Suggested further readings

Further reading on linear dynamical systems models in neuroscience:

Costa, A. C., Ahamed, T., & Stephens, G. J. (2019). Adaptive, locally linear models of complex dynamics. Proceedings of the National Academy of Sciences, 116(5), 1501-1510. doi: 10.1073/pnas.1813476116 .

Billeh, Y. N., Cai, B., Gratiy, S. L., Dai, K., Iyer, R., Gouwens, N. W., … & Arkhipov, A. (2020). Systematic integration of structural and functional data into multi-scale models of mouse primary visual cortex. Neuron, 106(3), 388-403. doi: 10.1016/j.neuron.2020.01.040 .

Brunton, B. W., Botvinick, M. M., & Brody, C. D. (2013). Rats and humans can optimally accumulate evidence for decision-making. Science, 340(6128), 95-98. doi: 10.1126/science.1233912 .

Brunton, B. W., Johnson, L. A., Ojemann, J. G., & Kutz, J. N. (2016). Extracting spatial–temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition. Journal of neuroscience methods, 258, 1-15. doi: 10.1016/j.jneumeth.2015.10.010 .

Gilson, M., Burkitt, A. N., Grayden, D. B., Thomas, D. A., & van Hemmen, J. L. (2009). Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks. I. Input selectivity–strengthening correlated input pathways. Biological cybernetics, 101(2), 81-102. doi: 10.1007/s00422-009-0319-4 .

Harris, K. D., Aravkin, A., Rao, R., & Brunton, B. W. (2021). Time-Varying Autoregression with Low-Rank Tensors. SIAM Journal on Applied Dynamical Systems, 20(4), 2335-2358. doi: 10.1137/20M1338058 (preprint: arxiv.org/pdf/1905.08389 ).

Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of physiology, 117(4), 500–544. doi: 10.1113/jphysiol.1952.sp004764 .

Hu, Y., Brunton, S. L., Cain, N., Mihalas, S., Kutz, J. N., & Shea-Brown, E. (2018). Feedback through graph motifs relates structure and function in complex networks. Physical Review E, 98(6), 062312. doi: 10.1103/physreve.98.062312 .

Izhikevich, E.M. (2007). Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting. MIT Press.

Linderman, S. W., Miller, A. C., Adams, R. P., Blei, D. M., Paninski, L., & Johnson, M. J. (2016). Recurrent switching linear dynamical systems. arXiv preprint. arXiv:1610.08466.

Mante, V., Sussillo, D., Shenoy, K. V., & Newsome, W. T. (2013). Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature, 503(7474), 78-84. doi: 10.1038/nature12742 (postprint: www.ncbi.nlm.nih.gov/pmc/articles/PMC4121670 )

Morrison, K., & Curto, C. (2019). Predicting neural network dynamics via graphical analysis. In: Algebraic and Combinatorial Computational Biology (pp. 241-277). Academic Press. doi: 10.1016/B978-0-12-814066-6.00008-8 (preprint: arxiv.org/pdf/1804.01487 ) or arxiv:1804.01487.

Ocker, G. K., Litwin-Kumar, A., & Doiron, B. (2015). Self-organization of microcircuits in networks of spiking neurons with plastic synapses. PLoS computational biology, 11(8), e1004458. doi: 10.1371/journal.pcbi.1004458 .

Ocker, G. K., Josić, K., Shea-Brown, E., & Buice, M. A. (2017). Linking structure and activity in nonlinear spiking networks. PLoS computational biology, 13(6), e1005583. doi: 10.1371/journal.pcbi.1005583 .

Pillow, J. W., Shlens, J., Paninski, L., Sher, A., Litke, A. M., Chichilnisky, E. J., & Simoncelli, E. P. (2008). Spatio-temporal correlations and visual signalling in a complete neuronal population. Nature, 454(7207), 995-999. doi: 10.1038/nature07140 (postprint: europepmc.org/articles/pmc2684455?pdf=render ).

Seung, H. S. (1996). How the brain keeps the eyes still. Proceedings of the National Academy of Sciences, 93(23), 13339-13344. doi: 10.1073/pnas.93.23.13339 .

Usher, M., & McClelland, J. L. (2001). The time course of perceptual choice: the leaky, competing accumulator model. Psychological review, 108(3), 550. doi: 10.1037/0033-295X.108.3.550 .

