diff --git a/.gitignore b/.gitignore
index 597d790..3b78d49 100644
--- a/.gitignore
+++ b/.gitignore
@@ -132,3 +132,14 @@ dmypy.json
test.py
.vscode/settings.json
.vscode/
+test.ipynb
+test2.ipynb
+minitest.ipynb
+dspikes_benchmarks.ipynb
+dspikes.ipynb
+nikos.ipynb
+viz.py
+bench_test_old.py
+bench_test.py
+test2.py
+examples_new/val_tau.py
diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index f85cfb3..fee7ee8 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -18,5 +18,4 @@ formats:
# Optionally declare the Python requirements required to build your docs
python:
install:
- - requirements: docs_sphinx/source/requirements_docs.txt
- system_packages: true
\ No newline at end of file
+ - requirements: docs_sphinx/source/requirements_docs.txt
\ No newline at end of file
diff --git a/README.rst b/README.rst
index 2290cf0..cdc791c 100644
--- a/README.rst
+++ b/README.rst
@@ -14,9 +14,23 @@ Dendrify
:target: CODE_OF_CONDUCT.md
:alt: Contributor Covenant
-Although neuronal dendrites greatly influence how single neurons process incoming information, their role in network-level functions remain largely unexplored. Current SNNs are usually quite simplistic, overlooking essential dendritic properties. Conversely, circuit models with morphologically detailed neuron models are computationally costly, thus impractical for large-network simulations.
-To bridge the gap between these two, we introduce Dendrify, a free, open-source Python package compatible with the `Brian 2 simulator `_. Dendrify, through simple commands, automatically generates reduced compartmental neuron models with simplified yet biologically relevant dendritic and synaptic integrative properties. Such models strike a good balance between flexibility, performance, and biological accuracy, allowing us to explore dendritic contributions to network-level functions.
+Although neuronal dendrites play a crucial role in shaping how individual
+neurons process synaptic information, their contribution to network-level
+functions has remained largely unexplored. Current spiking neural networks
+(SNNs) often oversimplify dendritic properties or overlook their essential
+functions. On the other hand, circuit models with morphologically detailed
+neuron representations are computationally intensive, making them impractical
+for simulating large networks.
+
+In an effort to bridge this gap, we present Dendrify—a freely available,
+open-source Python package that seamlessly integrates with the
+`Brian 2 simulator `_. Dendrify,
+through simple commands, automatically generates reduced compartmental neuron
+models with simplified yet biologically relevant dendritic and synaptic
+integrative properties. These models offer a well-rounded compromise between
+flexibility, performance, and biological accuracy, enabling us to investigate
+the impact of dendrites on network-level functions.
.. image:: https://github.com/Poirazi-Lab/dendrify/blob/main/docs_sphinx/source/_static/intro.png
:width: 70 %
@@ -24,8 +38,13 @@ To bridge the gap between these two, we introduce Dendrify, a free, open-source
If you use Dendrify for your published research, we kindly ask you to cite our article:
-Pagkalos, M., Chavlis, S., & Poirazi, P. (2023). Introducing the Dendrify framework for incorporating dendrites to spiking neural networks. Nature Communications, 14(1), 131. https://doi.org/10.1038/s41467-022-35747-8
+Pagkalos, M., Chavlis, S., & Poirazi, P. (2023). Introducing the Dendrify framework
+for incorporating dendrites to spiking neural networks.
+Nature Communications, 14(1), 131. https://doi.org/10.1038/s41467-022-35747-8
+
Documentation for Dendrify can be found at https://dendrify.readthedocs.io/en/latest/
-The project presentation for the INCF/OCNS Software Working Group is available `on google drive `_ and an interactive notebook with a short demo `on google colab `_.
+
+The project presentation for the INCF/OCNS Software Working Group is available
+`on google drive `_.
\ No newline at end of file
diff --git a/dendrify/__init__.py b/dendrify/__init__.py
index b9c0141..92a8c03 100644
--- a/dendrify/__init__.py
+++ b/dendrify/__init__.py
@@ -1,4 +1,5 @@
from .compartment import Compartment, Dendrite, Soma
-from .ephysproperties import EphysProperties
+from .ephysproperties import (EphysProperties, default_params,
+ update_default_params)
from .equations import library
-from .neuronmodel import NeuronModel
+from .neuronmodel import NeuronModel, PointNeuronModel
diff --git a/dendrify/compartment.py b/dendrify/compartment.py
index 84bd7d2..e1da2b6 100644
--- a/dendrify/compartment.py
+++ b/dendrify/compartment.py
@@ -1,20 +1,19 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-# conda version : 4.8.3
-# conda-build version : 3.18.12
-# python version : 3.7.6.final.0
-# brian2 version : 2.3 (py37hc9558a2_0)
-
from __future__ import annotations
-import sys
+import pprint as pp
from typing import Optional, Union
import numpy as np
-from brian2.units import Quantity, ms, pA
+from brian2 import defaultclock
+from brian2.core.functions import timestep
+from brian2.units import Quantity, ms, mV, pA
from .ephysproperties import EphysProperties
from .equations import library
+from .utils import (DimensionlessCompartmentError, DuplicateEquationsError,
+ get_logger)
+
+logger = get_logger(__name__)
class Compartment:
@@ -35,10 +34,26 @@ class Compartment:
also be provided but they should be in the same formattable structure as
the library models. Available options: ``'passive'`` (default),
``'adaptiveIF'``, ``'leakyIF'``, ``'adex'``.
- kwargs : :class:`~brian2.units.fundamentalunits.Quantity`, optional
- Kwargs are used to specify important electrophysiological properties,
- such as the specific capacitance or resistance. For more information
- see: :class:`.EphysProperties`.
+ length : ~brian2.units.fundamentalunits.Quantity, optional
+ A compartment's length.
+ diameter : ~brian2.units.fundamentalunits.Quantity, optional
+ A compartment's diameter.
+ cm : ~brian2.units.fundamentalunits.Quantity, optional
+ Specific capacitance (usually μF / cm^2).
+ gl : ~brian2.units.fundamentalunits.Quantity, optional
+ Specific leakage conductance (usually μS / cm^2).
+ cm_abs : ~brian2.units.fundamentalunits.Quantity, optional
+ Absolute capacitance (usually pF).
+ gl_abs : ~brian2.units.fundamentalunits.Quantity, optional
+ Absolute leakage conductance (usually nS).
+ r_axial : ~brian2.units.fundamentalunits.Quantity, optional
+ Axial resistance (usually Ohm * cm).
+ v_rest : ~brian2.units.fundamentalunits.Quantity, optional
+ Resting membrane voltage.
+ scale_factor : float, optional
+ A global area scale factor, by default ``1.0``.
+ spine_factor : float, optional
+ A dendritic area scale factor to account for spines, by default ``1.0``.
Examples
--------
@@ -47,35 +62,56 @@ class Compartment:
>>> # specifying equations and ephys properties:
>>> compY = Compartment('nameY', 'adaptiveIF', length=100*um, diameter=1*um,
>>> cm=1*uF/(cm**2), gl=50*uS/(cm**2))
+ >>> # specifying equations and absolute ephys properties:
+ >>> compY = Compartment('nameZ', 'adaptiveIF', cm_abs=100*pF, gl_abs=20*nS)
"""
- def __init__(self, name: str, model: str = 'passive', **kwargs: Quantity):
+ def __init__(
+ self,
+ name: str,
+ model: str = 'passive',
+ length: Optional[Quantity] = None,
+ diameter: Optional[Quantity] = None,
+ cm: Optional[Quantity] = None,
+ gl: Optional[Quantity] = None,
+ cm_abs: Optional[Quantity] = None,
+ gl_abs: Optional[Quantity] = None,
+ r_axial: Optional[Quantity] = None,
+ v_rest: Optional[Quantity] = None,
+ scale_factor: Optional[float] = 1.0,
+ spine_factor: Optional[float] = 1.0
+ ):
self.name = name
self._equations = None
self._params = None
self._connections = None
+ self._synapses = None
# Add membrane equations:
self._add_equations(model)
# Keep track of electrophysiological properties:
- self._ephys_object = EphysProperties(name=self.name, **kwargs)
+ self._ephys_object = EphysProperties(
+ name=self.name,
+ length=length,
+ diameter=diameter,
+ cm=cm,
+ gl=gl,
+ cm_abs=cm_abs,
+ gl_abs=gl_abs,
+ r_axial=r_axial,
+ v_rest=v_rest,
+ scale_factor=scale_factor,
+ spine_factor=spine_factor
+ )
def __str__(self):
- ephys_dict = self._ephys_object.__dict__
- ephys = '\n'.join([f"\u2192 {i}:\n [{ephys_dict[i]}]\n"
- for i in ephys_dict])
- equations = self.equations.replace('\n', '\n ')
-
- parameters = '\n'.join([f" '{i[0]}': {i[1]}"
- for i in self.parameters.items()
- ]) if self.parameters else ' None'
- msg = (f"OBJECT TYPE:\n\n {self.__class__}\n\n"
- f"{'-'*45}\n\n"
- f"USER PARAMETERS:\n\n{ephys}"
- f"\n{'-'*45}\n\n"
- "PROPERTIES: \n\n"
- f"\u2192 equations:\n {equations}\n\n"
- f"\u2192 parameters:\n{parameters}\n")
- return msg
+ equations = self.equations
+ parameters = pp.pformat(self.parameters)
+ user = pp.pformat(self._ephys_object.__dict__)
+ txt = (f"\nOBJECT\n{6*'-'}\n{self.__class__}\n\n\n"
+ f"EQUATIONS\n{9*'-'}\n{equations}\n\n\n"
+ f"PARAMETERS\n{10*'-'}\n{parameters}\n\n\n"
+ f"USER PARAMETERS\n{15*'-'}\n{user}")
+ return txt
def _add_equations(self, model: str):
"""
@@ -89,12 +125,15 @@ def _add_equations(self, model: str):
if model in library:
self._equations = library[model].format('_'+self.name)
else:
- self._equations = model.format('_'+self.name)
+ logger.warning(("The model you provided is not found. The default "
+ "'passive' membrane model will be used instead."))
+ self._equations = library['passive'].format('_'+self.name)
- def connect(self, other: Compartment,
+ def connect(self,
+ other: Compartment,
g: Union[Quantity, str] = 'half_cylinders'):
"""
- Allows the connection (electrical coupling) of two compartments.
+ Connects two compartments (electrical coupling).
Parameters
----------
@@ -102,13 +141,13 @@ def connect(self, other: Compartment,
Another compartment.
g : str or :class:`~brian2.units.fundamentalunits.Quantity`, optional
The coupling conductance. It can be set explicitly or calculated
- automatically (provided all necessary parameters exist).
- Available options: ``'half_cylinders'`` (default),
+ automatically (provided all necessary parameters exist).
+ Available options: ``'half_cylinders'`` (default),
``'cylinder_'``.
Warning
-------
- The automatic approaches require that both compartments to be connected
+ The automatic approaches require that both compartments to be connected
have specified **length**, **diameter** and **axial resistance**.
Examples
@@ -124,9 +163,20 @@ def connect(self, other: Compartment,
# Prohibit connecting compartments with the same name
if self.name == other.name:
- print(("ERROR: Cannot connect to compartments with the same name.\n"
- "Program exited"))
- sys.exit()
+ raise ValueError(
+ "Cannot connect compartments with the same name.\n")
+ if (self.dimensionless or other.dimensionless) and type(g) == str:
+ raise DimensionlessCompartmentError(
+ ("Cannot automatically calculate the coupling \nconductance of "
+ "dimensionless compartments. To resolve this error, perform\n"
+ "one of the following:\n\n"
+ f"1. Provide [length, diameter, r_axial] for both '{self.name}'"
+ f" and '{other.name}'.\n\n"
+ f"2. Turn both compartment into dimensionless by providing only"
+ " values for \n [cm_abs, gl_abs] and then connect them using "
+ "an exact coupling conductance."
+ )
+ )
# Current from Comp2 -> Comp1
I_forward = 'I_{1}_{0} = (V_{1}-V_{0}) * g_{1}_{0} :amp'.format(
@@ -175,10 +225,12 @@ def connect(self, other: Compartment,
other._connections.append(
(g_to_other, ctype, comp._ephys_object))
else:
- print('Please select a valid conductance.')
+ raise ValueError(
+ "Please provide a valid conductance option."
+ )
- def synapse(self, channel: Optional[str] = None,
- pre: Optional[str] = None,
+ def synapse(self, channel: str,
+ tag: str,
g: Optional[Quantity] = None,
t_rise: Optional[Quantity] = None,
t_decay: Optional[Quantity] = None,
@@ -189,24 +241,23 @@ def synapse(self, channel: Optional[str] = None,
instantaneous rise of the synaptic conductance followed by an exponential
decay. When both the rise ``t_rise`` and decay ``t_decay`` constants are
provided, synapses are modelled as a sum of two exponentials. For more
- information see:
- `Modeling Synapses by Arnd Roth & Mark C. W. van Rossum
+ information see:
+ `Modeling Synapses by Arnd Roth & Mark C. W. van Rossum
`_
Parameters
----------
channel : str
Synaptic channel type. Available options: ``'AMPA'``, ``'NMDA'``,
- ``'GABA'``, by default ``None``
- pre : str
- A unique name to distinguish synapses of the same type coming from
- different input sources, by default ``None``
+ ``'GABA'``.
+ tag : str
+ A unique name to distinguish synapses of the same type.
g : :class:`~brian2.units.fundamentalunits.Quantity`
- Maximum synaptic conductance, by default ``None``
+ Maximum synaptic conductance
t_rise : :class:`~brian2.units.fundamentalunits.Quantity`
- Rise time constant, by default ``None``
+ Rise time constant
t_decay : :class:`~brian2.units.fundamentalunits.Quantity`
- Decay time constant, by default ``None``
+ Decay time constant
scale_g : bool, optional
Option to add a normalization factor to scale the maximum
conductance at 1 when synapses are modelled as a difference of
@@ -217,51 +268,66 @@ def synapse(self, channel: Optional[str] = None,
--------
>>> comp = Compartment('comp')
>>> # adding an AMPA synapse with instant rise & exponential decay:
- >>> comp.synapse('AMPA', g=1*nS, t_decay=5*ms, pre='X')
+ >>> comp.synapse('AMPA', tag='X', g=1*nS, t_decay=5*ms)
>>> # same channel, different conductance & source:
- >>> comp.synapse('AMPA', g=2*nS, t_decay=5*ms, pre='Y')
- >>> # different channel with both rise & decay kinetics:
- >>> comp.synapse('NMDA', g=1*nS, t_rise=5*ms, t_decay=50*ms, pre='X')
+ >>> comp.synapse('AMPA', tag='Y', g=2*nS, t_decay=5*ms)
+ >>> # different channel with both rise & decay kinetics:
+ >>> comp.synapse('NMDA', tag='X' g=1*nS, t_rise=5*ms, t_decay=50*ms)
"""
- # Make sure that the user provides a synapse source
- if not pre:
- print((f"Warning:
argument missing for '{channel}' "
- f"synapse @ '{self.name}'\n"
- "Program exited.\n"))
- sys.exit()
- # Switch to rise/decay equations if t_rise & t_decay are provided
- if all([t_rise, t_decay]):
- key = f"{channel}_rd"
+ synapse_id = "_".join([channel, tag, self.name])
+
+ if self._synapses:
+ # Check if this synapse already exists
+ if synapse_id in self._synapses:
+ raise DuplicateEquationsError(
+ f"The equations of '{channel}_{tag}' have already been "
+ f"added to '{self.name}'. \nPlease use a different "
+ f"combination of [channel, tag] when calling the synapse() "
+ "method \nmultiple times on a single compartment. You might"
+ " also see this error if you are using \nJupyter/iPython "
+ "which store variable values in memory. Try cleaning all "
+ "variables or \nrestart the kernel before running your "
+ "code. If this problem persists, please report it \n"
+ "by creating a new issue here: "
+ "https://github.com/Poirazi-Lab/dendrify/issues."
+ )
else:
- key = channel
+ self._synapses = []
- current_name = f'I_{channel}_{pre}_{self.name}'
- current_eqs = library[key].format(self.name, pre)
+ # Switch to rise/decay equations if t_rise & t_decay are provided
+ key = f"{channel}_rd" if all([t_rise, t_decay]) else channel
+ current_name = f'I_{channel}_{tag}_{self.name}'
+ current_eqs = library[key].format(self.name, tag)
to_replace = f'= I_ext_{self.name}'
self._equations = self._equations.replace(
- to_replace, f'{to_replace} + {current_name}')
+ to_replace,
+ f'{to_replace} + {current_name}'
+ )
self._equations += '\n'+current_eqs
if not self._params:
self._params = {}
- weight = f"w_{channel}_{pre}_{self.name}"
- self._params[weight] = 1
+ weight = f"w_{channel}_{tag}_{self.name}"
+ self._params[weight] = 1.0
+
# If user provides a value for g, then add it to _params
if g:
- self._params[f'g_{channel}_{pre}_{self.name}'] = g
+ self._params[f'g_{channel}_{tag}_{self.name}'] = g
if t_rise:
- self._params[f't_{channel}_rise_{pre}_{self.name}'] = t_rise
+ self._params[f't_{channel}_rise_{tag}_{self.name}'] = t_rise
if t_decay:
- self._params[f't_{channel}_decay_{pre}_{self.name}'] = t_decay
+ self._params[f't_{channel}_decay_{tag}_{self.name}'] = t_decay
if scale_g:
if all([t_rise, t_decay, g]):
norm_factor = Compartment.g_norm_factor(t_rise, t_decay)
- self._params[f'g_{channel}_{pre}_{self.name}'] *= norm_factor
+ self._params[f'g_{channel}_{tag}_{self.name}'] *= norm_factor
+
+ self._synapses.append(synapse_id)
- def noise(self, tau: Quantity = 20*ms, sigma: Quantity = 3*pA,
+ def noise(self, tau: Quantity = 20*ms, sigma: Quantity = 1*pA,
mean: Quantity = 0*pA):
"""
Adds a stochastic noise current. For more information see the Noise
@@ -277,10 +343,24 @@ def noise(self, tau: Quantity = 20*ms, sigma: Quantity = 3*pA,
Mean of the Gaussian noise, by default ``0*pA``
"""
I_noise_name = f'I_noise_{self.name}'
+
+ if I_noise_name in self.equations:
+ raise DuplicateEquationsError(
+ f"The equations of '{I_noise_name}' have already been "
+ f"added to '{self.name}'. \nYou might be seeing this error if "
+ "you are using Jupyter/iPython "
+ "which store variable values \nin memory. Try cleaning all "
+ "variables or restart the kernel before running your "
+ "code. If this \nproblem persists, please report it "
+ "by creating a new issue here:\n"
+ "https://github.com/Poirazi-Lab/dendrify/issues."
+ )
noise_eqs = library['noise'].format(self.name)
to_change = f'= I_ext_{self.name}'
self._equations = self._equations.replace(
- to_change, f'{to_change} + {I_noise_name}')
+ to_change,
+ f'{to_change} + {I_noise_name}'
+ )
self._equations += '\n'+noise_eqs
# Add _params:
@@ -293,7 +373,8 @@ def noise(self, tau: Quantity = 20*ms, sigma: Quantity = 3*pA,
@property
def parameters(self) -> dict:
"""
- All parameters that have been generated for a single compartment.
+ Returns all the parameters that have been generated for a single
+ compartment.
Returns
-------
@@ -310,59 +391,42 @@ def parameters(self) -> dict:
@property
def area(self) -> Quantity:
"""
- A compartment's surface area (open cylinder) based on its length
+ Returns a compartment's surface area (open cylinder) based on its length
and diameter.
Returns
-------
:class:`~brian2.units.fundamentalunits.Quantity`
"""
- try:
- return self._ephys_object.area
- except AttributeError:
- print(("Error: Missing Parameters\n"
- f"Cannot calculate the area of <{self.name}>, "
- "returned None instead.\n"))
+ return self._ephys_object.area
@property
def capacitance(self) -> Quantity:
"""
- A compartment's absolute capacitance based on its specific capacitance
- (cm) and surface area.
+ Returns a compartment's absolute capacitance.
Returns
-------
:class:`~brian2.units.fundamentalunits.Quantity`
"""
- try:
- return self._ephys_object.capacitance
- except AttributeError:
- print(("Error: Missing Parameters\n"
- f"Cannot calculate the capacitance of <{self.name}>, "
- "returned None instead.\n"))
+ return self._ephys_object.capacitance
@property
def g_leakage(self) -> Quantity:
"""
- A compartment's absolute leakage conductance based on its specific
- leakage conductance (gl) and surface area.
+ A compartment's absolute leakage conductance.
Returns
-------
:class:`~brian2.units.fundamentalunits.Quantity`
"""
- try:
- return self._ephys_object.g_leakage
- except AttributeError:
- print(("Error: Missing Parameters\n"
- f"Cannot calculate the g leakage of <{self.name}>, "
- "returned None instead.\n"))
+ return self._ephys_object.g_leakage
@property
def equations(self) -> str:
"""
- All differential equations that have been generated for a single
- compartment.
+ Returns all differential equations that describe a single compartment
+ and the mechanisms that have been added to it.
Returns
-------
@@ -371,7 +435,7 @@ def equations(self) -> str:
return self._equations
@property
- def _g_couples(self) -> dict:
+ def _g_couples(self) -> Union[dict, None]:
# If not _connections have been specified yet
if not self._connections:
return None
@@ -405,6 +469,17 @@ def g_norm_factor(trise: Quantity, tdecay: Quantity):
/ ms)
return 1/factor
+ @property
+ def dimensionless(self) -> bool:
+ """
+ Checks if a compartment has been flagged as dimensionless.
+
+ Returns
+ -------
+ bool
+ """
+ return True if self._ephys_object._dimensionless else False
+
class Soma(Compartment):
"""
@@ -429,48 +504,70 @@ class Soma(Compartment):
also be provided but they should be in the same formattable structure as
the library models. Available options: ``'leakyIF'`` (default),
``'adaptiveIF'``, ``'adex'``.
- kwargs : :class:`~brian2.units.fundamentalunits.Quantity`, optional
- Kwargs are used to specify important electrophysiological properties,
- such as the specific capacitance or resistance. For more information
- see: :class:`.EphysProperties`.
+ length : ~brian2.units.fundamentalunits.Quantity, optional
+ A compartment's length.
+ diameter : ~brian2.units.fundamentalunits.Quantity, optional
+ A compartment's diameter.
+ cm : ~brian2.units.fundamentalunits.Quantity, optional
+ Specific capacitance (usually μF / cm^2).
+ gl : ~brian2.units.fundamentalunits.Quantity, optional
+ Specific leakage conductance (usually μS / cm^2).
+ cm_abs : ~brian2.units.fundamentalunits.Quantity, optional
+ Absolute capacitance (usually pF).
+ gl_abs : ~brian2.units.fundamentalunits.Quantity, optional
+ Absolute leakage conductance (usually nS).
+ r_axial : ~brian2.units.fundamentalunits.Quantity, optional
+ Axial resistance (usually Ohm * cm).
+ v_rest : ~brian2.units.fundamentalunits.Quantity, optional
+ Resting membrane voltage.
+ scale_factor : float, optional
+ A global area scale factor, by default ``1.0``.
+ spine_factor : float, optional
+ A dendritic area scale factor to account for spines, by default ``1.0``.
Examples
--------
>>> # specifying equations only:
- >>> somaX = Soma('nameX', 'leakyIF')
+ >>> compX = Soma('nameX', 'leakyIF')
>>> # specifying equations and ephys properties:
- >>> somaY = Soma('nameY', 'adaptiveIF', length=100*um, diameter=1*um,
- >>> cm=1*uF/(cm**2), gl=50*uS/(cm**2))
+ >>> compY = Soma('nameY', 'adaptiveIF', length=100*um, diameter=1*um,
+ >>> cm=1*uF/(cm**2), gl=50*uS/(cm**2))
+ >>> # specifying equations and absolute ephys properties:
+ >>> compY = Soma('nameZ', 'adaptiveIF', cm_abs=100*pF, gl_abs=20*nS)
"""
- def __init__(self, name: str, model: str = 'leakyIF', **kwargs: Quantity):
-
- super().__init__(name, model, **kwargs)
- self._events = None
- self._event_actions = None
-
- def __str__(self):
- ephys_dict = self._ephys_object.__dict__
- ephys = '\n'.join([f"\u2192 {i}:\n [{ephys_dict[i]}]\n"
- for i in ephys_dict])
- equations = self.equations.replace('\n', '\n ')
-
- parameters = '\n'.join([f" '{i[0]}': {i[1]}"
- for i in self.parameters.items()
- ]) if self.parameters else ' None'
-
- msg = (f"OBJECT TYPE:\n\n {self.__class__}\n\n"
- f"{'-'*45}\n\n"
- f"USER PARAMETERS:\n\n{ephys}"
- f"\n{'-'*45}\n\n"
- "PROPERTIES: \n\n"
- f"\u2192 equations:\n {equations}\n\n"
- f"\u2192 parameters:\n{parameters}\n")
- return msg
+ def __init__(
+ self,
+ name: str,
+ model: str = 'leakyIF',
+ length: Optional[Quantity] = None,
+ diameter: Optional[Quantity] = None,
+ cm: Optional[Quantity] = None,
+ gl: Optional[Quantity] = None,
+ cm_abs: Optional[Quantity] = None,
+ gl_abs: Optional[Quantity] = None,
+ r_axial: Optional[Quantity] = None,
+ v_rest: Optional[Quantity] = None,
+ scale_factor: Optional[float] = 1.0,
+ spine_factor: Optional[float] = 1.0
+ ):
+ super().__init__(
+ name=name,
+ model=model,
+ length=length,
+ diameter=diameter,
+ cm=cm,
+ gl=gl,
+ cm_abs=cm_abs,
+ gl_abs=gl_abs,
+ r_axial=r_axial,
+ v_rest=v_rest,
+ scale_factor=scale_factor,
+ spine_factor=spine_factor
+ )
class Dendrite(Compartment):
- # TODO: restrict to passive
"""
A class that automatically generates and handles all differential equations
and parameters needed to describe a dendritic compartment, its active
@@ -491,46 +588,104 @@ class Dendrite(Compartment):
model : str, optional
A keyword for accessing Dendrify's library models. Dendritic compartments
are by default set to ``'passive'``.
+ length : ~brian2.units.fundamentalunits.Quantity, optional
+ A compartment's length.
+ diameter : ~brian2.units.fundamentalunits.Quantity, optional
+ A compartment's diameter.
+ cm : ~brian2.units.fundamentalunits.Quantity, optional
+ Specific capacitance (usually μF / cm^2).
+ gl : ~brian2.units.fundamentalunits.Quantity, optional
+ Specific leakage conductance (usually μS / cm^2).
+ cm_abs : ~brian2.units.fundamentalunits.Quantity, optional
+ Absolute capacitance (usually pF).
+ gl_abs : ~brian2.units.fundamentalunits.Quantity, optional
+ Absolute leakage conductance (usually nS).
+ r_axial : ~brian2.units.fundamentalunits.Quantity, optional
+ Axial resistance (usually Ohm * cm).
+ v_rest : ~brian2.units.fundamentalunits.Quantity, optional
+ Resting membrane voltage.
+ scale_factor : float, optional
+ A global area scale factor, by default ``1.0``.
+ spine_factor : float, optional
+ A dendritic area scale factor to account for spines, by default ``1.0``.
+
+ Examples
+ --------
+ >>> # specifying equations only:
+ >>> compX = Dendrite('nameX')
+ >>> # specifying equations and ephys properties:
+ >>> compY = Dendrite('nameY', length=100*um, diameter=1*um,
+ >>> cm=1*uF/(cm**2), gl=50*uS/(cm**2))
+ >>> # specifying equations and absolute ephys properties:
+ >>> compY = Dendrite('nameZ', cm_abs=100*pF, gl_abs=20*nS)
"""
- def __init__(self, name: str, model: str = 'passive', **kwargs: Quantity):
- super().__init__(name, model, **kwargs)
+ def __init__(
+ self,
+ name: str,
+ model: str = 'passive',
+ length: Optional[Quantity] = None,
+ diameter: Optional[Quantity] = None,
+ cm: Optional[Quantity] = None,
+ gl: Optional[Quantity] = None,
+ cm_abs: Optional[Quantity] = None,
+ gl_abs: Optional[Quantity] = None,
+ r_axial: Optional[Quantity] = None,
+ v_rest: Optional[Quantity] = None,
+ scale_factor: Optional[float] = 1.0,
+ spine_factor: Optional[float] = 1.0
+ ):
+ super().__init__(
+ name=name,
+ model=model,
+ length=length,
+ diameter=diameter,
+ cm=cm,
+ gl=gl,
+ cm_abs=cm_abs,
+ gl_abs=gl_abs,
+ r_axial=r_axial,
+ v_rest=v_rest,
+ scale_factor=scale_factor,
+ spine_factor=spine_factor
+ )
self._events = None
self._event_actions = None
+ self._dspike_params = None
def __str__(self):
- ephys_dict = self._ephys_object.__dict__
- ephys = '\n'.join([f"\u2192 {i}:\n [{ephys_dict[i]}]\n"
- for i in ephys_dict])
- equations = self.equations.replace('\n', '\n ')
- events = '\n'.join([f" '{key}': '{self.events[key]}'"
- for key in self.events
- ]) if self.events else ' None'
- parameters = '\n'.join([f" '{i[0]}': {i[1]}"
- for i in self.parameters.items()
- ]) if self.parameters else ' None'
- msg = (f"OBJECT TYPE:\n\n {self.__class__}\n\n"
- f"{'-'*45}\n\n"
- f"USER PARAMETERS:\n\n{ephys}"
- f"\n{'-'*45}\n\n"
- "PROPERTIES: \n\n"
- f"\u2192 equations:\n {equations}\n\n"
- f"\u2192 events:\n{events}\n\n"
- f"\u2192 parameters:\n{parameters}\n")
- return msg
-
- def dspikes(self, channel: str,
+ equations = self.equations
+ parameters = pp.pformat(self.parameters)
+ events = pp.pformat(self.events, width=120)
+ event_names = pp.pformat(self.event_names)
+ user = self._ephys_object.__dict__
+ user_clean = pp.pformat({k: v for k, v in user.items() if v})
+ txt = (f"\nOBJECT\n{6*'-'}\n{self.__class__}\n\n\n"
+ f"EQUATIONS\n{9*'-'}\n{equations}\n\n\n"
+ f"PARAMETERS\n{10*'-'}\n{parameters}\n\n\n"
+ f"EVENTS\n{6*'-'}\n{event_names}\n\n\n"
+ f"EVENT CONDITIONS\n{16*'-'}\n{events}\n\n\n"
+ f"USER PARAMETERS\n{15*'-'}\n{user_clean}")
+ return txt
+
+ def dspikes(self, name: str,
threshold: Optional[Quantity] = None,
g_rise: Optional[Quantity] = None,
- g_fall: Optional[Quantity] = None):
- # TODO: show error if channel does not exist.
+ g_fall: Optional[Quantity] = None,
+ duration_rise: Optional[Quantity] = None,
+ duration_fall: Optional[Quantity] = None,
+ reversal_rise: Union[Quantity, str, None] = None,
+ reversal_fall: Union[Quantity, str, None] = None,
+ offset_fall: Optional[Quantity] = None,
+ refractory: Optional[Quantity] = None
+ ):
"""
- Adds the mechanisms and parameters needed for dendritic spiking. Under
- the hood, this method creates all equations, conditions and actions to
- utilize Brian's custom events functionality. Spikes are generated through
- the sequential activation of a positive (sodium or calcium-like) and a
- negative current (potassium-like current) when a specified dSpike
- threshold is crossed.
+ Adds the ionic mechanisms and parameters needed for dendritic spiking.
+ Under the hood, this method creates the equations, conditions and
+ actions to take advantage of Brian's custom events. dSpikes are
+ generated through the sequential activation of a positive (sodium or
+ calcium-like) and a negative current (potassium-like current) when a
+ specified dSpike threshold is crossed.
.. hint::
@@ -542,152 +697,228 @@ def dspikes(self, channel: str,
within the refractory period. dSpikes cannot be generated during this
phase.
- **DEPOLARIZATION PHASE:**\n
+ **RISE PHASE:**\n
When the dendritic voltage crosses the dSpike threshold AND the
refractory period has elapsed. This triggers the instant activation
- of a positive current that enters the dendrite and then decays
- exponentially.
+ of a positive current that is deactivated after a specified amount
+ of time (``duration_rise``). Also a new refractory period begins.
- **REPOLARIZATION PHASE:**\n
- This phase starts automatically after a specified delay from the
- initiation of the dSpike. A negative current is activated instantly
- and then decays exponentially. Also a new refractory period begins.
+ **FALL PHASE:**\n
+ This phase starts automatically with a delay (``offset_fall``) after
+ the dSpike threshold is crossed. A negative current is activated
+ instantly and then is deactivated after a specified amount of time
+ (``duration_fall``).
Parameters
----------
- channel : str
- Ion channel type. Available options: ``'Na'``, ``'Ca'`` (coming soon).
- threshold : :class:`~brian2.units.fundamentalunits.Quantity`
- The membrane voltage threshold for dendritic spiking, by default
- ``None``.
- g_rise : :class:`~brian2.units.fundamentalunits.Quantity`
- The conductance of the current that is activated during the
- depolarization phase, by default ``None``.
- g_fall : :class:`~brian2.units.fundamentalunits.Quantity`
- The conductance of the current that is activated during the
- repolarization phase, by default ``None``.
+ name : str
+ A unique name to describe a single dSpike type.
+ threshold : ~brian2.units.fundamentalunits.Quantity, optional
+ The membrane voltage threshold for dendritic spiking.
+ g_rise : ~brian2.units.fundamentalunits.Quantity, optional
+ The max conductance of the channel that is activated during the rise
+ (depolarization phase).
+ g_fall : ~brian2.units.fundamentalunits.Quantity, optional
+ The max conductance of the channel that is activated during the fall
+ (repolarization phase).
+ duration_rise : ~brian2.units.fundamentalunits.Quantity, optional
+ The duration of g_rise staying open.
+ duration_fall : ~brian2.units.fundamentalunits.Quantity, optional
+ The duration of g_fall staying open.
+ reversal_rise : (~brian2.units.fundamentalunits.Quantity, str), optional
+ The reversal potential of the channel that is activated during the rise
+ (depolarization) phase.
+ reversal_fall : (~brian2.units.fundamentalunits.Quantity, str), optional
+ The reversal potential of the channel that is activated during the fall
+ (repolarization) phase.
+ offset_fall : ~brian2.units.fundamentalunits.Quantity, optional
+ The delay for the activation of g_rise.
+ refractory : ~brian2.units.fundamentalunits.Quantity, optional
+ The time interval required before dSpike can be activated again.
"""
- if channel == 'Na':
- self._Na_spikes(threshold=threshold, g_rise=g_rise, g_fall=g_fall)
- elif channel == 'Ca':
- self._Ca_spikes(threshold=threshold, g_rise=g_rise, g_fall=g_fall)
-
- def _Na_spikes(self, threshold: Optional[Quantity] = None,
- g_rise: Optional[Quantity] = None,
- g_fall: Optional[Quantity] = None):
+
# The following code creates all necessary equations for dspikes:
- name = self.name
- dspike_currents = f'I_Na_{name} + I_Kn_{name}'
+ comp = self.name
+ ID = f"{name}_{comp}"
+ event_name = f"spike_{ID}"
+
+ if self._events:
+ # Check if this event already exists
+ if event_name in self._events:
+ raise DuplicateEquationsError(
+ f"The equations for '{event_name}' have already been "
+ f"added to '{self.name}'. \nPlease use a different "
+ f"[name] when adding multiple dSpike mechanisms to "
+ " a single compartment. \nYou might"
+ " also see this error if you are using Jupyter/iPython "
+ "which store variable values in \nmemory. Try cleaning all "
+ "variables or restart the kernel before running your "
+ "code. If this \nproblem persists, please report it "
+ "by creating a new issue here: \n"
+ "https://github.com/Poirazi-Lab/dendrify/issues."
+ )
+ else:
+ self._events = {}
+
+ dspike_currents = f"I_rise_{ID} + I_fall_{ID}"
+
# Both currents take into account the reversal potential of Na/K
- I_Na_eqs = f'I_Na_{name} = g_Na_{name} * (E_Na-V_{name}) :amp'
- I_Kn_eqs = f'I_Kn_{name} = g_Kn_{name} * (E_K-V_{name}) :amp'
- # Ion conductances simply decay exponentially
- g_Na_eqs = f'dg_Na_{name}/dt = -g_Na_{name}/tau_Na :siemens'
- g_Kn_eqs = f'dg_Kn_{name}/dt = -g_Kn_{name}/tau_Kn :siemens'
- # Parameters needed for the dSpike custom events
- I_Na_check = f'allow_I_Na_{name} :boolean'
- I_Kn_check = f'allow_I_Kn_{name} :boolean'
- refractory_var = f'timer_Na_{name} :second'
+ I_rise_eqs = f"I_rise_{ID} = g_rise_{ID} * (E_rise_{name}-V_{comp}) :amp"
+ I_fall_eqs = f"I_fall_{ID} = g_fall_{ID} * (E_fall_{name}-V_{comp}) :amp"
+
+ # Ion conductances
+ g_rise_eqs = (
+ f"g_rise_{ID} = "
+ f"g_rise_max_{ID} * "
+ f"int(t_in_timesteps <= spiketime_{ID} + duration_rise_{ID}) * "
+ f"gate_{ID} "
+ ":siemens"
+ )
+ g_fall_eqs = (
+ f"g_fall_{ID} = "
+ f"g_fall_max_{ID} * "
+ f"int(t_in_timesteps <= spiketime_{ID} + offset_fall_{ID} + duration_fall_{ID}) * "
+ f"int(t_in_timesteps >= spiketime_{ID} + offset_fall_{ID}) * "
+ f"gate_{ID} "
+ ":siemens"
+ )
+ spiketime = f'spiketime_{ID} :1' # in units of timestep
+ gate = f'gate_{ID} :1' # zero or one
+
# Add equations to a compartment
- to_replace = f'= I_ext_{name}'
+ to_replace = f'= I_ext_{comp}'
self._equations = self._equations.replace(
- to_replace, f'{to_replace} + {dspike_currents}')
- self._equations += '\n'.join(['', I_Na_eqs, I_Kn_eqs, g_Na_eqs, g_Kn_eqs,
- I_Na_check, I_Kn_check, refractory_var])
- # Create all necessary custom _events for dspikes:
- condition_I_Na = library['condition_I_Na']
- condition_I_Kn = library['condition_I_Kn']
- if not self._events:
- self._events = {}
- self._events[f"activate_I_Na_{name}"] = condition_I_Na.format(name)
- self._events[f"activate_I_Kn_{name}"] = condition_I_Kn.format(name)
+ to_replace,
+ f'{to_replace} + {dspike_currents}'
+ )
+ self._equations += '\n'.join(['', I_rise_eqs, I_fall_eqs,
+ g_rise_eqs, g_fall_eqs,
+ spiketime, gate]
+ )
+
+ # Create and add custom dspike event
+ event_name = f"spike_{ID}"
+ condition = (f"V_{comp} >= Vth_{ID} and "
+ f"t_in_timesteps >= spiketime_{ID} + refractory_{ID} * gate_{ID}"
+ )
+
+ self._events[event_name] = condition
# Specify what is going to happen inside run_on_event()
+ action = {f"spike_{ID}": f"spiketime_{ID} = t_in_timesteps; gate_{ID} = 1"}
if not self._event_actions:
- self._event_actions = library['run_on_Na_spike'].format(name)
+ self._event_actions = action
else:
- self._event_actions += "\n" + \
- library['run_on_Na_spike'].format(name)
+ self._event_actions.update(action)
+
# Include params needed
- if not self._params:
- self._params = {}
- if threshold:
- self._params[f"Vth_Na_{self.name}"] = threshold
- if g_rise:
- self._params[f"g_Na_{self.name}_max"] = g_rise
- if g_fall:
- self._params[f"g_Kn_{self.name}_max"] = g_fall
-
- def _Ca_spikes(self, threshold: Quantity = None, g_rise: Quantity = None,
- g_fall: Quantity = None):
- # TODO: check that it works as expected.
