Lim S., McKee, J. L., Woloszyn, L., Amit, Y., Freedman, D. J., Sheinberg, D. L., and Brunel, N. (2015). Inferring learning rules from distributions of firing rates in cortical neurons. Nat Neurosci http://dx.doi.org/10.1038/nn.4158
Extracting Low-dimensional Latent Structure From Time Series in the Presence of Delays Karthik C. Lakshmanan, Patrick T Sadtler, Elizabeth C Tyler-Kabara, Aaron P Batista, and Byron M. Yu http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00759
A Tractable Method for Describing Complex Couplings between Neurons and Population Rate. eNeuro (2016) Christophe Gardella, Olivier Marre, Thierry Mora (suggested by I. Memming Park) http://eneuro.org/content/3/4/ENEURO.0160-15.2016
Knierim, J. J. and Neunuebel, J. P. (2016). Tracking the flow of hippocampal computation: Pattern separation, pattern completion, and attractor dynamics. Neurobiology of Learning and Memory, 129:38-49. (suggested by I. Memming Park) http://dx.doi.org/10.1016/j.nlm.2015.10.008
Tavoni, G., Cocco, S., and Monasson, R. Neural assemblies revealed by inferred connectivity-based models of prefrontal cortex recordings, J. Comp. Neuro (2016), pp1-25, Doi:10.1007/s10827-016-0617-5 (suggested by David Hocker)
Pengcheng Zhou, Shawn D. Burton, Adam C. Snyder, Matthew A. Smith, Nathaniel N. Urban, Robert E. Kass , Establishing a Statistical Link between Network Oscillations and Neural Synchrony, PLOS Comp. Bio. 2015. (Suggested by I. Memming Park) http://dx.doi.org/10.1371/journal.pcbi.1004549
Madhu Advani and Surya Ganguli. Statistical Mechanics of Optimal Convex Inference in High Dimensions. (Suggested by Memming) https://journals.aps.org/prx/abstract/10.1103/PhysRevX.6.031034
Diversity in neural firing dynamics supports both rigid and learned hippocampal sequences. Grosmark, A. D., & Buzsáki, G. (2016). Science, 351(6280), 1440–3. (Suggested by Memming)
P. E. LATHAM, B. J. RICHMOND, P. G. NELSON, S. NIRENBERG. Intrinsic dynamics in neuronal networks. I. Theory. Am Physiol Soc 2000. (Suggested by Memming) http://www.variational-bayes.org/~pel/papers/idyn-th.pdf
Reading Out Olfactory Receptors: Feedforward Circuits Detect Odors in Mixtures without Demixing (Suggested by Memming) http://www.cell.com/neuron/fulltext/S0896-6273(16)30499-8
Peter H Rudebeck, Richard C Saunders, Anna T. Prescott, Lily S. Chau and Elisabeth A Murray (2013) Prefrontal mechanisms of emotion, value and behavioural flexibility. Nature Neuroscience, 16(8): 1140-5. (Suggested by David Hocker) http://www.jneurosci.org/content/35/33/11751.short
Tavares,R, et al. (2015) A map for social navigation in the human brain. Neuron 87, 231-243. (Suggested by David Hocker) http://www.sciencedirect.com/science/article/pii/S0896627315005243
Pagan, M., Simoncelli, E. P., and Rust, N. C. (2016). Neural quadratic discriminant analysis: Nonlinear decoding with V1-Like computation. Neural Computation, pages 1-29. (by Memming, ask me for the PDF) http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00890
