trepka_etal_jneuro_2023

Overview

This repository contains code for fitting AR models to estimate timescales and for generating the figures for the following paper:

Trepka et al. (2023) Training-dependent gradients of timescales of neural dynamics in the primate prefrontal cortex and their contributions to working memory. The Journal of Neuroscience.

Data format

We include intermediate data files in the data/plot_input directory that can be used for replicating most figures in the paper. The intermediate files contain tables of neurons along with model parameters estimated for each neuron.

Reproducing figures

To reproduce all figures, clone the repo and run make_figures.m, plot_corr_vs_err_timescales.m, plot_decoder_accuracy_over_time.m and plot_decoder_accuracy_over_neurons.m.

Reproducing all analyses

To reproduce all analyses from raw data, run the analysis scripts in the order illustrated in run_all.m.

Code organization

The remaining scripts in the main directory are organized as follows:

• config.m contains constants and parameters for model fitting and postprocessing data
• preprocess/ contains functions for preprocessing the raw data
• model/ contains functions for fitting the AR model
• postprocess/ contains functions for postprocessing the model output
• plot/ contains helper functions for plotting figures
• decoder/ contains functions for training and testing decoders
• revisions/ contains functions related to additional analyses associated with the revisions of the paper