+ if not self._dspike_params:
+ self._dspike_params = {}
+
+ dt = defaultclock.dt
+
+ params = [threshold,
+ g_rise,
+ g_fall,
+ self._ionic_param(reversal_rise),
+ self._ionic_param(reversal_fall),
+ self._timestep(duration_rise, dt),
+ self._timestep(duration_fall, dt),
+ self._timestep(offset_fall, dt),
+ self._timestep(refractory, dt)]
+
+ vars = [f"Vth_{ID}",
+ f"g_rise_max_{ID}",
+ f"g_fall_max_{ID}",
+ f"E_rise_{name}",
+ f"E_fall_{name}",
+ f"duration_rise_{ID}",
+ f"duration_fall_{ID}",
+ f"offset_fall_{ID}",
+ f"refractory_{ID}"]
+
+ d = dict(zip(vars, params))
+ self._dspike_params[ID] = d
+
+ def _timestep(self,
+ param: Union[Quantity, None], dt
+ ) -> Union[int, None]:
+ if not param:
+ return None
+ if isinstance(param, Quantity):
+ return timestep(param, dt)
+ else:
+ raise ValueError(
+ f"Please provide a valid time parameter for '{self.name}'."
+ )
+
+ def _ionic_param(self,
+ param: Union[str, Quantity, None],
+ ) -> Union[Quantity, None]:
+ DEFAULT_PARAMS = EphysProperties.DEFAULT_PARAMS
+ valid_params = {k: v for k, v in DEFAULT_PARAMS.items() if k[0] == 'E'}
+ if not param:
+ return None
+ if isinstance(param, Quantity):
+ return param
+ elif isinstance(param, str):
+ try:
+ return DEFAULT_PARAMS[param]
+ except KeyError:
+ raise ValueError(
+ f"Please provide a valid ionic parameter for '{self.name}'."
+ " Available options:\n"
+ f"{pp.pformat(valid_params)}"
+ )
+ else:
+ raise ValueError(
+ f"Please provide a valid ionic parameter for '{self.name}'."
+ " Available options:\n"
+ f"{pp.pformat(valid_params)}"
+ )
+
+ @property
+ def parameters(self) -> dict:
"""
- Coming soon.
+ Returns a dictionary of all parameters that have been generated for a
+ single compartment.
+
+ Returns
+ -------
+ dict
"""
- pass
-
- # # The following code creates all necessary equations for dspikes:
- # name = self.name
- # dspike_currents = f'I_Ca_{name} + I_Kc_{name}'
-
- # I_Ca_eqs = f'dI_Ca_{name}/dt = -I_Ca_{name}/tau_Ca :amp'
- # I_Kc_eqs = f'dI_Kc_{name}/dt = -I_Kc_{name}/tau_Kc :amp'
-
- # I_Ca_check = f'allow_I_Ca_{name} :boolean'
- # I_Kc_check = f'allow_I_Kc_{name} :boolean'
-
- # refractory_var = f'timer_Ca_{name} :second'
- # to_replace = f'= I_ext_{name}'
-
- # self._equations = self._equations.replace(
- # to_replace, f'{to_replace} + {dspike_currents}')
- # self._equations += '\n'.join(['', I_Ca_eqs, I_Kc_eqs, I_Ca_check,
- # I_Kc_check, refractory_var])
-
- # # Create all necessary custom _events for dspikes:
- # condition_I_Ca = library['condition_I_Ca']
- # condition_I_Kc = library['condition_I_Kc']
- # if not self._events:
- # self._events = {}
- # self._events[f"activate_I_Ca_{name}"] = condition_I_Ca.format(name)
- # self._events[f"activate_I_Kc_{name}"] = condition_I_Kc.format(name)
-
- # # Specify what is going to happen inside run_on_event()
- # if not self._event_actions:
- # self._event_actions = library['run_on_Ca_spike'].format(name)
- # else:
- # self._event_actions += "\n" + \
- # library['run_on_Ca_spike'].format(name)
- # # Include params needed
- # if not self._params:
- # self._params = {}
- # if threshold:
- # self._params[f"Vth_Ca_{self.name}"] = threshold
- # if g_rise:
- # self._params[f"g_Ca_{self.name}_max"] = g_rise
- # if g_fall:
- # self._params[f"g_Kc_{self.name}_max"] = g_fall
+ d_out = {}
+ for i in [self._params, self._g_couples]:
+ if i:
+ d_out.update(i)
+ if self._dspike_params:
+ for d in self._dspike_params.values():
+ d_out.update(d)
+ if self._ephys_object:
+ d_out.update(self._ephys_object.parameters)
+ return d_out
@property
def events(self) -> dict:
"""
- A dictionary of all dSpike events created for a single dendrite.
+ Returns a dictionary of all dSpike events created for a single dendrite.
Returns
-------
dict
Keys: event names, values: events conditions.
"""
- return self._events
+ return self._events if self._events else {}
@property
- def event_actions(self) -> str:
+ def event_names(self) -> list:
"""
- A string that is used to tell Brian how to handle the dSpike events.
+ Returns a list of all dSpike event names created for a single dendrite.
Returns
-------
- str
- Executable code that runs automatically in the background.
+ list
"""
- return self._event_actions
+ if not self._events:
+ return []
+ return list(self._events.keys())
diff --git a/dendrify/ephysproperties.py b/dendrify/ephysproperties.py
index e05a402..5f07d55 100644
--- a/dendrify/ephysproperties.py
+++ b/dendrify/ephysproperties.py
@@ -1,9 +1,37 @@
from __future__ import annotations
+import pprint as pp
from math import pi
-from typing import List, Optional, Tuple, Union
+from typing import Optional
-from brian2.units import Quantity
+from brian2.units import *
+
+from .utils import DimensionlessCompartmentError, get_logger
+
+logger = get_logger(__name__)
+
+
+def default_params() -> dict:
+ """
+ Returns the default ephys parameters.
+
+ Returns
+ -------
+ dict
+ """
+ return EphysProperties.DEFAULT_PARAMS
+
+
+def update_default_params(params: dict) -> None:
+ """
+ Updates the default ephys parameters.
+
+ Parameters
+ ----------
+ params : dict
+ A dictionary of ionic parameters
+ """
+ EphysProperties.DEFAULT_PARAMS.update(params)
class EphysProperties(object):
@@ -13,62 +41,111 @@ class EphysProperties(object):
Note
----
- An EphysProperties object is automatically created and linked to a
- :class:`.Compartment`, :class:`.Soma`, or :class:`.Dendrite` object
- during the instantiation of the latter.
+ An EphysProperties object is automatically created and linked to a
+ single compartment during the initialization of the latter.
Parameters
----------
name : str, optional
- A compartment's name, by default ``None``
+ A compartment's name.
length : ~brian2.units.fundamentalunits.Quantity, optional
- A compartment's length, by default ``None``
+ A compartment's length.
diameter : ~brian2.units.fundamentalunits.Quantity, optional
- A compartment's diameter, by default ``None``
+ A compartment's diameter.
cm : ~brian2.units.fundamentalunits.Quantity, optional
- Specific capacitance (usually μF / cm^2), by default ``None``
+ Specific capacitance (usually μF / cm^2).
gl : ~brian2.units.fundamentalunits.Quantity, optional
- Specific leakage conductance (usually μS / cm^2), by default ``None``
+ Specific leakage conductance (usually μS / cm^2).
+ cm_abs : ~brian2.units.fundamentalunits.Quantity, optional
+ Absolute capacitance (usually pF).
+ gl_abs : ~brian2.units.fundamentalunits.Quantity, optional
+ Absolute leakage conductance (usually nS).
r_axial : ~brian2.units.fundamentalunits.Quantity, optional
- Axial resistance (usually Ohm * cm), by default ``None``
+ Axial resistance (usually Ohm * cm).
v_rest : ~brian2.units.fundamentalunits.Quantity, optional
- Resting membrane voltage, by default ``None``
+ Resting membrane voltage.
scale_factor : float, optional
- A global area scale factor, by default ``1.0``
+ A global area scale factor, by default ``1.0``.
spine_factor : float, optional
- A dendritic area scale factor to account for spines, by default ``1.0``
+ A dendritic area scale factor to account for spines, by default ``1.0``.
"""
- def __init__(self, name: Optional[str] = None,
- length: Optional[Quantity] = None,
- diameter: Optional[Quantity] = None,
- cm: Optional[Quantity] = None,
- gl: Optional[Quantity] = None,
- r_axial: Optional[Quantity] = None,
- v_rest: Optional[Quantity] = None,
- scale_factor: float = 1.0,
- spine_factor: float = 1.0):
+ DEFAULT_PARAMS = {
+ "E_AMPA": 0 * mV,
+ "E_NMDA": 0 * mV,
+ "E_GABA": -80 * mV,
+ "E_Na": 70 * mV,
+ "E_K": -89 * mV,
+ "E_Ca": 136 * mV,
+ "Mg_con": 1.0,
+ "Alpha_NMDA": 0.062,
+ "Beta_NMDA": 3.57,
+ "Gamma_NMDA": 0
+ }
+
+ def __init__(
+ self,
+ name: Optional[str] = None,
+ length: Optional[Quantity] = None,
+ diameter: Optional[Quantity] = None,
+ cm: Optional[Quantity] = None,
+ gl: Optional[Quantity] = None,
+ cm_abs: Optional[Quantity] = None,
+ gl_abs: Optional[Quantity] = None,
+ r_axial: Optional[Quantity] = None,
+ v_rest: Optional[Quantity] = None,
+ scale_factor: Optional[float] = 1.0,
+ spine_factor: Optional[float] = 1.0
+ ):
self.name = name
self.length = length
self.diameter = diameter
self.cm = cm
self.gl = gl
+ self.cm_abs = cm_abs
+ self.gl_abs = gl_abs
self.r_axial = r_axial
self.v_rest = v_rest
- self.scale_factor = scale_factor
- self.spine_factor = spine_factor
+ self.scale_factor = scale_factor if not any([cm_abs, gl_abs]) else None
+ self.spine_factor = spine_factor if not any([cm_abs, gl_abs]) else None
+ self._dimensionless = True if any([cm_abs, gl_abs]) else False
+ self._check_dimensionless()
def __str__(self):
- attrs = self.__dict__
- details = [f"\u2192 {i}: \n{attrs[i]}\n" for i in attrs]
- msg = ("OBJECT:\n{0}\n"
- "\n=======================================================\n\n"
- "ATTRIBUTES:\n{1}")
- return msg.format(self.__class__, "\n".join(details))
+ attrs = pp.pformat(self.__dict__)
+ txt = (f"OBJECT:\n{self.__class__}\n\n"
+ f"ATTRIBUTES:\n{attrs}"
+ )
+ return txt
+
+ def _check_dimensionless(self):
+ """
+ Ensure that no redundant parameters are provided when a dimensionless
+ compartment is created (i.e., when absolute values of capacitance and
+ leakage conductance are provided).
+ """
+ not_dimensionless = [self.length, self.diameter,
+ self.cm, self.gl, self.r_axial]
+
+ if self._dimensionless and any(not_dimensionless):
+ raise DimensionlessCompartmentError(
+ ("\nRedundant or incompatible parameters were detected "
+ f"during \nthe initialization of '{self.name}'. "
+ "When absolute values of \ncapacitance [cm_abs] or leakage "
+ "conductance [gl_abs] are \nused, a dimensionless "
+ "compartment is created by default. \nTo resolve this error, "
+ "you can perform one of the following:\n\n"
+ "1. Discard these parameters [length, diameter, cm,"
+ "gl, r_axial]\n if you want to create a dimensionless "
+ "compartment.\n\n"
+ "2. Discard these parameters [cm_abs, gl_abs] if you want to\n"
+ " create a compartment with physical dimensions."
+ )
+ )
@property
- def total_area_factor(self) -> float:
+ def _total_area_factor(self) -> float:
"""
The total surface are factor.
@@ -79,91 +156,118 @@ def total_area_factor(self) -> float:
return self.scale_factor * self.spine_factor
@property
- def area(self) -> Quantity:
+ def area(self) -> Quantity | None:
"""
- A compartment's surface area (open cylinder) based on its length and
- diameter.
+ Returns compartment's surface area (open cylinder) based on its length
+ and diameter.
Returns
-------
~brian2.units.fundamentalunits.Quantity
A compartment's surface area
"""
- try:
- return pi * self.length * self.diameter * self.total_area_factor
- except TypeError:
- print(("ERROR: Missing Parameters ('length' or 'diameter')\n"
- f"Could not calculate the area of <{self.name}>, "
- "returned None instead\n"))
+ if self._dimensionless:
+ logger.warning(
+ (f"Surface area is not defined for the dimensionless "
+ f"compartment: '{self.name}'"
+ f"\nReturning None instead."
+ )
+ )
+ else:
+ try:
+ return pi * self.length * self.diameter * self._total_area_factor
+ except TypeError:
+ logger.warning(
+ (f"Missing parameters [length | diameter] for '{self.name}'."
+ f"\nCould not calculate the area of '{self.name}', "
+ "returned None."
+ )
+ )
@property
- def capacitance(self) -> Quantity:
+ def capacitance(self) -> Quantity | None:
"""
- A compartment's absolute capacitance based on its specific capacitance
- (cm) and surface area.
+ Returns a compartment's capacitance based on its specific capacitance
+ (cm) and surface area. If an absolute capacitance (cm_abs) has been
+ provided by the user, it returns this value instead.
Returns
-------
:class:`~brian2.units.fundamentalunits.Quantity`
"""
- try:
- return self.area * self.cm
- except TypeError:
- print(("ERROR: Missing Parameters ('cm')\n"
- f"Could not calculate the capacitance of <{self.name}>, "
- "returned None instead"))
+ if self._dimensionless:
+ if self.cm_abs:
+ return self.cm_abs
+ else:
+ logger.warning(
+ f"Missing parameter [cm_abs] for '{self.name}', "
+ "returned None."
+ )
+ else:
+ try:
+ return self.area * self.cm
+ except TypeError:
+ logger.warning(
+ (f"Could not calculate the [capacitance] of '{self.name}', "
+ "returned None."
+ )
+ )
@property
def g_leakage(self) -> Quantity:
"""
- A compartment's absolute leakage conductance based on its specific
- leakage conductance (gl) and surface area.
+ Returns a compartment's absolute leakage conductance based on its
+ specific leakage conductance (gl) and surface area. If an absolute
+ leakage conductance (gl_abs) has been provided by the user, it returns
+ this value instead.
Returns
-------
:class:`~brian2.units.fundamentalunits.Quantity`
"""
- try:
- return self.area * self.gl
- except TypeError:
- print(("ERROR: Missing Parameters ('gl')\n"
- f"Could not calculate the g_leakage of <{self.name}>, "
- "returned None instead"))
+ if self._dimensionless:
+ if self.gl_abs:
+ return self.gl_abs
+ else:
+ logger.warning(
+ f"Missing parameter [gl_abs] for '{self.name}', "
+ "returned None."
+ )
+ else:
+ try:
+ return self.area * self.gl
+ except TypeError:
+ logger.warning(
+ (f"Could not calculate the [g_leakage] of '{self.name}', "
+ "returned None."
+ )
+ )
@property
def parameters(self) -> dict:
"""
- Returns a dictionary of all electrophysiological parameters.
+ Returns a dictionary of all the major electrophysiological parameters
+ that describe a single compartment.
Returns
-------
dict
"""
- d = {}
- error = None
-
- if self.v_rest:
- d[f"EL_{self.name}"] = self.v_rest
- else:
- print(f"ERROR: Could not resolve 'EL_{self.name}'\n")
- error = True
-
- if self.capacitance:
- d[f"C_{self.name}"] = self.capacitance
- else:
- print(f"Could not resolve 'C_{self.name}'\n")
- error = True
-
- if self.g_leakage:
- d[f"gL_{self.name}"] = self.g_leakage
- else:
- print(f"Could not resolve 'gL_{self.name}'")
- error = True
+ d_out = {}
+ EL, C, gL = self.v_rest, self.capacitance, self.g_leakage
- if error:
- print("\nWARNING: One or more parameters are "
- f"missing for '{self.name}' !!!\n")
- return d
+ for value, var in zip([EL, C, gL], ['EL', 'C', 'gL']):
+ if value:
+ if self.name:
+ d_out[f"{var}_{self.name}"] = value
+ else:
+ d_out[f"{var}"] = value
+ else:
+ logger.error(
+ f"Could not resolve [{var}_{self.name}] for '{self.name}'."
+ )
+ d_out.update(self.DEFAULT_PARAMS)
+ return d_out
@property
def g_cylinder(self) -> Quantity:
@@ -177,12 +281,20 @@ def g_cylinder(self) -> Quantity:
-------
:class:`~brian2.units.fundamentalunits.Quantity`
"""
+ if self._dimensionless:
+ raise DimensionlessCompartmentError(
+ f"Calculating [g_cylinder] is invalid for '{self.name}', since\n"
+ "it is a dimensionless compartment. To connect two dimensionless"
+ " compartments, an exact \nvalue for g_couple must be provided."
+ )
try:
ri = (4*self.r_axial*self.length) / (pi*self.diameter**2)
except TypeError:
- print(("ERROR: Missing Parameters \n"
- f"Could not calculate the g_cylinder of <{self.name}>, "
- "returned None instead."))
+ logger.warning(
+ (f"Could not calculate [g_cylinder] for '{self.name}'.\n"
+ "Please make sure that [length, diameter, r_axial]\n"
+ "are available.")
+ )
else:
return 1/ri
@@ -204,13 +316,28 @@ def g_couple(comp1: EphysProperties, comp2: EphysProperties) -> Quantity:
-------
:class:`~brian2.units.fundamentalunits.Quantity`
"""
+ if any([comp1._dimensionless, comp2._dimensionless]):
+ raise DimensionlessCompartmentError(
+ ("Cannot automatically calculate the coupling \nconductance of "
+ "dimensionless compartments. To resolve this error, perform\n"
+ "one of the following:\n\n"
+ f"1. Provide [length, diameter, r_axial] for both '{comp1.name}'"
+ f" and '{comp2.name}'.\n\n"
+ f"2. Turn both compartment into dimensionless by providing only"
+ " values for \n [cm_abs, gl_abs] and then connect them using "
+ "an exact coupling conductance."
+ )
+ )
try:
r1 = (4 * comp1.r_axial * comp1.length) / (pi * comp1.diameter**2)
r2 = (4 * comp2.r_axial * comp2.length) / (pi * comp2.diameter**2)
ri = (r1+r2) / 2
except TypeError:
- print(("ERROR: Missing Parameters \n"
- f"Could not calculate the g_couple of <{comp1.name}> "
- f"& <{comp2.name}>, returned None instead."))
+ logger.error(
+ (f"Could not calculate the g_couple for '{comp1.name}' and "
+ f"'{comp2.name}'.\n"
+ "Please make sure that [length, diameter, r_axial] are\n"
+ "available for both compartments.")
+ )
else:
return 1/ri
diff --git a/dendrify/equations.py b/dendrify/equations.py
index 75fbc43..eb4fd16 100644
--- a/dendrify/equations.py
+++ b/dendrify/equations.py
@@ -42,28 +42,68 @@
'ds_GABA_{1}_{0}/dt = -s_GABA_{1}_{0} / t_GABA_rise_{1}_{0} :1'),
# NMDA equations with rise and decay kinetics:
- 'NMDA': ('I_NMDA_{1}_{0} = g_NMDA_{1}_{0} * (E_NMDA-V_{0}) * s_NMDA_{1}_{0} / (1 + Mg * exp(-alpha*(V_{0}/mV+gamma)) / beta) * w_NMDA_{1}_{0} :amp\n'
+ 'NMDA': ('I_NMDA_{1}_{0} = g_NMDA_{1}_{0} * (E_NMDA-V_{0}) * s_NMDA_{1}_{0} / (1 + Mg_con * exp(-Alpha_NMDA*(V_{0}/mV+Gamma_NMDA)) / Beta_NMDA) * w_NMDA_{1}_{0} :amp\n'
'ds_NMDA_{1}_{0}/dt = -s_NMDA_{1}_{0}/t_NMDA_decay_{1}_{0} :1'),
# NMDA equations with rise and decay kinetics:
- 'NMDA_rd': ('I_NMDA_{1}_{0} = g_NMDA_{1}_{0} * (E_NMDA-V_{0}) * x_NMDA_{1}_{0} / (1 + Mg * exp(-alpha*(V_{0}/mV+gamma)) / beta) * w_NMDA_{1}_{0} :amp\n'
+ 'NMDA_rd': ('I_NMDA_{1}_{0} = g_NMDA_{1}_{0} * (E_NMDA-V_{0}) * x_NMDA_{1}_{0} / (1 + Mg_con * exp(-Alpha_NMDA*(V_{0}/mV+Gamma_NMDA)) / Beta_NMDA) * w_NMDA_{1}_{0} :amp\n'
'dx_NMDA_{1}_{0}/dt = (-x_NMDA_{1}_{0}/t_NMDA_decay_{1}_{0}) + s_NMDA_{1}_{0}/ms :1\n'
'ds_NMDA_{1}_{0}/dt = -s_NMDA_{1}_{0} / t_NMDA_rise_{1}_{0} :1'),
# Random white noise equations:
- 'noise': 'dI_noise_{0}/dt = (mean_noise_{0}-I_noise_{0}) / tau_noise_{0} + sigma_noise_{0} * (sqrt(2/(tau_noise_{0}*dt)) * randn()) :amp',
+ 'noise': 'dI_noise_{0}/dt = (mean_noise_{0}-I_noise_{0}) / tau_noise_{0} + sigma_noise_{0} * (sqrt(2/(tau_noise_{0}*dt)) * randn()) :amp'
+}
- 'condition_I_Na': 'V_{0} > Vth_Na_{0} and allow_I_Na_{0} and t > timer_Na_{0} + refractory_Na',
+library_point = {
+ # Adaptive exponential integrate & fire:
+ 'adex': ('dV/dt = (gL * (EL-V) + gL*DeltaT*exp((V-Vth)/DeltaT) + I - w) / C :volt\n'
+ 'dw/dt = (a * (V-EL) -w) / tauw :amp\n'
+ 'I = I_ext :amp\n'
+ 'I_ext :amp'),
- 'condition_I_Kn': 't > (timer_Na_{0} + offset_Kn) and allow_I_Kn_{0}',
+ # Leaky integrate and fire with adaptation:
+ 'adaptiveIF': ('dV/dt = (gL * (EL-V) + I - w) / C :volt\n'
+ 'dw/dt = (a * (V-EL) - w) / tauw :amp\n'
+ 'I = I_ext :amp\n'
+ 'I_ext :amp'),
- 'condition_I_Ca': 'V_{0} > Vth_Ca_{0} and allow_I_Ca_{0} and t > timer_Ca_{0} + refractory_Ca',
+ # Leaky integrate and fire:
+ 'leakyIF': ('dV/dt = (gL * (EL-V) + I) / C :volt\n'
+ 'I = I_ext :amp\n'
+ 'I_ext :amp'),
- 'condition_I_Kc': 't > (timer_Ca_{0} + offset_Kc) and allow_I_Kc_{0}',
+ # Leaky membrane:
+ 'passive': ('dV/dt = (gL * (EL-V) + I) / C :volt\n'
+ 'I = I_ext :amp\n'
+ 'I_ext :amp'),
- 'run_on_Na_spike': ("run_on_event('activate_I_Na_{0}', 'g_Na_{0} += g_Na_{0}_max; allow_I_Na_{0}=False; allow_I_Kn_{0}=True; timer_Na_{0} = t') \n"
- "run_on_event('activate_I_Kn_{0}', 'g_Kn_{0} += g_Kn_{0}_max; allow_I_Kn_{0}=False; allow_I_Na_{0}=True')"),
+ # AMPA equations with instant rise (only decay kinetics):
+ 'AMPA': ('I_AMPA_{0} = g_AMPA_{0} * (E_AMPA-V) * s_AMPA_{0} * w_AMPA_{0} :amp\n'
+ 'ds_AMPA_{0}/dt = -s_AMPA_{0} / t_AMPA_decay_{0} :1'),
+
+ # AMPA equations with rise and decay kinetics:
+ 'AMPA_rd': ('I_AMPA_{0} = g_AMPA_{0} * (E_AMPA-V) * x_AMPA_{0} * w_AMPA_{0} :amp\n'
+ 'dx_AMPA_{0}/dt = (-x_AMPA_{0}/t_AMPA_decay_{0}) + s_AMPA_{0}/ms :1\n'
+ 'ds_AMPA_{0}/dt = -s_AMPA_{0} / t_AMPA_rise_{0} :1'),
- 'run_on_Ca_spike': ("run_on_event('activate_I_Ca_{0}', 'g_Ca_{0} += g_Ca_{0}_max; allow_I_Ca_{0}=False; allow_I_Kc_{0}=True; timer_Ca_{0} = t') \n"
- "run_on_event('activate_I_Kc_{0}', 'g_Kc_{0} += g_Kc_{0}_max; allow_I_Kc_{0}=False; allow_I_Ca_{0}=True')")
+ # GABA equations with instant rise (only decay kinetics):
+ 'GABA': ('I_GABA_{0} = g_GABA_{0} * (E_GABA-V) * s_GABA_{0} * w_GABA_{0} :amp\n'
+ 'ds_GABA_{0}/dt = -s_GABA_{0} / t_GABA_decay_{0} :1'),
+
+ # GABA equations with rise and decay kinetics:
+ 'GABA_rd': ('I_GABA_{0} = g_GABA_{0} * (E_GABA-V) * x_GABA_{0} * w_GABA_{0} :amp\n'
+ 'dx_GABA_{0}/dt = (-x_GABA_{0}/t_GABA_decay_{0}) + s_GABA_{0}/ms :1\n'
+ 'ds_GABA_{0}/dt = -s_GABA_{0} / t_GABA_rise_{0} :1'),
+
+ # NMDA equations with rise and decay kinetics:
+ 'NMDA': ('I_NMDA_{0} = g_NMDA_{0} * (E_NMDA-V) * s_NMDA_{0} / (1 + Mg_con * exp(-Alpha_NMDA*(V/mV+Gamma_NMDA)) / Beta_NMDA) * w_NMDA_{0} :amp\n'
+ 'ds_NMDA_{0}/dt = -s_NMDA_{0}/t_NMDA_decay_{0} :1'),
+
+ # NMDA equations with rise and decay kinetics:
+ 'NMDA_rd': ('I_NMDA_{0} = g_NMDA_{0} * (E_NMDA-V) * x_NMDA_{0} / (1 + Mg_con * exp(-Alpha_NMDA*(V/mV+Gamma_NMDA)) / Beta_NMDA) * w_NMDA_{0} :amp\n'
+ 'dx_NMDA_{0}/dt = (-x_NMDA_{0}/t_NMDA_decay_{0}) + s_NMDA_{0}/ms :1\n'
+ 'ds_NMDA_{0}/dt = -s_NMDA_{0} / t_NMDA_rise_{0} :1'),
+
+ # Random white noise equations:
+ 'noise': 'dI_noise/dt = (mean_noise-I_noise) / tau_noise + sigma_noise * (sqrt(2/(tau_noise*dt)) * randn()) :amp'
}
diff --git a/dendrify/neuronmodel.py b/dendrify/neuronmodel.py
index e013cdb..efdc291 100644
--- a/dendrify/neuronmodel.py
+++ b/dendrify/neuronmodel.py
@@ -1,11 +1,18 @@
-import sys
+import pprint as pp
+from copy import deepcopy
from typing import List, Optional, Tuple, Union
-import __main__ as main
-from brian2 import NeuronGroup
-from brian2.units import Quantity, mV, mvolt
+import numpy as np
+from brian2 import NeuronGroup, Synapses, defaultclock
+from brian2.units import Quantity, ms, pA
from .compartment import Compartment, Dendrite, Soma
+from .ephysproperties import EphysProperties
+from .equations import library_point
+from .utils import (DimensionlessCompartmentError, DuplicateEquationsError,
+ get_logger)
+
+logger = get_logger(__name__)
class NeuronModel:
@@ -25,7 +32,7 @@ class NeuronModel:
substitute morphologically and biophysically detailed neuron models,
commonly used for highly-accurate, single-cell simulations. If you are
interested in the latter category of models, please see Brian's
- :doc:`SpatialNeuron
+ :doc:`SpatialNeuron
`.
Parameters
@@ -33,15 +40,23 @@ class NeuronModel:
connections : list[tuple[Compartment, Compartment, str | Quantity]]
A description of how the various compartments belonging to the same
neuron model should be connected.
- kwargs : :class:`~brian2.units.fundamentalunits.Quantity`, optional
- Kwargs are used to specify important electrophysiological properties,
- such as the specific capacitance or resistance. For all available options
- see: :class:`.EphysProperties`.
+ cm : ~brian2.units.fundamentalunits.Quantity, optional
+ Specific capacitance (usually μF / cm^2).
+ gl : ~brian2.units.fundamentalunits.Quantity, optional
+ Specific leakage conductance (usually μS / cm^2).
+ r_axial : ~brian2.units.fundamentalunits.Quantity, optional
+ Axial resistance (usually Ohm * cm).
+ v_rest : ~brian2.units.fundamentalunits.Quantity, optional
+ Resting membrane voltage.
+ scale_factor : float, optional
+ A global area scale factor, by default ``1.0``.
+ spine_factor : float, optional
+ A dendritic area scale factor to account for spines, by default ``1.0``.
Warning
-------
Parameters set here affect all model compartments and can override any
- compartment-specific parameters.
+ compartment-specific parameters.
Example
-------
@@ -50,174 +65,326 @@ class NeuronModel:
>>> # y -> Soma or Dendrite object other than x
>>> # z -> 'half_cylinders' or 'cylinder_ + name' or brian2.nS unit
>>> # (by default 'half_cylinders')
- >>> soma = Soma('s', ...)
- >>> prox = Dendrite('p', ...)
- >>> dist = Dendrite('d', ...)
+ >>> soma = Soma(...)
+ >>> prox = Dendrite(...)
+ >>> dist = Dendrite(...)
>>> connections = [(soma, prox, 15*nS), (prox, dist, 10*nS)]
>>> model = NeuronModel(connections)
"""
- # Default values for key ionic mechanisms
- DEFAULTS = {"E_AMPA": 0 * mV,
- "E_NMDA": 0 * mV,
- "E_GABA": -80 * mV,
- "E_Na": 70 * mV,
- "E_K": -89 * mV,
- "E_Ca": 136 * mV,
- "Mg": 1.0,
- "alpha": 0.062,
- "beta": 3.57,
- "gamma": 0}
-
- def __init__(self, connections: List[
- Tuple[Compartment, Compartment, Union[str, Quantity]]], **kwargs):
- self._namespace = None
+ def __init__(
+ self,
+ connections: List[Tuple[Compartment,
+ Compartment,
+ Union[str, Quantity, None]]],
+ cm: Optional[Quantity] = None,
+ gl: Optional[Quantity] = None,
+ r_axial: Optional[Quantity] = None,
+ v_rest: Optional[Quantity] = None,
+ scale_factor: Optional[float] = None,
+ spine_factor: Optional[float] = None,
+ ):
self._compartments = None
- self._linked_neurongroup = None
- self._varscope = None
self._extra_equations = None
self._extra_params = None
self._graph = None
self._parse_compartments(connections)
- self._set_properties(**kwargs)
+ self._set_properties(cm=cm, gl=gl,
+ r_axial=r_axial,
+ v_rest=v_rest,
+ scale_factor=scale_factor,
+ spine_factor=spine_factor)
def __str__(self):
- equations = self.equations.replace('\n', '\n ')
- if self.parameters:
- params_sorted = {key: self.parameters[key]
- for key in sorted(self.parameters)}
- parameters = '\n'.join([f" '{i[0]}': {i[1]}"
- for i in params_sorted.items()])
- else:
- parameters = ' None'
-
- if self._extra_params:
- extra_params_sorted = {key: self._extra_params[key]
- for key in sorted(self._extra_params)}
- extra_params = '\n'.join([f" '{i[0]}': {i[1]}"
- for i in extra_params_sorted.items()])
- else:
- extra_params = ' None'
-
- events = '\n'.join([f" '{key}': '{self.events[key]}'"
- for key in self.events
- ]) if self.events else ' None'
-
- msg = (f"OBJECT TYPE:\n\n {self.__class__}\n\n"
- f"{'-'*45}\n\n"
- "PROPERTIES (type): \n\n"
- f"\u2192 equations (str):\n {equations}\n\n"
- f"\u2192 parameters (dict):\n{parameters}\n\n"
- f"\u2192 events (dict):\n{events}\n"
- f"\n{'-'*45}\n\n"
- f"USEFUL ATTRIBUTES:\n\n"
- f"\u2192 _linked_neurongroup:\n {self._linked_neurongroup}\n\n"
- f"\u2192 _extra_equations:\n {self._extra_equations}\n\n"
- f"\u2192 _extra_params:\n{extra_params}\n")
- return msg
+ equations = self.equations
+ parameters = pp.pformat(self.parameters)
+ events = pp.pformat(self.events, width=120)
+ event_names = pp.pformat(self.event_names)
+ txt = (f"\nOBJECT\n{6*'-'}\n{self.__class__}\n\n\n"
+ f"EQUATIONS\n{9*'-'}\n{equations}\n\n\n"
+ f"PARAMETERS\n{10*'-'}\n{parameters}\n\n\n"
+ f"EVENTS\n{6*'-'}\n{event_names}\n\n\n"
+ f"EVENT CONDITIONS\n{16*'-'}\n{events}\n\n\n"
+ )
+ return txt
def _parse_compartments(self, comp_list):
- error_msg = (
- "\nValid format: [*(x, y, z)] \n"
- "- x -> Soma or Dendrite object\n"
- "- y -> Soma or Dendrite object other than x\n"
- "- z -> 'half_cylinders' or 'cylinder_ + name' or brian2.nS unit\n"
- " (default is 'half_cylinders' if left blank)\n\n"
- "Example:\n"
- "[(comp1, comp2), (comp2, comp3, 10*nS), "
- "(comp3, comp4, 'cylinder_c3')]\n")
-
+ # Ensure that all compartments have a unique names
+ ids, names = [], []
+ for tup in comp_list:
+ for i in tup:
+ if isinstance(i, Compartment):
+ ids.append(id(i))
+ names.append(i.name)
+ if len(set(ids)) != len(set(names)):
+ raise ValueError(
+ ("Please make sure that all compartments included to a single "
+ "NeuronModel have unique names.")
+ )
+ # Start parsing
self._compartments = []
self._graph = []
- for comp in comp_list:
- pre, post = comp[0], comp[1]
-
- # Prohibit self connections
- if pre is post:
- print(f"ERROR: Cannot connect '{pre.name}' to itself.")
- print(error_msg)
- sys.exit()
-
- # Ensure that users do not use objects that make no sense
- if not (isinstance(pre, Compartment) and
- isinstance(post, Compartment)):
- print(f"ERROR: Unknown compartment type provided.")
- print(error_msg)
- sys.exit()
-
+ # Copy compartments to avoid modifying the original objects
+ copied_list = self._copy_compartments(comp_list)
+ for tup in copied_list:
+ pre, post = tup[0], tup[1]
# Store graph-like representation for debugging or visualization
self._graph.append((pre.name, post.name))
-
# Include all compartments in a list for easy access
if pre not in self._compartments:
self._compartments.append(pre)
if post not in self._compartments:
self._compartments.append(post)
-
- # Call the connect method from the Compartment class
- if len(comp) == 2:
+ # Call the connect method from the Compartment class
+ if len(tup) == 2:
pre.connect(post)
else:
- pre.connect(post, g=comp[2])
+ pre.connect(post, g=tup[2])
+ # Check if all compartments are dimensionless or not
+ is_dimensionless = [i.dimensionless for i in self._compartments]
+ if True in is_dimensionless and False in is_dimensionless:
+ raise DimensionlessCompartmentError(
+ "When creating a NeuronModel, either all of its\n"
+ "compartments must be dimensionless or none of them. "
+ "To resolve this issue, you\n"
+ "can perform one of the following:\n\n"
+ "1. Discard these parameters [length, diameter, cm,"
+ "gl, r_axial]\n if you want to create dimensionless "
+ "compartments.\n\n"
+ "2. Discard these parameters [cm_abs, gl_abs] if you want to\n"
+ " create compartments with physical dimensions."
+ )
+
+ def _copy_compartments(self, comp_list):
+ error_msg = (
+ "\n\nValid format: [*(x, y, z)]\n"
+ f"{26*'-'}\n"
+ "x -> Soma or Dendrite object.\n"
+ "y -> Soma or Dendrite object other than x.\n"
+ "z -> 'half_cylinders' or 'cylinder_ + name' or conductance unit.\n"
+ " (default: 'half_cylinders' if left blank).\n\n"
+ "Example:\n"
+ "[(comp1, comp2), (comp2, comp3, 10*nS)] \n"
+ )
+ used = {} # Keep track of copied compartments to avoid duplicates
+ new_list = []
+ for tup in comp_list:
+ # Ensure that users provide correct format
+ if len(tup) < 2 or len(tup) > 3:
+ raise ValueError(
+ f"Invalid number of arguments provided. {error_msg}"
+ )
+ # Ensure that users do not use objects that make no sense
+ if not (isinstance(tup[0], Compartment) and
+ isinstance(tup[1], Compartment)):
+ raise TypeError(
+ f"Invalid compartment type provided. {error_msg}"
+ )
+ # Prohibit self connections
+ if tup[0] is tup[1]:
+ raise ValueError(
+ f"ERROR: Cannot connect '{tup[0].name}' to itself. "
+ f"{error_msg}"
+ )
+
+ if tup[0].name in used:
+ pre = used[tup[0].name]
+ else:
+ pre = deepcopy(tup[0])
+ used[pre.name] = pre
+ if tup[1].name in used:
+ post = used[tup[1].name]
+ else:
+ post = deepcopy(tup[1])
+ used[post.name] = post
+ if len(tup) == 2:
+ new_tup = (pre, post)
+ elif len(tup) == 3:
+ new_tup = (pre, post, tup[2])
+ new_list.append(new_tup)
+ return new_list
def _set_properties(self, cm=None, gl=None, r_axial=None, v_rest=None,
scale_factor=None, spine_factor=None):
- for i in self._compartments:
- if cm and (not i._ephys_object.cm):
- i._ephys_object.cm = cm
- if gl and (not i._ephys_object.gl):
- i._ephys_object.gl = gl
- if r_axial and (not i._ephys_object.r_axial):
- i._ephys_object.r_axial = r_axial
- if v_rest and (not i._ephys_object.v_rest):
- i._ephys_object.v_rest = v_rest
- if scale_factor:
- i._ephys_object.scale_factor = scale_factor
- if spine_factor:
- if isinstance(i, Dendrite):
- i._ephys_object.spine_factor = spine_factor
-
- def dspike_properties(self, channel: str = None,
- tau_rise: Optional[Quantity] = None,
- tau_fall: Optional[Quantity] = None,
- offset_fall: Optional[Quantity] = None,
- refractory: Optional[Quantity] = None):
- """
- Allows specifying essential dSpike properties affecting all compartments.
+
+ params = {'cm': cm, 'gl': gl, 'r_axial': r_axial,
+ 'scale_factor': scale_factor, 'spine_factor': spine_factor}
+
+ for comp in self._compartments:
+ # update v_rest for all compartments if provided
+ if v_rest:
+ comp._ephys_object.v_rest = v_rest
+ # prohibit dimensionless compartments from taking area-related params
+ if comp.dimensionless and any(params.values()):
+ raise DimensionlessCompartmentError(
+ f"The dimensionless compartment '{comp.name}' cannot take "
+ "the \nfollowing parameters: "
+ "[cm, gl, r_axial, scale_factor, spine_factor]."
+ )
+ # update all other params if provided
+ if not comp.dimensionless and any(params.values()):
+ for param, value in params.items():
+ if value:
+ setattr(comp._ephys_object, param, value)
+ # make sure to initialize area factors if not provided
+ if not value and param in ['scale_factor', 'spine_factor']:
+ setattr(comp._ephys_object, param, 1.0)
+
+
+ def config_dspikes(self, event_name: str,
+ threshold: Union[Quantity, None] = None,
+ duration_rise: Union[Quantity, None] = None,
+ duration_fall: Union[Quantity, None] = None,
+ reversal_rise: Union[Quantity, str, None] = None,
+ reversal_fall: Union[Quantity, str, None] = None,
+ offset_fall: Union[Quantity, None] = None,
+ refractory: Union[Quantity, None] = None
+ ):
+ """
+ Configure the parameters for dendritic spiking.
Parameters
----------
- channel : str
- Ion channel type. Available options: ``'Na'``, ``'Ca'`` (coming
- soon).
- tau_rise : :class:`~brian2.units.fundamentalunits.Quantity`
- The decay time constant of the current causing the dSpike's
- **depolarization** phase, by default ``None``.
- tau_fall : :class:`~brian2.units.fundamentalunits.Quantity`
- The decay time constant of the current causing the dSpike's
- **repolarization** phase, by default ``None``.
- offset_fall : :class:`~brian2.units.fundamentalunits.Quantity`
- The delay for starting the dSpike repolarization phase, by default
- ``None``.