Eric Jonas and Konrad Kording. Could a neuroscientist understand a microprocessor? bioRxiv 2016 (by Memming, this is fun.) Satohiro Tajima, Toru Yanagawa, Naotaka Fujii, Taro Toyoizumi. Untangling Brain-Wide Dynamics in Consciousness by Cross-Embedding. PLoS CB 2015 http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004537 (by Memming; for those interested in dimensionality of neural dynamics)
Maria del Mar Quiroga, Adam P. Morris, Bart Krekelberg. Adaptation without Plasticity. Cell Reports. 2016 (by Memming) http://dx.doi.org/10.1016/j.celrep.2016.08.089
Mohammad-Reza Dehaqani, Abdol-Hossein Vahabie, Mohammadbagher Parsa, Behrad Noudoost, Alireza Soltani, Enhanced representation of space by prefrontal neuronal ensembles and its dependence on cognitive states. bioRxiv, doi: http://biorxiv.org/content/early/2016/10/10/065581 (GLC; dimensionality; ongoing activity; prefrontal cortex)
Iigaya K, Adaptive learning and decision-making under uncertainty by metaplastic synapses guided by a surprise detection system, Elife. 2016 Aug 9;5. pii: e18073. doi: 10.7554/eLife.18073. (GLC)
Orbán, G, Berkes, P, Fiser, J & Lengyel, M, 2016, Neural Variability and Sampling-Based Probabilistic Representations in the Visual Cortex, vol. 92, no. 2, pp. 530-543. (Memming; Sampling hypothesis / Bayesian Brain) http://www.cell.com/neuron/fulltext/S0896-6273(16)30639-0
Jérémie Barral & Alex D Reyes, Synaptic scaling rule preserves excitatory–inhibitory balance and salient neuronal network dynamics, Nat Neurosci 2016, doi:10.1038/nn.4415 (GLC; experimental demonstration in cultured networks of the 1/sqrt(K) synaptic scaling predicted by the theory of balanced networks, along with predicted results on CV, FF, and firing rate distributions) Adam N. Sanborn, Nick Chater. Bayesian Brains without Probabilities. Cell 2016 (Memming; Opinion piece on sampling hypothesis) http://dx.doi.org/10.1016/j.tics.2016.10.003
Carsen Stringer, Marius Pachitariu, Michael Okun, Peter Bartho, Kenneth Harris, Peter Latham, Maneesh Sahani, Nicholas Lesica. Inhibitory control of shared variability in cortical networks. (Memming) http://biorxiv.org/content/early/2016/07/16/041103
Peng Sun and Michael S. Landy. A Two-Stage Process Model of Sensory Discrimination: An Alternative to Drift-Diffusion. (Memming; theory of sensory integration) http://www.jneurosci.org/content/36/44/11259
Neural Variability and Sampling-Based Probabilistic Representations in the Visual Cortex http://www.cell.com/neuron/abstract/S0896-6273(16)30639-0 (suggested by Luca Mazzucato)
History-dependent variability in population dynamics during evidence accumulation in cortex, http://www.nature.com/neuro/journal/vaop/ncurrent/full/nn.4403.html (suggested by Luca Mazzucato)
Pawel Zmarz, Georg B. Keller. Mismatch Receptive Fields in Mouse Visual Cortex. (Memming; predictive coding) http://dx.doi.org/10.1016/j.neuron.2016.09.057
Akihiro Funamizu, Bernd Kuhn and Kenji Doya. Neural substrate of dynamic Bayesian inference in the cerebral cortex. Nature Neuroscience. (2016) (Memming) http://dx.doi.org/10.1038/nn.4390
Peter Lakatos et al. Global dynamics of selective attention and its lapses in primary auditory cortex. Nat Neurosci 2016 (Memming) http://dx.doi.org/10.1038/nn.4386