- refractory : :class:`~brian2.units.fundamentalunits.Quantity`
- The duration of the dSpike inactive period, by default ``None``.
- """
- # Make sure user provides a valid option:
- if channel not in ['Na', 'Ca']:
- print("Please select a valid dendritic spike type ('Na' or 'Ca')")
- sys.exit()
- # Choose param names based on user input:
- if channel == 'Na':
- dspike_params = {'refractory_Na': refractory,
- 'offset_Kn': offset_fall,
- 'tau_Na': tau_rise,
- 'tau_Kn': tau_fall}
- else:
- dspike_params = {'refractory_Ca': refractory,
- 'offset_Kc': offset_fall,
- 'tau_Ca': tau_rise,
- 'tau_Kc': tau_fall}
- self.add_params(dspike_params)
+ event_name : str
+ A unique name referring to a specific dSpike type.
+ threshold : ~brian2.units.fundamentalunits.Quantity, optional
+ The membrane voltage threshold for dendritic spiking.
+ duration_rise : ~brian2.units.fundamentalunits.Quantity, optional
+ The duration of g_rise staying open.
+ duration_fall : ~brian2.units.fundamentalunits.Quantity, optional
+ The duration of g_fall staying open.
+ reversal_rise : (~brian2.units.fundamentalunits.Quantity, str), optional
+ The reversal potential of the channel that is activated during the rise
+ (depolarization) phase.
+ reversal_fall : (~brian2.units.fundamentalunits.Quantity, str), optional
+ The reversal potential of the channel that is activated during the fall
+ (repolarization) phase.
+ offset_fall : ~brian2.units.fundamentalunits.Quantity, optional
+ The delay for the activation of g_rise.
+ refractory : ~brian2.units.fundamentalunits.Quantity, optional
+ The time interval required before dSpike can be activated again.
+ """
+
+ for comp in self._compartments:
+ if isinstance(comp, Dendrite) and comp._dspike_params:
+ ID = f"{event_name}_{comp.name}"
+ dt = defaultclock.dt
+ d = {f"Vth_{ID}": threshold,
+ f"duration_rise_{ID}": comp._timestep(duration_rise, dt),
+ f"duration_fall_{ID}": comp._timestep(duration_fall, dt),
+ f"E_rise_{event_name}": comp._ionic_param(reversal_rise),
+ f"E_fall_{event_name}": comp._ionic_param(reversal_fall),
+ f"offset_fall_{ID}": comp._timestep(offset_fall, dt),
+ f"refractory_{ID}": comp._timestep(refractory, dt)}
+ comp._dspike_params[ID].update(d)
+
+ def make_neurongroup(self,
+ N: int,
+ method: str = 'euler',
+ threshold: Optional[str] = None,
+ reset: Optional[str] = None,
+ second_reset: Optional[str] = None,
+ spike_width: Optional[Quantity] = None,
+ refractory: Union[Quantity, str, bool] = False,
+ init_rest: bool = True,
+ init_events: bool = True,
+ show: bool = False,
+ **kwargs
+ ) -> Union[NeuronGroup, Tuple]:
+ """
+ Returns a Brian2 NeuronGroup object from a NeuronModel. If a second
+ reset is provided, it also returns a Synapses object to implement
+ somatic action potentials with a more realistic shape which also unlocks
+ dendritic backpropagation. This method can also take all parameters that
+ are accepted by Brian's
+ :doc:`NeuronGroup `.
+
+ Parameters
+ ----------
+ N : int
+ The number of neurons in the group.
+ method : str, optional
+ The numerical integration method. Either a string with the name of a
+ registered method (e.g. "euler") or a function that receives an
+ `Equations` object and returns the corresponding abstract code, by
+ default ``'euler'``.
+ threshold : str, optional
+ The condition which produces spikes. Should be a single line boolean
+ expression.
+ reset : str, optional
+ The (possibly multi-line) string with the code to execute on reset.
+ refractory : (Quantity, str), optional
+ Either the length of the refractory period (e.g. ``2*ms``), a string
+ expression that evaluates to the length of the refractory period
+ after each spike (e.g. ``'(1 + rand())*ms'``), or a string expression
+ evaluating to a boolean value, given the condition under which the
+ neuron stays refractory after a spike (e.g. ``'v > -20*mV'``).
+ second_reset : str, optional
+ Option to include a second reset for more realistic somatic spikes.
+ spike_width : Quantity, optional
+ The time interval between the two resets.
+ init_rest : bool, optional
+ Option to automatically initialize the voltages of all compartments
+ at the specified resting potentials, by default True.
+ init_events : bool, optional
+ Option to automatically initialize all custom events that required
+ for dendritic spiking, by default True.
+ show : bool, optional
+ Option to print the automatically initialized parameters, by default
+ False.
+ **kwargs: optional
+ All other parameters accepted by Brian's NeuronGroup.
+
+ Returns
+ -------
+ Union[NeuronGroup, Tuple]
+ If no second reset is added, it returns a NeuronGroup object.
+ Otherwise, it returns a tuple of (NeuronGroup, Synapses) objects.
+ """
+
+ group = NeuronGroup(N,
+ method=method,
+ threshold=threshold,
+ reset=reset,
+ refractory=refractory,
+ model=self.equations,
+ events=self.events,
+ namespace=self.parameters,
+ **kwargs)
+
+ if init_rest:
+ for comp in self._compartments:
+ if show:
+ print(
+ f"Setting V_{comp.name} = {comp._ephys_object.v_rest}")
+ setattr(group, f'V_{comp.name}', comp._ephys_object.v_rest)
+
+ if init_events:
+ if self.event_actions:
+ for event, action in self.event_actions.items():
+ if show:
+ print(f"Setting run_on_event('{event}', '{action}')")
+ group.run_on_event(event, action)
+
+ ap_reset = None
+ if any([second_reset, spike_width]):
+ txt = (
+ "If you wish to have a more realistic action potential shape, "
+ "please provide \nvalid values for both [second_reset] and "
+ "[spike_width]."
+ )
+ if not all([second_reset, spike_width]):
+ raise ValueError(txt)
+ try:
+ ap_reset = Synapses(group, group,
+ on_pre=second_reset,
+ delay=spike_width)
+ ap_reset.connect(j='i')
+
+ except Exception:
+ raise ValueError(txt)
+
+ return (group, ap_reset) if ap_reset else group
def add_params(self, params_dict: dict):
"""
@@ -246,95 +413,7 @@ def add_equations(self, eqs: str):
else:
self._extra_equations += f"\n{eqs}"
- def link(self, ng: NeuronGroup, automate: str = 'all',
- verbose: bool = False):
- """
- Links a NeuronModel to a
- :doc:`NeuronGroup `.
- This allows dendrify to automatically handle the initialization of
- important simulation parameters.
-
- Parameters
- ----------
- ng : brian2.NeuronGroup
- A NeuronGroup that was created using a NeuronModel.
- automate : str, optional
- What to automate. Available options: ``'all'`` (default),
- ``'v_rest'``, ``'events'``.
- verbose : bool, optional
- If ``True`` it prints all the code that was created and run in the
- background by dendrify, by default ``False``
- """
- self._namespace = ng.namespace
- self._varscope = main.__dict__
- items = main.__dict__.items()
- ng_name = [k for k, v in items if v is ng][0]
- self._linked_neurongroup = ng_name, ng
-
- if automate == 'all':
- self._set_rest(verbose)
- self._handle_events(verbose)
-
- elif automate == 'v_rest':
- self._set_rest(verbose)
-
- elif automate == 'events':
- self._handle_events(verbose)
-
- def _set_rest(self, verbose=False):
- """
- Creates and runs executable code that initialises V rest across all
- NeuronModel _compartments.
- """
- command = '{0}.V_{1} = {2}'
-
- # When model parameters are passed as dict to the NeuronGroup:
- if self._namespace:
- commands = [command.format(self._linked_neurongroup[0], i.name,
- repr(self._namespace['EL_'+i.name]))
- for i in self._compartments]
- executable = '\n'.join(commands)
- if verbose:
- print(executable)
- exec(executable, self._varscope)
-
- def _handle_events(self, verbose=False):
- """
- Creates and runs executable code that:
- a) Initializes custom event checkpoint variables.
- b) Specifies what happens during custom events.
- """
- ng_name = self._linked_neurongroup[0]
- # Find all active _compartments:
- active_comps = [i for i in self._compartments if i._events]
- if active_comps == []:
- if verbose:
- print("\n")
- return
- # Na spike vs Ca spike branches
- comps_Na = filter(lambda x: '_I_Na_' in x.event_actions, active_comps)
- comps_Ca = filter(lambda x: '_I_Ca_' in x.event_actions, active_comps)
- # Initial compditions for the custom events needed for dspikes:
- checks_Na = ('{0}.allow_I_Na_{1} = True \n'
- '{0}.allow_I_Kn_{1} = False')
- checks_Ca = ('{0}.allow_I_Ca_{1} = True \n'
- '{0}.allow_I_Kc_{1} = False')
- # Compartment specific initial compditions:
- checks_Na_comp = [checks_Na.format(ng_name, i.name) for i in comps_Na]
- checks_Ca_comp = [checks_Ca.format(ng_name, i.name) for i in comps_Ca]
- # All initial compditions and actions needed for dspikes:
- all_checks = checks_Na_comp + checks_Ca_comp
- all_actions = [i.event_actions for i in active_comps]
- # Megrge all actions and checks into a single string:
- commands = '\n'.join(all_checks + all_actions)
-
- executable = commands.replace('run_on_event',
- f'{ng_name}.run_on_event')
- if verbose:
- print(executable)
- exec(executable, self._varscope)
-
- def as_graph(self, fontsize: int = 10, fontcolor: str = 'white',
+ def as_graph(self, figsize: list = [6, 4], fontsize: int = 10, fontcolor: str = 'white',
scale_nodes: float = 1, color_soma: str = '#4C6C92',
color_dendrites: str = '#A7361C', alpha: float = 1,
scale_edges: float = 1, seed: Optional[int] = None):
@@ -380,7 +459,7 @@ def as_graph(self, fontsize: int = 10, fontcolor: str = 'white',
G.add_edges_from(self._graph)
# Visualize it
- fig, ax = plt.subplots()
+ fig, ax = plt.subplots(figsize=figsize)
for d in ['right', 'top', 'left', 'bottom']:
ax.spines[d].set_visible(False)
pos = nx.spring_layout(G, fixed=soma, pos={soma[0]: (0, 0)},
@@ -402,7 +481,7 @@ def as_graph(self, fontsize: int = 10, fontcolor: str = 'white',
@property
def equations(self) -> str:
"""
- Merges all compartments' equations into a single string.
+ Returns a string containing all model equations.
Returns
-------
@@ -417,7 +496,7 @@ def equations(self) -> str:
@property
def parameters(self) -> dict:
"""
- Merges all compartments' parameters into a dictionary.
+ Returns a dictionary containing all model parameters.
Returns
-------
@@ -427,7 +506,6 @@ def parameters(self) -> dict:
d = {}
for i in self._compartments:
d.update(i.parameters)
- d.update(self.DEFAULTS)
if self._extra_params:
d.update(self._extra_params)
return d
@@ -435,7 +513,8 @@ def parameters(self) -> dict:
@property
def events(self) -> dict:
"""
- Organizes all custom events for dendritic spiking into a dictionary.
+ Returns a dictionary containing all model custom events for dendritic
+ spiking.
Returns
-------
@@ -443,22 +522,386 @@ def events(self) -> dict:
All model custom events for dendritic spiking.
"""
d_out = {}
- all_events = [i._events for i in self._compartments
- if i._events and isinstance(i, Dendrite)]
+ dendrites = [i for i in self._compartments if isinstance(i, Dendrite)]
+ all_events = [i._events for i in dendrites if i._events]
for d in all_events:
d_out.update(d)
return d_out
@property
- def event_actions(self) -> list:
+ def event_names(self) -> list:
"""
- Creates a list of all event actions for dendritic spiking.
+ Returns a list of all event names for dendritic spiking.
+
+ Returns
+ -------
+ list
+ All event names for dendritic spiking
+ """
+ return list(self.events.keys())
+
+ @property
+ def event_actions(self) -> dict:
+ """
+ Returns a dictionary containing all event actions for dendritic
+ spiking.
Returns
-------
list
All event actions for dendritic spiking
"""
- all_actions = [i._event_actions for i in self._compartments
- if i._event_actions and isinstance(i, Dendrite)]
- return all_actions
+ d_out = {}
+ dendrites = [i for i in self._compartments if isinstance(i, Dendrite)]
+ all_actions = [i._event_actions for i in dendrites if i._event_actions]
+ for d in all_actions:
+ d_out.update(d)
+ return d_out
+
+
+class PointNeuronModel:
+ """
+ Like a :class:`.NeuronModel` but for point-neuron (single-compartment)
+ models.
+
+ Parameters
+ ----------
+ model : str, optional
+ A keyword for accessing Dendrify's library models. Custom models can
+ also be provided but they should be in the same formattable structure as
+ the library models. Available options: ``'leakyIF'`` (default),
+ ``'adaptiveIF'``, ``'adex'``.
+ length : ~brian2.units.fundamentalunits.Quantity, optional
+ The point neuron's length.
+ diameter : ~brian2.units.fundamentalunits.Quantity, optional
+ The point neuron's diameter.
+ cm : ~brian2.units.fundamentalunits.Quantity, optional
+ Specific capacitance (usually μF / cm^2).
+ gl : ~brian2.units.fundamentalunits.Quantity, optional
+ Specific leakage conductance (usually μS / cm^2).
+ cm_abs : ~brian2.units.fundamentalunits.Quantity, optional
+ Absolute capacitance (usually pF).
+ gl_abs : ~brian2.units.fundamentalunits.Quantity, optional
+ Absolute leakage conductance (usually nS).
+ v_rest : ~brian2.units.fundamentalunits.Quantity, optional
+ Resting membrane voltage.
+ """
+
+ def __init__(
+ self,
+ model: str = 'leakyIF',
+ length: Optional[Quantity] = None,
+ diameter: Optional[Quantity] = None,
+ cm: Optional[Quantity] = None,
+ gl: Optional[Quantity] = None,
+ cm_abs: Optional[Quantity] = None,
+ gl_abs: Optional[Quantity] = None,
+ v_rest: Optional[Quantity] = None,
+ ):
+ self._equations = None
+ self._params = None
+ self._synapses = None
+ self._extra_equations = None
+ self._extra_params = None
+ # Add membrane equations:
+ self._add_equations(model)
+ # Keep track of electrophysiological properties:
+ self._ephys_object = EphysProperties(
+ name=None,
+ length=length,
+ diameter=diameter,
+ cm=cm,
+ gl=gl,
+ cm_abs=cm_abs,
+ gl_abs=gl_abs,
+ v_rest=v_rest,
+ )
+
+ def __str__(self):
+ equations = self.equations
+ parameters = pp.pformat(self.parameters)
+ user = pp.pformat(self._ephys_object.__dict__)
+ txt = (f"\nOBJECT\n{6*'-'}\n{self.__class__}\n\n\n"
+ f"EQUATIONS\n{9*'-'}\n{equations}\n\n\n"
+ f"PARAMETERS\n{10*'-'}\n{parameters}\n\n\n"
+ f"USER PARAMETERS\n{15*'-'}\n{user}")
+ return txt
+
+ def _add_equations(self, model: str):
+ """
+ Adds equations to a compartment.
+
+ Parameters
+ ----------
+ model : str
+ """
+ # Pick a model template or provide a custom model:
+ if model in library_point:
+ self._equations = library_point[model]
+ else:
+ logger.warning(("The model you provided is not found. The default "
+ "'passive' membrane model will be used instead."))
+ self._equations = library_point['passive']
+
+ def synapse(self,
+ channel: str,
+ tag: str,
+ g: Optional[Quantity] = None,
+ t_rise: Optional[Quantity] = None,
+ t_decay: Optional[Quantity] = None,
+ scale_g: bool = False):
+ """
+ Adds synaptic currents equations and parameters. When only the decay
+ time constant ``t_decay`` is provided, the synaptic model assumes an
+ instantaneous rise of the synaptic conductance followed by an exponential
+ decay. When both the rise ``t_rise`` and decay ``t_decay`` constants are
+ provided, synapses are modelled as a sum of two exponentials. For more
+ information see:
+ `Modeling Synapses by Arnd Roth & Mark C. W. van Rossum
+ `_
+
+ Parameters
+ ----------
+ channel : str
+ Synaptic channel type. Available options: ``'AMPA'``, ``'NMDA'``,
+ ``'GABA'``.
+ tag : str
+ A unique name to distinguish synapses of the same type.
+ g : :class:`~brian2.units.fundamentalunits.Quantity`
+ Maximum synaptic conductance
+ t_rise : :class:`~brian2.units.fundamentalunits.Quantity`
+ Rise time constant
+ t_decay : :class:`~brian2.units.fundamentalunits.Quantity`
+ Decay time constant
+ scale_g : bool, optional
+ Option to add a normalization factor to scale the maximum
+ conductance at 1 when synapses are modelled as a difference of
+ exponentials (have both rise and decay kinetics), by default
+ ``False``.
+
+ Examples
+ --------
+ >>> neuron = PointNeuronModel(...)
+ >>> # adding an AMPA synapse with instant rise & exponential decay:
+ >>> neuron.synapse('AMPA', tag='X', g=1*nS, t_decay=5*ms)
+ >>> # same channel, different conductance & source:
+ >>> neuron.synapse('AMPA', tag='Y', g=2*nS, t_decay=5*ms)
+ >>> # different channel with both rise & decay kinetics:
+ >>> neuron.synapse('NMDA', tag='X' g=1*nS, t_rise=5*ms, t_decay=50*ms)
+ """
+
+ synapse_id = "_".join([channel, tag])
+
+ if self._synapses:
+ # Check if this synapse already exists
+ if synapse_id in self._synapses:
+ raise DuplicateEquationsError(
+ f"The equations of '{channel}_{tag}' have already been "
+ f"added. \nPlease use a different "
+ f"combination of [channel, tag] when calling the synapse() "
+ "method \nmultiple times on a single compartment. You might"
+ " also see this error if you are using \nJupyter/iPython "
+ "which store variable values in memory. Try cleaning all "
+ "variables or \nrestart the kernel before running your "
+ "code. If this problem persists, please report it \n"
+ "by creating a new issue here: "
+ "https://github.com/Poirazi-Lab/dendrify/issues."
+ )
+ else:
+ self._synapses = []
+
+ # Switch to rise/decay equations if t_rise & t_decay are provided
+ key = f"{channel}_rd" if all([t_rise, t_decay]) else channel
+ current_name = f'I_{channel}_{tag}'
+ current_eqs = library_point[key].format(tag)
+
+ to_replace = f'= I_ext'
+ self._equations = self._equations.replace(
+ to_replace,
+ f'{to_replace} + {current_name}'
+ )
+ self._equations += '\n'+current_eqs
+
+ if not self._params:
+ self._params = {}
+
+ weight = f"w_{channel}_{tag}"
+ self._params[weight] = 1.0
+
+ # If user provides a value for g, then add it to _params
+ if g:
+ self._params[f'g_{channel}_{tag}'] = g
+ if t_rise:
+ self._params[f't_{channel}_rise_{tag}'] = t_rise
+ if t_decay:
+ self._params[f't_{channel}_decay_{tag}'] = t_decay
+ if scale_g:
+ if all([t_rise, t_decay, g]):
+ norm_factor = Compartment.g_norm_factor(t_rise, t_decay)
+ self._params[f'g_{channel}_{tag}'] *= norm_factor
+
+ self._synapses.append(synapse_id)
+
+ def noise(self, tau: Quantity = 20*ms, sigma: Quantity = 1*pA,
+ mean: Quantity = 0*pA):
+ """
+ Adds a stochastic noise current. For more information see the Noise
+ section: of :doc:`brian2:user/models`
+
+ Parameters
+ ----------
+ tau : :class:`~brian2.units.fundamentalunits.Quantity`, optional
+ Time constant of the Gaussian noise, by default ``20*ms``
+ sigma : :class:`~brian2.units.fundamentalunits.Quantity`, optional
+ Standard deviation of the Gaussian noise, by default ``3*pA``
+ mean : :class:`~brian2.units.fundamentalunits.Quantity`, optional
+ Mean of the Gaussian noise, by default ``0*pA``
+ """
+ I_noise_name = f'I_noise'
+
+ if I_noise_name in self.equations:
+ raise DuplicateEquationsError(
+ f"The equations of '{I_noise_name}' have already been "
+ f"added to the model. \nYou might be seeing this error if "
+ "you are using Jupyter/iPython "
+ "which store variable values \nin memory. Try cleaning all "
+ "variables or restart the kernel before running your "
+ "code. If this \nproblem persists, please report it "
+ "by creating a new issue here:\n"
+ "https://github.com/Poirazi-Lab/dendrify/issues."
+ )
+ noise_eqs = library_point['noise']
+ to_change = f'= I_ext'
+ self._equations = self._equations.replace(
+ to_change,
+ f'{to_change} + {I_noise_name}'
+ )
+ self._equations = f"{self._equations}\n{noise_eqs}"
+
+ # Add _params:
+ if not self._params:
+ self._params = {}
+ self._params[f'tau_noise'] = tau
+ self._params[f'sigma_noise'] = sigma
+ self._params[f'mean_noise'] = mean
+
+
+ def make_neurongroup(self, N: int, **kwargs) -> NeuronGroup:
+ group = NeuronGroup(N, model=self.equations,
+ namespace=self.parameters,
+ **kwargs)
+ setattr(group, 'V', self._ephys_object.v_rest)
+ return group
+
+ def add_params(self, params_dict: dict):
+ """
+ Allows specifying extra/custom parameters.
+
+ Parameters
+ ----------
+ params_dict : dict
+ A dictionary of parameters.
+ """
+ if not self._extra_params:
+ self._extra_params = {}
+ self._extra_params.update(params_dict)
+
+ def add_equations(self, eqs: str):
+ """
+ Allows adding custom equations.
+
+ Parameters
+ ----------
+ eqs : str
+ A string of Brian-compatible equations.
+ """
+ if not self._extra_equations:
+ self._extra_equations = f"{eqs}"
+ else:
+ self._extra_equations += f"\n{eqs}"
+
+ @property
+ def parameters(self) -> dict:
+ """
+ Returns all the parameters that have been generated for a single
+ compartment.
+
+ Returns
+ -------
+ dict
+ """
+ d_out = {}
+ if self._params:
+ d_out.update(self._params)
+ if self._extra_params:
+ d_out.update(self._extra_params)
+ if self._ephys_object:
+ d_out.update(self._ephys_object.parameters)
+ return d_out
+
+ @property
+ def area(self) -> Quantity:
+ """
+ Returns a compartment's surface area (open cylinder) based on its length
+ and diameter.
+
+ Returns
+ -------
+ :class:`~brian2.units.fundamentalunits.Quantity`
+ """
+ return self._ephys_object.area
+
+ @property
+ def capacitance(self) -> Quantity:
+ """
+ Returns a compartment's absolute capacitance.
+
+ Returns
+ -------
+ :class:`~brian2.units.fundamentalunits.Quantity`
+ """
+ return self._ephys_object.capacitance
+
+ @property
+ def g_leakage(self) -> Quantity:
+ """
+ A compartment's absolute leakage conductance.
+
+ Returns
+ -------
+ :class:`~brian2.units.fundamentalunits.Quantity`
+ """
+ return self._ephys_object.g_leakage
+
+ @property
+ def equations(self) -> str:
+ """
+ Returns all differential equations that describe a single compartment
+ and the mechanisms that have been added to it.
+
+ Returns
+ -------
+ str
+ """
+ if self._extra_equations:
+ return f"{self._equations}\n\n{self._extra_equations}"
+ return self._equations
+
+ @staticmethod
+ def g_norm_factor(trise: Quantity, tdecay: Quantity):
+ tpeak = (tdecay*trise / (tdecay-trise)) * np.log(tdecay/trise)
+ factor = (((tdecay*trise) / (tdecay-trise))
+ * (-np.exp(-tpeak/trise) + np.exp(-tpeak/tdecay))
+ / ms)
+ return 1/factor
+
+ @property
+ def dimensionless(self) -> bool:
+ """
+ Checks if a compartment has been flagged as dimensionless.
+
+ Returns
+ -------
+ bool
+ """
+ return True if self._ephys_object._dimensionless else False
diff --git a/dendrify/tests/simple_test.py b/dendrify/tests/simple_test.py
new file mode 100644
index 0000000..924a820
--- /dev/null
+++ b/dendrify/tests/simple_test.py
@@ -0,0 +1,7 @@
+# content of test_sample.py
+def func(x):
+ return x + 1
+
+
+def test_answer():
+ assert func(5) == 6
\ No newline at end of file
diff --git a/dendrify/utils.py b/dendrify/utils.py
new file mode 100644
index 0000000..aa9511c
--- /dev/null
+++ b/dendrify/utils.py
@@ -0,0 +1,41 @@
+import logging
+
+
+def get_logger(name):
+ """A simple function that returns a logger
+
+ Parameters
+ ----------
+ name : str
+ The name used for logging, should normally be the module name as
+ returned by ``__name__``.
+
+ Returns
+ -------
+ logger : logging.Logger
+ Used for nicely displaying error, warnings etc.
+ """
+ logger = logging.getLogger(name)
+ logger.setLevel(logging.INFO)
+ handler = logging.StreamHandler()
+ handler.setLevel(logging.INFO)
+ formatter = logging.Formatter(
+ '%(levelname)s [%(name)s:%(lineno)d]\n%(message)s\n')
+ handler.setFormatter(formatter)
+ logger.addHandler(handler)
+ return logger
+
+
+class DimensionlessCompartmentError(Exception):
+ """
+ Raise this error when an operation that is invalid for dimensionless
+ compartments is performed.
+ """
+ pass
+
+
+class DuplicateEquationsError(Exception):
+ """
+ Raise this error when a user tries to add the same equations twice.
+ """
+ pass
diff --git a/docs_sphinx/examples_to_rst.py b/docs_sphinx/examples_to_rst.py
new file mode 100644
index 0000000..1032195
--- /dev/null
+++ b/docs_sphinx/examples_to_rst.py
@@ -0,0 +1,107 @@
+from os import listdir, path
+
+import brian2 as b # needed for exec
+import matplotlib.pyplot as plt
+from brian2.units import * # needed for exec
+from scipy.optimize import curve_fit # needed for exec
+
+
+# Analysis code
+def func(t, a, tau):
+ """Exponential decay function"""
+ return a * b.exp(-t / tau)
+
+def get_tau(trace, t0):
+ dt = b.defaultclock.dt
+ Vmin = min(trace)
+ time_to_peak = list(trace).index(Vmin)
+
+ # Find voltage from current-start to min value
+ voltages = trace[int(t0/dt): time_to_peak] / mV
+
+ # Min-max normalize voltages
+ v_norm = (voltages - voltages.min()) / (voltages.max() - voltages.min())
+
+ # Fit exp decay function to normalized data
+ X = b.arange(0, len(v_norm)) * dt / ms
+ popt, _ = curve_fit(func, X, v_norm)
+
+ return popt, X, v_norm
+
+class Parser:
+ def __init__(self, file, docs_path):
+ self.file = file
+ self.lines = self.read_file()
+ self.info_lines = self.get_info_lines()
+ self.docs_path = docs_path
+ self.figs_path = path.join(docs_path, 'source/examples/_static')
+
+ def read_file(self):
+ with open(self.file, 'r') as f:
+ return f.read().splitlines()
+
+ def get_info_lines(self):
+ return [i for i, x in enumerate(self.lines) if x == '"""']
+
+ def run_code(self):
+ figname = self.filename.replace('.py', '.png')
+ fig_path = path.join(self.figs_path, figname)
+ exec(self.code.replace('show()', f'savefig("{fig_path}", dpi=300)'), locals())
+
+ def write_rst(self):
+ filename = path.join(self.docs_path, 'source', 'examples',
+ self.filename.replace('.py', '.rst'))
+ with open(filename, 'w') as f:
+ f.write(self.rst_file)
+
+ @property
+ def filename(self):
+ return path.basename(self.file)
+
+ @property
+ def title(self):
+ for index, value in enumerate(self.lines):
+ if value == 'Title':
+ return self.lines[index + 2]
+ @property
+ def description(self):
+ start = [i for i, x in enumerate(self.lines) if x == 'Description'][0]
+ end = self.info_lines[-1]
+ return '\n'.join(self.lines[start + 2:end])
+
+ @property
+ def code(self):
+ start = self.info_lines[-1] + 1
+ return '\n'.join(self.lines[start:])
+
+ @property
+ def rst_code(self):
+ directive = '.. code-block:: python'
+ indented_code = self.code.replace('\n', '\n ')
+ return '\n'.join([directive, indented_code])
+
+ @property
+ def rst_file(self):
+ image_name = path.join('_static', self.filename.replace('.py', '.png'))
+ image = (f'.. image:: {image_name}\n'
+ ' :align: center')
+ content = [f"{self.title}\n{'=' * len(self.title)}",
+ self.description,
+ self.rst_code, image]
+ return '\n\n\n'.join(content)
+
+
+if __name__ == '__main__':
+ # Get the path to the directory that is one level up
+ root = path.abspath(path.join(path.dirname(__file__), '..'))
+ docs = path.join(root, 'docs_sphinx')
+ examples = path.join(root, 'examples_new')
+ files = listdir(examples)
+
+ for file in files:
+ filename = path.join(examples, file)
+ example = Parser(filename, docs)
+ print(example.title)
+ example.run_code()
+ example.write_rst()
+ plt.close('all')
diff --git a/docs_sphinx/source/_static/dendrify_logo_dark.png b/docs_sphinx/source/_static/dendrify_logo_dark.png
new file mode 100644
index 0000000..53678ea
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diff --git a/docs_sphinx/source/_static/dendrify_logo_light.png b/docs_sphinx/source/_static/dendrify_logo_light.png
new file mode 100644
index 0000000..c58dccc
Binary files /dev/null and b/docs_sphinx/source/_static/dendrify_logo_light.png differ
diff --git a/docs_sphinx/source/_static/intro.png b/docs_sphinx/source/_static/intro.png
index 93e67d8..f3913e5 100644
Binary files a/docs_sphinx/source/_static/intro.png and b/docs_sphinx/source/_static/intro.png differ
diff --git a/docs_sphinx/source/_static/intro_dark.png b/docs_sphinx/source/_static/intro_dark.png
index 5eb4415..12420fe 100644
Binary files a/docs_sphinx/source/_static/intro_dark.png and b/docs_sphinx/source/_static/intro_dark.png differ
diff --git a/docs_sphinx/source/api/classes.rst b/docs_sphinx/source/api/classes.rst
index 42c5684..cd596aa 100644
--- a/docs_sphinx/source/api/classes.rst
+++ b/docs_sphinx/source/api/classes.rst
@@ -4,8 +4,10 @@ Classes
.. toctree::
:maxdepth: 1
- compartment
+
soma
dendrite
neuronmodel
+ pointneuronmodel
+ compartment
ephys
\ No newline at end of file
diff --git a/docs_sphinx/source/api/models.rst b/docs_sphinx/source/api/models.rst
index 38d0884..49cb7a3 100644
--- a/docs_sphinx/source/api/models.rst
+++ b/docs_sphinx/source/api/models.rst
@@ -1,21 +1,20 @@
Model library
=============
-.. image:: ../_static/under-construction.png
- :width: 20 %
- :align: center
-
.. note::
Dendrify relies on Brian's :doc:`Equations-based `
approach to define models as systems of first order ordinary differential
equations. For convenience, Dendrify includes a library of default models
- (see below) however users can also provide custom model equations.
+ (see below), however users can also provide custom equations. **The list
+ below is not exhaustive and will be updated in the future**.
+
.. _somatic_models:
Somatic models [1]_ [2]_
------------------------
+|
Leaky Integrate-and-Fire
~~~~~~~~~~~~~~~~~~~~~~~~
@@ -33,6 +32,8 @@ where
When the firing threshold :math:`V_\theta` is crossed, :math:`V` resets to a
fixed value :math:`V_r`.
+|
+
Adaptive Integrate-and-Fire
~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -52,6 +53,7 @@ When the firing threshold :math:`V_\theta` is crossed, :math:`V` resets to a
fixed value :math:`V_r` and :math:`w \rightarrow w+b`, where :math:`b` is the
spike-triggered adaptation current.
+|
Adaptive Exponential Integrate-and-Fire
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -72,17 +74,16 @@ spike-triggered adaptation current.
----
-.. _dendritic_models:
-
-Dendritic models
-----------------
-
+.. .. _dendritic_models:
+.. Dendritic models
+.. ----------------
.. _synaptic_models:
Synaptic models [3]_ [4]_
-------------------------
+|
AMPA
~~~~
@@ -104,6 +105,8 @@ where
:math:`\tau_{\text{AMPA}}^{\text{decay}}` the AMPA decay time constant. When a
pre-synaptic spike arrives :math:`s \rightarrow s+1`.
+|
+
AMPA (rise & decay)
~~~~~~~~~~~~~~~~~~~~
@@ -127,6 +130,8 @@ where
:math:`\tau_{\text{AMPA}}^{\text{decay}}` is the AMPA decay time constant.
When a pre-synaptic spike arrives :math:`s \rightarrow s+1`.
+|
+
NMDA
~~~~
@@ -153,6 +158,7 @@ magnesium and voltage dependencies and :math:`[\rm{Mg}^{2+}]_{o}`
denotes the external magnesium concentration (mM).
When a pre-synaptic spike arrives :math:`s \rightarrow s+1`.
+----
References
~~~~~~~~~~
diff --git a/docs_sphinx/source/api/pointneuronmodel.rst b/docs_sphinx/source/api/pointneuronmodel.rst
new file mode 100644
index 0000000..fd89a04
--- /dev/null
+++ b/docs_sphinx/source/api/pointneuronmodel.rst
@@ -0,0 +1,7 @@
+PointNeuronModel
+================
+
+.. autoclass:: dendrify.neuronmodel.PointNeuronModel
+ :members:
+ :autosummary:
+ :autosummary-nosignatures:
\ No newline at end of file
diff --git a/docs_sphinx/source/changelog.rst b/docs_sphinx/source/changelog.rst
index 831bf47..74a2383 100644
--- a/docs_sphinx/source/changelog.rst
+++ b/docs_sphinx/source/changelog.rst
@@ -1,6 +1,30 @@
Release notes
===============
+Version 2.0.0
+-------------
+ * New and improved implementation of dendritic spikes.
+ * New PointNeuronModel class for creating point-neuron models.
+ * New way for specifying the electrophysiological properties of neurons.
+ * Significantly improved error catching and exception handling.
+ * Fixed compatibility issues with Jupyter notebooks.
+ * More stable and robust code overall.
+ * Added tutorials and code examples.
+ * Improved documentation page.
+ * Added a support e-mail address.
+ * Many minor improvements, bug fixes and quality of life improvements.
+ * New logo.
+
+ Special thanks to Marcel Stimberg, Spyros Chavlis, Nikos Malakasis, Christos
+ Karageorgiou Kaneen and Elisavet Kapetanou for their valuable feedback
+ and suggestions for improving Dendrify.
+
+
+Version 1.0.9
+-------------
+ * Minor improvements.
+
+
Version 1.0.8
-------------
* Improved documentation.
@@ -15,7 +39,6 @@ Version 1.0.5
Version 1.0.4
-------------
-
* Redesigned documentation page.
* Added more type hints.
* Improved compatibility with older Python versions.
diff --git a/docs_sphinx/source/code_of_conduct.rst b/docs_sphinx/source/code_of_conduct.rst
index 0a6af11..f237df3 100644
--- a/docs_sphinx/source/code_of_conduct.rst
+++ b/docs_sphinx/source/code_of_conduct.rst
@@ -61,7 +61,7 @@ Enforcement
-----------
Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported by contacting the project team at mpagkalos93@gmail.com. All
+reported by contacting the project team at dendrify@dendrites.gr. All
complaints will be reviewed and investigated and will result in a response that
is deemed necessary and appropriate to the circumstances. The project team is
obligated to maintain confidentiality with regard to the reporter of an incident.
diff --git a/docs_sphinx/source/conf.py b/docs_sphinx/source/conf.py
index b5848b0..961c325 100644
--- a/docs_sphinx/source/conf.py
+++ b/docs_sphinx/source/conf.py
@@ -13,7 +13,8 @@
project = 'Dendrify'
copyright = '2022, Michalis Pagkalos'
author = 'Michalis Pagkalos'
-release = '1.0.9'
+release = '2.0.0'
+
# -- General configuration -----------------------------------------------------
extensions = [
@@ -35,6 +36,8 @@
exclude_patterns = ['_build', '**.ipynb_checkpoints']
html_static_path = ['_static']
autosummary_generate = True
+nbsphinx_input_prompt = "%.0s"
+nbsphinx_output_prompt = "%.0s"
autodoc_default_options = {'show-inheritance': True}
autodoc_typehints = "none"
intersphinx_mapping = {
@@ -46,15 +49,25 @@
myst_url_schemes = ["http", "https", ]
# -- HTML settings -------------------------------------------------------------
-mathjax3_config = {'chtml': {'displayAlign': 'left'}}
+mathjax3_config = {'chtml': {'displayAlign': 'center'}}
copybutton_prompt_text = r">>> (?!#)"
copybutton_prompt_is_regexp = True
copybutton_only_copy_prompt_lines = True
-html_title = f"{project}"
+html_scaled_image_link = False
+html_title = f"{project} {release}"
html_theme = 'furo'
pygments_style = "default"
pygments_dark_style = "material"
html_theme_options = {
+ "sidebar_hide_name": True,
+ "navigation_with_keys": True,
+ "light_logo": "dendrify_logo_light.png",
+ "dark_logo": "dendrify_logo_dark.png",
+ "dark_css_variables": {
+ "color-brand-primary": "#78b2ff",
+ "color-brand-content": "#78b2ff",
+ "color-background-hover": "#ffffff33"
+ },
"footer_icons": [
{
"name": "GitHub",
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diff --git a/docs_sphinx/source/examples/comp_amplification.rst b/docs_sphinx/source/examples/comp_amplification.rst
new file mode 100644
index 0000000..6e7f975
--- /dev/null
+++ b/docs_sphinx/source/examples/comp_amplification.rst
@@ -0,0 +1,107 @@
+Active vs passive dendrites
+===========================
+
+
+In pyramidal neurons, distal synapses have often a minute effect on the somatic
+membrane potential due to strong dendritic attenuation. However, the activation
+of dendritic spikes can amplify synaptic inputs that are temporally correlated,
+increasing the probability of somatic AP generation.
+
+In this example we show:
+
+- How to create a compartmental model with passive or active dendrites.
+- How dendritic spiking may affect somatic AP generation.