Zhang, W., Falkner, A. L., Krishna, B. S., Goldberg, M. E., and Miller, K. D. (2016). Coupling between One-Dimensional networks reconciles conflicting dynamics in LIP and reveals its recurrent circuitry. Neuron. (Memming) http://www.cell.com/neuron/abstract/S0896-6273(16)30861-3 http://dx.doi.org/10.1016/j.neuron.2016.11.023
Rosenbaum, R., Smith, M. A., Kohn, A., Rubin, J. E., and Doiron, B. (2017). The spatial structure of correlated neuronal variability. Nature Neuroscience, 20(1):107-114. (Balanced network; News & Views by P. Latham; suggested by Memming) http://www.nature.com/neuro/journal/v20/n1/full/nn.4433.html
Angela M Bruno, William N Frost, Mark D Humphries. A spiral attractor network drives locomotion in Aplysia. (nonlinear dynamics; Memming) http://biorxiv.org/content/early/2017/01/31/104562 -> Taken by Phillip Kang
Stefano Panzeri, Christopher D. Harvey, Eugenio Piasini, Peter E. Latham, Tommaso Fellin. Cracking the Neural Code for Sensory Perception by Combining Statistics, Intervention, and Behavior. Neuron 2017 (Review article; Memming) http://www.cell.com/neuron/fulltext/S0896-6273(16)31009-1
Xaq Pitkow and Dora E Angelaki. How the Brain Might Work: Statistics Flowing in Redundant Population Codes. PREPRINT (Perspective; Bayesian brain; Neural dynamics; Memming) http://xaqlab.com/wp-content/uploads/2017/01/HowTheBrainWorks_1.3b.pdf
Prsa, M., Galiñanes, G. L., and Huber, D. (2017). Rapid integration of artificial sensory feedback during operant conditioning of motor cortex neurons. Neuron, 93(4):929-939.e6. (M1 optogenetic single neuron perturbation; Memming) http://dx.doi.org/10.1016/j.neuron.2017.01.023
Gordon J. Berman, William Bialek, and Joshua W. Shaevitz, Predictability and hierarchy in Drosophila behavior, PNAS, 113, no 42. 11943-11948 (2016) http://www.pnas.org/content/113/42/11943.full.pdf
Yoshua Bengio, Thomas Mesnard, Asja Fischer, Saizheng Zhang, Yuhuai Wu. STDP as Presynaptic Activity Times Rate of Change of Postsynaptic Activity Approximates Backpropagation. Neural Computation. 2017 Feb. (Memming)
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., Hassabis, D., Clopath, C., Kumaran, D., and Hadsell, R. (2017). Overcoming catastrophic forgetting in neural networks. Proceedings of the National Academy of Sciences (PNAS), pages 201611835+. (memory capacity in neural networks; Memming)
Naumann, E. A., Fitzgerald, J. E., Dunn, T. W., Rihel, J., Sompolinsky, H., and Engert, F. (2016). From Whole-Brain data to functional circuit models: The zebrafish optomotor response. Cell, 167(4):947-960.e20. (Memming)
Moore, J. J., Ravassard, P. M., Ho, D., Acharya, L., Kees, A. L., Vuong, C., and Mehta, M. R. (2017). Dynamics of cortical dendritic membrane potential and spikes in freely behaving rats. Science, 355(6331):eaaj1497+. (Dendritic spikes; Not really computational, but a new finding that could change how we think about neural computation; Memming)
Romano, S. A., Pérez-Schuster, V., Jouary, A., Candeo, A., Boulanger-Weill, J., and Sumbre, G. (2017). A computational toolbox and step-by-step tutorial for the analysis of neuronal population dynamics in calcium imaging data. bioRxiv, pages 103879+. (Data analysis pipeline for calcium imaging; Memming)
Bolding, K. A. and Franks, K. M. (2017). Complementary codes for odor identity and intensity in olfactory cortex. eLife, 6:e22630+. (Memming)