+
+
+.. code-block:: python
+
+ import brian2 as b
+ from brian2.units import Hz, cm, ms, mV, nS, ohm, pA, pF, uF, um, uS
+
+ from dendrify import Dendrite, NeuronModel, Soma
+
+ b.prefs.codegen.target = 'numpy' # faster for simple simulations
+
+ # Create neuron model with passive dendrites
+ soma = Soma('soma', cm_abs=200*pF, gl_abs=10*nS)
+ dend = Dendrite('dend', cm_abs=50*pF, gl_abs=2.5*nS)
+ dend.synapse('AMPA', tag='x', g=3*nS, t_decay=2*ms)
+ dend.synapse('NMDA', tag='x', g=3*nS, t_decay=60*ms)
+ model_passive = NeuronModel([(soma, dend, 15*nS)], v_rest=-60*mV)
+
+ # Add dendritic spikes and create a neuron model with active dendrites
+ dend.dspikes('Na', g_rise=30*nS, g_fall=14*nS)
+ model_active = NeuronModel([(soma, dend, 15*nS)], v_rest=-60*mV)
+ model_active.config_dspikes('Na', threshold=-35*mV,
+ duration_rise=1.2*ms, duration_fall=2.4*ms,
+ offset_fall=0.2*ms, refractory=5*ms,
+ reversal_rise='E_Na', reversal_fall='E_K')
+
+ # Create a neuron group with passive dendrites
+ neuron_passive, reset_p = model_passive.make_neurongroup(1, method='euler',
+ threshold='V_soma > -40*mV',
+ reset='V_soma = 40*mV',
+ second_reset='V_soma=-50*mV',
+ spike_width=0.8*ms,
+ refractory=4*ms)
+
+ # Create a neuron group with active dendrites
+ neuron_active, reset_a = model_active.make_neurongroup(1, method='euler',
+ threshold='V_soma > -40*mV',
+ reset='V_soma = 40*mV',
+ second_reset='V_soma=-50*mV',
+ spike_width=0.8*ms,
+ refractory=4*ms)
+
+ # # Create random Poisson input
+ Input_p = b.PoissonGroup(5, rates=20*Hz)
+ Input_a = b.PoissonGroup(5, rates=20*Hz)
+
+ # Create synapses
+ S_p = b.Synapses(Input_p, neuron_passive, on_pre='s_AMPA_x_dend += 1; s_NMDA_x_dend += 1')
+ S_p.connect(p=1)
+
+ S_a = b.Synapses(Input_a, neuron_active, on_pre='s_AMPA_x_dend += 1; s_NMDA_x_dend += 1')
+ S_a.connect(p=1)
+
+ # Record voltages
+ vars = ['V_soma', 'V_dend']
+ M_p = b.StateMonitor(neuron_passive, vars, record=True)
+ M_a = b.StateMonitor(neuron_active, vars, record=True)
+
+ # Run simulation
+ b.seed(123) # for reproducibility
+ net_passive = b.Network(neuron_passive, reset_p, Input_p, S_p, M_p)
+ net_passive.run(500*ms)
+ b.start_scope() # clear previous simulation
+ b.seed(123) # for reproducibility
+ net_active = b.Network(neuron_active, reset_a, Input_a, S_a, M_a)
+ net_active.run(500*ms)
+
+ # Visualize results
+ time_p = M_p.t/ms
+ vs_p = M_p.V_soma[0]/mV
+ vd_p = M_p.V_dend[0]/mV
+ time_a = M_a.t/ms
+ vs_a = M_a.V_soma[0]/mV
+ vd_a = M_a.V_dend[0]/mV
+
+ fig, axes = b.subplots(2, 1, figsize=(6, 4), sharex=True)
+ ax0, ax1 = axes
+ ax0.plot(time_a, vd_a, label='Vdend', c='red')
+ ax0.plot(time_p, vd_p, '--', label='Vdend (passive)', c='black')
+ ax0.set_ylabel('Voltage (mV)')
+ ax0.legend(loc=2)
+
+ ax1.plot(time_a, vs_a, label='Vsoma', c='navy')
+ ax1.plot(time_p, vs_p, '--', label='Vsoma\n(passive dend)', c='orange')
+ ax1.set_xlabel('Time (ms)')
+ ax1.set_ylabel('Voltage (mV)')
+ ax1.legend(loc=2)
+
+ fig.tight_layout()
+ b.show()
+
+
+.. image:: _static/comp_amplification.png
+ :align: center
\ No newline at end of file
diff --git a/docs_sphinx/source/examples/comp_backprop.rst b/docs_sphinx/source/examples/comp_backprop.rst
new file mode 100644
index 0000000..562aa18
--- /dev/null
+++ b/docs_sphinx/source/examples/comp_backprop.rst
@@ -0,0 +1,107 @@
+Back-propagating dSpikes
+========================
+
+
+An important property of biological neurons is that action potentials (APs)
+initiated in the axon can invade the soma and nearby dendrites and propagate
+backwards toward the dendritic tips. The transmission efficacy of these
+back-propagating action potentials (bAPs) relies on the dendritic morphology
+and the presence of dendritic voltage-gated ion channels.
+
+In Dendrify, to achieve this behavior one needs to first recreate a more
+realistic somatic AP shape by using the ``second_reset`` and ``spike_width``
+arguments in ``make_neurongroup``. In this way, the somatic voltage can be first
+reset to a more positive value and then below threshold. This allows the passive
+depolarization of proximal dendrites in responses to somatic APs. If dendrites
+are also equipped with active ionic mechanisms, this depolarization can trigger
+the spontaneous generation of dendritic bAPs.
+
+In this example we show:
+
+- How to implement back-propagating dSpikes in Dendrify.
+- How to achieve a more realistic somatic AP shape in I&F models, that is
+ essential for the generation of bAPs.
+
+.. important::
+
+ Notice that when a ``second_reset`` is used, the ``make_neurongroup`` method
+ returns an additional object which is Brian's Synapses. If your simulation
+ code uses :doc:`Brian's Networks ` feature, this
+ additional object should be added to the network as well (also shown in the
+ example below).
+
+
+.. code-block:: python
+
+ import brian2 as b
+ from brian2.units import cm, ms, mV, nS, ohm, pA, uF, um, uS
+
+ from dendrify import Dendrite, NeuronModel, Soma
+
+ b.prefs.codegen.target = 'numpy' # faster for simple simulations
+
+ # Create neuron model
+ soma = Soma('soma', model='leakyIF', length=25*um, diameter=25*um)
+ trunk = Dendrite('trunk', length=100*um, diameter=2.5*um)
+ prox = Dendrite('prox', length=100*um, diameter=1*um)
+ dist = Dendrite('dist', length=100*um, diameter=0.5*um)
+
+ trunk.dspikes('Na', g_rise=22*nS, g_fall=14*nS)
+ prox.dspikes('Na', g_rise=9*nS, g_fall=5.7*nS)
+ dist.dspikes('Na', g_rise=3.7*nS, g_fall=2.4*nS)
+
+ con = [(soma, trunk, 15*nS), (trunk, prox, 6*nS), (prox, dist, 2*nS)]
+ model = NeuronModel(con, cm=1*uF/(cm**2), gl=40*uS/(cm**2),
+ v_rest=-65*mV, r_axial=150*ohm*cm,
+ scale_factor=2.8, spine_factor=1.5)
+ model.config_dspikes('Na', threshold=-35*mV,
+ duration_rise=1.2*ms, duration_fall=2.4*ms,
+ offset_fall=0.2*ms, refractory=5*ms,
+ reversal_rise='E_Na', reversal_fall='E_K')
+
+ # Make a new neurongroup
+ neuron, ap_reset = model.make_neurongroup(1, method='euler',
+ threshold='V_soma > -40*mV',
+ reset='V_soma = 40*mV',
+ second_reset= 'V_soma=-55*mV',
+ spike_width = 0.8*ms,
+ refractory=4*ms)
+
+ # Record voltages
+ vars = ['V_soma', 'V_trunk', 'V_prox', 'V_dist']
+ M = b.StateMonitor(neuron, vars, record=True)
+
+ # Run simulation
+ net = b.Network(neuron, ap_reset, M)
+ net.run(10*ms)
+ neuron.I_ext_soma = 150*pA
+ net.run(100*ms)
+ neuron.I_ext_soma = 0*pA
+ net.run(60*ms)
+
+ # Visualize results
+ fig, axes = b.subplots(2, 1, figsize=(6, 5))
+ ax0, ax1 = axes
+
+ ax0.plot(M.t/ms, M.V_soma[0]/mV, label='soma', zorder=3)
+ ax0.plot(M.t/ms, M.V_trunk[0]/mV, label='trunk')
+ ax0.plot(M.t/ms, M.V_prox[0]/mV, label='prox')
+ ax0.plot(M.t/ms, M.V_dist[0]/mV, label='dist')
+ ax0.set_ylabel('Voltage (mV)')
+ ax0.legend()
+
+ ax1.plot(M.t/ms, M.V_soma[0]/mV, zorder=3)
+ ax1.plot(M.t/ms, M.V_trunk[0]/mV)
+ ax1.plot(M.t/ms, M.V_prox[0]/mV)
+ ax1.plot(M.t/ms, M.V_dist[0]/mV)
+ ax1.set_title('(Zoomed)', y=1, pad=-12, fontsize=10)
+ ax1.set_xlabel('Time (ms)')
+ ax1.set_ylabel('Voltage (mV)')
+ ax1.set_xlim(50, 120)
+
+ fig.tight_layout()
+ b.show()
+
+
+.. image:: _static/comp_backprop.png
+ :align: center
\ No newline at end of file
diff --git a/docs_sphinx/source/examples/comp_understanding.rst b/docs_sphinx/source/examples/comp_understanding.rst
new file mode 100644
index 0000000..eeb9463
--- /dev/null
+++ b/docs_sphinx/source/examples/comp_understanding.rst
@@ -0,0 +1,94 @@
+Understanding dSpikes
+=====================
+
+
+Dendrify introduces a new event-driven mechanism for modeling dendritic spiking,
+which is significantly simpler and more efficient than the traditional
+Hodgkin-Huxley formalism. This mechanism has three distinct phases.
+
+**INACTIVE PHASE:**
+When the dendritic voltage is subthreshold OR the simulation step is within the
+refractory period. dSpikes cannot be generated during this phase.
+
+**RISE PHASE:**
+When the dendritic voltage crosses the dSpike threshold AND the refractory
+period has elapsed. This triggers the instant activation of a positive current
+that is deactivated after a specified amount of time (``duration_rise``). Also a
+new refractory period begins.
+
+**FALL PHASE:**
+This phase starts automatically with a delay (``offset_fall``) after the dSpike
+threshold is crossed. A negative current is activated instantly and then is
+deactivated after a specified amount of time (``duration_fall``).
+
+
+In this example:
+
+- How dendritic spiking is implemented in Dendrify.
+
+
+.. code-block:: python
+
+ import brian2 as b
+ from brian2.units import ms, mV, nS, pA, pF
+
+ from dendrify import Dendrite, NeuronModel, Soma
+
+ b.prefs.codegen.target = 'numpy' # faster for simple simulations
+
+ # Create neuron model
+ soma = Soma('soma', cm_abs=200*pF, gl_abs=10*nS)
+ dend = Dendrite('dend', cm_abs=50*pF, gl_abs=2.5*nS)
+ dend.dspikes('Na', g_rise=30*nS, g_fall=15*nS)
+
+ model = NeuronModel([(soma, dend, 15*nS)], v_rest=-60*mV)
+ model.config_dspikes('Na', threshold=-35*mV,
+ duration_rise=1.2*ms, duration_fall=2.4*ms,
+ offset_fall=0.5*ms, refractory=5*ms,
+ reversal_rise='E_Na', reversal_fall='E_K')
+
+ # Create neuron group
+ neuron = model.make_neurongroup(1, method='euler')
+
+ # Record variables of interest
+ vars = ['V_soma', 'V_dend', 'g_rise_Na_dend', 'g_fall_Na_dend',
+ 'I_rise_Na_dend', 'I_fall_Na_dend']
+ M = b.StateMonitor(neuron, vars, record=True)
+
+ # Run simulation
+ b.run(10*ms)
+ neuron.I_ext_dend = 213*pA
+ b.run(150*ms)
+ neuron.I_ext_dend = 0*pA
+ b.run(80*ms)
+
+ # Visualize results
+ time = M.t/ms
+ v1 = M.V_soma[0]/mV
+ v2 = M.V_dend[0]/mV
+
+ fig, axes = b.subplots(3, 1, figsize=(6, 6), sharex=True)
+ ax0, ax1, ax2 = axes
+
+ ax0.plot(time, v2, label='dendrite')
+ ax0.plot(time, v1, label='soma', c='C2')
+ ax0.axhline(-35, ls=':', c='black', label='threshold')
+ ax0.set_ylabel('Voltage (mV)')
+ ax0.set_xlim(110, 175)
+
+ ax1.plot(time, M.g_rise_Na_dend[0]/nS, label='g_rise', c='black')
+ ax1.plot(time, -M.g_fall_Na_dend[0]/nS, label='-g_fall', c='red')
+ ax1.set_ylabel('Conductance (nS)')
+
+ ax2.plot(time, M.I_rise_Na_dend[0]/pA, label='I_rise', c='gray')
+ ax2.plot(time, M.I_fall_Na_dend[0]/pA, label='I_fall', c='C1')
+ ax2.set_ylabel('Current (pA)')
+ ax2.set_xlabel('Time (ms)')
+
+ for ax in axes: ax.legend()
+ fig.tight_layout()
+ b.show()
+
+
+.. image:: _static/comp_understanding.png
+ :align: center
\ No newline at end of file
diff --git a/docs_sphinx/source/examples/compartmental.rst b/docs_sphinx/source/examples/compartmental.rst
new file mode 100644
index 0000000..147049e
--- /dev/null
+++ b/docs_sphinx/source/examples/compartmental.rst
@@ -0,0 +1,10 @@
+Compartmental models
+====================
+
+.. toctree::
+ :maxdepth: 1
+
+
+ comp_understanding
+ comp_backprop
+ comp_amplification
\ No newline at end of file
diff --git a/docs_sphinx/source/examples/point.rst b/docs_sphinx/source/examples/point.rst
new file mode 100644
index 0000000..0a780f3
--- /dev/null
+++ b/docs_sphinx/source/examples/point.rst
@@ -0,0 +1,11 @@
+Point-neuron models
+===================
+
+.. toctree::
+ :maxdepth: 1
+
+
+ point_adex
+ point_adex_noise
+ point_adex_synapses
+ point_lif_inhibition
\ No newline at end of file
diff --git a/docs_sphinx/source/examples/point_adex.rst b/docs_sphinx/source/examples/point_adex.rst
new file mode 100644
index 0000000..0d5e04f
--- /dev/null
+++ b/docs_sphinx/source/examples/point_adex.rst
@@ -0,0 +1,71 @@
+AdEx neuron
+===========
+
+
+The Dendrify implementation of the Adaptive exponential integrate-and-fire model
+(adapted from `Brian's examples `_).
+
+Resources:
+
+- http://www.scholarpedia.org/article/Adaptive_exponential_integrate-and-fire_model
+- https://pubmed.ncbi.nlm.nih.gov/16014787/
+
+
+.. code-block:: python
+
+ import brian2 as b
+ from brian2.units import ms, mV, nA, nS, pF
+
+ from dendrify import PointNeuronModel
+
+ b.prefs.codegen.target = 'numpy' # faster for simple simulations
+
+ # Create neuron model
+ model = PointNeuronModel(model='adex',
+ cm_abs=281*pF,
+ gl_abs=30*nS,
+ v_rest=-70.6*mV)
+
+ # Include adex parameters
+ model.add_params({'Vth': -50.4*mV,
+ 'DeltaT': 2*mV,
+ 'tauw': 144*ms,
+ 'a': 4*nS,
+ 'b': 0.0805*nA,
+ 'Vr': -70.6*mV,
+ 'Vcut': -50.4*mV + 5 * 2*mV})
+
+ # Create a NeuronGroup
+ neuron = model.make_neurongroup(N=1, threshold='V>Vcut',
+ reset='V=Vr; w+=b',
+ method='euler')
+
+ # Record voltages and spike times
+ trace = b.StateMonitor(neuron, 'V', record=True)
+ spikes = b.SpikeMonitor(neuron)
+
+ # Run simulation
+ b.run(20 * ms)
+ neuron.I_ext = 1*nA
+ b.run(100 * ms)
+ neuron.I_ext = 0*nA
+ b.run(20 * ms)
+
+ # Trick to draw nicer spikes in I&F models
+ vm = trace[0].V[:]
+ for t in spikes.t:
+ i = int(t / b.defaultclock.dt)
+ vm[i] = 20*mV
+
+ # Plot results
+ b.figure(figsize=[6, 3])
+ b.plot(trace.t / ms, vm / mV, label='V')
+ b.xlabel('Time (ms)')
+ b.ylabel('Voltage (mV)')
+ b.legend()
+ b.tight_layout()
+ b.show()
+
+
+.. image:: _static/point_adex.png
+ :align: center
\ No newline at end of file
diff --git a/docs_sphinx/source/examples/point_adex_noise.rst b/docs_sphinx/source/examples/point_adex_noise.rst
new file mode 100644
index 0000000..b2d9b45
--- /dev/null
+++ b/docs_sphinx/source/examples/point_adex_noise.rst
@@ -0,0 +1,96 @@
+AdEx neuron + noise
+===================
+
+
+The Dendrify implementation of the Adaptive exponential integrate-and-fire model
+(adapted from `Brian's examples `_).
+
+In this example, we also explore:
+
+- How to add gaussian noise.
+- How to create NeuronGroups with different properties using a single PointNeuronModel.
+
+Resources:
+
+- http://www.scholarpedia.org/article/Adaptive_exponential_integrate-and-fire_model
+- https://pubmed.ncbi.nlm.nih.gov/16014787/
+
+
+.. code-block:: python
+
+
+ import brian2 as b
+ from brian2.units import ms, mV, nA, nS, pA, pF
+
+ from dendrify import PointNeuronModel
+
+ b.prefs.codegen.target = 'numpy' # faster for simple simulations
+ b.seed(1234) # for reproducibility
+
+ # Create neuron model
+ model = PointNeuronModel(model='adex',
+ cm_abs=281*pF,
+ gl_abs=30*nS,
+ v_rest=-70.6*mV)
+
+ model.add_params({'Vth': -50.4*mV,
+ 'DeltaT': 2*mV,
+ 'tauw': 144*ms,
+ 'a': 4*nS,
+ 'b': 0.0805*nA,
+ 'Vr': -70.6*mV,
+ 'Vcut': -50.4*mV + 5 * 2*mV})
+
+
+ # Create a NeuronGroup
+ neuron = model.make_neurongroup(N=1, threshold='V>Vcut',
+ reset='V=Vr; w+=b',
+ method='euler')
+
+ # Update model with noise and create a new NeuronGroup
+ model.noise(mean=50*pA, sigma=300*pA, tau=2*ms)
+ noisy_neuron = model.make_neurongroup(N=1, threshold='V>Vcut',
+ reset='V=Vr; w+=b',
+ method='euler')
+
+ # Record voltages and spike times
+ trace = b.StateMonitor(neuron, 'V', record=True)
+ spikes = b.SpikeMonitor(neuron)
+ noisy_trace = b.StateMonitor(noisy_neuron, 'V', record=True)
+ noisy_spikes = b.SpikeMonitor(noisy_neuron)
+
+ # Run simulation
+ b.run(20 * ms)
+ neuron.I_ext = 1*nA
+ noisy_neuron.I_ext = 1*nA
+ b.run(100 * ms)
+ neuron.I_ext = 0*nA
+ noisy_neuron.I_ext = 0*nA
+ b.run(20 * ms)
+
+ # Trick to draw nicer spikes in I&F models
+ vm = trace[0].V[:]
+ noisy_vm = noisy_trace[0].V[:]
+ for t1, t2 in zip(spikes.t, noisy_spikes.t):
+ i = int(t1 / b.defaultclock.dt)
+ j = int(t2 / b.defaultclock.dt)
+ vm[i] = 20*mV
+ noisy_vm[j] = 20*mV
+
+ # Plot results
+ fig, axes = b.subplots(2, 1, figsize=[6, 6])
+ ax1, ax2 = axes
+ ax1.plot(trace.t / ms, vm / mV, label='V')
+ ax1.set_ylabel('Voltage (mV)')
+ ax1.legend()
+
+ ax2.plot(noisy_trace.t / ms, noisy_vm / mV, label='V (with noise)')
+ ax2.set_ylabel('Voltage (mV)')
+ ax2.set_xlabel('Time (ms)')
+ ax2.legend()
+ fig.tight_layout()
+ b.show()
+
+
+.. image:: _static/point_adex_noise.png
+ :align: center
\ No newline at end of file
diff --git a/docs_sphinx/source/examples/point_adex_synapses.rst b/docs_sphinx/source/examples/point_adex_synapses.rst
new file mode 100644
index 0000000..1316ebb
--- /dev/null
+++ b/docs_sphinx/source/examples/point_adex_synapses.rst
@@ -0,0 +1,86 @@
+AdEx network + synapses
+=======================
+
+
+The Dendrify implementation of the Adaptive exponential integrate-and-fire model
+(adapted from `Brian's examples `_).
+
+In this example, we also explore:
+
+- How to add random Poisson synaptic input.
+- How to create a basic network model.
+
+Resources:
+
+- http://www.scholarpedia.org/article/Adaptive_exponential_integrate-and-fire_model
+- https://pubmed.ncbi.nlm.nih.gov/16014787/
+
+
+.. code-block:: python
+
+ import brian2 as b
+ from brian2.units import Hz, ms, mV, nA, nS, pF
+
+ from dendrify import PointNeuronModel
+
+ b.prefs.codegen.target = 'numpy' # faster for simple simulations
+ b.seed(1234) # for reproducibility
+
+ # Create neuron model and add AMPA equations
+ model = PointNeuronModel(model='adex',
+ cm_abs=281*pF,
+ gl_abs=30*nS,
+ v_rest=-70.6*mV)
+ model.synapse('AMPA', tag='x', g=2*nS, t_decay=2*ms)
+
+ # Include adex parameters
+ model.add_params({'Vth': -50.4*mV,
+ 'DeltaT': 2*mV,
+ 'tauw': 144*ms,
+ 'a': 4*nS,
+ 'b': 0.0805*nA,
+ 'Vr': -70.6*mV,
+ 'Vcut': -50.4*mV + 5 * 2*mV})
+
+ # Create a NeuronGroup
+ neuron = model.make_neurongroup(N=100, threshold='V>Vcut',
+ reset='V=Vr; w+=b',
+ method='euler')
+
+ # Create a Poisson input
+ Input = b.PoissonGroup(200, rates=100*Hz)
+
+ # Randomly connect Poisson input to NeuronGroup
+ S = b.Synapses(Input, neuron, on_pre='s_AMPA_x += 1')
+ S.connect(p=0.25)
+
+ # Record voltages and spike times
+ trace = b.StateMonitor(neuron, 'V', record=0)
+ spikes = b.SpikeMonitor(neuron)
+
+ # Run simulation
+ b.run(200 * ms)
+
+ # Trick to draw nicer spikes in I&F models
+ vm = trace[0].V[:]
+ for t in spikes.spike_trains()[0]:
+ i = int(t / b.defaultclock.dt)
+ vm[i] = 20*mV
+
+ # Plot results
+ fig, axes = b.subplots(2, 1, figsize=[6, 6])
+ ax1, ax2 = axes
+ ax1.plot(trace.t / ms, vm / mV, label='$V_0$')
+ ax1.set_ylabel('Voltage (mV)')
+ ax1.legend()
+ ax2.plot(spikes.t/ms, spikes.i, '.', label='spikes')
+ ax2.set_xlabel('Time (ms)')
+ ax2.set_ylabel('Neuron index')
+ ax2.legend()
+ fig.tight_layout()
+ b.show()
+
+
+
+.. image:: _static/point_adex_synapses.png
+ :align: center
\ No newline at end of file
diff --git a/docs_sphinx/source/examples/point_lif_inhibition.rst b/docs_sphinx/source/examples/point_lif_inhibition.rst
new file mode 100644
index 0000000..89bb8bd
--- /dev/null
+++ b/docs_sphinx/source/examples/point_lif_inhibition.rst
@@ -0,0 +1,79 @@
+LIF network + inhibition
+========================
+
+
+In this example, we present a simple network of generic leaky integrate-and-fire
+units comprising interconnected excitatory and inhibitory neurons.
+
+In this example, we also explore:
+
+- How to add different types of synaptic equations.
+- How to achieve more complex network connectivity.
+
+
+.. code-block:: python
+
+ import brian2 as b
+ from brian2.units import Hz, ms, mV, nS, pF
+
+ from dendrify import PointNeuronModel
+
+ b.prefs.codegen.target = 'numpy' # faster for simple simulations
+ b.seed(123) # for reproducibility
+
+ N_e = 700
+ N_i = 300
+
+ # Create a neuron model
+ model = PointNeuronModel(model='leakyIF', cm_abs=281*pF, gl_abs=30*nS,
+ v_rest=-70.6*mV)
+
+ model.synapse('AMPA', tag='ext', g=2*nS, t_decay=2.5*ms) # external excitatory input
+ model.synapse('GABA', tag='inh', g=2*nS, t_decay=7.5*ms) # feedback inhibition
+ model.add_params({'Vth': -40.4*mV, 'Vr': -65.6*mV})
+
+ # Create a NeuronGroup
+ neurons = model.make_neurongroup(N=N_e+N_i, threshold='V>Vth',
+ reset='V=Vr', method='euler')
+
+ # Subpopulation of 300 inhibitory neurons
+ inhibitory = neurons[:N_i]
+
+ # Subpopulation of 700 excitatory neurons
+ excitatory = neurons[N_i:]
+
+ # Create a Poisson input
+ Input = b.PoissonGroup(200, rates=90*Hz)
+
+ # Specify synaptic connections
+ Syn_ext_a = b.Synapses(Input, excitatory, on_pre='s_AMPA_ext += 1')
+ Syn_ext_a.connect(p=0.2)
+
+ Syn_ext_b = b.Synapses(Input, inhibitory, on_pre='s_AMPA_ext += 1')
+ Syn_ext_b.connect(p=0) # initially no connections to inhibitory neurons
+
+ Syn_inh = b.Synapses(inhibitory, excitatory, on_pre='s_GABA_inh += 1')
+ Syn_inh.connect(p=0.15)
+
+ # Record voltages and spike times
+ spikes_e = b.SpikeMonitor(excitatory)
+ spikes_i = b.SpikeMonitor(inhibitory)
+
+ # Run simulation
+ b.run(250 * ms)
+ Syn_ext_b.connect(p=0.2) # add connections to inhibitory neurons
+ b.run(250 * ms)
+
+ # Plot results
+ b.figure(figsize=[6, 5])
+ b.plot(spikes_e.t/ms, spikes_e.i+N_i, '.', label='excitatory')
+ b.plot(spikes_i.t/ms, spikes_i.i, '.', label='inhibitory', c='crimson')
+ b.xlabel('Time (ms)')
+ b.ylabel('Neuron index')
+ b.legend()
+ b.tight_layout()
+ b.show()
+
+
+.. image:: _static/point_lif_inhibition.png
+ :align: center
\ No newline at end of file
diff --git a/docs_sphinx/source/examples/val_dendritic_attenuation.rst b/docs_sphinx/source/examples/val_dendritic_attenuation.rst
new file mode 100644
index 0000000..4c26b13
--- /dev/null
+++ b/docs_sphinx/source/examples/val_dendritic_attenuation.rst
@@ -0,0 +1,79 @@
+Dendritic attenuation
+=====================
+
+
+The attenuation of currents traveling along the somatodendritic axis is an
+intrinsic property of biological neurons and is due to the morphology and cable
+properties of their dendritic trees. (also see `Tran-van-Minh et al, 2015
+`_).
+
+In this example, we show:
+
+- How to measure the dendritic, distance-dependent voltage attenuation of a long
+ current pulse injected at the soma.
+
+
+
+.. code-block:: python
+
+ import brian2 as b
+ from brian2.units import cm, ms, mV, ohm, pA, pF, uF, um, uS
+
+ from dendrify import Dendrite, NeuronModel, Soma
+
+ b.prefs.codegen.target = 'numpy' # faster for simple simulations
+
+ # Create neuron model
+ soma = Soma('soma', length=25*um, diameter=25*um)
+ trunk = Dendrite('trunk', length=100*um, diameter=1.5*um)
+ prox = Dendrite('prox', length=100*um, diameter=1.2*um)
+ dist = Dendrite('dist', length=100*um, diameter=1*um)
+
+ # Create a neuron group
+ connections = [(soma, trunk), (trunk, prox), (prox, dist)]
+ model = NeuronModel(connections, cm=1*uF/(cm**2), gl=50*uS/(cm**2),
+ v_rest=-70*mV, r_axial=400*ohm*cm)
+ neuron = model.make_neurongroup(1, method='euler') # no spiking for simplicity
+
+ # Monitor voltages
+ M = b.StateMonitor(neuron, ['V_soma', 'V_trunk', 'V_prox', 'V_dist'],
+ record=True)
+
+ # Run simulation
+ b.run(20*ms)
+ neuron.I_ext_soma = -10*pA
+ b.run(500*ms)
+ neuron.I_ext_soma = 0*pA
+ b.run(100*ms)
+
+ # Analyse and plot results
+ time = M.t/ms
+ vs = M.V_soma[0]/mV
+ vt = M.V_trunk[0]/mV
+ vp = M.V_prox[0]/mV
+ vd = M.V_dist[0]/mV
+ voltages = [vs, vt, vp, vd]
+ delta_v = [min(v) - v[0] for v in voltages]
+ ratio = [i/delta_v[0] for i in delta_v]
+ distances = range(0, 400, 100)
+ names = ['soma', 'trunk', 'prox', 'dist']
+
+ fig, axes = b.subplots(1, 2, figsize=(6, 3))
+ ax0, ax1 = axes
+ for i, v in enumerate(voltages):
+ ax0.plot(time, v, label=names[i])
+ ax0.set_ylabel('Voltage (mV)')
+ ax0.set_xlabel('Time (ms)')
+ ax0.legend()
+
+ ax1.plot(distances, ratio, 'ko-', ms=4)
+ ax1.set_ylabel(r'$dV_{dend}$ / $dV_{soma}$')
+ ax1.set_xlabel('Distance from soma (μm)')
+ ax1.set_yticks(b.arange(.7, 1, .1))
+
+ fig.tight_layout()
+ b.show()
+
+
+.. image:: _static/val_dendritic_attenuation.png
+ :align: center
\ No newline at end of file
diff --git a/docs_sphinx/source/examples/val_dendritic_io.rst b/docs_sphinx/source/examples/val_dendritic_io.rst
new file mode 100644
index 0000000..104378c
--- /dev/null
+++ b/docs_sphinx/source/examples/val_dendritic_io.rst
@@ -0,0 +1,97 @@
+Dendritic I/O curve
+===================
+
+
+Dendritic integration can be quantified by comparing the observed depolarization
+resulting from the quasi-simultaneous activation of the same synaptic inputs, and
+the arithmetic sum of individual EPSPs (expected membrane depolarization). The
+dendritic input-output (I/O) relationship is easily described by plotting
+observed vs. expected depolarizations for different numbers of co-activated
+synapses (also see `Tran-van-Minh et al, 2015
+`_).
+
+In this example, we show:
+
+- How to calculate the dendritic I/O curve in a simple compartmental model.
+- How active dendritic conductances affect the I/O curve.
+- How to perform the above experiment in a vectorized and efficient manner.
+
+
+.. code-block:: python
+
+ import brian2 as b
+ from brian2.units import ms, mV, nS, pF
+
+ from dendrify import Dendrite, NeuronModel, Soma
+
+ b.prefs.codegen.target = 'numpy' # faster for simple simulations
+
+ # Create neuron model
+ soma = Soma('soma', cm_abs=200*pF, gl_abs=10*nS)
+ dend = Dendrite('dend', cm_abs=50*pF, gl_abs=2.5*nS)
+ dend.dspikes('Na', g_rise=30*nS, g_fall=15*nS)
+ dend.synapse('AMPA', tag='x', g=3*nS, t_decay=2*ms)
+ dend.synapse('NMDA', tag='x', g=3*nS, t_decay=50*ms)
+
+ model = NeuronModel([(soma, dend, 15*nS)], v_rest=-65*mV)
+ model.config_dspikes('Na', threshold=-35*mV,
+ duration_rise=1.2*ms, duration_fall=2.4*ms,
+ offset_fall=0.2*ms, refractory=5*ms,
+ reversal_rise='E_Na', reversal_fall='E_K')
+
+ # Create neuron group
+ """Instead of creating a single neuron, we create a group of neurons, each
+ receiving a different number of synapses. This allows us to calculate the
+ dendritic I/O curve efficiently in a single simulation."""
+ N_syn = 15 # number of synapses
+ neurons = model.make_neurongroup(N_syn, method='euler',
+ threshold='V_soma > -40*mV',
+ reset='V_soma = -55*mV',
+ refractory=4*ms)
+
+ # Create input source
+ start = 10*ms
+ isi = 0.1*ms # inter-spike interval of input synapses
+ spiketimes = [(start + (i*isi)) for i in range(N_syn)]
+ I = b.SpikeGeneratorGroup(N_syn, range(N_syn), spiketimes)
+
+ # Connect input to neurons
+ synaptic_effect = "s_AMPA_x_dend += 1.0; s_NMDA_x_dend += 1.0"
+ S = b.Synapses(I, neurons, on_pre=synaptic_effect)
+ S.connect('j >= i') # 1st neuron receives 1 synapse, 2nd neuron receives 2 synapses, etc.
+
+ # Record dendritic voltage
+ M = b.StateMonitor(neurons, ['V_dend'], record=True)
+
+ # Run simulation
+ b.run(200 *ms)
+
+ # Visualize results
+ time = M.t/ms
+ v = M.V_dend/mV
+ v_rest = v[0][0]
+ u_epsp = max(v[0]) - v_rest
+ expected = [u_epsp * (i+1) for i in range(N_syn)]
+ actual = [max(v[i]) - v_rest for i in range(N_syn)]
+ linear = b.linspace(0, max(actual))
+
+ fig, axes = b.subplots(1, 2, figsize=(6, 4))
+ ax0, ax1 = axes
+
+ ax0.plot(expected, actual, 'o-', label='Dendritic I/O')
+ ax0.plot(linear, linear, '--', color='gray', label='Linear')
+ ax0.set_xlabel('Expected EPSP (mV)')
+ ax0.set_ylabel('Actual EPSP (mV)')
+ ax0.legend()
+
+ ax1.plot(time, v[7], label='#8 synapses', c='black', alpha=0.8)
+ ax1.plot(time, v[8], label='#9 synapses', c='crimson', alpha=0.8)
+ ax1.set_xlabel('Time (ms)')
+ ax1.set_ylabel('Dendritic voltage (mV)')
+ ax1.legend()
+ fig.tight_layout()
+ b.show()
+
+
+.. image:: _static/val_dendritic_io.png
+ :align: center
\ No newline at end of file
diff --git a/docs_sphinx/source/examples/val_fi_curve.rst b/docs_sphinx/source/examples/val_fi_curve.rst
new file mode 100644
index 0000000..3dcdbeb
--- /dev/null
+++ b/docs_sphinx/source/examples/val_fi_curve.rst
@@ -0,0 +1,59 @@
+Frequency-current curve
+=======================
+
+
+A frequency-current curve (F-I curve) is the function that relates the net
+current ``I`` flowing into a neuron to its firing rate ``F``.
+
+In this example we show:
+
+- How to calculate the somatic F-I curve for a simple 2-compartment neuron model.
+- How to perform the above experiment in a vectorized and efficient manner.
+
+
+.. code-block:: python
+
+ import brian2 as b
+ from brian2.units import ms, mV, nS, pA, pF
+
+ from dendrify import Dendrite, NeuronModel, Soma
+
+ b.prefs.codegen.target = 'numpy' # faster for simple simulations
+
+ # Create neuron model
+ soma = Soma('soma', cm_abs=200*pF, gl_abs=10*nS)
+ dend = Dendrite('dend', cm_abs=50*pF, gl_abs=2.5*nS)
+ model = NeuronModel([(soma, dend, 15*nS)], v_rest=-65*mV)
+
+ # Range of current amplitudes to test
+ I = range(200, 620, 20) * pA
+
+ # Create neuron group
+ """Instead of creating a single neuron, we create a group of neurons, each with
+ a different value of ``I_ext``. This allows us to calculate the F-I curve in a
+ single simulation."""
+ neurons = model.make_neurongroup(len(I), method='euler',
+ threshold='V_soma > -40*mV',
+ reset='V_soma = -55*mV',
+ refractory=4*ms)
+
+ # Record spike times
+ spikes = b.SpikeMonitor(neurons)
+
+ # Run simulation
+ sim_time = 1000*ms
+ neurons.I_ext_soma = I
+ b.run(sim_time)
+
+ # Visualize F-I curve
+ F = [len(s) / sim_time for s in spikes.spike_trains().values()]
+ b.figure(figsize=(6, 4))
+ b.plot(I/pA, F, 'o-')
+ b.xlabel('I (pA)')
+ b.ylabel('F (Hz)')
+ b.tight_layout()
+ b.show()
+
+
+.. image:: _static/val_fi_curve.png
+ :align: center
\ No newline at end of file
diff --git a/docs_sphinx/source/examples/val_rinput.rst b/docs_sphinx/source/examples/val_rinput.rst
new file mode 100644
index 0000000..2339962
--- /dev/null
+++ b/docs_sphinx/source/examples/val_rinput.rst
@@ -0,0 +1,90 @@
+Input resistance
+================
+
+
+Input resistance (``Rin``) determines how much a neuron depolarizes in response
+to a steady current. It is a useful metric of a neuron's excitability; neurons
+with high ``Rin`` depolarize more in response to a given current than neurons
+with low ``Rin``. ``Rin`` is often measured experimentally by injecting a small
+current ``I`` into the neuron and measuring the steady-state change in its
+membrane potential ``ΔV``. Using Ohm's law, ``Rin`` can be estimated as
+``Rin = ΔV/I``.
+
+In this example we show:
+
+- How to calculate ``Rin`` in a point neuron model.
+- How ``Rin`` is affected by changes in the neuron's membrane leak conductance
+ ``gl``.
+
+Note: We also scale the neuron's membrane capacitance ``cm`` to maintain a
+constant membrane time constant (``τm = cm/gl``).
+
+
+.. code-block:: python
+
+ import brian2 as b
+ from brian2.units import Mohm, ms, mV, nS, pA, pF
+
+ from dendrify import PointNeuronModel
+
+ b.prefs.codegen.target = 'numpy' # faster for simple simulations
+
+ # Parameters
+ g_leakage = 20*nS # membrane leak conductance
+ capacitance = 250*pF # membrane capacitance
+ EL = -70*mV # resting potential
+
+ # Create neuron models
+ control = PointNeuronModel(model='leakyIF', cm_abs=capacitance,
+ gl_abs=g_leakage, v_rest=EL)
+
+ low_rin = PointNeuronModel(model='leakyIF', cm_abs=capacitance*1.2,
+ gl_abs=g_leakage*1.2, v_rest=EL)
+
+ high_rin = PointNeuronModel(model='leakyIF', cm_abs=capacitance*0.8,
+ gl_abs=g_leakage*0.8, v_rest=EL)
+
+ # Create NeuronGroups (no threshold or reset conditions for simplicity)
+ control_neuron = control.make_neurongroup(N=1, method='euler')
+ low_rin_neuron = low_rin.make_neurongroup(N=1, method='euler')
+ high_rin_neuron = high_rin.make_neurongroup(N=1, method='euler')
+
+ # Record voltages
+ control_monitor = b.StateMonitor(control_neuron, 'V', record=0)
+ low_rin_monitor = b.StateMonitor(low_rin_neuron, 'V', record=0)
+ high_rin_monitor = b.StateMonitor(high_rin_neuron, 'V', record=0)
+
+ # Run simulation
+ I = -20*pA # current pulse amplitude
+ b.run(50*ms)
+ for n in [control_neuron, low_rin_neuron, high_rin_neuron]:
+ n.I_ext = -20*pA
+ b.run(500*ms)
+ for n in [control_neuron, low_rin_neuron, high_rin_neuron]:
+ n.I_ext = 0*pA
+ b.run(100*ms)
+
+ # Calculate Rin
+ Rin_control = (min(control_monitor.V[0]) - control_monitor.V[0][500]) / I
+ Rin_low = (min(low_rin_monitor.V[0]) - low_rin_monitor.V[0][500]) / I
+ Rin_high = (min(high_rin_monitor.V[0]) - high_rin_monitor.V[0][500]) / I
+
+ # Plot results
+ b.figure(figsize=(6, 3.5))
+ b.plot(control_monitor.t/ms, control_monitor.V[0]/mV,
+ label='control Rin = {:.2f} MΩ'.format(Rin_control/ Mohm))
+ b.plot(low_rin_monitor.t/ms, low_rin_monitor.V[0]/mV,
+ label='low Rin = {:.2f} MΩ'.format(Rin_low/ Mohm))
+ b.plot(high_rin_monitor.t/ms, high_rin_monitor.V[0]/mV,
+ label='high Rin = {:.2f} MΩ'.format(Rin_high/ Mohm))
+ b.axvline(50, ls=':', c='gray', label='stimulation period')
+ b.axvline(550, ls=':', c='gray')
+ b.xlabel('Time (ms)')
+ b.ylabel('Membrane potential (mV)')
+ b.legend()
+ b.tight_layout()
+ b.show()
+
+
+.. image:: _static/val_rinput.png
+ :align: center
\ No newline at end of file
diff --git a/docs_sphinx/source/examples/val_tau.rst b/docs_sphinx/source/examples/val_tau.rst
new file mode 100644
index 0000000..6b76de3
--- /dev/null
+++ b/docs_sphinx/source/examples/val_tau.rst
@@ -0,0 +1,111 @@
+Membrane time constant
+======================
+
+
+In this example, we show how to calculate a neuron's membrane time constant
+``τm``, a metric that describes how quickly the membrane potential ``V`` decays
+to its steady-state value after some perturbation. In simple RC circuits, ``τm``
+is calculated as the product of the membrane capacitance ``C`` and the membrane
+resistance ``R``. However, in neurons, ``τm`` is also affected by voltage-gated
+conductances or other non-linearities.
+
+
+Experimentally, ``τm`` is often calculated by fitting an exponential function to
+the membrane potential ``V`` trace after applying a small negative current pulse
+at rest.
+
+
+Here we explore:
+
+- How to calculate ``τm`` for a neuron model experimentally.
+- How ``τm`` is affected by the presence of voltage-gated conductances, such as
+ an adaptation current.
+
+
+.. code-block:: python
+
+ import brian2 as b
+ from brian2.units import ms, mV, nS, pA, pF
+ from scipy.optimize import curve_fit
+
+ from dendrify import PointNeuronModel
+
+ b.prefs.codegen.target = 'numpy' # faster for simple simulations
+
+ # Create neuron models
+ GL = 20*nS # membrane leak conductance
+ CM = 250*pF # membrane capacitance
+ EL = -70*mV # resting potential
+ tau_theory = CM / GL # theoretical membrane time constant
+
+ lif = PointNeuronModel(model='leakyIF', cm_abs=CM, gl_abs=GL, v_rest=EL)
+ aif = PointNeuronModel(model='adaptiveIF', cm_abs=CM, gl_abs=GL, v_rest=EL)
+ aif.add_params({'tauw': 100*ms, 'a': 2*nS})
+
+ # Create NeuronGroups (no threshold or reset conditions for simplicity)
+ lif_neuron = lif.make_neurongroup(N=1, method='euler')
+ aif_neuron = aif.make_neurongroup(N=1, method='euler')
+
+ # Record voltages
+ lif_monitor = b.StateMonitor(lif_neuron, 'V', record=0)
+ aif_monitor = b.StateMonitor(aif_neuron, 'V', record=0)
+
+ # Run simulation
+ I = -10*pA # current pulse amplitude
+ t0 = 20*ms # time to start current pulse
+ t_stim = 200*ms # duration of current pulse
+
+ b.run(t0)
+ lif_neuron.I_ext, aif_neuron.I_ext = I, I
+ b.run(t_stim)
+ lif_neuron.I_ext, aif_neuron.I_ext = 0*pA, 0*pA
+ b.run(100*ms)
+
+ # Analysis code
+ def func(t, a, tau):
+ """Exponential decay function"""
+ return a * b.exp(-t / tau)
+
+ def get_tau(trace, t0):
+ dt = b.defaultclock.dt
+ Vmin = min(trace)
+ time_to_peak = list(trace).index(Vmin)
+ # Find voltage from current-start to min value
+ voltages = trace[int(t0/dt): time_to_peak] / mV
+ # Min-max normalize voltages
+ v_norm = (voltages - voltages.min()) / (voltages.max() - voltages.min())
+ # Fit exp decay function to normalized data
+ X = b.arange(0, len(v_norm)) * dt / ms
+ popt, _ = curve_fit(func, X, v_norm)
+ return popt, X, v_norm
+
+ # Plot results
+ popt_lif, X_lif, v_norm_lif = get_tau(lif_monitor.V[0], t0)
+ popt_aif, X_aif, v_norm_aif = get_tau(aif_monitor.V[0], t0)
+
+ fig, axes = b.subplot_mosaic("""
+ AA
+ BC
+ """, layout='constrained', figsize=[6, 5])
+ ax0, ax1, ax2 = axes.values()
+ ax0.plot(lif_monitor.t/ms, lif_monitor.V[0]/mV, label='Leaky IF')
+ ax0.plot(aif_monitor.t/ms, aif_monitor.V[0]/mV, label='Adaptive IF', zorder=0)
+ ax0.set_title('Theoretical τm: {:.2f} ms'.format(tau_theory/ms))
+ ax0.set_ylabel('Membrane potential (mV)')
+ ax0.legend()
+ ax1.plot(X_lif, v_norm_lif, 'ko-', ms=3)
+ ax1.plot(X_lif, func(X_lif, *popt_lif), c='tomato')
+ ax1.set_ylabel('Normalized potential (mV)')
+ ax1.set_title(f'LIF | τm: {popt_lif[1]:.2f} ms')
+ ax2.plot(X_aif, v_norm_aif, 'ko-', label='V (rest \u2192 min)', ms=3)
+ ax2.plot(X_aif, func(X_aif, *popt_aif), label='a * exp(-t / τm)', c='tomato')
+ ax2.set_title(f'AIF | τm: {popt_aif[1]:.2f} ms')
+ ax2.legend()
+ for ax in axes.values():
+ ax.set_xlabel('Time (ms)')
+ fig.tight_layout()
+ b.show()
+
+
+.. image:: _static/val_tau.png
+ :align: center
\ No newline at end of file
diff --git a/docs_sphinx/source/examples/validation.rst b/docs_sphinx/source/examples/validation.rst
new file mode 100644
index 0000000..9f2d679
--- /dev/null
+++ b/docs_sphinx/source/examples/validation.rst
@@ -0,0 +1,12 @@
+Validation tests
+================
+
+.. toctree::
+ :maxdepth: 1
+
+
+ val_rinput
+ val_fi_curve
+ val_tau
+ val_dendritic_attenuation
+ val_dendritic_io
\ No newline at end of file
diff --git a/docs_sphinx/source/index.rst b/docs_sphinx/source/index.rst
index ba9e4eb..32a7ecb 100644
--- a/docs_sphinx/source/index.rst
+++ b/docs_sphinx/source/index.rst
@@ -12,21 +12,24 @@ Introduction
:target: CODE_OF_CONDUCT.md
:alt: Contributor Covenant
-Although neuronal dendrites greatly influence how single neurons process incoming
-information, their role in network-level functions remain largely unexplored.
-Current SNNs are usually quite simplistic, overlooking essential dendritic
-properties. Conversely, circuit models with morphologically detailed neuron
-models are computationally costly, thus impractical for large-network
-simulations.
-
-To bridge the gap between these two, we introduce Dendrify, a free,
-open-source Python package compatible with the
+
+Although neuronal dendrites play a crucial role in shaping how individual
+neurons process synaptic information, their contribution to network-level
+functions has remained largely unexplored. Current spiking neural networks
+(SNNs) often oversimplify dendritic properties or overlook their essential
+functions. On the other hand, circuit models with morphologically detailed
+neuron representations are computationally intensive, making them impractical
+for simulating large networks.
+
+In an effort to bridge this gap, we present Dendrify—a freely available,
+open-source Python package that seamlessly integrates with the
`Brian 2 simulator `_. Dendrify,
through simple commands, automatically generates reduced compartmental neuron
models with simplified yet biologically relevant dendritic and synaptic
-integrative properties. Such models strike a good balance between flexibility,
-performance, and biological accuracy, allowing us to explore dendritic
-contributions to network-level functions.
+integrative properties. These models offer a well-rounded compromise between
+flexibility, performance, and biological accuracy, enabling us to investigate
+the impact of dendrites on network-level functions.
+
.. image:: _static/intro.png
:width: 75 %
@@ -38,6 +41,7 @@ contributions to network-level functions.
:align: center
:class: only-dark
+
.. tip::
If you use Dendrify for your published research, we kindly ask you to cite our
article:|br|
@@ -58,11 +62,21 @@ contributions to network-level functions.
.. toctree::
- :maxdepth: 2
- :caption: Using Dendrify
+ :maxdepth: 1
+ :caption: Tutorials
+
+ tutorials/Dendrify_101
+ tutorials/Dendrify_simulations
+
+
+.. toctree::
+ :maxdepth: 1
+ :caption: Examples
- usage/tutorial
- usage/examples
+ examples/compartmental
+ examples/point
+ examples/validation
+
.. toctree::
@@ -78,8 +92,9 @@ contributions to network-level functions.
:maxdepth: 1
:caption: Useful information
- papers
+ support
changelog
+ papers
code_of_conduct
diff --git a/docs_sphinx/source/installation.rst b/docs_sphinx/source/installation.rst
index ec22ed8..867ec36 100644
--- a/docs_sphinx/source/installation.rst
+++ b/docs_sphinx/source/installation.rst
@@ -6,6 +6,13 @@ The easiest way to install it is through ``pip``, using the command::
pip install dendrify
+The above command will automatically install Brian 2.5.4, if it is not already
+installed. If you wish to work with a different Brian version, you can install
+Dendrify without its dependencies using the command::
+
+ pip install dendrify --no-deps
+
+and then install the desired version of Brian separately (see bellow).
Dependencies
------------
@@ -15,11 +22,11 @@ Dependencies
on almost all platforms. Brian is designed to be easy to learn and use, highly
flexible and easily extensible.
- * :doc:`How to install Brian 2 `
+ * :doc:`Brian 2 installation guidelines `
-* `Netwokx `_ (optional) is a Python package for the creation,
+* `Networkx `_ (optional) is a Python package for the creation,
manipulation, and study of the structure, dynamics, and functions of complex
- networks. If you wish Dendrify to have access to certain experimental model
+ networks. If you wish Dendrify to have access to some experimental model
visualization features, you can install it using the command::
pip install networkx
@@ -30,8 +37,7 @@ GPU support
Dendrify is compatible with `Brian2CUDA `_,
a Python package for simulating spiking neural networks on graphics processing
units (GPUs). Brian2CUDA is an extension of Brian2 that uses the code generation
-system of the latter to generate simulation code in C++/CUDA, which is then executed
-on NVIDIA GPUs.
-
-* :doc:`How to install Brian2CUDA `
+system of the latter to generate simulation code in C++/CUDA, which is then
+executed on NVIDIA GPUs.
+* :doc:`Brian2CUDA installation guidelines `
diff --git a/docs_sphinx/source/support.rst b/docs_sphinx/source/support.rst
new file mode 100644
index 0000000..259acb4
--- /dev/null
+++ b/docs_sphinx/source/support.rst
@@ -0,0 +1,18 @@
+Support
+=======
+
+Dendrify was created by `Michalis Pagkalos `_
+and is currently maintained by Michalis Pagkalos and Spyros Chavlis, both
+currently members of the `Poirazi Lab `_ .
+
+If you run into any issues or have questions about using Dendrify, you can
+either:
+
+- E-mail us at dendrify@dendrites.gr
+- Open a new issue on `GitHub `_
+
+.. tip::
+
+ To facilitate communication, please include a minimal working example that
+ reproduces your issue. This will help us understand your problem and
+ provide a faster solution.
diff --git a/docs_sphinx/source/tutorials/Dendrify_101.ipynb b/docs_sphinx/source/tutorials/Dendrify_101.ipynb
new file mode 100644
index 0000000..eb98c8a
--- /dev/null
+++ b/docs_sphinx/source/tutorials/Dendrify_101.ipynb
@@ -0,0 +1,1543 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "4y7lu4Q9-IHe"
+ },
+ "source": [
+ "# Dendrify basics \n",
+ "\n",
+ "In this tutorial, we are going to cover the following topics:\n",
+ "\n",
+ "* Getting to know Dendrify's basic object types and their functions\n",
+ "* How to generate model equations\n",
+ "* How to set model parameters"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "fg5O2CCigZKS"
+ },
+ "source": [
+ "## Imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "id": "BR17RLDWfy76"
+ },
+ "outputs": [],
+ "source": [
+ "import brian2 as b\n",
+ "import dendrify as d\n",
+ "from brian2.units import *\n",
+ "from dendrify import Soma, Dendrite, PointNeuronModel\n",
+ "\n",
+ "b.prefs.codegen.target = 'numpy' # faster for simple models and short simulations"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NATfjiUXiqen"
+ },
+ "source": [
+ "## Generating model equations\n",
+ "\n",
+ "*In this first part of the tutorial we are going to focus on how to create single compartments and how to equip them with desired mechanisms.*"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LrJj7WlSzVmc"
+ },
+ "source": [
+ "### Creating compartments"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "id": "4CSCYDemipZZ"
+ },
+ "outputs": [],
+ "source": [
+ "# Setting a compartment's name is the barely minimum you need to create it\n",
+ "soma = Soma('soma')\n",
+ "dend = Dendrite('dend')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "eoFMZcrujD9O",
+ "outputId": "5af66c97-05df-4677-ef8b-cd50eeb3aaf3"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "True\n",
+ "True\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Soma and Dendrite objects share many functions since they both inherit from\n",
+ "# the same class\n",
+ "print(isinstance(soma, d.Compartment))\n",
+ "print(isinstance(dend, d.Compartment))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1A09v3WBmo10"
+ },
+ "source": [
+ "### Accessing equations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "EdtyDYEqkJnU",
+ "outputId": "74c2bb5e-d647-4263-d229-b50d6a6c7bbf"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "dV_soma/dt = (gL_soma * (EL_soma-V_soma) + I_soma) / C_soma :volt\n",
+ "I_soma = I_ext_soma :amp\n",
+ "I_ext_soma :amp\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(soma.equations)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "2T98I4v0kiBH",
+ "outputId": "689fb64d-6f19-41c3-fbe7-df8da8d608f9"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "dV_dend/dt = (gL_dend * (EL_dend-V_dend) + I_dend) / C_dend :volt\n",
+ "I_dend = I_ext_dend :amp\n",
+ "I_ext_dend :amp\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(dend.equations)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Ha7jqirqz05p"
+ },
+ "source": [
+ "### Synaptic currents"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "id": "OVHJYxV8mgAo"
+ },
+ "outputs": [],
+ "source": [
+ "# The usage of tags helps differentiate between same type of synapses that reach\n",
+ "# a single compartment.\n",
+ "dend.synapse('AMPA', tag='A')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "kd-uoy-n4TDY",
+ "outputId": "fe1a5889-ecf3-4631-8956-10f43cab02c2"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "dV_dend/dt = (gL_dend * (EL_dend-V_dend) + I_dend) / C_dend :volt\n",
+ "I_dend = I_ext_dend + I_AMPA_A_dend :amp\n",
+ "I_ext_dend :amp\n",
+ "I_AMPA_A_dend = g_AMPA_A_dend * (E_AMPA-V_dend) * s_AMPA_A_dend * w_AMPA_A_dend :amp\n",
+ "ds_AMPA_A_dend/dt = -s_AMPA_A_dend / t_AMPA_decay_A_dend :1\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(dend.equations)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Vi5MhW8J1FE4"
+ },
+ "source": [
+ "* **s_AMPA_x_dend** -> the state variable for this channel (0 -> closed).\n",
+ "* **w_AMPA_x_dend** -> the weight variable. Useful for plasticity (1 by default)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "hitkBvw-uwoq",
+ "outputId": "1f09c289-7536-43ee-e329-c556354dac6b"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "dV_dend/dt = (gL_dend * (EL_dend-V_dend) + I_dend) / C_dend :volt\n",
+ "I_dend = I_ext_dend + I_AMPA_B_dend + I_AMPA_A_dend :amp\n",
+ "I_ext_dend :amp\n",
+ "I_AMPA_A_dend = g_AMPA_A_dend * (E_AMPA-V_dend) * s_AMPA_A_dend * w_AMPA_A_dend :amp\n",
+ "ds_AMPA_A_dend/dt = -s_AMPA_A_dend / t_AMPA_decay_A_dend :1\n",
+ "I_AMPA_B_dend = g_AMPA_B_dend * (E_AMPA-V_dend) * s_AMPA_B_dend * w_AMPA_B_dend :amp\n",
+ "ds_AMPA_B_dend/dt = -s_AMPA_B_dend / t_AMPA_decay_B_dend :1\n"
+ ]
+ }
+ ],
+ "source": [
+ "dend.synapse('AMPA', tag='B')\n",
+ "print(dend.equations)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "IyfXL37q7qlY",
+ "outputId": "30730644-5341-4d5d-94ae-02dbda097047"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "dV_dend/dt = (gL_dend * (EL_dend-V_dend) + I_dend) / C_dend :volt\n",
+ "I_dend = I_ext_dend + I_NMDA_A_dend + I_AMPA_B_dend + I_AMPA_A_dend :amp\n",
+ "I_ext_dend :amp\n",
+ "I_AMPA_A_dend = g_AMPA_A_dend * (E_AMPA-V_dend) * s_AMPA_A_dend * w_AMPA_A_dend :amp\n",
+ "ds_AMPA_A_dend/dt = -s_AMPA_A_dend / t_AMPA_decay_A_dend :1\n",
+ "I_AMPA_B_dend = g_AMPA_B_dend * (E_AMPA-V_dend) * s_AMPA_B_dend * w_AMPA_B_dend :amp\n",
+ "ds_AMPA_B_dend/dt = -s_AMPA_B_dend / t_AMPA_decay_B_dend :1\n",
+ "I_NMDA_A_dend = g_NMDA_A_dend * (E_NMDA-V_dend) * s_NMDA_A_dend / (1 + Mg_con * exp(-Alpha_NMDA*(V_dend/mV+Gamma_NMDA)) / Beta_NMDA) * w_NMDA_A_dend :amp\n",
+ "ds_NMDA_A_dend/dt = -s_NMDA_A_dend/t_NMDA_decay_A_dend :1\n"
+ ]
+ }
+ ],
+ "source": [
+ "dend.synapse('NMDA', tag='A')\n",
+ "print(dend.equations)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5dFQAGxw46hv"
+ },
+ "source": [
+ "### Random noise"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "u7WEPnjA42B2",
+ "outputId": "f8373d8f-4231-4771-c82b-62907d2a7d90"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "dV_dend/dt = (gL_dend * (EL_dend-V_dend) + I_dend) / C_dend :volt\n",
+ "I_dend = I_ext_dend + I_noise_dend + I_NMDA_A_dend + I_AMPA_B_dend + I_AMPA_A_dend :amp\n",
+ "I_ext_dend :amp\n",
+ "I_AMPA_A_dend = g_AMPA_A_dend * (E_AMPA-V_dend) * s_AMPA_A_dend * w_AMPA_A_dend :amp\n",
+ "ds_AMPA_A_dend/dt = -s_AMPA_A_dend / t_AMPA_decay_A_dend :1\n",
+ "I_AMPA_B_dend = g_AMPA_B_dend * (E_AMPA-V_dend) * s_AMPA_B_dend * w_AMPA_B_dend :amp\n",
+ "ds_AMPA_B_dend/dt = -s_AMPA_B_dend / t_AMPA_decay_B_dend :1\n",
+ "I_NMDA_A_dend = g_NMDA_A_dend * (E_NMDA-V_dend) * s_NMDA_A_dend / (1 + Mg_con * exp(-Alpha_NMDA*(V_dend/mV+Gamma_NMDA)) / Beta_NMDA) * w_NMDA_A_dend :amp\n",
+ "ds_NMDA_A_dend/dt = -s_NMDA_A_dend/t_NMDA_decay_A_dend :1\n",
+ "dI_noise_dend/dt = (mean_noise_dend-I_noise_dend) / tau_noise_dend + sigma_noise_dend * (sqrt(2/(tau_noise_dend*dt)) * randn()) :amp\n"
+ ]
+ }
+ ],
+ "source": [
+ "dend.noise()\n",
+ "print(dend.equations)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "GoZLHuaP5c8T"
+ },
+ "source": [
+ "*NOTE: You can find more info about how random noise is impemented in [Brian's documentation](https://brian2.readthedocs.io/en/stable/user/models.html?highlight=noise#noise).*"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "GspVuWJsEMlc"
+ },
+ "source": [
+ "### Dendritic spikes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "xOzgQ0QivWMr",
+ "outputId": "6f9a23aa-56b2-41dc-d0e5-6a68e5a7f39b"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "dV_dend/dt = (gL_dend * (EL_dend-V_dend) + I_dend) / C_dend :volt\n",
+ "I_dend = I_ext_dend + I_rise_Na_dend + I_fall_Na_dend + I_noise_dend + I_NMDA_A_dend + I_AMPA_B_dend + I_AMPA_A_dend :amp\n",
+ "I_ext_dend :amp\n",
+ "I_AMPA_A_dend = g_AMPA_A_dend * (E_AMPA-V_dend) * s_AMPA_A_dend * w_AMPA_A_dend :amp\n",
+ "ds_AMPA_A_dend/dt = -s_AMPA_A_dend / t_AMPA_decay_A_dend :1\n",
+ "I_AMPA_B_dend = g_AMPA_B_dend * (E_AMPA-V_dend) * s_AMPA_B_dend * w_AMPA_B_dend :amp\n",
+ "ds_AMPA_B_dend/dt = -s_AMPA_B_dend / t_AMPA_decay_B_dend :1\n",
+ "I_NMDA_A_dend = g_NMDA_A_dend * (E_NMDA-V_dend) * s_NMDA_A_dend / (1 + Mg_con * exp(-Alpha_NMDA*(V_dend/mV+Gamma_NMDA)) / Beta_NMDA) * w_NMDA_A_dend :amp\n",
+ "ds_NMDA_A_dend/dt = -s_NMDA_A_dend/t_NMDA_decay_A_dend :1\n",
+ "dI_noise_dend/dt = (mean_noise_dend-I_noise_dend) / tau_noise_dend + sigma_noise_dend * (sqrt(2/(tau_noise_dend*dt)) * randn()) :amp\n",
+ "I_rise_Na_dend = g_rise_Na_dend * (E_rise_Na-V_dend) :amp\n",
+ "I_fall_Na_dend = g_fall_Na_dend * (E_fall_Na-V_dend) :amp\n",
+ "g_rise_Na_dend = g_rise_max_Na_dend * int(t_in_timesteps <= spiketime_Na_dend + duration_rise_Na_dend) * gate_Na_dend :siemens\n",
+ "g_fall_Na_dend = g_fall_max_Na_dend * int(t_in_timesteps <= spiketime_Na_dend + offset_fall_Na_dend + duration_fall_Na_dend) * int(t_in_timesteps >= spiketime_Na_dend + offset_fall_Na_dend) * gate_Na_dend :siemens\n",
+ "spiketime_Na_dend :1\n",
+ "gate_Na_dend :1\n"
+ ]
+ }
+ ],
+ "source": [
+ "dend.dspikes('Na')\n",
+ "print(dend.equations)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dI2QKeh3FI2S"
+ },
+ "source": [
+ "### Connecting compartments"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "EIpkNOaJEpT5",
+ "outputId": "d67c2b30-c60c-4329-e58a-c083b7c71f09"
+ },
+ "outputs": [],
+ "source": [
+ "dend.connect(soma)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "zzba0-KoD12O"
+ },
+ "source": [
+ "*NOTE: Ιgnore the above errors for now. They will make sense in a while.*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "RGnKjqUoEw-4",
+ "outputId": "ca62ee2f-76fe-42bd-c6e3-f5af77beff24"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "dV_dend/dt = (gL_dend * (EL_dend-V_dend) + I_dend) / C_dend :volt\n",
+ "I_dend = I_ext_dend + I_soma_dend + I_rise_Na_dend + I_fall_Na_dend + I_noise_dend + I_NMDA_A_dend + I_AMPA_B_dend + I_AMPA_A_dend :amp\n",
+ "I_ext_dend :amp\n",
+ "I_AMPA_A_dend = g_AMPA_A_dend * (E_AMPA-V_dend) * s_AMPA_A_dend * w_AMPA_A_dend :amp\n",
+ "ds_AMPA_A_dend/dt = -s_AMPA_A_dend / t_AMPA_decay_A_dend :1\n",
+ "I_AMPA_B_dend = g_AMPA_B_dend * (E_AMPA-V_dend) * s_AMPA_B_dend * w_AMPA_B_dend :amp\n",
+ "ds_AMPA_B_dend/dt = -s_AMPA_B_dend / t_AMPA_decay_B_dend :1\n",
+ "I_NMDA_A_dend = g_NMDA_A_dend * (E_NMDA-V_dend) * s_NMDA_A_dend / (1 + Mg_con * exp(-Alpha_NMDA*(V_dend/mV+Gamma_NMDA)) / Beta_NMDA) * w_NMDA_A_dend :amp\n",
+ "ds_NMDA_A_dend/dt = -s_NMDA_A_dend/t_NMDA_decay_A_dend :1\n",
+ "dI_noise_dend/dt = (mean_noise_dend-I_noise_dend) / tau_noise_dend + sigma_noise_dend * (sqrt(2/(tau_noise_dend*dt)) * randn()) :amp\n",
+ "I_rise_Na_dend = g_rise_Na_dend * (E_rise_Na-V_dend) :amp\n",
+ "I_fall_Na_dend = g_fall_Na_dend * (E_fall_Na-V_dend) :amp\n",
+ "g_rise_Na_dend = g_rise_max_Na_dend * int(t_in_timesteps <= spiketime_Na_dend + duration_rise_Na_dend) * gate_Na_dend :siemens\n",
+ "g_fall_Na_dend = g_fall_max_Na_dend * int(t_in_timesteps <= spiketime_Na_dend + offset_fall_Na_dend + duration_fall_Na_dend) * int(t_in_timesteps >= spiketime_Na_dend + offset_fall_Na_dend) * gate_Na_dend :siemens\n",
+ "spiketime_Na_dend :1\n",
+ "gate_Na_dend :1\n",
+ "I_soma_dend = (V_soma-V_dend) * g_soma_dend :amp\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(dend.equations)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "RZF6f7VcFQTF",
+ "outputId": "4a722c2a-5712-44fd-ca4a-5e76607cf1de"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "dV_soma/dt = (gL_soma * (EL_soma-V_soma) + I_soma) / C_soma :volt\n",
+ "I_soma = I_ext_soma + I_dend_soma :amp\n",
+ "I_ext_soma :amp\n",
+ "I_dend_soma = (V_dend-V_soma) * g_dend_soma :amp\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(soma.equations)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HlFu9pEOGtS4"
+ },
+ "source": [
+ "### User-defined equations"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cUH-lXneEFiU"
+ },
+ "source": [
+ "*Equations are Python strings, thus you can adapt them with standard string formatting practices.*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "wiJsv5IXG0Vi",
+ "outputId": "87633fe7-a056-4ade-ca20-e682d360db66"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "str"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "type(dend.equations)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "9LEl6oRLHob8",
+ "outputId": "9362240a-89a1-415d-ceef-9136b0fbd51e"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "dV_dend/dt = (gL_dend * (EL_dend-V_dend) + I_dend) / C_dend :volt\n",
+ "I_dend = I_ext_dend + I_soma_dend + I_rise_Na_dend + I_fall_Na_dend + I_noise_dend + I_NMDA_A_dend + I_AMPA_B_dend + I_AMPA_A_dend :amp\n",
+ "I_ext_dend :amp\n",
+ "I_AMPA_A_dend = g_AMPA_A_dend * (E_AMPA-V_dend) * s_AMPA_A_dend * w_AMPA_A_dend :amp\n",
+ "ds_AMPA_A_dend/dt = -s_AMPA_A_dend / t_AMPA_decay_A_dend :1\n",
+ "I_AMPA_B_dend = g_AMPA_B_dend * (E_AMPA-V_dend) * s_AMPA_B_dend * w_AMPA_B_dend :amp\n",
+ "ds_AMPA_B_dend/dt = -s_AMPA_B_dend / t_AMPA_decay_B_dend :1\n",
+ "I_NMDA_A_dend = g_NMDA_A_dend * (E_NMDA-V_dend) * s_NMDA_A_dend / (1 + Mg_con * exp(-Alpha_NMDA*(V_dend/mV+Gamma_NMDA)) / Beta_NMDA) * w_NMDA_A_dend :amp\n",
+ "ds_NMDA_A_dend/dt = -s_NMDA_A_dend/t_NMDA_decay_A_dend :1\n",
+ "dI_noise_dend/dt = (mean_noise_dend-I_noise_dend) / tau_noise_dend + sigma_noise_dend * (sqrt(2/(tau_noise_dend*dt)) * randn()) :amp\n",
+ "I_rise_Na_dend = g_rise_Na_dend * (E_rise_Na-V_dend) :amp\n",
+ "I_fall_Na_dend = g_fall_Na_dend * (E_fall_Na-V_dend) :amp\n",
+ "g_rise_Na_dend = g_rise_max_Na_dend * int(t_in_timesteps <= spiketime_Na_dend + duration_rise_Na_dend) * gate_Na_dend :siemens\n",
+ "g_fall_Na_dend = g_fall_max_Na_dend * int(t_in_timesteps <= spiketime_Na_dend + offset_fall_Na_dend + duration_fall_Na_dend) * int(t_in_timesteps >= spiketime_Na_dend + offset_fall_Na_dend) * gate_Na_dend :siemens\n",
+ "spiketime_Na_dend :1\n",
+ "gate_Na_dend :1\n",
+ "I_soma_dend = (V_soma-V_dend) * g_soma_dend :amp\n",
+ "dcns/dt = -cns/tau_cns :1\n"
+ ]
+ }
+ ],
+ "source": [
+ "custom_model = \"dcns/dt = -cns/tau_cns :1\"\n",
+ "eqs = f\"{dend.equations}\\n{custom_model}\"\n",
+ "print(eqs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LWwQ57oWFnoG"
+ },
+ "source": [
+ "## Setting model parameters\n",
+ "\n",
+ "*In this second part of the tutorial we are going to explore how to access, generate or update all model parameters.*"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "18EB0MRkK3WR"
+ },
+ "source": [
+ "### Accessing model properties"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ECmM9IGcvmE4",
+ "outputId": "d2a63116-383c-47e4-da8c-8878a8006002"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "ERROR [dendrify.ephysproperties:336]\n",
+ "Could not calculate the g_couple for 'dend' and 'soma'.\n",
+ "Please make sure that [length, diameter, r_axial] are\n",
+ "available for both compartments.\n",
+ "\n",
+ "WARNING [dendrify.ephysproperties:180]\n",
+ "Missing parameters [length | diameter] for 'dend'.\n",
+ "Could not calculate the area of 'dend', returned None.\n",
+ "\n",
+ "WARNING [dendrify.ephysproperties:210]\n",
+ "Could not calculate the [capacitance] of 'dend', returned None.\n",
+ "\n",
+ "WARNING [dendrify.ephysproperties:180]\n",
+ "Missing parameters [length | diameter] for 'dend'.\n",
+ "Could not calculate the area of 'dend', returned None.\n",
+ "\n",
+ "WARNING [dendrify.ephysproperties:240]\n",
+ "Could not calculate the [g_leakage] of 'dend', returned None.\n",
+ "\n",
+ "ERROR [dendrify.ephysproperties:266]\n",
+ "Could not resolve [EL_dend] for 'dend'.\n",
+ "\n",
+ "ERROR [dendrify.ephysproperties:266]\n",
+ "Could not resolve [C_dend] for 'dend'.\n",
+ "\n",
+ "ERROR [dendrify.ephysproperties:266]\n",
+ "Could not resolve [gL_dend] for 'dend'.\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "{'w_AMPA_A_dend': 1.0,\n",
+ " 'w_AMPA_B_dend': 1.0,\n",
+ " 'w_NMDA_A_dend': 1.0,\n",
+ " 'tau_noise_dend': 20. * msecond,\n",
+ " 'sigma_noise_dend': 1. * pamp,\n",
+ " 'mean_noise_dend': 0. * amp,\n",
+ " 'g_soma_dend': None,\n",
+ " 'Vth_Na_dend': None,\n",
+ " 'g_rise_max_Na_dend': None,\n",
+ " 'g_fall_max_Na_dend': None,\n",
+ " 'E_rise_Na': None,\n",
+ " 'E_fall_Na': None,\n",
+ " 'duration_rise_Na_dend': None,\n",
+ " 'duration_fall_Na_dend': None,\n",
+ " 'offset_fall_Na_dend': None,\n",
+ " 'refractory_Na_dend': None,\n",
+ " 'E_AMPA': 0. * volt,\n",
+ " 'E_NMDA': 0. * volt,\n",
+ " 'E_GABA': -80. * mvolt,\n",
+ " 'E_Na': 70. * mvolt,\n",
+ " 'E_K': -89. * mvolt,\n",
+ " 'E_Ca': 136. * mvolt,\n",
+ " 'Mg_con': 1.0,\n",
+ " 'Alpha_NMDA': 0.062,\n",
+ " 'Beta_NMDA': 3.57,\n",
+ " 'Gamma_NMDA': 0}"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dend.parameters"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "q_mi6gmP6L40"
+ },
+ "source": [
+ "*Dendrify is designed to fail loudly!!! Errors and warnings are raised if you try to access parameters that do not exist, or if something important is missing.*"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cXS_FuIuLApo"
+ },
+ "source": [
+ "### Default parameters"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nvST35V1EkFt"
+ },
+ "source": [
+ "*NOTE: Dendrify has a built-in library of default simulation parameters that can be views or adjusted using default_params() and update_default_params() respectively.*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "zNC-AFP9pJeX",
+ "outputId": "726a8d08-aade-4fda-be9b-5294790cded1"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'E_AMPA': 0. * volt,\n",
+ " 'E_NMDA': 0. * volt,\n",
+ " 'E_GABA': -80. * mvolt,\n",
+ " 'E_Na': 70. * mvolt,\n",
+ " 'E_K': -89. * mvolt,\n",
+ " 'E_Ca': 136. * mvolt,\n",
+ " 'Mg_con': 1.0,\n",
+ " 'Alpha_NMDA': 0.062,\n",
+ " 'Beta_NMDA': 3.57,\n",
+ " 'Gamma_NMDA': 0}"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "d.default_params()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "qWmWEYWiqE01",
+ "outputId": "506b93a9-fc87-4a1d-f2ce-66a3ef1a44ea"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'E_AMPA': 0. * volt,\n",
+ " 'E_NMDA': 0. * volt,\n",
+ " 'E_GABA': -80. * mvolt,\n",
+ " 'E_Na': 70. * mvolt,\n",
+ " 'E_K': -89. * mvolt,\n",
+ " 'E_Ca': 2023,\n",
+ " 'Mg_con': 1.0,\n",
+ " 'Alpha_NMDA': 0.062,\n",
+ " 'Beta_NMDA': 3.57,\n",
+ " 'Gamma_NMDA': 0}"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "d.update_default_params({\"E_Ca\":2023})\n",
+ "d.default_params()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "eb0zei_i2r0f"
+ },
+ "source": [
+ "### Ephys parameters"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "K8SHIG5y7E9g"
+ },
+ "source": [
+ "*In Dendrify, each compartment is treated as an open cylinder. Although an RC circuit does not have physical dimensions, length and diameter are needed to estimate a compartment's theoretical surface area.*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "id": "EcGBrUj82z8_"
+ },
+ "outputs": [],
+ "source": [
+ "soma = Soma('soma', length=20*um, diameter=20*um,\n",
+ " cm=1*uF/(cm**2), gl=40*uS/(cm**2),\n",
+ " r_axial=150*ohm*cm, v_rest=-70*mV)\n",
+ "\n",
+ "dend = Dendrite('dend', length=20*um, diameter=20*um,\n",
+ " cm=1*uF/(cm**2), gl=40*uS/(cm**2),\n",
+ " r_axial=150*ohm*cm, v_rest=-70*mV)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "UcPgE9843bJo",
+ "outputId": "ed020d71-d351-4e24-b096-5f333449e201"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "OBJECT\n",
+ "------\n",
+ "\n",
+ "\n",
+ "\n",
+ "EQUATIONS\n",
+ "---------\n",
+ "dV_soma/dt = (gL_soma * (EL_soma-V_soma) + I_soma) / C_soma :volt\n",
+ "I_soma = I_ext_soma :amp\n",
+ "I_ext_soma :amp\n",
+ "\n",
+ "\n",
+ "PARAMETERS\n",
+ "----------\n",
+ "{'Alpha_NMDA': 0.062,\n",
+ " 'Beta_NMDA': 3.57,\n",
+ " 'C_soma': 12.56637061 * pfarad,\n",
+ " 'EL_soma': -70. * mvolt,\n",
+ " 'E_AMPA': 0. * volt,\n",
+ " 'E_Ca': 2023,\n",
+ " 'E_GABA': -80. * mvolt,\n",
+ " 'E_K': -89. * mvolt,\n",
+ " 'E_NMDA': 0. * volt,\n",
+ " 'E_Na': 70. * mvolt,\n",
+ " 'Gamma_NMDA': 0,\n",
+ " 'Mg_con': 1.0,\n",
+ " 'gL_soma': 0.50265482 * nsiemens}\n",
+ "\n",
+ "\n",
+ "USER PARAMETERS\n",
+ "---------------\n",
+ "{'_dimensionless': False,\n",
+ " 'cm': 0.01 * metre ** -4 * kilogram ** -1 * second ** 4 * amp ** 2,\n",
+ " 'cm_abs': None,\n",
+ " 'diameter': 20. * umetre,\n",
+ " 'gl': 0.4 * metre ** -4 * kilogram ** -1 * second ** 3 * amp ** 2,\n",
+ " 'gl_abs': None,\n",
+ " 'length': 20. * umetre,\n",
+ " 'name': 'soma',\n",
+ " 'r_axial': 1.5 * metre ** 3 * kilogram * second ** -3 * amp ** -2,\n",
+ " 'scale_factor': 1.0,\n",
+ " 'spine_factor': 1.0,\n",
+ " 'v_rest': -70. * mvolt}\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(soma) # no more errors :)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 37
+ },
+ "id": "lEGbWHAO3iOw",
+ "outputId": "bc668298-0aed-4d9c-d56f-3c32a157b134"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/latex": [
+ "$1256.637061435917\\,\\mathrm{um^2}$"
+ ],
+ "text/plain": [
+ "1256.63706144 * umetre2"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# The surface area of an equivalent open cylinder\n",
+ "soma.area"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 37
+ },
+ "id": "Za2WRvIt4T_z",
+ "outputId": "93ed1e09-03bc-4de7-9859-b084a5b86143"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/latex": [
+ "$12.566370614359167\\,\\mathrm{p}\\mathrm{F}$"
+ ],
+ "text/plain": [
+ "12.56637061 * pfarad"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Absolute capacitance (specific capacitance [cm] multiplied by area)\n",
+ "soma.capacitance"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 37
+ },
+ "id": "Q-vEq2Ou4VCd",
+ "outputId": "5db0c7f9-624c-4545-c810-82a338855de8"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/latex": [
+ "$0.5026548245743667\\,\\mathrm{n}\\mathrm{S}$"
+ ],
+ "text/plain": [
+ "0.50265482 * nsiemens"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Absolute leakage conductance (specific leakage conductance [gl] multiplied by area)\n",
+ "soma.g_leakage"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_sG16pZZdiyi"
+ },
+ "source": [
+ "### Synaptic parameters"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "61zf_Lm0cngU",
+ "outputId": "9366e9f1-4903-4d7c-cc0e-8f94956bb523"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "OBJECT\n",
+ "------\n",
+ "\n",
+ "\n",
+ "\n",
+ "EQUATIONS\n",
+ "---------\n",
+ "dV_dend/dt = (gL_dend * (EL_dend-V_dend) + I_dend) / C_dend :volt\n",
+ "I_dend = I_ext_dend + I_AMPA_A_dend :amp\n",
+ "I_ext_dend :amp\n",
+ "I_AMPA_A_dend = g_AMPA_A_dend * (E_AMPA-V_dend) * s_AMPA_A_dend * w_AMPA_A_dend :amp\n",