Shiva Farashahi, Christopher H. Donahue, Peyman Khorsand, Hyojung Seo, Daeyeol Lee, Alireza Soltani. (2017) Metaplasticity as a Neural Substrate for Adaptive Learning and Choice under Uncertainty. Neuron (Memming)
Hardcastle, K., Maheswaranathan, N., Ganguli, S., and Giocomo, L. M. (2017). A multiplexed, heterogeneous, and adaptive code for navigation in medial entorhinal cortex. Neuron, 94(2):375-387.e7. (Memming)
Rubin, R., Abbott, L. F., and Sompolinsky, H. (2017). Balanced excitation and inhibition are required for High-Capacity, Noise-Robust neuronal selectivity. arXiv 2017 (Memming)
Kim, S. S., Rouault, H., Druckmann, S., and Jayaraman, V. (2017). Ring attractor dynamics in the drosophila central brain. Science, pages eaal4835+. (Memming)
Guo, Z. V., Inagaki, H. K., Daie, K., Druckmann, S., Gerfen, C. R., and Svoboda, K. (2017). Maintenance of persistent activity in a frontal thalamocortical loop. Nature, advance online publication. (ALM working memory; Memming)
Friedemann Zenke, Surya Ganguli. SuperSpike: Supervised learning in multi-layer spiking neural networks. arXiv 1705.11146 (Memming) Matthew L. Leavitt, Diego Mendoza-Halliday, Julio C. Martinez-Trujillo. Sustained Activity Encoding Working Memories: Not Fully Distributed. Trends in Neuroscience 2017. (Memming)
Juan A. Gallego, Matthew G. Perich, Lee E. Miller, Sara A. Solla. Neural Manifolds for the Control of Movement. Neuron 2017. (Memming) Scott, B. B., Constantinople, C. M., Akrami, A., Hanks, T. D., Brody, C. D., and Tank, D. W. (2017). Fronto-parietal cortical circuits encode accumulated evidence with a diversity of timescales. Neuron. (Memming)
Alyson K. Fletcher, Sundeep Rangan. Inference in Deep Networks in High Dimensions. https://arxiv.org/abs/1706.06549 (Memming; probably too technical)
Watanabe, T., Masuda, N., Megumi, F., Kanai, R., and Rees, G. (2014). Energy landscape and dynamics of brain activity during human bistable perception. Nature Communications, 5:4765+. (Memming; related to https://www.nature.com/articles/ncomms16048)
Linderman, S.W., Gershman, S.J. Using computational theory to constrain statistical models of neural data. Current Opinion in Neurobiology volume 46, 2017, pp. 14 - 24 (Memming)
Elsayed, G. F. and Cunningham, J. P. (2017). Structure in neural population recordings: an expected byproduct of simpler phenomena? Nature Neuroscience, 20(9):1310-1318. (Memming; there’s an associated news & views by Pillow & Aoi)
Stefano Recanatesi, Mikhail Katkov, and Misha Tsodyks. Memory States and Transitions Between Them in Attractor Neural Networks. Neural Computation 2017. (Memming; Mean field and finite size effects. E-I balance)
Hiroshi M Shiozaki and Hokto Kazama. Parallel encoding of recent visual experience and self-motion during navigation in Drosophila. Nature Neuroscience 2017 (Memming; central complex, visual integration)
Lapish, C. C., Balaguer-Ballester, E., Seamans, J. K., Phillips, A. G., and Durstewitz, D. (2015). Amphetamine exerts Dose-Dependent changes in prefrontal cortex attractor dynamics during working memory. Journal of Neuroscience, 35(28):10172-10187.