+ "ds_AMPA_A_dend/dt = -s_AMPA_A_dend / t_AMPA_decay_A_dend :1\n",
+ "\n",
+ "\n",
+ "PARAMETERS\n",
+ "----------\n",
+ "{'Alpha_NMDA': 0.062,\n",
+ " 'Beta_NMDA': 3.57,\n",
+ " 'C_dend': 12.56637061 * pfarad,\n",
+ " 'EL_dend': -70. * mvolt,\n",
+ " 'E_AMPA': 0. * volt,\n",
+ " 'E_Ca': 2023,\n",
+ " 'E_GABA': -80. * mvolt,\n",
+ " 'E_K': -89. * mvolt,\n",
+ " 'E_NMDA': 0. * volt,\n",
+ " 'E_Na': 70. * mvolt,\n",
+ " 'Gamma_NMDA': 0,\n",
+ " 'Mg_con': 1.0,\n",
+ " 'gL_dend': 0.50265482 * nsiemens,\n",
+ " 'g_AMPA_A_dend': 1. * nsiemens,\n",
+ " 't_AMPA_decay_A_dend': 5. * msecond,\n",
+ " 'w_AMPA_A_dend': 1.0}\n",
+ "\n",
+ "\n",
+ "EVENTS\n",
+ "------\n",
+ "[]\n",
+ "\n",
+ "\n",
+ "EVENT CONDITIONS\n",
+ "----------------\n",
+ "{}\n",
+ "\n",
+ "\n",
+ "USER PARAMETERS\n",
+ "---------------\n",
+ "{'cm': 0.01 * metre ** -4 * kilogram ** -1 * second ** 4 * amp ** 2,\n",
+ " 'diameter': 20. * umetre,\n",
+ " 'gl': 0.4 * metre ** -4 * kilogram ** -1 * second ** 3 * amp ** 2,\n",
+ " 'length': 20. * umetre,\n",
+ " 'name': 'dend',\n",
+ " 'r_axial': 1.5 * metre ** 3 * kilogram * second ** -3 * amp ** -2,\n",
+ " 'scale_factor': 1.0,\n",
+ " 'spine_factor': 1.0,\n",
+ " 'v_rest': -70. * mvolt}\n"
+ ]
+ }
+ ],
+ "source": [
+ "dend.synapse('AMPA', 'A', g=1*nS, t_decay=5*ms)\n",
+ "print(dend)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "9plJm75LdItc",
+ "outputId": "ed2499a1-dc39-48cb-a38b-d389c315479a"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "OBJECT\n",
+ "------\n",
+ "\n",
+ "\n",
+ "\n",
+ "EQUATIONS\n",
+ "---------\n",
+ "dV_dend/dt = (gL_dend * (EL_dend-V_dend) + I_dend) / C_dend :volt\n",
+ "I_dend = I_ext_dend + I_NMDA_B_dend + I_NMDA_A_dend + I_AMPA_A_dend :amp\n",
+ "I_ext_dend :amp\n",
+ "I_AMPA_A_dend = g_AMPA_A_dend * (E_AMPA-V_dend) * s_AMPA_A_dend * w_AMPA_A_dend :amp\n",
+ "ds_AMPA_A_dend/dt = -s_AMPA_A_dend / t_AMPA_decay_A_dend :1\n",
+ "I_NMDA_A_dend = g_NMDA_A_dend * (E_NMDA-V_dend) * s_NMDA_A_dend / (1 + Mg_con * exp(-Alpha_NMDA*(V_dend/mV+Gamma_NMDA)) / Beta_NMDA) * w_NMDA_A_dend :amp\n",
+ "ds_NMDA_A_dend/dt = -s_NMDA_A_dend/t_NMDA_decay_A_dend :1\n",
+ "I_NMDA_B_dend = g_NMDA_B_dend * (E_NMDA-V_dend) * x_NMDA_B_dend / (1 + Mg_con * exp(-Alpha_NMDA*(V_dend/mV+Gamma_NMDA)) / Beta_NMDA) * w_NMDA_B_dend :amp\n",
+ "dx_NMDA_B_dend/dt = (-x_NMDA_B_dend/t_NMDA_decay_B_dend) + s_NMDA_B_dend/ms :1\n",
+ "ds_NMDA_B_dend/dt = -s_NMDA_B_dend / t_NMDA_rise_B_dend :1\n",
+ "\n",
+ "\n",
+ "PARAMETERS\n",
+ "----------\n",
+ "{'Alpha_NMDA': 0.062,\n",
+ " 'Beta_NMDA': 3.57,\n",
+ " 'C_dend': 12.56637061 * pfarad,\n",
+ " 'EL_dend': -70. * mvolt,\n",
+ " 'E_AMPA': 0. * volt,\n",
+ " 'E_Ca': 2023,\n",
+ " 'E_GABA': -80. * mvolt,\n",
+ " 'E_K': -89. * mvolt,\n",
+ " 'E_NMDA': 0. * volt,\n",
+ " 'E_Na': 70. * mvolt,\n",
+ " 'Gamma_NMDA': 0,\n",
+ " 'Mg_con': 1.0,\n",
+ " 'gL_dend': 0.50265482 * nsiemens,\n",
+ " 'g_AMPA_A_dend': 1. * nsiemens,\n",
+ " 'g_NMDA_A_dend': 1. * nsiemens,\n",
+ " 'g_NMDA_B_dend': 1. * nsiemens,\n",
+ " 't_AMPA_decay_A_dend': 5. * msecond,\n",
+ " 't_NMDA_decay_A_dend': 60. * msecond,\n",
+ " 't_NMDA_decay_B_dend': 60. * msecond,\n",
+ " 't_NMDA_rise_B_dend': 5. * msecond,\n",
+ " 'w_AMPA_A_dend': 1.0,\n",
+ " 'w_NMDA_A_dend': 1.0,\n",
+ " 'w_NMDA_B_dend': 1.0}\n",
+ "\n",
+ "\n",
+ "EVENTS\n",
+ "------\n",
+ "[]\n",
+ "\n",
+ "\n",
+ "EVENT CONDITIONS\n",
+ "----------------\n",
+ "{}\n",
+ "\n",
+ "\n",
+ "USER PARAMETERS\n",
+ "---------------\n",
+ "{'cm': 0.01 * metre ** -4 * kilogram ** -1 * second ** 4 * amp ** 2,\n",
+ " 'diameter': 20. * umetre,\n",
+ " 'gl': 0.4 * metre ** -4 * kilogram ** -1 * second ** 3 * amp ** 2,\n",
+ " 'length': 20. * umetre,\n",
+ " 'name': 'dend',\n",
+ " 'r_axial': 1.5 * metre ** 3 * kilogram * second ** -3 * amp ** -2,\n",
+ " 'scale_factor': 1.0,\n",
+ " 'spine_factor': 1.0,\n",
+ " 'v_rest': -70. * mvolt}\n"
+ ]
+ }
+ ],
+ "source": [
+ "# NMDA synapse with instant activation and exponential decay:\n",
+ "dend.synapse('NMDA', 'A', g=1*nS, t_decay=60*ms)\n",
+ "\n",
+ "# NMDA synapse as a sum of two exponentials with rise and decay kinetics:\n",
+ "dend.synapse('NMDA', 'B', g=1*nS, t_decay=60*ms, t_rise=5*ms)\n",
+ "\n",
+ "print(dend)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "S1cO0OnODFIM"
+ },
+ "source": [
+ "### Random noise parameters"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "id": "jW9cizORC-7Z"
+ },
+ "outputs": [],
+ "source": [
+ "dend.noise(mean=0*pA, sigma=10*pA, tau=1*ms)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2GmemYW843qc"
+ },
+ "source": [
+ "### dSpike parameters"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "srIhg8ywDOXu"
+ },
+ "source": [
+ "*We will explore this topic in another tutorial...*"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0a4bqv1bGpaO"
+ },
+ "source": [
+ "### Coupling parameters"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "id": "ywUg7Tsd450K"
+ },
+ "outputs": [],
+ "source": [
+ "# Automatic approach\n",
+ "soma.connect(dend, g=10*nS)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "MKbPUsQPHXrz",
+ "outputId": "e7f26114-59eb-4fae-cc3c-fde0eb0308d5"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "OBJECT\n",
+ "------\n",
+ "\n",
+ "\n",
+ "\n",
+ "EQUATIONS\n",
+ "---------\n",
+ "dV_soma/dt = (gL_soma * (EL_soma-V_soma) + I_soma) / C_soma :volt\n",
+ "I_soma = I_ext_soma + I_dend_soma :amp\n",
+ "I_ext_soma :amp\n",
+ "I_dend_soma = (V_dend-V_soma) * g_dend_soma :amp\n",
+ "\n",
+ "\n",
+ "PARAMETERS\n",
+ "----------\n",
+ "{'Alpha_NMDA': 0.062,\n",
+ " 'Beta_NMDA': 3.57,\n",
+ " 'C_soma': 12.56637061 * pfarad,\n",
+ " 'EL_soma': -70. * mvolt,\n",
+ " 'E_AMPA': 0. * volt,\n",
+ " 'E_Ca': 2023,\n",
+ " 'E_GABA': -80. * mvolt,\n",
+ " 'E_K': -89. * mvolt,\n",
+ " 'E_NMDA': 0. * volt,\n",
+ " 'E_Na': 70. * mvolt,\n",
+ " 'Gamma_NMDA': 0,\n",
+ " 'Mg_con': 1.0,\n",
+ " 'gL_soma': 0.50265482 * nsiemens,\n",
+ " 'g_dend_soma': 10. * nsiemens}\n",
+ "\n",
+ "\n",
+ "USER PARAMETERS\n",
+ "---------------\n",
+ "{'_dimensionless': False,\n",
+ " 'cm': 0.01 * metre ** -4 * kilogram ** -1 * second ** 4 * amp ** 2,\n",
+ " 'cm_abs': None,\n",
+ " 'diameter': 20. * umetre,\n",
+ " 'gl': 0.4 * metre ** -4 * kilogram ** -1 * second ** 3 * amp ** 2,\n",
+ " 'gl_abs': None,\n",
+ " 'length': 20. * umetre,\n",
+ " 'name': 'soma',\n",
+ " 'r_axial': 1.5 * metre ** 3 * kilogram * second ** -3 * amp ** -2,\n",
+ " 'scale_factor': 1.0,\n",
+ " 'spine_factor': 1.0,\n",
+ " 'v_rest': -70. * mvolt}\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(soma)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "BVC5W-wqUAw3"
+ },
+ "source": [
+ "### Dimensionless compartments"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cZ7hn5vbVMRb"
+ },
+ "source": [
+ "*If you know the model's desired capacitance and leakage conductance you can pass them dirrectly as a parameters when creating a compartment.*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {
+ "id": "hq8jwM4oHYgY"
+ },
+ "outputs": [],
+ "source": [
+ "soma = Soma('soma', cm_abs=200*pF, gl_abs=20*nS, v_rest=-70*mV)\n",
+ "dend = Dendrite('dend', cm_abs=200*pF, gl_abs=20*nS, v_rest=-70*mV)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dc91WzlfWOsA"
+ },
+ "source": [
+ "*Since these compartments have no dimensions, the automatic connection approach will not work, since it calculates the resistance between two adjucent half-cylinders.*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 418
+ },
+ "id": "bRzVy6XvV4as",
+ "outputId": "4570210b-08c5-4ca2-b860-7a3dc97abd41"
+ },
+ "outputs": [
+ {
+ "ename": "DimensionlessCompartmentError",
+ "evalue": "Cannot automatically calculate the coupling \nconductance of dimensionless compartments. To resolve this error, perform\none of the following:\n\n1. Provide [length, diameter, r_axial] for both 'soma' and 'dend'.\n\n2. Turn both compartment into dimensionless by providing only values for \n [cm_abs, gl_abs] and then connect them using an exact coupling conductance.",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mDimensionlessCompartmentError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[31], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m soma\u001b[39m.\u001b[39;49mconnect(dend)\n",
+ "File \u001b[0;32m~/anaconda3/envs/dendrify/lib/python3.11/site-packages/dendrify/compartment.py:169\u001b[0m, in \u001b[0;36mCompartment.connect\u001b[0;34m(self, other, g)\u001b[0m\n\u001b[1;32m 166\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 167\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mCannot connect compartments with the same name.\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 168\u001b[0m \u001b[39mif\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdimensionless \u001b[39mor\u001b[39;00m other\u001b[39m.\u001b[39mdimensionless) \u001b[39mand\u001b[39;00m \u001b[39mtype\u001b[39m(g) \u001b[39m==\u001b[39m \u001b[39mstr\u001b[39m:\n\u001b[0;32m--> 169\u001b[0m \u001b[39mraise\u001b[39;00m DimensionlessCompartmentError(\n\u001b[1;32m 170\u001b[0m (\u001b[39m\"\u001b[39m\u001b[39mCannot automatically calculate the coupling \u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39mconductance of \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 171\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mdimensionless compartments. To resolve this error, perform\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[1;32m 172\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mone of the following:\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[1;32m 173\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m1. Provide [length, diameter, r_axial] for both \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mname\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 174\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m and \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mother\u001b[39m.\u001b[39mname\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[1;32m 175\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m2. Turn both compartment into dimensionless by providing only\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 176\u001b[0m \u001b[39m\"\u001b[39m\u001b[39m values for \u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m [cm_abs, gl_abs] and then connect them using \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 177\u001b[0m \u001b[39m\"\u001b[39m\u001b[39man exact coupling conductance.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 178\u001b[0m )\n\u001b[1;32m 179\u001b[0m )\n\u001b[1;32m 181\u001b[0m \u001b[39m# Current from Comp2 -> Comp1\u001b[39;00m\n\u001b[1;32m 182\u001b[0m I_forward \u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39mI_\u001b[39m\u001b[39m{1}\u001b[39;00m\u001b[39m_\u001b[39m\u001b[39m{0}\u001b[39;00m\u001b[39m = (V_\u001b[39m\u001b[39m{1}\u001b[39;00m\u001b[39m-V_\u001b[39m\u001b[39m{0}\u001b[39;00m\u001b[39m) * g_\u001b[39m\u001b[39m{1}\u001b[39;00m\u001b[39m_\u001b[39m\u001b[39m{0}\u001b[39;00m\u001b[39m :amp\u001b[39m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mformat(\n\u001b[1;32m 183\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mname, other\u001b[39m.\u001b[39mname)\n",
+ "\u001b[0;31mDimensionlessCompartmentError\u001b[0m: Cannot automatically calculate the coupling \nconductance of dimensionless compartments. To resolve this error, perform\none of the following:\n\n1. Provide [length, diameter, r_axial] for both 'soma' and 'dend'.\n\n2. Turn both compartment into dimensionless by providing only values for \n [cm_abs, gl_abs] and then connect them using an exact coupling conductance."
+ ]
+ }
+ ],
+ "source": [
+ "soma.connect(dend)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "lCCfJCJJWxEt"
+ },
+ "source": [
+ "*However, you can still connect them by explicitly specifying the coupling conductance as shown bellow. **IMPORTANT: This trick also works for compartments that have dimensions as well**.*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "id": "Y3mLnFyYWC5L"
+ },
+ "outputs": [],
+ "source": [
+ "soma.connect(dend, g=10*nS)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ebpd-TbhXdZE",
+ "outputId": "711a3985-c56d-427c-dba5-0107e975f17a"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "OBJECT\n",
+ "------\n",
+ "\n",
+ "\n",
+ "\n",
+ "EQUATIONS\n",
+ "---------\n",
+ "dV_soma/dt = (gL_soma * (EL_soma-V_soma) + I_soma) / C_soma :volt\n",
+ "I_soma = I_ext_soma + I_dend_soma :amp\n",
+ "I_ext_soma :amp\n",
+ "I_dend_soma = (V_dend-V_soma) * g_dend_soma :amp\n",
+ "\n",
+ "\n",
+ "PARAMETERS\n",
+ "----------\n",
+ "{'Alpha_NMDA': 0.062,\n",
+ " 'Beta_NMDA': 3.57,\n",
+ " 'C_soma': 200. * pfarad,\n",
+ " 'EL_soma': -70. * mvolt,\n",
+ " 'E_AMPA': 0. * volt,\n",
+ " 'E_Ca': 2023,\n",
+ " 'E_GABA': -80. * mvolt,\n",
+ " 'E_K': -89. * mvolt,\n",
+ " 'E_NMDA': 0. * volt,\n",
+ " 'E_Na': 70. * mvolt,\n",
+ " 'Gamma_NMDA': 0,\n",
+ " 'Mg_con': 1.0,\n",
+ " 'gL_soma': 20. * nsiemens,\n",
+ " 'g_dend_soma': 10. * nsiemens}\n",
+ "\n",
+ "\n",
+ "USER PARAMETERS\n",
+ "---------------\n",
+ "{'_dimensionless': True,\n",
+ " 'cm': None,\n",
+ " 'cm_abs': 200. * pfarad,\n",
+ " 'diameter': None,\n",
+ " 'gl': None,\n",
+ " 'gl_abs': 20. * nsiemens,\n",
+ " 'length': None,\n",
+ " 'name': 'soma',\n",
+ " 'r_axial': None,\n",
+ " 'scale_factor': None,\n",
+ " 'spine_factor': None,\n",
+ " 'v_rest': -70. * mvolt}\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(soma)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Creating point neurons"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "*Dendrify also supports point (single-compartment) neuron models that share most of the functionalities of compartment objects (Soma/Dendrite). Notice that here, there is no need to specify a compartment's name.*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "OBJECT\n",
+ "------\n",
+ "\n",
+ "\n",
+ "\n",
+ "EQUATIONS\n",
+ "---------\n",
+ "dV/dt = (gL * (EL-V) + I) / C :volt\n",
+ "I = I_ext + I_AMPA_x + I_noise :amp\n",
+ "I_ext :amp\n",
+ "dI_noise/dt = (mean_noise-I_noise) / tau_noise + sigma_noise * (sqrt(2/(tau_noise*dt)) * randn()) :amp\n",
+ "I_AMPA_x = g_AMPA_x * (E_AMPA-V) * s_AMPA_x * w_AMPA_x :amp\n",
+ "ds_AMPA_x/dt = -s_AMPA_x / t_AMPA_decay_x :1\n",
+ "\n",
+ "\n",
+ "PARAMETERS\n",
+ "----------\n",
+ "{'Alpha_NMDA': 0.062,\n",
+ " 'Beta_NMDA': 3.57,\n",
+ " 'C': 200. * pfarad,\n",
+ " 'EL': -60. * mvolt,\n",
+ " 'E_AMPA': 0. * volt,\n",
+ " 'E_Ca': 2023,\n",
+ " 'E_GABA': -80. * mvolt,\n",
+ " 'E_K': -89. * mvolt,\n",
+ " 'E_NMDA': 0. * volt,\n",
+ " 'E_Na': 70. * mvolt,\n",
+ " 'Gamma_NMDA': 0,\n",
+ " 'Mg_con': 1.0,\n",
+ " 'gL': 10. * nsiemens,\n",
+ " 'g_AMPA_x': 2. * nsiemens,\n",
+ " 'mean_noise': 10. * pamp,\n",
+ " 'sigma_noise': 100. * pamp,\n",
+ " 't_AMPA_decay_x': 2. * msecond,\n",
+ " 'tau_noise': 20. * msecond,\n",
+ " 'w_AMPA_x': 1.0}\n",
+ "\n",
+ "\n",
+ "USER PARAMETERS\n",
+ "---------------\n",
+ "{'_dimensionless': True,\n",
+ " 'cm': None,\n",
+ " 'cm_abs': 200. * pfarad,\n",
+ " 'diameter': None,\n",
+ " 'gl': None,\n",
+ " 'gl_abs': 10. * nsiemens,\n",
+ " 'length': None,\n",
+ " 'name': None,\n",
+ " 'r_axial': None,\n",
+ " 'scale_factor': None,\n",
+ " 'spine_factor': None,\n",
+ " 'v_rest': -60. * mvolt}\n"
+ ]
+ }
+ ],
+ "source": [
+ "model = PointNeuronModel(model='leakyIF', v_rest=-60*mV,\n",
+ " cm_abs=200*pF, gl_abs=10*nS)\n",
+ "model.noise(mean=10*pA, sigma=100*pA, tau=20*ms)\n",
+ "model.synapse('AMPA', tag='x', g=2*nS, t_decay=2*ms)\n",
+ "\n",
+ "print(model)"
+ ]
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "provenance": [],
+ "toc_visible": true
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/docs_sphinx/source/tutorials/Dendrify_simulations.ipynb b/docs_sphinx/source/tutorials/Dendrify_simulations.ipynb
new file mode 100644
index 0000000..44a121c
--- /dev/null
+++ b/docs_sphinx/source/tutorials/Dendrify_simulations.ipynb
@@ -0,0 +1,1342 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "McBiAX2GtIhy"
+ },
+ "source": [
+ "# Running simulations \n",
+ "\n",
+ "In this tutorial, we are going to cover the following topics:\n",
+ "\n",
+ "* How to merge compartments into compartmental neuron models\n",
+ "* How to make Dendrify and Brian interact with each other\n",
+ "* How to run simulations of Dendrify models in Brian\n",
+ "\n",
+ "`Disclaimer: Below, we present a generic \"toy\" model developed solely for educational purposes. However, please note that its parameters and behavior have not been validated using real data. If you intend to utilize Dendrify in a project, we strongly advise against using this model as it is, unless you first calibrate its parameters to align with your specific requirements.`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_DY92XwdtIRn"
+ },
+ "source": [
+ "## Imports & settings"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "id": "AsdN3SL3sw03"
+ },
+ "outputs": [],
+ "source": [
+ "import brian2 as b\n",
+ "import matplotlib.pyplot as plt\n",
+ "from brian2.units import *\n",
+ "from dendrify import Soma, Dendrite, NeuronModel\n",
+ "\n",
+ "b.prefs.codegen.target = 'numpy' # faster for basic models and short simulations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "cellView": "form",
+ "id": "Lw68q2p-_b_f"
+ },
+ "outputs": [],
+ "source": [
+ "# Plot settings\n",
+ "blue = '#005c94ff'\n",
+ "green = '#338000ff'\n",
+ "orange = '#ff6600ff'\n",
+ "notred = '#aa0044ff'\n",
+ "params = {\n",
+ " \"legend.fontsize\": 10,\n",
+ " \"legend.handlelength\": 1.5,\n",
+ " \"legend.edgecolor\": 'inherit',\n",
+ " \"legend.columnspacing\": 0.8,\n",
+ " \"legend.handletextpad\": 0.5,\n",
+ " \"axes.labelsize\": 10,\n",
+ " \"axes.titlesize\": 11,\n",
+ " \"axes.spines.right\": False,\n",
+ " \"axes.spines.top\": False,\n",
+ " \"xtick.labelsize\": 10,\n",
+ " \"ytick.labelsize\": 10,\n",
+ " 'lines.markersize': 3,\n",
+ " 'lines.linewidth': 1.25,\n",
+ " 'grid.color': \"#d3d3d3\",\n",
+ " 'figure.dpi': 150,\n",
+ " 'axes.prop_cycle': b.cycler(color=[blue, green, orange, notred])\n",
+ " }\n",
+ "\n",
+ "plt.rcParams.update(params)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "d4gD9Sdy-Gdp"
+ },
+ "source": [
+ "## Create a compartmental model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "zza4XGmH-VM7"
+ },
+ "source": [
+ "Lets try to recreate the following basic 4-compartment model:\n",
+ "\n",
+ "
\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "w3qaNaQREaAW"
+ },
+ "source": [
+ "According to the previous tutorial the code should look somethink like this:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "id": "KnB8V4uSDxIz"
+ },
+ "outputs": [],
+ "source": [
+ "# create soma\n",
+ "soma = Soma('soma', model='leakyIF', length=25*um, diameter=25*um,\n",
+ " cm=1*uF/(cm**2), gl=40*uS/(cm**2),\n",
+ " v_rest=-65*mV)\n",
+ "\n",
+ "# create trunk\n",
+ "trunk = Dendrite('trunk', length=100*um, diameter=2.5*um,\n",
+ " cm=1*uF/(cm**2), gl=40*uS/(cm**2),\n",
+ " v_rest=-65*mV)\n",
+ "\n",
+ "# create proximal dendrite\n",
+ "prox = Dendrite('prox', length=100*um, diameter=1*um,\n",
+ " cm=1*uF/(cm**2), gl=40*uS/(cm**2),\n",
+ " v_rest=-65*mV)\n",
+ "prox.synapse('AMPA', tag='pathY', g=1*nS, t_decay=2*ms)\n",
+ "prox.synapse('NMDA', tag='pathY', g=1*nS, t_decay=60*ms)\n",
+ "\n",
+ "# create distal dendrite\n",
+ "dist = Dendrite('dist', length=100*um, diameter=0.5*um,\n",
+ " cm=1*uF/(cm**2), gl=40*uS/(cm**2),\n",
+ " v_rest=-65*mV)\n",
+ "dist.synapse('AMPA', tag='pathX', g=1*nS, t_decay=2*ms)\n",
+ "dist.synapse('NMDA', tag='pathX', g=1*nS, t_decay=60*ms)\n",
+ "\n",
+ "soma.connect(trunk, g=15*nS)\n",
+ "trunk.connect(prox, g=6*nS)\n",
+ "prox.connect(dist, g=2*nS)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xZv2KH5iE-L8"
+ },
+ "source": [
+ "HOWEVER: There is a far better way for creating a compartmental model!!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "id": "aELa4K9es_EJ"
+ },
+ "outputs": [],
+ "source": [
+ "# create compartments\n",
+ "soma = Soma('soma', model='leakyIF', length=25*um, diameter=25*um)\n",
+ "trunk = Dendrite('trunk', length=100*um, diameter=2.5*um)\n",
+ "prox = Dendrite('prox', length=100*um, diameter=1*um)\n",
+ "dist = Dendrite('dist', length=100*um, diameter=0.5*um)\n",
+ "\n",
+ "# add AMPA/NMDA synapses\n",
+ "prox.synapse('AMPA', tag='pathY', g=1*nS, t_decay=2*ms)\n",
+ "prox.synapse('NMDA', tag='pathY', g=1*nS, t_decay=60*ms)\n",
+ "dist.synapse('AMPA', tag='pathX', g=1*nS, t_decay=2*ms)\n",
+ "dist.synapse('NMDA', tag='pathX', g=1*nS, t_decay=60*ms)\n",
+ "\n",
+ "# merge compartments into a neuron model and set its basic properties\n",
+ "graph = [(soma, trunk, 15*nS), (trunk, prox, 6*nS), (prox, dist, 2*nS)]\n",
+ "model = NeuronModel(graph, cm=1*uF/(cm**2), gl=40*uS/(cm**2),\n",
+ " v_rest=-65*mV, scale_factor=2.8, spine_factor=1.5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mJCpyFkmGH4n"
+ },
+ "source": [
+ "The NeuronModel class, not only allows to set model parameters, but also unlocks many cool functions that we are going to explore now."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "JN48EqDVGmbp",
+ "outputId": "24c48223-baf1-458b-b19d-c69a6ed320e6"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "OBJECT\n",
+ "------\n",
+ "\n",
+ "\n",
+ "\n",
+ "EQUATIONS\n",
+ "---------\n",
+ "dV_soma/dt = (gL_soma * (EL_soma-V_soma) + I_soma) / C_soma :volt\n",
+ "I_soma = I_ext_soma + I_trunk_soma :amp\n",
+ "I_ext_soma :amp\n",
+ "I_trunk_soma = (V_trunk-V_soma) * g_trunk_soma :amp\n",
+ "\n",
+ "dV_trunk/dt = (gL_trunk * (EL_trunk-V_trunk) + I_trunk) / C_trunk :volt\n",
+ "I_trunk = I_ext_trunk + I_prox_trunk + I_soma_trunk :amp\n",
+ "I_ext_trunk :amp\n",
+ "I_soma_trunk = (V_soma-V_trunk) * g_soma_trunk :amp\n",
+ "I_prox_trunk = (V_prox-V_trunk) * g_prox_trunk :amp\n",
+ "\n",
+ "dV_prox/dt = (gL_prox * (EL_prox-V_prox) + I_prox) / C_prox :volt\n",
+ "I_prox = I_ext_prox + I_dist_prox + I_trunk_prox + I_NMDA_pathY_prox + I_AMPA_pathY_prox :amp\n",
+ "I_ext_prox :amp\n",
+ "I_AMPA_pathY_prox = g_AMPA_pathY_prox * (E_AMPA-V_prox) * s_AMPA_pathY_prox * w_AMPA_pathY_prox :amp\n",
+ "ds_AMPA_pathY_prox/dt = -s_AMPA_pathY_prox / t_AMPA_decay_pathY_prox :1\n",
+ "I_NMDA_pathY_prox = g_NMDA_pathY_prox * (E_NMDA-V_prox) * s_NMDA_pathY_prox / (1 + Mg_con * exp(-Alpha_NMDA*(V_prox/mV+Gamma_NMDA)) / Beta_NMDA) * w_NMDA_pathY_prox :amp\n",
+ "ds_NMDA_pathY_prox/dt = -s_NMDA_pathY_prox/t_NMDA_decay_pathY_prox :1\n",
+ "I_trunk_prox = (V_trunk-V_prox) * g_trunk_prox :amp\n",
+ "I_dist_prox = (V_dist-V_prox) * g_dist_prox :amp\n",
+ "\n",
+ "dV_dist/dt = (gL_dist * (EL_dist-V_dist) + I_dist) / C_dist :volt\n",
+ "I_dist = I_ext_dist + I_prox_dist + I_NMDA_pathX_dist + I_AMPA_pathX_dist :amp\n",
+ "I_ext_dist :amp\n",
+ "I_AMPA_pathX_dist = g_AMPA_pathX_dist * (E_AMPA-V_dist) * s_AMPA_pathX_dist * w_AMPA_pathX_dist :amp\n",
+ "ds_AMPA_pathX_dist/dt = -s_AMPA_pathX_dist / t_AMPA_decay_pathX_dist :1\n",
+ "I_NMDA_pathX_dist = g_NMDA_pathX_dist * (E_NMDA-V_dist) * s_NMDA_pathX_dist / (1 + Mg_con * exp(-Alpha_NMDA*(V_dist/mV+Gamma_NMDA)) / Beta_NMDA) * w_NMDA_pathX_dist :amp\n",
+ "ds_NMDA_pathX_dist/dt = -s_NMDA_pathX_dist/t_NMDA_decay_pathX_dist :1\n",
+ "I_prox_dist = (V_prox-V_dist) * g_prox_dist :amp\n",
+ "\n",
+ "\n",
+ "PARAMETERS\n",
+ "----------\n",
+ "{'Alpha_NMDA': 0.062,\n",
+ " 'Beta_NMDA': 3.57,\n",
+ " 'C_dist': 6.59734457 * pfarad,\n",
+ " 'C_prox': 13.19468915 * pfarad,\n",
+ " 'C_soma': 82.46680716 * pfarad,\n",
+ " 'C_trunk': 32.98672286 * pfarad,\n",
+ " 'EL_dist': -65. * mvolt,\n",
+ " 'EL_prox': -65. * mvolt,\n",
+ " 'EL_soma': -65. * mvolt,\n",
+ " 'EL_trunk': -65. * mvolt,\n",
+ " 'E_AMPA': 0. * volt,\n",
+ " 'E_Ca': 136. * mvolt,\n",
+ " 'E_GABA': -80. * mvolt,\n",
+ " 'E_K': -89. * mvolt,\n",
+ " 'E_NMDA': 0. * volt,\n",
+ " 'E_Na': 70. * mvolt,\n",
+ " 'Gamma_NMDA': 0,\n",
+ " 'Mg_con': 1.0,\n",
+ " 'gL_dist': 263.8937829 * psiemens,\n",
+ " 'gL_prox': 0.52778757 * nsiemens,\n",
+ " 'gL_soma': 3.29867229 * nsiemens,\n",
+ " 'gL_trunk': 1.31946891 * nsiemens,\n",
+ " 'g_AMPA_pathX_dist': 1. * nsiemens,\n",
+ " 'g_AMPA_pathY_prox': 1. * nsiemens,\n",
+ " 'g_NMDA_pathX_dist': 1. * nsiemens,\n",
+ " 'g_NMDA_pathY_prox': 1. * nsiemens,\n",
+ " 'g_dist_prox': 2. * nsiemens,\n",
+ " 'g_prox_dist': 2. * nsiemens,\n",
+ " 'g_prox_trunk': 6. * nsiemens,\n",
+ " 'g_soma_trunk': 15. * nsiemens,\n",
+ " 'g_trunk_prox': 6. * nsiemens,\n",
+ " 'g_trunk_soma': 15. * nsiemens,\n",
+ " 't_AMPA_decay_pathX_dist': 2. * msecond,\n",
+ " 't_AMPA_decay_pathY_prox': 2. * msecond,\n",
+ " 't_NMDA_decay_pathX_dist': 60. * msecond,\n",
+ " 't_NMDA_decay_pathY_prox': 60. * msecond,\n",
+ " 'w_AMPA_pathX_dist': 1.0,\n",
+ " 'w_AMPA_pathY_prox': 1.0,\n",
+ " 'w_NMDA_pathX_dist': 1.0,\n",
+ " 'w_NMDA_pathY_prox': 1.0}\n",
+ "\n",
+ "\n",
+ "EVENTS\n",
+ "------\n",
+ "[]\n",
+ "\n",
+ "\n",
+ "EVENT CONDITIONS\n",
+ "----------------\n",
+ "{}\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(model)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 602
+ },
+ "id": "NOqzKvOsGwus",
+ "outputId": "bf2f1bb0-c447-4795-c9ee-1fd35b21d95d"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "model.as_graph()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 451
+ },
+ "id": "JW4khry_Ln6u",
+ "outputId": "d021a828-8fb7-4662-844e-c6ede1358a01"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "model.as_graph(figsize=[4,3], scale_nodes=0.5, fontsize=8, color_soma='black')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "MuGEWDxDKQac"
+ },
+ "source": [
+ "## Dendrify meets Brian"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "id": "U7vbOssEHMrd"
+ },
+ "outputs": [],
+ "source": [
+ "neuron, ap_reset = model.make_neurongroup(1, method='euler', threshold='V_soma > -40*mV',\n",
+ " reset='V_soma = 40*mV',\n",
+ " second_reset= 'V_soma=-55*mV',\n",
+ " spike_width = 0.5*ms,\n",
+ " refractory=4*ms)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "id": "6vLaNX_uNKj4"
+ },
+ "outputs": [],
+ "source": [
+ "# Set a monitor to record the voltages of all compartments\n",
+ "voltages = ['V_soma', 'V_trunk', 'V_prox', 'V_dist']\n",
+ "M = b.StateMonitor(neuron, voltages, record=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "9BJSB5NQQObO"
+ },
+ "source": [
+ "## Run simulation and plot results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "id": "h1DSWPf-N3Ok"
+ },
+ "outputs": [],
+ "source": [
+ "net = b.Network(neuron, ap_reset, M) # organize everythink into a network\n",
+ "net.run(10*ms) # no input\n",
+ "neuron.I_ext_soma = 200*pA\n",
+ "net.run(100*ms) # 200 pA injected at the soma for 100 ms\n",
+ "neuron.I_ext_soma = 0*pA\n",
+ "net.run(60*ms) # run another 60 ms without any input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 571
+ },
+ "id": "9goZFVmDNx2h",
+ "outputId": "256700a6-d4ca-4745-ad9a-b07ea988b707"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot voltages\n",
+ "fig, ax = plt.subplots(figsize=(7, 4))\n",
+ "ax.plot(M.t/ms, M.V_soma[0]/mV, label='soma')\n",
+ "ax.plot(M.t/ms, M.V_trunk[0]/mV, label='trunk')\n",
+ "ax.plot(M.t/ms, M.V_prox[0]/mV, label='prox')\n",
+ "ax.plot(M.t/ms, M.V_dist[0]/mV, label='dist')\n",
+ "ax.set_xlabel('Time (ms)')\n",
+ "ax.set_ylabel('Voltage (mV)')\n",
+ "ax.legend(loc='best');"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 571
+ },
+ "id": "vEC1jiVrOK0v",
+ "outputId": "12cd9928-ad9d-4220-f72c-7e3f48780452"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Zoom in\n",
+ "fig, ax = plt.subplots(figsize=(7, 4))\n",
+ "ax.plot(M.t/ms, M.V_soma[0]/mV, label='soma')\n",
+ "ax.plot(M.t/ms, M.V_trunk[0]/mV, label='trunk')\n",
+ "ax.plot(M.t/ms, M.V_prox[0]/mV, label='prox')\n",
+ "ax.plot(M.t/ms, M.V_dist[0]/mV, label='dist')\n",
+ "ax.set_xlabel('Time (ms)')\n",
+ "ax.set_ylabel('Voltage (mV)')\n",
+ "ax.set_xlim(left=40, right=80)\n",
+ "ax.legend(loc='best');"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pMDJ-StzMZP3"
+ },
+ "source": [
+ "NOTE: The dendritic voltage fluctuations that follow somatic APs are due to the electrical coupling of these compartments. They are not backpropagating dSpikes (not included yet in the model)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2WuTNpz8RCVV"
+ },
+ "source": [
+ "## A network with random input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "id": "HiXz1qoqP0j0"
+ },
+ "outputs": [],
+ "source": [
+ "b.start_scope() # clear previous run\n",
+ "\n",
+ "# First create 2 input groups\n",
+ "inputX = b.PoissonGroup(50, 10*Hz) # 50 neurons firing at 10 Hz\n",
+ "inputY = b.PoissonGroup(50, 10*Hz) # 50 neurons firing at 10 Hz\n",
+ "\n",
+ "# And a NEWronGroup (I am so funny)\n",
+ "group, ap_reset = model.make_neurongroup(100, method='euler', threshold='V_soma > -40*mV',\n",
+ " reset='V_soma = 40*mV',\n",
+ " second_reset= 'V_soma=-55*mV',\n",
+ " spike_width = 0.5*ms,\n",
+ " refractory=4*ms)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ZlGUdMxOVweY",
+ "outputId": "d2582da8-43be-4410-99be-cdc3a097e803"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "dV_dist/dt = (gL_dist * (EL_dist-V_dist) + I_dist) / C_dist :volt\n",
+ "I_dist = I_ext_dist + I_NMDA_pathX_dist + I_AMPA_pathX_dist :amp\n",
+ "I_ext_dist :amp\n",
+ "I_AMPA_pathX_dist = g_AMPA_pathX_dist * (E_AMPA-V_dist) * s_AMPA_pathX_dist * w_AMPA_pathX_dist :amp\n",
+ "ds_AMPA_pathX_dist/dt = -s_AMPA_pathX_dist / t_AMPA_decay_pathX_dist :1\n",
+ "I_NMDA_pathX_dist = g_NMDA_pathX_dist * (E_NMDA-V_dist) * s_NMDA_pathX_dist / (1 + Mg_con * exp(-Alpha_NMDA*(V_dist/mV+Gamma_NMDA)) / Beta_NMDA) * w_NMDA_pathX_dist :amp\n",
+ "ds_NMDA_pathX_dist/dt = -s_NMDA_pathX_dist/t_NMDA_decay_pathX_dist :1\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Let's remember how the equations look like:\n",
+ "print(dist.equations)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "id": "i49dF9gWV3_c"
+ },
+ "outputs": [],
+ "source": [
+ "# Define what happens when a presynaptic spike arrives at the synapse\n",
+ "synX = b.Synapses(inputX, group,\n",
+ " on_pre='s_AMPA_pathX_dist += 1; s_NMDA_pathX_dist += 1')\n",
+ "synY = b.Synapses(inputY, group,\n",
+ " on_pre='s_AMPA_pathY_prox += 1; s_NMDA_pathY_prox += 1')\n",
+ "\n",
+ "# This is the actual connection part\n",
+ "synX.connect(p=0.5) # 50% of the inputs X connect to the group\n",
+ "synY.connect(p=0.5) # 50% of the inputs Y connect to the group"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "id": "31L0xML5WD41"
+ },
+ "outputs": [],
+ "source": [
+ "# Set spike and voltage monitors\n",
+ "S = b.SpikeMonitor(group)\n",
+ "\n",
+ "voltages = ['V_soma', 'V_trunk', 'V_prox', 'V_dist']\n",
+ "M = b.StateMonitor(group, voltages, record=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "id": "cWZp0srva6zn"
+ },
+ "outputs": [],
+ "source": [
+ "# Run simulation\n",
+ "net = b.Network(group, ap_reset, synX, synY, S)\n",
+ "b.run(1000*ms)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 571
+ },
+ "id": "MRKKv3qYM_TM",
+ "outputId": "9885cdb8-b5a7-439c-a4a5-85b2fce91482"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot spiketimes\n",
+ "fig, ax = plt.subplots(figsize=(7, 4))\n",
+ "ax.plot(S.t/ms, S.i, '.')\n",
+ "ax.set_xlabel('Time (ms)')\n",
+ "ax.set_ylabel('Neuron index');"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 571
+ },
+ "id": "BJL6gISoWuJ-",
+ "outputId": "4f1f6c48-28bd-4e20-e384-f7555674b8fe"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot voltages\n",
+ "fig, ax = plt.subplots(figsize=(7, 4))\n",
+ "ax.plot(M.t/ms, M.V_soma[0]/mV, label='soma', zorder=3)\n",
+ "ax.plot(M.t/ms, M.V_trunk[0]/mV, label='trunk')\n",
+ "ax.plot(M.t/ms, M.V_prox[0]/mV, label='prox')\n",
+ "ax.plot(M.t/ms, M.V_dist[0]/mV, label='dist')\n",
+ "ax.set_xlabel('Time (ms)')\n",
+ "ax.set_ylabel('Voltage (mV)')\n",
+ "ax.legend(loc='best');"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "jzzSg2wWx3f8"
+ },
+ "source": [
+ "## Playing with dSpikes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "id": "q0ilgEHkx18q"
+ },
+ "outputs": [],
+ "source": [
+ "b.start_scope() # clear previous run\n",
+ "\n",
+ "# add channels to compartments\n",
+ "dist.dspikes('Na', g_rise=3.7*nS, g_fall=2.4*nS)\n",
+ "prox.dspikes('Na', g_rise=9*nS, g_fall=5.7*nS)\n",
+ "trunk.dspikes('Na', g_rise=22*nS, g_fall=14*nS)\n",
+ "\n",
+ "\n",
+ "model = NeuronModel(graph, cm=1*uF/(cm**2), gl=40*uS/(cm**2),\n",
+ " v_rest=-65*mV, r_axial=150*ohm*cm,\n",
+ " scale_factor=2.8, spine_factor=1.5)\n",
+ "\n",
+ "model.config_dspikes('Na', threshold=-35*mV,\n",
+ " duration_rise=1.2*ms, duration_fall=2.4*ms,\n",
+ " offset_fall=0.2*ms, refractory=5*ms,\n",
+ " reversal_rise='E_Na', reversal_fall='E_K')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "qDWiO-A1NUuA",
+ "outputId": "fbed67de-37ce-41cd-d635-13f66055c336"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "OBJECT\n",
+ "------\n",
+ "\n",
+ "\n",
+ "\n",
+ "EQUATIONS\n",
+ "---------\n",
+ "dV_soma/dt = (gL_soma * (EL_soma-V_soma) + I_soma) / C_soma :volt\n",
+ "I_soma = I_ext_soma + I_trunk_soma :amp\n",
+ "I_ext_soma :amp\n",
+ "I_trunk_soma = (V_trunk-V_soma) * g_trunk_soma :amp\n",
+ "\n",
+ "dV_trunk/dt = (gL_trunk * (EL_trunk-V_trunk) + I_trunk) / C_trunk :volt\n",
+ "I_trunk = I_ext_trunk + I_prox_trunk + I_soma_trunk + I_rise_Na_trunk + I_fall_Na_trunk :amp\n",
+ "I_ext_trunk :amp\n",
+ "I_rise_Na_trunk = g_rise_Na_trunk * (E_rise_Na-V_trunk) :amp\n",
+ "I_fall_Na_trunk = g_fall_Na_trunk * (E_fall_Na-V_trunk) :amp\n",
+ "g_rise_Na_trunk = g_rise_max_Na_trunk * int(t_in_timesteps <= spiketime_Na_trunk + duration_rise_Na_trunk) * gate_Na_trunk :siemens\n",
+ "g_fall_Na_trunk = g_fall_max_Na_trunk * int(t_in_timesteps <= spiketime_Na_trunk + offset_fall_Na_trunk + duration_fall_Na_trunk) * int(t_in_timesteps >= spiketime_Na_trunk + offset_fall_Na_trunk) * gate_Na_trunk :siemens\n",
+ "spiketime_Na_trunk :1\n",
+ "gate_Na_trunk :1\n",
+ "I_soma_trunk = (V_soma-V_trunk) * g_soma_trunk :amp\n",
+ "I_prox_trunk = (V_prox-V_trunk) * g_prox_trunk :amp\n",
+ "\n",
+ "dV_prox/dt = (gL_prox * (EL_prox-V_prox) + I_prox) / C_prox :volt\n",
+ "I_prox = I_ext_prox + I_dist_prox + I_trunk_prox + I_rise_Na_prox + I_fall_Na_prox + I_NMDA_pathY_prox + I_AMPA_pathY_prox :amp\n",
+ "I_ext_prox :amp\n",
+ "I_AMPA_pathY_prox = g_AMPA_pathY_prox * (E_AMPA-V_prox) * s_AMPA_pathY_prox * w_AMPA_pathY_prox :amp\n",
+ "ds_AMPA_pathY_prox/dt = -s_AMPA_pathY_prox / t_AMPA_decay_pathY_prox :1\n",
+ "I_NMDA_pathY_prox = g_NMDA_pathY_prox * (E_NMDA-V_prox) * s_NMDA_pathY_prox / (1 + Mg_con * exp(-Alpha_NMDA*(V_prox/mV+Gamma_NMDA)) / Beta_NMDA) * w_NMDA_pathY_prox :amp\n",
+ "ds_NMDA_pathY_prox/dt = -s_NMDA_pathY_prox/t_NMDA_decay_pathY_prox :1\n",
+ "I_rise_Na_prox = g_rise_Na_prox * (E_rise_Na-V_prox) :amp\n",
+ "I_fall_Na_prox = g_fall_Na_prox * (E_fall_Na-V_prox) :amp\n",
+ "g_rise_Na_prox = g_rise_max_Na_prox * int(t_in_timesteps <= spiketime_Na_prox + duration_rise_Na_prox) * gate_Na_prox :siemens\n",
+ "g_fall_Na_prox = g_fall_max_Na_prox * int(t_in_timesteps <= spiketime_Na_prox + offset_fall_Na_prox + duration_fall_Na_prox) * int(t_in_timesteps >= spiketime_Na_prox + offset_fall_Na_prox) * gate_Na_prox :siemens\n",
+ "spiketime_Na_prox :1\n",
+ "gate_Na_prox :1\n",
+ "I_trunk_prox = (V_trunk-V_prox) * g_trunk_prox :amp\n",
+ "I_dist_prox = (V_dist-V_prox) * g_dist_prox :amp\n",
+ "\n",
+ "dV_dist/dt = (gL_dist * (EL_dist-V_dist) + I_dist) / C_dist :volt\n",
+ "I_dist = I_ext_dist + I_prox_dist + I_rise_Na_dist + I_fall_Na_dist + I_NMDA_pathX_dist + I_AMPA_pathX_dist :amp\n",
+ "I_ext_dist :amp\n",
+ "I_AMPA_pathX_dist = g_AMPA_pathX_dist * (E_AMPA-V_dist) * s_AMPA_pathX_dist * w_AMPA_pathX_dist :amp\n",
+ "ds_AMPA_pathX_dist/dt = -s_AMPA_pathX_dist / t_AMPA_decay_pathX_dist :1\n",
+ "I_NMDA_pathX_dist = g_NMDA_pathX_dist * (E_NMDA-V_dist) * s_NMDA_pathX_dist / (1 + Mg_con * exp(-Alpha_NMDA*(V_dist/mV+Gamma_NMDA)) / Beta_NMDA) * w_NMDA_pathX_dist :amp\n",
+ "ds_NMDA_pathX_dist/dt = -s_NMDA_pathX_dist/t_NMDA_decay_pathX_dist :1\n",
+ "I_rise_Na_dist = g_rise_Na_dist * (E_rise_Na-V_dist) :amp\n",
+ "I_fall_Na_dist = g_fall_Na_dist * (E_fall_Na-V_dist) :amp\n",
+ "g_rise_Na_dist = g_rise_max_Na_dist * int(t_in_timesteps <= spiketime_Na_dist + duration_rise_Na_dist) * gate_Na_dist :siemens\n",
+ "g_fall_Na_dist = g_fall_max_Na_dist * int(t_in_timesteps <= spiketime_Na_dist + offset_fall_Na_dist + duration_fall_Na_dist) * int(t_in_timesteps >= spiketime_Na_dist + offset_fall_Na_dist) * gate_Na_dist :siemens\n",
+ "spiketime_Na_dist :1\n",
+ "gate_Na_dist :1\n",
+ "I_prox_dist = (V_prox-V_dist) * g_prox_dist :amp\n",
+ "\n",
+ "\n",
+ "PARAMETERS\n",
+ "----------\n",
+ "{'Alpha_NMDA': 0.062,\n",
+ " 'Beta_NMDA': 3.57,\n",
+ " 'C_dist': 6.59734457 * pfarad,\n",
+ " 'C_prox': 13.19468915 * pfarad,\n",
+ " 'C_soma': 82.46680716 * pfarad,\n",
+ " 'C_trunk': 32.98672286 * pfarad,\n",
+ " 'EL_dist': -65. * mvolt,\n",
+ " 'EL_prox': -65. * mvolt,\n",
+ " 'EL_soma': -65. * mvolt,\n",
+ " 'EL_trunk': -65. * mvolt,\n",
+ " 'E_AMPA': 0. * volt,\n",
+ " 'E_Ca': 136. * mvolt,\n",
+ " 'E_GABA': -80. * mvolt,\n",
+ " 'E_K': -89. * mvolt,\n",
+ " 'E_NMDA': 0. * volt,\n",
+ " 'E_Na': 70. * mvolt,\n",
+ " 'E_fall_Na': -89. * mvolt,\n",
+ " 'E_rise_Na': 70. * mvolt,\n",
+ " 'Gamma_NMDA': 0,\n",
+ " 'Mg_con': 1.0,\n",
+ " 'Vth_Na_dist': -35. * mvolt,\n",
+ " 'Vth_Na_prox': -35. * mvolt,\n",
+ " 'Vth_Na_trunk': -35. * mvolt,\n",
+ " 'duration_fall_Na_dist': 24,\n",
+ " 'duration_fall_Na_prox': 24,\n",
+ " 'duration_fall_Na_trunk': 24,\n",
+ " 'duration_rise_Na_dist': 12,\n",
+ " 'duration_rise_Na_prox': 12,\n",
+ " 'duration_rise_Na_trunk': 12,\n",
+ " 'gL_dist': 263.8937829 * psiemens,\n",
+ " 'gL_prox': 0.52778757 * nsiemens,\n",
+ " 'gL_soma': 3.29867229 * nsiemens,\n",
+ " 'gL_trunk': 1.31946891 * nsiemens,\n",
+ " 'g_AMPA_pathX_dist': 1. * nsiemens,\n",
+ " 'g_AMPA_pathY_prox': 1. * nsiemens,\n",
+ " 'g_NMDA_pathX_dist': 1. * nsiemens,\n",
+ " 'g_NMDA_pathY_prox': 1. * nsiemens,\n",
+ " 'g_dist_prox': 2. * nsiemens,\n",
+ " 'g_fall_max_Na_dist': 2.4 * nsiemens,\n",
+ " 'g_fall_max_Na_prox': 5.7 * nsiemens,\n",
+ " 'g_fall_max_Na_trunk': 14. * nsiemens,\n",
+ " 'g_prox_dist': 2. * nsiemens,\n",
+ " 'g_prox_trunk': 6. * nsiemens,\n",
+ " 'g_rise_max_Na_dist': 3.7 * nsiemens,\n",
+ " 'g_rise_max_Na_prox': 9. * nsiemens,\n",
+ " 'g_rise_max_Na_trunk': 22. * nsiemens,\n",
+ " 'g_soma_trunk': 15. * nsiemens,\n",
+ " 'g_trunk_prox': 6. * nsiemens,\n",
+ " 'g_trunk_soma': 15. * nsiemens,\n",
+ " 'offset_fall_Na_dist': 2,\n",
+ " 'offset_fall_Na_prox': 2,\n",
+ " 'offset_fall_Na_trunk': 2,\n",
+ " 'refractory_Na_dist': 50,\n",
+ " 'refractory_Na_prox': 50,\n",
+ " 'refractory_Na_trunk': 50,\n",
+ " 't_AMPA_decay_pathX_dist': 2. * msecond,\n",
+ " 't_AMPA_decay_pathY_prox': 2. * msecond,\n",
+ " 't_NMDA_decay_pathX_dist': 60. * msecond,\n",
+ " 't_NMDA_decay_pathY_prox': 60. * msecond,\n",
+ " 'w_AMPA_pathX_dist': 1.0,\n",
+ " 'w_AMPA_pathY_prox': 1.0,\n",
+ " 'w_NMDA_pathX_dist': 1.0,\n",
+ " 'w_NMDA_pathY_prox': 1.0}\n",
+ "\n",
+ "\n",
+ "EVENTS\n",
+ "------\n",
+ "['spike_Na_trunk', 'spike_Na_prox', 'spike_Na_dist']\n",
+ "\n",
+ "\n",
+ "EVENT CONDITIONS\n",
+ "----------------\n",
+ "{'spike_Na_dist': 'V_dist >= Vth_Na_dist and t_in_timesteps >= spiketime_Na_dist + refractory_Na_dist * gate_Na_dist',\n",
+ " 'spike_Na_prox': 'V_prox >= Vth_Na_prox and t_in_timesteps >= spiketime_Na_prox + refractory_Na_prox * gate_Na_prox',\n",
+ " 'spike_Na_trunk': 'V_trunk >= Vth_Na_trunk and t_in_timesteps >= spiketime_Na_trunk + refractory_Na_trunk * '\n",
+ " 'gate_Na_trunk'}\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(model)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "id": "xDDD6rHw9JVc"
+ },
+ "outputs": [],
+ "source": [
+ "# Make a new neurongroup\n",
+ "neuron, ap_reset = model.make_neurongroup(1, method='euler', threshold='V_soma > -40*mV',\n",
+ " reset='V_soma = 40*mV',\n",
+ " second_reset= 'V_soma=-55*mV',\n",
+ " spike_width = 0.8*ms,\n",
+ " refractory=4*ms)\n",
+ "\n",
+ "vars = ['V_soma', 'V_trunk', 'V_prox', 'V_dist']\n",
+ "M = b.StateMonitor(neuron, vars, record=True)\n",
+ "\n",
+ "net = b.Network(neuron, ap_reset, M)\n",
+ "net.run(10*ms)\n",
+ "neuron.I_ext_soma = 150*pA\n",
+ "net.run(100*ms)\n",
+ "neuron.I_ext_soma = 0*pA\n",
+ "net.run(60*ms)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "cellView": "form",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 571
+ },
+ "id": "K3-CUCCT-L3k",
+ "outputId": "b1022c01-0066-4e29-a034-cbf84b6b81b5"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# @title Plot voltages\n",
+ "fig, ax = plt.subplots(figsize=(7, 4))\n",
+ "ax.plot(M.t/ms, M.V_soma[0]/mV, label='soma', zorder=3)\n",
+ "ax.plot(M.t/ms, M.V_trunk[0]/mV, label='trunk')\n",
+ "ax.plot(M.t/ms, M.V_prox[0]/mV, label='prox')\n",
+ "ax.plot(M.t/ms, M.V_dist[0]/mV, label='dist')\n",
+ "ax.set_xlabel('Time (ms)')\n",
+ "ax.set_ylabel('Voltage (mV)')\n",
+ "ax.legend(loc='best');"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "yvOuvVdsN8SY"
+ },
+ "source": [
+ "Now these are some actual backpropagating dendritic spikes with sodium-like characteristics."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "cellView": "form",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 571
+ },
+ "id": "mAbec7fs-P2-",
+ "outputId": "9f1434fe-4c81-42f3-d31f-73163afde9c9"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Zoom in\n",
+ "fig, ax = plt.subplots(figsize=(7, 4))\n",
+ "ax.plot(M.t/ms, M.V_soma[0]/mV, label='soma')\n",
+ "ax.plot(M.t/ms, M.V_trunk[0]/mV, label='trunk')\n",
+ "ax.plot(M.t/ms, M.V_prox[0]/mV, label='prox')\n",
+ "ax.plot(M.t/ms, M.V_dist[0]/mV, label='dist')\n",
+ "ax.set_xlabel('Time (ms)')\n",
+ "ax.set_ylabel('Voltage (mV)')\n",
+ "ax.set_xlim(left=50, right=100)\n",
+ "ax.legend(loc='best');"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "sWI8eGzwQWHq"
+ },
+ "source": [
+ "## Adding some noise"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "id": "k3xlQ9YIQdIH"
+ },
+ "outputs": [],
+ "source": [
+ "b.start_scope() # clear previous run\n",
+ "\n",
+ "# add noise\n",
+ "a = 2\n",
+ "soma.noise(mean=a*25*pA, sigma=25*pA, tau=1*ms)\n",
+ "trunk.noise(mean=a*20*pA, sigma=20*pA, tau=1*ms)\n",
+ "prox.noise(mean=a*15*pA, sigma=15*pA, tau=1*ms)\n",
+ "dist.noise(mean=a*6*pA, sigma=10*pA, tau=1*ms)\n",
+ "\n",
+ "\n",
+ "# merge compartments into a neuron model and set its basic properties\n",
+ "edges = [(soma, trunk, 15*nS), (trunk, prox, 8*nS), (prox, dist, 3*nS)]\n",
+ "model = NeuronModel(edges, cm=1*uF/(cm**2), gl=40*uS/(cm**2),\n",
+ " v_rest=-65*mV, r_axial=150*ohm*cm,\n",
+ " scale_factor=2.8, spine_factor=1.5)\n",
+ "\n",
+ "model.config_dspikes('Na', threshold=-35*mV,\n",
+ " duration_rise=1.2*ms, duration_fall=2.4*ms,\n",
+ " offset_fall=0.2*ms, refractory=5*ms,\n",
+ " reversal_rise='E_Na', reversal_fall='E_K')\n",
+ "\n",
+ "# make a neuron group\n",
+ "neuron, ap_reset = model.make_neurongroup(1, method='euler', threshold='V_soma > -40*mV',\n",
+ " reset='V_soma = 40*mV',\n",
+ " second_reset= 'V_soma=-55*mV',\n",
+ " spike_width = 0.8*ms,\n",
+ " refractory=4*ms)\n",
+ "\n",
+ "# record voltages\n",
+ "vars = ['V_soma', 'V_trunk', 'V_prox', 'V_dist']\n",
+ "M = b.StateMonitor(neuron, vars, record=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "3K6jfeMaTcel",
+ "outputId": "93145e2f-05af-4662-f286-677edbd17fcd"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "OBJECT\n",
+ "------\n",
+ "\n",
+ "\n",
+ "\n",
+ "EQUATIONS\n",
+ "---------\n",
+ "dV_soma/dt = (gL_soma * (EL_soma-V_soma) + I_soma) / C_soma :volt\n",
+ "I_soma = I_ext_soma + I_trunk_soma + I_noise_soma :amp\n",
+ "I_ext_soma :amp\n",
+ "dI_noise_soma/dt = (mean_noise_soma-I_noise_soma) / tau_noise_soma + sigma_noise_soma * (sqrt(2/(tau_noise_soma*dt)) * randn()) :amp\n",
+ "I_trunk_soma = (V_trunk-V_soma) * g_trunk_soma :amp\n",
+ "\n",
+ "dV_trunk/dt = (gL_trunk * (EL_trunk-V_trunk) + I_trunk) / C_trunk :volt\n",
+ "I_trunk = I_ext_trunk + I_prox_trunk + I_soma_trunk + I_noise_trunk + I_rise_Na_trunk + I_fall_Na_trunk :amp\n",
+ "I_ext_trunk :amp\n",
+ "I_rise_Na_trunk = g_rise_Na_trunk * (E_rise_Na-V_trunk) :amp\n",
+ "I_fall_Na_trunk = g_fall_Na_trunk * (E_fall_Na-V_trunk) :amp\n",
+ "g_rise_Na_trunk = g_rise_max_Na_trunk * int(t_in_timesteps <= spiketime_Na_trunk + duration_rise_Na_trunk) * gate_Na_trunk :siemens\n",
+ "g_fall_Na_trunk = g_fall_max_Na_trunk * int(t_in_timesteps <= spiketime_Na_trunk + offset_fall_Na_trunk + duration_fall_Na_trunk) * int(t_in_timesteps >= spiketime_Na_trunk + offset_fall_Na_trunk) * gate_Na_trunk :siemens\n",
+ "spiketime_Na_trunk :1\n",
+ "gate_Na_trunk :1\n",
+ "dI_noise_trunk/dt = (mean_noise_trunk-I_noise_trunk) / tau_noise_trunk + sigma_noise_trunk * (sqrt(2/(tau_noise_trunk*dt)) * randn()) :amp\n",
+ "I_soma_trunk = (V_soma-V_trunk) * g_soma_trunk :amp\n",
+ "I_prox_trunk = (V_prox-V_trunk) * g_prox_trunk :amp\n",
+ "\n",
+ "dV_prox/dt = (gL_prox * (EL_prox-V_prox) + I_prox) / C_prox :volt\n",
+ "I_prox = I_ext_prox + I_dist_prox + I_trunk_prox + I_noise_prox + I_rise_Na_prox + I_fall_Na_prox + I_NMDA_pathY_prox + I_AMPA_pathY_prox :amp\n",
+ "I_ext_prox :amp\n",
+ "I_AMPA_pathY_prox = g_AMPA_pathY_prox * (E_AMPA-V_prox) * s_AMPA_pathY_prox * w_AMPA_pathY_prox :amp\n",
+ "ds_AMPA_pathY_prox/dt = -s_AMPA_pathY_prox / t_AMPA_decay_pathY_prox :1\n",
+ "I_NMDA_pathY_prox = g_NMDA_pathY_prox * (E_NMDA-V_prox) * s_NMDA_pathY_prox / (1 + Mg_con * exp(-Alpha_NMDA*(V_prox/mV+Gamma_NMDA)) / Beta_NMDA) * w_NMDA_pathY_prox :amp\n",
+ "ds_NMDA_pathY_prox/dt = -s_NMDA_pathY_prox/t_NMDA_decay_pathY_prox :1\n",
+ "I_rise_Na_prox = g_rise_Na_prox * (E_rise_Na-V_prox) :amp\n",
+ "I_fall_Na_prox = g_fall_Na_prox * (E_fall_Na-V_prox) :amp\n",
+ "g_rise_Na_prox = g_rise_max_Na_prox * int(t_in_timesteps <= spiketime_Na_prox + duration_rise_Na_prox) * gate_Na_prox :siemens\n",
+ "g_fall_Na_prox = g_fall_max_Na_prox * int(t_in_timesteps <= spiketime_Na_prox + offset_fall_Na_prox + duration_fall_Na_prox) * int(t_in_timesteps >= spiketime_Na_prox + offset_fall_Na_prox) * gate_Na_prox :siemens\n",
+ "spiketime_Na_prox :1\n",
+ "gate_Na_prox :1\n",
+ "dI_noise_prox/dt = (mean_noise_prox-I_noise_prox) / tau_noise_prox + sigma_noise_prox * (sqrt(2/(tau_noise_prox*dt)) * randn()) :amp\n",
+ "I_trunk_prox = (V_trunk-V_prox) * g_trunk_prox :amp\n",
+ "I_dist_prox = (V_dist-V_prox) * g_dist_prox :amp\n",
+ "\n",
+ "dV_dist/dt = (gL_dist * (EL_dist-V_dist) + I_dist) / C_dist :volt\n",
+ "I_dist = I_ext_dist + I_prox_dist + I_noise_dist + I_rise_Na_dist + I_fall_Na_dist + I_NMDA_pathX_dist + I_AMPA_pathX_dist :amp\n",
+ "I_ext_dist :amp\n",
+ "I_AMPA_pathX_dist = g_AMPA_pathX_dist * (E_AMPA-V_dist) * s_AMPA_pathX_dist * w_AMPA_pathX_dist :amp\n",
+ "ds_AMPA_pathX_dist/dt = -s_AMPA_pathX_dist / t_AMPA_decay_pathX_dist :1\n",
+ "I_NMDA_pathX_dist = g_NMDA_pathX_dist * (E_NMDA-V_dist) * s_NMDA_pathX_dist / (1 + Mg_con * exp(-Alpha_NMDA*(V_dist/mV+Gamma_NMDA)) / Beta_NMDA) * w_NMDA_pathX_dist :amp\n",
+ "ds_NMDA_pathX_dist/dt = -s_NMDA_pathX_dist/t_NMDA_decay_pathX_dist :1\n",
+ "I_rise_Na_dist = g_rise_Na_dist * (E_rise_Na-V_dist) :amp\n",
+ "I_fall_Na_dist = g_fall_Na_dist * (E_fall_Na-V_dist) :amp\n",
+ "g_rise_Na_dist = g_rise_max_Na_dist * int(t_in_timesteps <= spiketime_Na_dist + duration_rise_Na_dist) * gate_Na_dist :siemens\n",
+ "g_fall_Na_dist = g_fall_max_Na_dist * int(t_in_timesteps <= spiketime_Na_dist + offset_fall_Na_dist + duration_fall_Na_dist) * int(t_in_timesteps >= spiketime_Na_dist + offset_fall_Na_dist) * gate_Na_dist :siemens\n",
+ "spiketime_Na_dist :1\n",
+ "gate_Na_dist :1\n",
+ "dI_noise_dist/dt = (mean_noise_dist-I_noise_dist) / tau_noise_dist + sigma_noise_dist * (sqrt(2/(tau_noise_dist*dt)) * randn()) :amp\n",
+ "I_prox_dist = (V_prox-V_dist) * g_prox_dist :amp\n",
+ "\n",
+ "\n",
+ "PARAMETERS\n",
+ "----------\n",
+ "{'Alpha_NMDA': 0.062,\n",
+ " 'Beta_NMDA': 3.57,\n",
+ " 'C_dist': 6.59734457 * pfarad,\n",
+ " 'C_prox': 13.19468915 * pfarad,\n",
+ " 'C_soma': 82.46680716 * pfarad,\n",
+ " 'C_trunk': 32.98672286 * pfarad,\n",
+ " 'EL_dist': -65. * mvolt,\n",
+ " 'EL_prox': -65. * mvolt,\n",
+ " 'EL_soma': -65. * mvolt,\n",
+ " 'EL_trunk': -65. * mvolt,\n",
+ " 'E_AMPA': 0. * volt,\n",
+ " 'E_Ca': 136. * mvolt,\n",
+ " 'E_GABA': -80. * mvolt,\n",
+ " 'E_K': -89. * mvolt,\n",
+ " 'E_NMDA': 0. * volt,\n",
+ " 'E_Na': 70. * mvolt,\n",
+ " 'E_fall_Na': -89. * mvolt,\n",
+ " 'E_rise_Na': 70. * mvolt,\n",
+ " 'Gamma_NMDA': 0,\n",
+ " 'Mg_con': 1.0,\n",
+ " 'Vth_Na_dist': -35. * mvolt,\n",
+ " 'Vth_Na_prox': -35. * mvolt,\n",
+ " 'Vth_Na_trunk': -35. * mvolt,\n",
+ " 'duration_fall_Na_dist': 24,\n",
+ " 'duration_fall_Na_prox': 24,\n",
+ " 'duration_fall_Na_trunk': 24,\n",
+ " 'duration_rise_Na_dist': 12,\n",
+ " 'duration_rise_Na_prox': 12,\n",
+ " 'duration_rise_Na_trunk': 12,\n",
+ " 'gL_dist': 263.8937829 * psiemens,\n",
+ " 'gL_prox': 0.52778757 * nsiemens,\n",
+ " 'gL_soma': 3.29867229 * nsiemens,\n",
+ " 'gL_trunk': 1.31946891 * nsiemens,\n",
+ " 'g_AMPA_pathX_dist': 1. * nsiemens,\n",
+ " 'g_AMPA_pathY_prox': 1. * nsiemens,\n",
+ " 'g_NMDA_pathX_dist': 1. * nsiemens,\n",
+ " 'g_NMDA_pathY_prox': 1. * nsiemens,\n",
+ " 'g_dist_prox': 3. * nsiemens,\n",
+ " 'g_fall_max_Na_dist': 2.4 * nsiemens,\n",
+ " 'g_fall_max_Na_prox': 5.7 * nsiemens,\n",
+ " 'g_fall_max_Na_trunk': 14. * nsiemens,\n",
+ " 'g_prox_dist': 3. * nsiemens,\n",
+ " 'g_prox_trunk': 8. * nsiemens,\n",
+ " 'g_rise_max_Na_dist': 3.7 * nsiemens,\n",
+ " 'g_rise_max_Na_prox': 9. * nsiemens,\n",
+ " 'g_rise_max_Na_trunk': 22. * nsiemens,\n",
+ " 'g_soma_trunk': 15. * nsiemens,\n",
+ " 'g_trunk_prox': 8. * nsiemens,\n",
+ " 'g_trunk_soma': 15. * nsiemens,\n",
+ " 'mean_noise_dist': 12. * pamp,\n",
+ " 'mean_noise_prox': 30. * pamp,\n",
+ " 'mean_noise_soma': 50. * pamp,\n",
+ " 'mean_noise_trunk': 40. * pamp,\n",
+ " 'offset_fall_Na_dist': 2,\n",
+ " 'offset_fall_Na_prox': 2,\n",
+ " 'offset_fall_Na_trunk': 2,\n",
+ " 'refractory_Na_dist': 50,\n",
+ " 'refractory_Na_prox': 50,\n",
+ " 'refractory_Na_trunk': 50,\n",
+ " 'sigma_noise_dist': 10. * pamp,\n",
+ " 'sigma_noise_prox': 15. * pamp,\n",
+ " 'sigma_noise_soma': 25. * pamp,\n",
+ " 'sigma_noise_trunk': 20. * pamp,\n",
+ " 't_AMPA_decay_pathX_dist': 2. * msecond,\n",
+ " 't_AMPA_decay_pathY_prox': 2. * msecond,\n",
+ " 't_NMDA_decay_pathX_dist': 60. * msecond,\n",
+ " 't_NMDA_decay_pathY_prox': 60. * msecond,\n",
+ " 'tau_noise_dist': 1. * msecond,\n",
+ " 'tau_noise_prox': 1. * msecond,\n",
+ " 'tau_noise_soma': 1. * msecond,\n",
+ " 'tau_noise_trunk': 1. * msecond,\n",
+ " 'w_AMPA_pathX_dist': 1.0,\n",
+ " 'w_AMPA_pathY_prox': 1.0,\n",
+ " 'w_NMDA_pathX_dist': 1.0,\n",
+ " 'w_NMDA_pathY_prox': 1.0}\n",
+ "\n",
+ "\n",
+ "EVENTS\n",
+ "------\n",
+ "['spike_Na_trunk', 'spike_Na_prox', 'spike_Na_dist']\n",
+ "\n",
+ "\n",
+ "EVENT CONDITIONS\n",
+ "----------------\n",
+ "{'spike_Na_dist': 'V_dist >= Vth_Na_dist and t_in_timesteps >= spiketime_Na_dist + refractory_Na_dist * gate_Na_dist',\n",
+ " 'spike_Na_prox': 'V_prox >= Vth_Na_prox and t_in_timesteps >= spiketime_Na_prox + refractory_Na_prox * gate_Na_prox',\n",
+ " 'spike_Na_trunk': 'V_trunk >= Vth_Na_trunk and t_in_timesteps >= spiketime_Na_trunk + refractory_Na_trunk * '\n",
+ " 'gate_Na_trunk'}\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(model)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "id": "B0wtAya-Tb7E"
+ },
+ "outputs": [],
+ "source": [
+ "# run simulation\n",
+ "net = b.Network(neuron, ap_reset, M)\n",
+ "net.run(500*ms)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 571
+ },
+ "id": "8CZ2UN_3ToU3",
+ "outputId": "847f7f10-1c2b-49de-b741-68bfadddfde9"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# @title Plot voltages\n",
+ "fig, ax = plt.subplots(figsize=(7, 4))\n",
+ "ax.plot(M.t/ms, M.V_soma[0]/mV, label='soma', zorder=3)\n",
+ "ax.plot(M.t/ms, M.V_trunk[0]/mV, label='trunk')\n",
+ "ax.plot(M.t/ms, M.V_prox[0]/mV, label='prox')\n",
+ "ax.plot(M.t/ms, M.V_dist[0]/mV, label='dist')\n",
+ "ax.set_xlabel('Time (ms)')\n",
+ "ax.set_ylabel('Voltage (mV)')\n",
+ "ax.legend(loc='best');"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IGZjZPlWltKs"
+ },
+ "source": [
+ "## Point neurons"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 602
+ },
+ "id": "AxdasPX7lzID",
+ "outputId": "07865acb-d1c5-4ccc-8d85-de1cdd9950aa"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from dendrify import PointNeuronModel\n",
+ "\n",
+ "b.start_scope()\n",
+ "\n",
+ "# create a point-neuron model and add some Poisson input\n",
+ "point_model = PointNeuronModel(model='leakyIF', v_rest=-60*mV,\n",
+ " cm_abs=200*pF, gl_abs=10*nS)\n",
+ "point_model.synapse('AMPA', tag='x', g=1*nS, t_decay=2*ms)\n",
+ "point_neuron = point_model.make_neurongroup(1, method='euler') # no spiking\n",
+ "\n",
+ "Input = b.PoissonGroup(10, rates=100*Hz)\n",
+ "S = b.Synapses(Input, point_neuron, on_pre='s_AMPA_x += 1')\n",
+ "S.connect(p=1)\n",
+ "\n",
+ "# monitor\n",
+ "M2 = b.StateMonitor(point_neuron, 'V', record=True)\n",
+ "\n",
+ "# simulation\n",
+ "b.run(250*ms)\n",
+ "\n",
+ "# plot\n",
+ "fig, ax = plt.subplots(1, 1, figsize=[7, 4])\n",
+ "ax.plot(M2.t/ms, M2.V[0]/mV)\n",
+ "ax.set_xlabel('Time (ms)')\n",
+ "ax.set_ylabel('Voltage (mV)')\n",
+ "fig.tight_layout();"
+ ]
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "provenance": [],
+ "toc_visible": true
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/docs_sphinx/source/usage/examples.rst b/docs_sphinx/source/usage/examples.rst
deleted file mode 100644
index 223fb36..0000000
--- a/docs_sphinx/source/usage/examples.rst
+++ /dev/null
@@ -1,82 +0,0 @@
-Examples
-========
-Bellow you will find two model examples adopted from the `Dendrify paper`_.
-
-* :ref:`Example 1 | A basic compartmental model with passive dendrites `
-* :ref:`Example 2 | A reduced compartmental model capturing active dendritic properties `
-
-.. tip::
- By clicking the "**Open in Colab**" button located under each example, you
- can run in your browser (without locally installing Dendrify or Brian) an
- interactive Jupyter notebook that reproduces the respective neuron models and
- simulation results.
-
-----
-
-.. _example 1:
-
-**Example 1 | A basic compartmental model with passive dendrites.**
-
-In this example we show that even rudimentary models can reproduce essentia
-neuronal properties such as the electrical segmentation caused by dendrites
-This allows multiple integration sites to coexist within a neuron and dendrite
-to operate semi-autonomously from the soma, while greatly affecting neuronal output.
-
-.. image:: ../_static/Fig2.png
- :width: 80 %
-
-**a)** Schematic illustration of a compartmental model consisting of a soma
-(spiking unit) and two dendrites (passive integrators). The apical dendrite
-can integrate excitatory synapses comprising AMPA and NMDA currents. **b)**
-Membrane voltage responses to current injections of the same amplitude are
-applied individually to each compartment. Notice the electrical segregation
-caused by the resistance between the three neuronal compartments. **c** Somatic
-responses to a varying number of simultaneous synaptic inputs (5–35 synapses).
-*Left*: control EPSPs, *Right*: EPSPs in the presence of NMDA blockers. **d)**
-Input-output function of the apical dendrite as recorded at the soma. The
-dotted line represents a linear function. Notice the shift from supralinear
-to the sublinear mode when NMDARs are blocked.
-
-.. image:: https://colab.research.google.com/assets/colab-badge.svg
- :target: https://colab.research.google.com/github/Poirazi-Lab/dendrify/blob/main/paper_figures/Fig2_notebook.ipynb
- :alt: Open in Colab
-
-----
-
-.. _example 2:
-
-**Example 2: A reduced compartmental model capturing active dendritic properties.**
-
-In this example we show that reduced compartmental I&F models, equipped with
-event-driven dendritic spiking mechanisms can faithfully reproduce a broad range
-of dendritic properties such as: i) Supralinear input integration, ii) dendrite-specific
-spiking threshold, iii) distance-dependent filtering, iv) backpropagation
-of somatic spikes.
-
-.. image:: ../_static/Fig3.png
- :width: 80 %
-
-**a)** Schematic illustration of a compartmental model consisting of a soma
-(leaky I&F) and three dendritic segments (trunk, proximal, distal) equipped
-with Na+ VGICs. The distal and proximal segments can also receive AMPA
-and NMDA synapses. **b–d)** Rheobase current injections (5 ms square pulses) for
-dSpike generation were applied individually to each dendritic segment. *Shaded
-areas*: location of current injection and dSpike initiation. *Top*: stimulation
-protocol showing the current threshold for a single dSpike (rheobase current).
-**e)** First temporal derivative of dendritic (left) and somatic (right) voltage
-traces from panels (**b–d**). **f)** Input–output function of the distal (left) and
-proximal (right) segment as recorded from the corresponding dendritic locations.
-We also indicate the number of quasi-simultaneously activated synapses (ISI = 0.1 ms)
-needed to elicit a single dSpike in each case. *OFF*: deactivation of Na+ dSpikes.
-*Dashed lines*: linear input–output relationship. **g)** *Left*: Backpropagating dSpikes
-are generated in response to somatic current injections. The short-amplitude
-spikelets detected in the distal branch are subthreshold voltage responses for
-dSpike initiation. *Right*: Magnified and superimposed voltage traces (top) from
-the dashed box (left). *Bottom*: dendritic voltage-activated currents responsible
-for dSpikes generation in each dendritic segment.
-
-.. image:: https://colab.research.google.com/assets/colab-badge.svg
- :target: https://colab.research.google.com/github/Poirazi-Lab/dendrify/blob/main/paper_figures/Fig3_notebook.ipynb
- :alt: Open in Colab
-
-.. _Dendrify paper: https://doi.org/10.1038/s41467-022-35747-8
diff --git a/docs_sphinx/source/usage/tutorial.rst b/docs_sphinx/source/usage/tutorial.rst
deleted file mode 100644
index 2c57e06..0000000
--- a/docs_sphinx/source/usage/tutorial.rst
+++ /dev/null
@@ -1,12 +0,0 @@
-Tutorial
-========
-
-Coming soon
-
-|
-|
-|
-
-.. image:: ../_static/under-construction.png
- :width: 20 %
- :align: center
\ No newline at end of file
diff --git a/examples_new/comp_amplification.py b/examples_new/comp_amplification.py
new file mode 100644
index 0000000..ac97c22
--- /dev/null
+++ b/examples_new/comp_amplification.py
@@ -0,0 +1,104 @@
+"""
+Title
+-----
+Active vs passive dendrites
+
+Description
+-----------
+In pyramidal neurons, distal synapses have often a minute effect on the somatic
+membrane potential due to strong dendritic attenuation. However, the activation
+of dendritic spikes can amplify synaptic inputs that are temporally correlated,
+increasing the probability of somatic AP generation.
+
+In this example we show:
+
+- How to create a compartmental model with passive or active dendrites.
+- How dendritic spiking may affect somatic AP generation.
+"""
+
+import brian2 as b
+from brian2.units import Hz, cm, ms, mV, nS, ohm, pA, pF, uF, um, uS
+
+from dendrify import Dendrite, NeuronModel, Soma
+
+b.prefs.codegen.target = 'numpy' # faster for simple simulations
+
+# Create neuron model with passive dendrites
+soma = Soma('soma', cm_abs=200*pF, gl_abs=10*nS)
+dend = Dendrite('dend', cm_abs=50*pF, gl_abs=2.5*nS)
+dend.synapse('AMPA', tag='x', g=3*nS, t_decay=2*ms)
+dend.synapse('NMDA', tag='x', g=3*nS, t_decay=60*ms)
+model_passive = NeuronModel([(soma, dend, 15*nS)], v_rest=-60*mV)
+
+# Add dendritic spikes and create a neuron model with active dendrites
+dend.dspikes('Na', g_rise=30*nS, g_fall=14*nS)
+model_active = NeuronModel([(soma, dend, 15*nS)], v_rest=-60*mV)
+model_active.config_dspikes('Na', threshold=-35*mV,
+ duration_rise=1.2*ms, duration_fall=2.4*ms,
+ offset_fall=0.2*ms, refractory=5*ms,
+ reversal_rise='E_Na', reversal_fall='E_K')
+
+# Create a neuron group with passive dendrites
+neuron_passive, reset_p = model_passive.make_neurongroup(1, method='euler',
+ threshold='V_soma > -40*mV',
+ reset='V_soma = 40*mV',
+ second_reset='V_soma=-50*mV',
+ spike_width=0.8*ms,
+ refractory=4*ms)
+
+# Create a neuron group with active dendrites
+neuron_active, reset_a = model_active.make_neurongroup(1, method='euler',
+ threshold='V_soma > -40*mV',
+ reset='V_soma = 40*mV',
+ second_reset='V_soma=-50*mV',
+ spike_width=0.8*ms,
+ refractory=4*ms)
+
+# # Create random Poisson input
+Input_p = b.PoissonGroup(5, rates=20*Hz)
+Input_a = b.PoissonGroup(5, rates=20*Hz)
+
+# Create synapses
+S_p = b.Synapses(Input_p, neuron_passive, on_pre='s_AMPA_x_dend += 1; s_NMDA_x_dend += 1')
+S_p.connect(p=1)
+
+S_a = b.Synapses(Input_a, neuron_active, on_pre='s_AMPA_x_dend += 1; s_NMDA_x_dend += 1')
+S_a.connect(p=1)
+
+# Record voltages
+vars = ['V_soma', 'V_dend']
+M_p = b.StateMonitor(neuron_passive, vars, record=True)
+M_a = b.StateMonitor(neuron_active, vars, record=True)
+
+# Run simulation
+b.seed(123) # for reproducibility
+net_passive = b.Network(neuron_passive, reset_p, Input_p, S_p, M_p)
+net_passive.run(500*ms)
+b.start_scope() # clear previous simulation
+b.seed(123) # for reproducibility
+net_active = b.Network(neuron_active, reset_a, Input_a, S_a, M_a)
+net_active.run(500*ms)
+
+# Visualize results
+time_p = M_p.t/ms
+vs_p = M_p.V_soma[0]/mV
+vd_p = M_p.V_dend[0]/mV
+time_a = M_a.t/ms
+vs_a = M_a.V_soma[0]/mV
+vd_a = M_a.V_dend[0]/mV
+
+fig, axes = b.subplots(2, 1, figsize=(6, 4), sharex=True)
+ax0, ax1 = axes
+ax0.plot(time_a, vd_a, label='Vdend', c='red')
+ax0.plot(time_p, vd_p, '--', label='Vdend (passive)', c='black')
+ax0.set_ylabel('Voltage (mV)')
+ax0.legend(loc=2)
+
+ax1.plot(time_a, vs_a, label='Vsoma', c='navy')
+ax1.plot(time_p, vs_p, '--', label='Vsoma\n(passive dend)', c='orange')
+ax1.set_xlabel('Time (ms)')
+ax1.set_ylabel('Voltage (mV)')
+ax1.legend(loc=2)
+
+fig.tight_layout()
+b.show()
\ No newline at end of file
diff --git a/examples_new/comp_backprop.py b/examples_new/comp_backprop.py
new file mode 100644
index 0000000..7c54409
--- /dev/null
+++ b/examples_new/comp_backprop.py
@@ -0,0 +1,104 @@
+"""
+Title
+-----
+Back-propagating dSpikes
+
+Description
+-----------
+An important property of biological neurons is that action potentials (APs)
+initiated in the axon can invade the soma and nearby dendrites and propagate
+backwards toward the dendritic tips. The transmission efficacy of these
+back-propagating action potentials (bAPs) relies on the dendritic morphology
+and the presence of dendritic voltage-gated ion channels.