Liang, H. et al. Interactions between feedback and lateral connections in the primary visual cortex. PNAS Proceedings of the National Academy of Sciences 114, 8637-8642 (2017). (Memming; Neural computation in vision)
Satohiro Tajima, et al.. Task-dependent recurrent dynamics in visual cortex. eLife 2017 (Memming; Vision, dynamics)
Wang, J., Narain, D., Hosseini, E. A. & Jazayeri, M. Flexible timing by temporal scaling of cortical responses. Nature Neuroscience 21, 102-110 (2017/2018). URL http://dx.doi.org/10.1038/s41593-017-0028-6. (Memming)
Rossi-Pool, R. et al. Decoding a decision process in the neuronal population of dorsal premotor cortex. Neuron 96, 1432-1446.e7 (2017). URL http://dx.doi.org/10.1016/j.neuron.2017.11.023 (Memming; decision-making, more dPCA)
Mackevicius, E. L., & Fee, M. S. (2018). Building a state space for song learning. Current Opinion in Neurobiology, 49 , 59-68. URL http://dx.doi.org/10.1016/j.conb.2017.12.001 (Memming; reinforcement learning)
Parthasarathy, A., Herikstad, R., Bong, J. H., Medina, F. S., Libedinsky, C., & Yen, S.-C. (2017). Mixed selectivity morphs population codes in prefrontal cortex. Nature Neuroscience, 20 (12), 1770-1779. URL http://dx.doi.org/10.1038/s41593-017-0003-2 (Memming; mixed selectivity)
Caballero, J., Humphries, M., Gurney, K,. (2018). A probabilistic, distributed, recursive mechanism for decision-making in the brain. PLOS Computational Biology. URL https://doi.org/10.1371/journal.pcbi.1006033 (Recommended by Memming: Decision making)
G. Hennequin, Y. Ahmadian, D. B. Rubin, M. Lengyel, and K. D. Miller, “The Dynamical Regime of Sensory Cortex: Stable Dynamics around a Single Stimulus-Tuned Attractor Account for Patterns of Noise Variability,” Neuron, vol. 98, no. 4, pp. 846-860.e5, May 2018. https://doi.org/10.1016/j.neuron.2018.04.017 (Memming)
M. F. Panichello, B. DePasquale, J. W. Pillow, and T. Buschman, “Error-correcting dynamics in visual working memory,” bioRxiv, p. 319103, May 2018. https://www.biorxiv.org/content/early/2018/05/10/319103 (Memming)
Chandrasekaran, Chandramouli, Joana Soldado-Magraner, Diogo Peixoto, William T. Newsome, Krishna Shenoy, and Maneesh Sahani. 2018. “Brittleness in Model Selection Analysis of Single Neuron Firing Rates.” bioRxiv. https://doi.org/10.1101/430710 (Memming)Learning arbitrary dynamics in efficient, balanced spiking networks using local plasticity rules. Alemi, Machens, Deneve & Slotine arxiv (2017).
A unifying motif for spatial and directional surround suppression. Liu, Miller & Pack biorxiv (2017).
Neuron’s eye view: Inferring features of complex stimuli from neural responses. Chen, Beck & Pearson PLOS Computational Biology (2017).
Nonlinear hebbian learning as a unifying principle in receptive field formation. Brito & Gerstner PLOS Computational Biology (2016).
Adaptation without plasticity. del Mar Quiroga, Morris & Krekelberg Cell (2016).
A Global Geometric Framework for Nonlinear Dimensionality Reduction. Tenenbaum, de Silva & Langford. Science (2000).
Distinct recurrent versus afferent dynamics in cortical visual processing. Reinhold, Lien, & Scanziani, Nat Neurosci (2015).
Modular deconstruction reveals the dynamical and physical building blocks of a locomotion motor program. Bruno, Frost & Humphries,Neuron (2015).
Spatial segregation of adaptation and predictive sensitization in retinal ganglion cells. Kastner & Baccus, Neuron, (2013).
Marginalization in neural circuits with divisive normalization. Beck, Latham & Pouget J. Neuroscience, (2011).
Computational account of spontaneous activity as a signature of predictive coding. Koran & Deneve, PLOS Comp. Bio., (2017).
Visual motion computation in recurrent neural networks. Pachitariu & Sahani NIPS, (2017).
Computing by robust transience: How the fronto-parietal network performs sequential, category-based decisions. Chaisangmongkon, Swaminathan, Freedman & Wang, Neuron, (2017).
A modeling framework for deriving the structural and functional architecture of a short-term memory microcircuit. Fisher et al., Neuron, (1997).