+
+In Dendrify, to achieve this behavior one needs to first recreate a more
+realistic somatic AP shape by using the ``second_reset`` and ``spike_width``
+arguments in ``make_neurongroup``. In this way, the somatic voltage can be first
+reset to a more positive value and then below threshold. This allows the passive
+depolarization of proximal dendrites in responses to somatic APs. If dendrites
+are also equipped with active ionic mechanisms, this depolarization can trigger
+the spontaneous generation of dendritic bAPs.
+
+In this example we show:
+
+- How to implement back-propagating dSpikes in Dendrify.
+- How to achieve a more realistic somatic AP shape in I&F models, that is
+ essential for the generation of bAPs.
+
+.. important::
+
+ Notice that when a ``second_reset`` is used, the ``make_neurongroup`` method
+ returns an additional object which is Brian's Synapses. If your simulation
+ code uses :doc:`Brian's Networks ` feature, this
+ additional object should be added to the network as well (also shown in the
+ example below).
+"""
+
+import brian2 as b
+from brian2.units import cm, ms, mV, nS, ohm, pA, uF, um, uS
+
+from dendrify import Dendrite, NeuronModel, Soma
+
+b.prefs.codegen.target = 'numpy' # faster for simple simulations
+
+# Create neuron model
+soma = Soma('soma', model='leakyIF', length=25*um, diameter=25*um)
+trunk = Dendrite('trunk', length=100*um, diameter=2.5*um)
+prox = Dendrite('prox', length=100*um, diameter=1*um)
+dist = Dendrite('dist', length=100*um, diameter=0.5*um)
+
+trunk.dspikes('Na', g_rise=22*nS, g_fall=14*nS)
+prox.dspikes('Na', g_rise=9*nS, g_fall=5.7*nS)
+dist.dspikes('Na', g_rise=3.7*nS, g_fall=2.4*nS)
+
+con = [(soma, trunk, 15*nS), (trunk, prox, 6*nS), (prox, dist, 2*nS)]
+model = NeuronModel(con, cm=1*uF/(cm**2), gl=40*uS/(cm**2),
+ v_rest=-65*mV, r_axial=150*ohm*cm,
+ scale_factor=2.8, spine_factor=1.5)
+model.config_dspikes('Na', threshold=-35*mV,
+ duration_rise=1.2*ms, duration_fall=2.4*ms,
+ offset_fall=0.2*ms, refractory=5*ms,
+ reversal_rise='E_Na', reversal_fall='E_K')
+
+# Make a new neurongroup
+neuron, ap_reset = model.make_neurongroup(1, method='euler',
+ threshold='V_soma > -40*mV',
+ reset='V_soma = 40*mV',
+ second_reset= 'V_soma=-55*mV',
+ spike_width = 0.8*ms,
+ refractory=4*ms)
+
+# Record voltages
+vars = ['V_soma', 'V_trunk', 'V_prox', 'V_dist']
+M = b.StateMonitor(neuron, vars, record=True)
+
+# Run simulation
+net = b.Network(neuron, ap_reset, M)
+net.run(10*ms)
+neuron.I_ext_soma = 150*pA
+net.run(100*ms)
+neuron.I_ext_soma = 0*pA
+net.run(60*ms)
+
+# Visualize results
+fig, axes = b.subplots(2, 1, figsize=(6, 5))
+ax0, ax1 = axes
+
+ax0.plot(M.t/ms, M.V_soma[0]/mV, label='soma', zorder=3)
+ax0.plot(M.t/ms, M.V_trunk[0]/mV, label='trunk')
+ax0.plot(M.t/ms, M.V_prox[0]/mV, label='prox')
+ax0.plot(M.t/ms, M.V_dist[0]/mV, label='dist')
+ax0.set_ylabel('Voltage (mV)')
+ax0.legend()
+
+ax1.plot(M.t/ms, M.V_soma[0]/mV, zorder=3)
+ax1.plot(M.t/ms, M.V_trunk[0]/mV)
+ax1.plot(M.t/ms, M.V_prox[0]/mV)
+ax1.plot(M.t/ms, M.V_dist[0]/mV)
+ax1.set_title('(Zoomed)', y=1, pad=-12, fontsize=10)
+ax1.set_xlabel('Time (ms)')
+ax1.set_ylabel('Voltage (mV)')
+ax1.set_xlim(50, 120)
+
+fig.tight_layout()
+b.show()
\ No newline at end of file
diff --git a/examples_new/comp_understanding.py b/examples_new/comp_understanding.py
new file mode 100644
index 0000000..f584954
--- /dev/null
+++ b/examples_new/comp_understanding.py
@@ -0,0 +1,91 @@
+"""
+Title
+-----
+Understanding dSpikes
+
+Description
+-----------
+Dendrify introduces a new event-driven mechanism for modeling dendritic spiking,
+which is significantly simpler and more efficient than the traditional
+Hodgkin-Huxley formalism. This mechanism has three distinct phases.
+
+**INACTIVE PHASE:**
+When the dendritic voltage is subthreshold OR the simulation step is within the
+refractory period. dSpikes cannot be generated during this phase.
+
+**RISE PHASE:**
+When the dendritic voltage crosses the dSpike threshold AND the refractory
+period has elapsed. This triggers the instant activation of a positive current
+that is deactivated after a specified amount of time (``duration_rise``). Also a
+new refractory period begins.
+
+**FALL PHASE:**
+This phase starts automatically with a delay (``offset_fall``) after the dSpike
+threshold is crossed. A negative current is activated instantly and then is
+deactivated after a specified amount of time (``duration_fall``).
+
+
+In this example:
+
+- How dendritic spiking is implemented in Dendrify.
+"""
+
+import brian2 as b
+from brian2.units import ms, mV, nS, pA, pF
+
+from dendrify import Dendrite, NeuronModel, Soma
+
+b.prefs.codegen.target = 'numpy' # faster for simple simulations
+
+# Create neuron model
+soma = Soma('soma', cm_abs=200*pF, gl_abs=10*nS)
+dend = Dendrite('dend', cm_abs=50*pF, gl_abs=2.5*nS)
+dend.dspikes('Na', g_rise=30*nS, g_fall=15*nS)
+
+model = NeuronModel([(soma, dend, 15*nS)], v_rest=-60*mV)
+model.config_dspikes('Na', threshold=-35*mV,
+ duration_rise=1.2*ms, duration_fall=2.4*ms,
+ offset_fall=0.5*ms, refractory=5*ms,
+ reversal_rise='E_Na', reversal_fall='E_K')
+
+# Create neuron group
+neuron = model.make_neurongroup(1, method='euler')
+
+# Record variables of interest
+vars = ['V_soma', 'V_dend', 'g_rise_Na_dend', 'g_fall_Na_dend',
+ 'I_rise_Na_dend', 'I_fall_Na_dend']
+M = b.StateMonitor(neuron, vars, record=True)
+
+# Run simulation
+b.run(10*ms)
+neuron.I_ext_dend = 213*pA
+b.run(150*ms)
+neuron.I_ext_dend = 0*pA
+b.run(80*ms)
+
+# Visualize results
+time = M.t/ms
+v1 = M.V_soma[0]/mV
+v2 = M.V_dend[0]/mV
+
+fig, axes = b.subplots(3, 1, figsize=(6, 6), sharex=True)
+ax0, ax1, ax2 = axes
+
+ax0.plot(time, v2, label='dendrite')
+ax0.plot(time, v1, label='soma', c='C2')
+ax0.axhline(-35, ls=':', c='black', label='threshold')
+ax0.set_ylabel('Voltage (mV)')
+ax0.set_xlim(110, 175)
+
+ax1.plot(time, M.g_rise_Na_dend[0]/nS, label='g_rise', c='black')
+ax1.plot(time, -M.g_fall_Na_dend[0]/nS, label='-g_fall', c='red')
+ax1.set_ylabel('Conductance (nS)')
+
+ax2.plot(time, M.I_rise_Na_dend[0]/pA, label='I_rise', c='gray')
+ax2.plot(time, M.I_fall_Na_dend[0]/pA, label='I_fall', c='C1')
+ax2.set_ylabel('Current (pA)')
+ax2.set_xlabel('Time (ms)')
+
+for ax in axes: ax.legend()
+fig.tight_layout()
+b.show()
\ No newline at end of file
diff --git a/examples_new/point_adex.py b/examples_new/point_adex.py
new file mode 100644
index 0000000..3d6f173
--- /dev/null
+++ b/examples_new/point_adex.py
@@ -0,0 +1,68 @@
+"""
+Title
+-----
+AdEx neuron
+
+Description
+-----------
+The Dendrify implementation of the Adaptive exponential integrate-and-fire model
+(adapted from `Brian's examples `_).
+
+Resources:
+
+- http://www.scholarpedia.org/article/Adaptive_exponential_integrate-and-fire_model
+- https://pubmed.ncbi.nlm.nih.gov/16014787/
+"""
+
+import brian2 as b
+from brian2.units import ms, mV, nA, nS, pF
+
+from dendrify import PointNeuronModel
+
+b.prefs.codegen.target = 'numpy' # faster for simple simulations
+
+# Create neuron model
+model = PointNeuronModel(model='adex',
+ cm_abs=281*pF,
+ gl_abs=30*nS,
+ v_rest=-70.6*mV)
+
+# Include adex parameters
+model.add_params({'Vth': -50.4*mV,
+ 'DeltaT': 2*mV,
+ 'tauw': 144*ms,
+ 'a': 4*nS,
+ 'b': 0.0805*nA,
+ 'Vr': -70.6*mV,
+ 'Vcut': -50.4*mV + 5 * 2*mV})
+
+# Create a NeuronGroup
+neuron = model.make_neurongroup(N=1, threshold='V>Vcut',
+ reset='V=Vr; w+=b',
+ method='euler')
+
+# Record voltages and spike times
+trace = b.StateMonitor(neuron, 'V', record=True)
+spikes = b.SpikeMonitor(neuron)
+
+# Run simulation
+b.run(20 * ms)
+neuron.I_ext = 1*nA
+b.run(100 * ms)
+neuron.I_ext = 0*nA
+b.run(20 * ms)
+
+# Trick to draw nicer spikes in I&F models
+vm = trace[0].V[:]
+for t in spikes.t:
+ i = int(t / b.defaultclock.dt)
+ vm[i] = 20*mV
+
+# Plot results
+b.figure(figsize=[6, 3])
+b.plot(trace.t / ms, vm / mV, label='V')
+b.xlabel('Time (ms)')
+b.ylabel('Voltage (mV)')
+b.legend()
+b.tight_layout()
+b.show()
\ No newline at end of file
diff --git a/examples_new/point_adex_noise.py b/examples_new/point_adex_noise.py
new file mode 100644
index 0000000..9cac603
--- /dev/null
+++ b/examples_new/point_adex_noise.py
@@ -0,0 +1,93 @@
+"""
+Title
+-----
+AdEx neuron + noise
+
+Description
+-----------
+The Dendrify implementation of the Adaptive exponential integrate-and-fire model
+(adapted from `Brian's examples `_).
+
+In this example, we also explore:
+
+- How to add gaussian noise.
+- How to create NeuronGroups with different properties using a single PointNeuronModel.
+
+Resources:
+
+- http://www.scholarpedia.org/article/Adaptive_exponential_integrate-and-fire_model
+- https://pubmed.ncbi.nlm.nih.gov/16014787/
+"""
+
+
+import brian2 as b
+from brian2.units import ms, mV, nA, nS, pA, pF
+
+from dendrify import PointNeuronModel
+
+b.prefs.codegen.target = 'numpy' # faster for simple simulations
+b.seed(1234) # for reproducibility
+
+# Create neuron model
+model = PointNeuronModel(model='adex',
+ cm_abs=281*pF,
+ gl_abs=30*nS,
+ v_rest=-70.6*mV)
+
+model.add_params({'Vth': -50.4*mV,
+ 'DeltaT': 2*mV,
+ 'tauw': 144*ms,
+ 'a': 4*nS,
+ 'b': 0.0805*nA,
+ 'Vr': -70.6*mV,
+ 'Vcut': -50.4*mV + 5 * 2*mV})
+
+
+# Create a NeuronGroup
+neuron = model.make_neurongroup(N=1, threshold='V>Vcut',
+ reset='V=Vr; w+=b',
+ method='euler')
+
+# Update model with noise and create a new NeuronGroup
+model.noise(mean=50*pA, sigma=300*pA, tau=2*ms)
+noisy_neuron = model.make_neurongroup(N=1, threshold='V>Vcut',
+ reset='V=Vr; w+=b',
+ method='euler')
+
+# Record voltages and spike times
+trace = b.StateMonitor(neuron, 'V', record=True)
+spikes = b.SpikeMonitor(neuron)
+noisy_trace = b.StateMonitor(noisy_neuron, 'V', record=True)
+noisy_spikes = b.SpikeMonitor(noisy_neuron)
+
+# Run simulation
+b.run(20 * ms)
+neuron.I_ext = 1*nA
+noisy_neuron.I_ext = 1*nA
+b.run(100 * ms)
+neuron.I_ext = 0*nA
+noisy_neuron.I_ext = 0*nA
+b.run(20 * ms)
+
+# Trick to draw nicer spikes in I&F models
+vm = trace[0].V[:]
+noisy_vm = noisy_trace[0].V[:]
+for t1, t2 in zip(spikes.t, noisy_spikes.t):
+ i = int(t1 / b.defaultclock.dt)
+ j = int(t2 / b.defaultclock.dt)
+ vm[i] = 20*mV
+ noisy_vm[j] = 20*mV
+
+# Plot results
+fig, axes = b.subplots(2, 1, figsize=[6, 6])
+ax1, ax2 = axes
+ax1.plot(trace.t / ms, vm / mV, label='V')
+ax1.set_ylabel('Voltage (mV)')
+ax1.legend()
+
+ax2.plot(noisy_trace.t / ms, noisy_vm / mV, label='V (with noise)')
+ax2.set_ylabel('Voltage (mV)')
+ax2.set_xlabel('Time (ms)')
+ax2.legend()
+fig.tight_layout()
+b.show()
\ No newline at end of file
diff --git a/examples_new/point_adex_synapses.py b/examples_new/point_adex_synapses.py
new file mode 100644
index 0000000..003b23e
--- /dev/null
+++ b/examples_new/point_adex_synapses.py
@@ -0,0 +1,83 @@
+"""
+Title
+-----
+AdEx network + synapses
+
+Description
+-----------
+The Dendrify implementation of the Adaptive exponential integrate-and-fire model
+(adapted from `Brian's examples `_).
+
+In this example, we also explore:
+
+- How to add random Poisson synaptic input.
+- How to create a basic network model.
+
+Resources:
+
+- http://www.scholarpedia.org/article/Adaptive_exponential_integrate-and-fire_model
+- https://pubmed.ncbi.nlm.nih.gov/16014787/
+"""
+
+import brian2 as b
+from brian2.units import Hz, ms, mV, nA, nS, pF
+
+from dendrify import PointNeuronModel
+
+b.prefs.codegen.target = 'numpy' # faster for simple simulations
+b.seed(1234) # for reproducibility
+
+# Create neuron model and add AMPA equations
+model = PointNeuronModel(model='adex',
+ cm_abs=281*pF,
+ gl_abs=30*nS,
+ v_rest=-70.6*mV)
+model.synapse('AMPA', tag='x', g=2*nS, t_decay=2*ms)
+
+# Include adex parameters
+model.add_params({'Vth': -50.4*mV,
+ 'DeltaT': 2*mV,
+ 'tauw': 144*ms,
+ 'a': 4*nS,
+ 'b': 0.0805*nA,
+ 'Vr': -70.6*mV,
+ 'Vcut': -50.4*mV + 5 * 2*mV})
+
+# Create a NeuronGroup
+neuron = model.make_neurongroup(N=100, threshold='V>Vcut',
+ reset='V=Vr; w+=b',
+ method='euler')
+
+# Create a Poisson input
+Input = b.PoissonGroup(200, rates=100*Hz)
+
+# Randomly connect Poisson input to NeuronGroup
+S = b.Synapses(Input, neuron, on_pre='s_AMPA_x += 1')
+S.connect(p=0.25)
+
+# Record voltages and spike times
+trace = b.StateMonitor(neuron, 'V', record=0)
+spikes = b.SpikeMonitor(neuron)
+
+# Run simulation
+b.run(200 * ms)
+
+# Trick to draw nicer spikes in I&F models
+vm = trace[0].V[:]
+for t in spikes.spike_trains()[0]:
+ i = int(t / b.defaultclock.dt)
+ vm[i] = 20*mV
+
+# Plot results
+fig, axes = b.subplots(2, 1, figsize=[6, 6])
+ax1, ax2 = axes
+ax1.plot(trace.t / ms, vm / mV, label='$V_0$')
+ax1.set_ylabel('Voltage (mV)')
+ax1.legend()
+ax2.plot(spikes.t/ms, spikes.i, '.', label='spikes')
+ax2.set_xlabel('Time (ms)')
+ax2.set_ylabel('Neuron index')
+ax2.legend()
+fig.tight_layout()
+b.show()
+
diff --git a/examples_new/point_lif_inhibition.py b/examples_new/point_lif_inhibition.py
new file mode 100644
index 0000000..5a4c354
--- /dev/null
+++ b/examples_new/point_lif_inhibition.py
@@ -0,0 +1,76 @@
+"""
+Title
+-----
+LIF network + inhibition
+
+Description
+-----------
+In this example, we present a simple network of generic leaky integrate-and-fire
+units comprising interconnected excitatory and inhibitory neurons.
+
+In this example, we also explore:
+
+- How to add different types of synaptic equations.
+- How to achieve more complex network connectivity.
+"""
+
+import brian2 as b
+from brian2.units import Hz, ms, mV, nS, pF
+
+from dendrify import PointNeuronModel
+
+b.prefs.codegen.target = 'numpy' # faster for simple simulations
+b.seed(123) # for reproducibility
+
+N_e = 700
+N_i = 300
+
+# Create a neuron model
+model = PointNeuronModel(model='leakyIF', cm_abs=281*pF, gl_abs=30*nS,
+ v_rest=-70.6*mV)
+
+model.synapse('AMPA', tag='ext', g=2*nS, t_decay=2.5*ms) # external excitatory input
+model.synapse('GABA', tag='inh', g=2*nS, t_decay=7.5*ms) # feedback inhibition
+model.add_params({'Vth': -40.4*mV, 'Vr': -65.6*mV})
+
+# Create a NeuronGroup
+neurons = model.make_neurongroup(N=N_e+N_i, threshold='V>Vth',
+ reset='V=Vr', method='euler')
+
+# Subpopulation of 300 inhibitory neurons
+inhibitory = neurons[:N_i]
+
+# Subpopulation of 700 excitatory neurons
+excitatory = neurons[N_i:]
+
+# Create a Poisson input
+Input = b.PoissonGroup(200, rates=90*Hz)
+
+# Specify synaptic connections
+Syn_ext_a = b.Synapses(Input, excitatory, on_pre='s_AMPA_ext += 1')
+Syn_ext_a.connect(p=0.2)
+
+Syn_ext_b = b.Synapses(Input, inhibitory, on_pre='s_AMPA_ext += 1')
+Syn_ext_b.connect(p=0) # initially no connections to inhibitory neurons
+
+Syn_inh = b.Synapses(inhibitory, excitatory, on_pre='s_GABA_inh += 1')
+Syn_inh.connect(p=0.15)
+
+# Record voltages and spike times
+spikes_e = b.SpikeMonitor(excitatory)
+spikes_i = b.SpikeMonitor(inhibitory)
+
+# Run simulation
+b.run(250 * ms)
+Syn_ext_b.connect(p=0.2) # add connections to inhibitory neurons
+b.run(250 * ms)
+
+# Plot results
+b.figure(figsize=[6, 5])
+b.plot(spikes_e.t/ms, spikes_e.i+N_i, '.', label='excitatory')
+b.plot(spikes_i.t/ms, spikes_i.i, '.', label='inhibitory', c='crimson')
+b.xlabel('Time (ms)')
+b.ylabel('Neuron index')
+b.legend()
+b.tight_layout()
+b.show()
\ No newline at end of file
diff --git a/examples_new/val_dendritic_attenuation.py b/examples_new/val_dendritic_attenuation.py
new file mode 100644
index 0000000..a65bd9b
--- /dev/null
+++ b/examples_new/val_dendritic_attenuation.py
@@ -0,0 +1,76 @@
+"""
+Title
+-----
+Dendritic attenuation
+
+Description
+-----------
+The attenuation of currents traveling along the somatodendritic axis is an
+intrinsic property of biological neurons and is due to the morphology and cable
+properties of their dendritic trees. (also see `Tran-van-Minh et al, 2015
+`_).
+
+In this example, we show:
+
+- How to measure the dendritic, distance-dependent voltage attenuation of a long
+ current pulse injected at the soma.
+
+"""
+
+import brian2 as b
+from brian2.units import cm, ms, mV, ohm, pA, pF, uF, um, uS
+
+from dendrify import Dendrite, NeuronModel, Soma
+
+b.prefs.codegen.target = 'numpy' # faster for simple simulations
+
+# Create neuron model
+soma = Soma('soma', length=25*um, diameter=25*um)
+trunk = Dendrite('trunk', length=100*um, diameter=1.5*um)
+prox = Dendrite('prox', length=100*um, diameter=1.2*um)
+dist = Dendrite('dist', length=100*um, diameter=1*um)
+
+# Create a neuron group
+connections = [(soma, trunk), (trunk, prox), (prox, dist)]
+model = NeuronModel(connections, cm=1*uF/(cm**2), gl=50*uS/(cm**2),
+ v_rest=-70*mV, r_axial=400*ohm*cm)
+neuron = model.make_neurongroup(1, method='euler') # no spiking for simplicity
+
+# Monitor voltages
+M = b.StateMonitor(neuron, ['V_soma', 'V_trunk', 'V_prox', 'V_dist'],
+ record=True)
+
+# Run simulation
+b.run(20*ms)
+neuron.I_ext_soma = -10*pA
+b.run(500*ms)
+neuron.I_ext_soma = 0*pA
+b.run(100*ms)
+
+# Analyse and plot results
+time = M.t/ms
+vs = M.V_soma[0]/mV
+vt = M.V_trunk[0]/mV
+vp = M.V_prox[0]/mV
+vd = M.V_dist[0]/mV
+voltages = [vs, vt, vp, vd]
+delta_v = [min(v) - v[0] for v in voltages]
+ratio = [i/delta_v[0] for i in delta_v]
+distances = range(0, 400, 100)
+names = ['soma', 'trunk', 'prox', 'dist']
+
+fig, axes = b.subplots(1, 2, figsize=(6, 3))
+ax0, ax1 = axes
+for i, v in enumerate(voltages):
+ ax0.plot(time, v, label=names[i])
+ax0.set_ylabel('Voltage (mV)')
+ax0.set_xlabel('Time (ms)')
+ax0.legend()
+
+ax1.plot(distances, ratio, 'ko-', ms=4)
+ax1.set_ylabel(r'$dV_{dend}$ / $dV_{soma}$')
+ax1.set_xlabel('Distance from soma (μm)')
+ax1.set_yticks(b.arange(.7, 1, .1))
+
+fig.tight_layout()
+b.show()
diff --git a/examples_new/val_dendritic_io.py b/examples_new/val_dendritic_io.py
new file mode 100644
index 0000000..9a7ca67
--- /dev/null
+++ b/examples_new/val_dendritic_io.py
@@ -0,0 +1,94 @@
+"""
+Title
+-----
+Dendritic I/O curve
+
+Description
+-----------
+Dendritic integration can be quantified by comparing the observed depolarization
+resulting from the quasi-simultaneous activation of the same synaptic inputs, and
+the arithmetic sum of individual EPSPs (expected membrane depolarization). The
+dendritic input-output (I/O) relationship is easily described by plotting
+observed vs. expected depolarizations for different numbers of co-activated
+synapses (also see `Tran-van-Minh et al, 2015
+`_).
+
+In this example, we show:
+
+- How to calculate the dendritic I/O curve in a simple compartmental model.
+- How active dendritic conductances affect the I/O curve.
+- How to perform the above experiment in a vectorized and efficient manner.
+"""
+
+import brian2 as b
+from brian2.units import ms, mV, nS, pF
+
+from dendrify import Dendrite, NeuronModel, Soma
+
+b.prefs.codegen.target = 'numpy' # faster for simple simulations
+
+# Create neuron model
+soma = Soma('soma', cm_abs=200*pF, gl_abs=10*nS)
+dend = Dendrite('dend', cm_abs=50*pF, gl_abs=2.5*nS)
+dend.dspikes('Na', g_rise=30*nS, g_fall=15*nS)
+dend.synapse('AMPA', tag='x', g=3*nS, t_decay=2*ms)
+dend.synapse('NMDA', tag='x', g=3*nS, t_decay=50*ms)
+
+model = NeuronModel([(soma, dend, 15*nS)], v_rest=-65*mV)
+model.config_dspikes('Na', threshold=-35*mV,
+ duration_rise=1.2*ms, duration_fall=2.4*ms,
+ offset_fall=0.2*ms, refractory=5*ms,
+ reversal_rise='E_Na', reversal_fall='E_K')
+
+# Create neuron group
+"""Instead of creating a single neuron, we create a group of neurons, each
+receiving a different number of synapses. This allows us to calculate the
+dendritic I/O curve efficiently in a single simulation."""
+N_syn = 15 # number of synapses
+neurons = model.make_neurongroup(N_syn, method='euler',
+ threshold='V_soma > -40*mV',
+ reset='V_soma = -55*mV',
+ refractory=4*ms)
+
+# Create input source
+start = 10*ms
+isi = 0.1*ms # inter-spike interval of input synapses
+spiketimes = [(start + (i*isi)) for i in range(N_syn)]
+I = b.SpikeGeneratorGroup(N_syn, range(N_syn), spiketimes)
+
+# Connect input to neurons
+synaptic_effect = "s_AMPA_x_dend += 1.0; s_NMDA_x_dend += 1.0"
+S = b.Synapses(I, neurons, on_pre=synaptic_effect)
+S.connect('j >= i') # 1st neuron receives 1 synapse, 2nd neuron receives 2 synapses, etc.
+
+# Record dendritic voltage
+M = b.StateMonitor(neurons, ['V_dend'], record=True)
+
+# Run simulation
+b.run(200 *ms)
+
+# Visualize results
+time = M.t/ms
+v = M.V_dend/mV
+v_rest = v[0][0]
+u_epsp = max(v[0]) - v_rest
+expected = [u_epsp * (i+1) for i in range(N_syn)]
+actual = [max(v[i]) - v_rest for i in range(N_syn)]
+linear = b.linspace(0, max(actual))
+
+fig, axes = b.subplots(1, 2, figsize=(6, 4))
+ax0, ax1 = axes
+
+ax0.plot(expected, actual, 'o-', label='Dendritic I/O')
+ax0.plot(linear, linear, '--', color='gray', label='Linear')
+ax0.set_xlabel('Expected EPSP (mV)')
+ax0.set_ylabel('Actual EPSP (mV)')
+ax0.legend()
+
+ax1.plot(time, v[7], label='#8 synapses', c='black', alpha=0.8)
+ax1.plot(time, v[8], label='#9 synapses', c='crimson', alpha=0.8)
+ax1.set_xlabel('Time (ms)')
+ax1.set_ylabel('Dendritic voltage (mV)')
+ax1.legend()
+fig.tight_layout()
+b.show()
\ No newline at end of file
diff --git a/examples_new/val_fi_curve.py b/examples_new/val_fi_curve.py
new file mode 100644
index 0000000..4cea699
--- /dev/null
+++ b/examples_new/val_fi_curve.py
@@ -0,0 +1,56 @@
+"""
+Title
+-----
+Frequency-current curve
+
+Description
+-----------
+A frequency-current curve (F-I curve) is the function that relates the net
+current ``I`` flowing into a neuron to its firing rate ``F``.
+
+In this example we show:
+
+- How to calculate the somatic F-I curve for a simple 2-compartment neuron model.
+- How to perform the above experiment in a vectorized and efficient manner.
+"""
+
+import brian2 as b
+from brian2.units import ms, mV, nS, pA, pF
+
+from dendrify import Dendrite, NeuronModel, Soma
+
+b.prefs.codegen.target = 'numpy' # faster for simple simulations
+
+# Create neuron model
+soma = Soma('soma', cm_abs=200*pF, gl_abs=10*nS)
+dend = Dendrite('dend', cm_abs=50*pF, gl_abs=2.5*nS)
+model = NeuronModel([(soma, dend, 15*nS)], v_rest=-65*mV)
+
+# Range of current amplitudes to test
+I = range(200, 620, 20) * pA
+
+# Create neuron group
+"""Instead of creating a single neuron, we create a group of neurons, each with
+a different value of ``I_ext``. This allows us to calculate the F-I curve in a
+single simulation."""
+neurons = model.make_neurongroup(len(I), method='euler',
+ threshold='V_soma > -40*mV',
+ reset='V_soma = -55*mV',
+ refractory=4*ms)
+
+# Record spike times
+spikes = b.SpikeMonitor(neurons)
+
+# Run simulation
+sim_time = 1000*ms
+neurons.I_ext_soma = I
+b.run(sim_time)
+
+# Visualize F-I curve
+F = [len(s) / sim_time for s in spikes.spike_trains().values()]
+b.figure(figsize=(6, 4))
+b.plot(I/pA, F, 'o-')
+b.xlabel('I (pA)')
+b.ylabel('F (Hz)')
+b.tight_layout()
+b.show()
diff --git a/examples_new/val_rinput.py b/examples_new/val_rinput.py
new file mode 100644
index 0000000..fbee0dc
--- /dev/null
+++ b/examples_new/val_rinput.py
@@ -0,0 +1,87 @@
+"""
+Title
+-----
+Input resistance
+
+Description
+-----------
+Input resistance (``Rin``) determines how much a neuron depolarizes in response
+to a steady current. It is a useful metric of a neuron's excitability; neurons
+with high ``Rin`` depolarize more in response to a given current than neurons
+with low ``Rin``. ``Rin`` is often measured experimentally by injecting a small
+current ``I`` into the neuron and measuring the steady-state change in its
+membrane potential ``ΔV``. Using Ohm's law, ``Rin`` can be estimated as
+``Rin = ΔV/I``.
+
+In this example we show:
+
+- How to calculate ``Rin`` in a point neuron model.
+- How ``Rin`` is affected by changes in the neuron's membrane leak conductance
+ ``gl``.
+
+Note: We also scale the neuron's membrane capacitance ``cm`` to maintain a
+constant membrane time constant (``τm = cm/gl``).
+"""
+
+import brian2 as b
+from brian2.units import Mohm, ms, mV, nS, pA, pF
+
+from dendrify import PointNeuronModel
+
+b.prefs.codegen.target = 'numpy' # faster for simple simulations
+
+# Parameters
+g_leakage = 20*nS # membrane leak conductance
+capacitance = 250*pF # membrane capacitance
+EL = -70*mV # resting potential
+
+# Create neuron models
+control = PointNeuronModel(model='leakyIF', cm_abs=capacitance,
+ gl_abs=g_leakage, v_rest=EL)
+
+low_rin = PointNeuronModel(model='leakyIF', cm_abs=capacitance*1.2,
+ gl_abs=g_leakage*1.2, v_rest=EL)
+
+high_rin = PointNeuronModel(model='leakyIF', cm_abs=capacitance*0.8,
+ gl_abs=g_leakage*0.8, v_rest=EL)
+
+# Create NeuronGroups (no threshold or reset conditions for simplicity)
+control_neuron = control.make_neurongroup(N=1, method='euler')
+low_rin_neuron = low_rin.make_neurongroup(N=1, method='euler')
+high_rin_neuron = high_rin.make_neurongroup(N=1, method='euler')
+
+# Record voltages
+control_monitor = b.StateMonitor(control_neuron, 'V', record=0)
+low_rin_monitor = b.StateMonitor(low_rin_neuron, 'V', record=0)
+high_rin_monitor = b.StateMonitor(high_rin_neuron, 'V', record=0)
+
+# Run simulation
+I = -20*pA # current pulse amplitude
+b.run(50*ms)
+for n in [control_neuron, low_rin_neuron, high_rin_neuron]:
+ n.I_ext = -20*pA
+b.run(500*ms)
+for n in [control_neuron, low_rin_neuron, high_rin_neuron]:
+ n.I_ext = 0*pA
+b.run(100*ms)
+
+# Calculate Rin
+Rin_control = (min(control_monitor.V[0]) - control_monitor.V[0][500]) / I
+Rin_low = (min(low_rin_monitor.V[0]) - low_rin_monitor.V[0][500]) / I
+Rin_high = (min(high_rin_monitor.V[0]) - high_rin_monitor.V[0][500]) / I
+
+# Plot results
+b.figure(figsize=(6, 3.5))
+b.plot(control_monitor.t/ms, control_monitor.V[0]/mV,
+ label='control Rin = {:.2f} MΩ'.format(Rin_control/ Mohm))
+b.plot(low_rin_monitor.t/ms, low_rin_monitor.V[0]/mV,
+ label='low Rin = {:.2f} MΩ'.format(Rin_low/ Mohm))
+b.plot(high_rin_monitor.t/ms, high_rin_monitor.V[0]/mV,
+ label='high Rin = {:.2f} MΩ'.format(Rin_high/ Mohm))
+b.axvline(50, ls=':', c='gray', label='stimulation period')
+b.axvline(550, ls=':', c='gray')
+b.xlabel('Time (ms)')
+b.ylabel('Membrane potential (mV)')
+b.legend()
+b.tight_layout()
+b.show()
diff --git a/examples_new/val_tau.py b/examples_new/val_tau.py
new file mode 100644
index 0000000..2a50208
--- /dev/null
+++ b/examples_new/val_tau.py
@@ -0,0 +1,108 @@
+"""
+Title
+-----
+Membrane time constant
+
+Description
+-----------
+In this example, we show how to calculate a neuron's membrane time constant
+``τm``, a metric that describes how quickly the membrane potential ``V`` decays
+to its steady-state value after some perturbation. In simple RC circuits, ``τm``
+is calculated as the product of the membrane capacitance ``C`` and the membrane
+resistance ``R``. However, in neurons, ``τm`` is also affected by voltage-gated
+conductances or other non-linearities.
+
+
+Experimentally, ``τm`` is often calculated by fitting an exponential function to
+the membrane potential ``V`` trace after applying a small negative current pulse
+at rest.
+
+
+Here we explore:
+
+- How to calculate ``τm`` for a neuron model experimentally.
+- How ``τm`` is affected by the presence of voltage-gated conductances, such as
+ an adaptation current.
+"""
+
+import brian2 as b
+from brian2.units import ms, mV, nS, pA, pF
+from scipy.optimize import curve_fit
+
+from dendrify import PointNeuronModel
+
+b.prefs.codegen.target = 'numpy' # faster for simple simulations
+
+# Create neuron models
+GL = 20*nS # membrane leak conductance
+CM = 250*pF # membrane capacitance
+EL = -70*mV # resting potential
+tau_theory = CM / GL # theoretical membrane time constant
+
+lif = PointNeuronModel(model='leakyIF', cm_abs=CM, gl_abs=GL, v_rest=EL)
+aif = PointNeuronModel(model='adaptiveIF', cm_abs=CM, gl_abs=GL, v_rest=EL)
+aif.add_params({'tauw': 100*ms, 'a': 2*nS})
+
+# Create NeuronGroups (no threshold or reset conditions for simplicity)
+lif_neuron = lif.make_neurongroup(N=1, method='euler')
+aif_neuron = aif.make_neurongroup(N=1, method='euler')
+
+# Record voltages
+lif_monitor = b.StateMonitor(lif_neuron, 'V', record=0)
+aif_monitor = b.StateMonitor(aif_neuron, 'V', record=0)
+
+# Run simulation
+I = -10*pA # current pulse amplitude
+t0 = 20*ms # time to start current pulse
+t_stim = 200*ms # duration of current pulse
+
+b.run(t0)
+lif_neuron.I_ext, aif_neuron.I_ext = I, I
+b.run(t_stim)
+lif_neuron.I_ext, aif_neuron.I_ext = 0*pA, 0*pA
+b.run(100*ms)
+
+# Analysis code
+def func(t, a, tau):
+ """Exponential decay function"""
+ return a * b.exp(-t / tau)
+
+def get_tau(trace, t0):
+ dt = b.defaultclock.dt
+ Vmin = min(trace)
+ time_to_peak = list(trace).index(Vmin)
+ # Find voltage from current-start to min value
+ voltages = trace[int(t0/dt): time_to_peak] / mV
+ # Min-max normalize voltages
+ v_norm = (voltages - voltages.min()) / (voltages.max() - voltages.min())
+ # Fit exp decay function to normalized data
+ X = b.arange(0, len(v_norm)) * dt / ms
+ popt, _ = curve_fit(func, X, v_norm)
+ return popt, X, v_norm
+
+# Plot results
+popt_lif, X_lif, v_norm_lif = get_tau(lif_monitor.V[0], t0)
+popt_aif, X_aif, v_norm_aif = get_tau(aif_monitor.V[0], t0)
+
+fig, axes = b.subplot_mosaic("""
+ AA
+ BC
+ """, layout='constrained', figsize=[6, 5])
+ax0, ax1, ax2 = axes.values()
+ax0.plot(lif_monitor.t/ms, lif_monitor.V[0]/mV, label='Leaky IF')
+ax0.plot(aif_monitor.t/ms, aif_monitor.V[0]/mV, label='Adaptive IF', zorder=0)
+ax0.set_title('Theoretical τm: {:.2f} ms'.format(tau_theory/ms))
+ax0.set_ylabel('Membrane potential (mV)')
+ax0.legend()
+ax1.plot(X_lif, v_norm_lif, 'ko-', ms=3)
+ax1.plot(X_lif, func(X_lif, *popt_lif), c='tomato')
+ax1.set_ylabel('Normalized potential (mV)')
+ax1.set_title(f'LIF | τm: {popt_lif[1]:.2f} ms')
+ax2.plot(X_aif, v_norm_aif, 'ko-', label='V (rest \u2192 min)', ms=3)
+ax2.plot(X_aif, func(X_aif, *popt_aif), label='a * exp(-t / τm)', c='tomato')
+ax2.set_title(f'AIF | τm: {popt_aif[1]:.2f} ms')
+ax2.legend()
+for ax in axes.values():
+ ax.set_xlabel('Time (ms)')
+fig.tight_layout()
+b.show()
\ No newline at end of file
diff --git a/setup.py b/setup.py
index 992c17b..1ae5ea6 100644
--- a/setup.py
+++ b/setup.py
@@ -1,6 +1,6 @@
from setuptools import find_packages, setup
-VERSION = '1.0.9'
+VERSION = '2.0.0'
DESCRIPTION = 'A package for adding dendrites to SNNs'
with open("README.rst") as f:
LONG_DESCRIPTION = f.read()
@@ -15,7 +15,7 @@
long_description_content_type="text/x-rst; charset=UTF-8",
long_description=LONG_DESCRIPTION,
packages=find_packages(),
- install_requires=['brian2==2.5.1'],
+ install_requires=['brian2==2.5.4'],
keywords=['python', 'brian2', 'dendrites', 'SNNs', 'network models'],
classifiers=[
"Development Status :: 4 - Beta",
![\"model\"](\"https://github.com/Poirazi-Lab/dendrify/blob/main/paper_figures/graphics/2.png?raw=true\")