Further reading from Outro lecture:

Ames, K. C., Ryu, S. I., & Shenoy, K. V. (2019). Simultaneous motor preparation and execution in a last-moment reach correction task. Nature communications, 10(1), 1-13. doi: 10.1038/s41467-019-10772-2 .

Churchland, M. M., Cunningham, J. P., Kaufman, M. T., Foster, J. D., Nuyujukian, P., Ryu, S. I., & Shenoy, K. V. (2012). Neural population dynamics during reaching. Nature, 487(7405), 51-56. doi: 10.1038/nature11129 (postprint: europepmc.org/articles/pmc3393826?pdf=render ).

Gilja, V., Pandarinath, C., Blabe, C. H., Nuyujukian, P., Simeral, J. D., Sarma, A. A., … & Henderson, J. M. (2015). Clinical translation of a high-performance neural prosthesis. Nature medicine, 21(10), 1142-1145. doi: 10.1038/nm.3953 (postprint: europepmc.org/articles/pmc4805425?pdf=render ).

Kao, J. C., Nuyujukian, P., Ryu, S. I., Churchland, M. M., Cunningham, J. P., & Shenoy, K. V. (2015). Single-trial dynamics of motor cortex and their applications to brain-machine interfaces. Nature communications, 6(1), 1-12. doi: 10.1038/ncomms8759 .

Kao, J. C., Nuyujukian, P., Ryu, S. I., & Shenoy, K. V. (2016). A high-performance neural prosthesis incorporating discrete state selection with hidden Markov models. IEEE Transactions on Biomedical Engineering, 64(4), 935-945. doi: 10.1109/TBME.2016.2582691 .

Nuyujukian, P., Kao, J. C., Ryu, S. I., & Shenoy, K. V. (2016). A nonhuman primate brain–computer typing interface. Proceedings of the IEEE, 105(1), 66-72. doi: 10.1109/JPROC.2016.2586967 (postprint: www.ncbi.nlm.nih.gov/pmc/articles/PMC7970827 ).

Nuyujukian, P., Albites Sanabria, J., Saab, J., Pandarinath, C., Jarosiewicz, B., Blabe, C. H., … & Henderson, J. M. (2018). Cortical control of a tablet computer by people with paralysis. PloS one, 13(11), e0204566. doi: 10.1371/journal.pone.0204566 .

Pandarinath, C., O’Shea, D. J., Collins, J., Jozefowicz, R., Stavisky, S. D., Kao, J. C., … & Sussillo, D. (2018). Inferring single-trial neural population dynamics using sequential auto-encoders. Nature methods, 15(10), 805-815. doi: 10.1038/s41592-018-0109-9 (postprint: europepmc.org/articles/pmc6380887?pdf=render ).

Pandarinath, C., Nuyujukian, P., Blabe, C. H., Sorice, B. L., Saab, J., Willett, F. R., … & Henderson, J. M. (2017). High performance communication by people with paralysis using an intracortical brain-computer interface. Elife, 6, e18554. doi: 10.7554/eLife.18554 .

Santhanam, G., Yu, B. M., Gilja, V., Ryu, S. I., Afshar, A., Sahani, M., & Shenoy, K. V. (2009). Factor-analysis methods for higher-performance neural prostheses. Journal of neurophysiology, 102(2), 1315-1330. doi: 10.1152/jn.00097.2009 .

Shenoy, K. V., Sahani, M., & Churchland, M. M. (2013). Cortical control of arm movements: a dynamical systems perspective. Annual review of neuroscience, 36, 337-359. doi: 10.1146/annurev-neuro-062111-150509 .

Stavisky, S. D., Kao, J. C., Ryu, S. I., & Shenoy, K. V. (2017). Motor cortical visuomotor feedback activity is initially isolated from downstream targets in output-null neural state space dimensions. Neuron, 95(1), 195-208. doi: 10.1016/j.neuron.2017.05.023 .

Stavisky, S. D., Willett, F. R., Wilson, G. H., Murphy, B. A., Rezaii, P., Avansino, D. T., … & Henderson, J. M. (2019). Neural ensemble dynamics in dorsal motor cortex during speech in people with paralysis. Elife, 8, e46015. doi: 10.7554/eLife.46015 .

Trautmann, E. M., Stavisky, S. D., Lahiri, S., Ames, K. C., Kaufman, M. T., O’Shea, D. J., … & Shenoy, K. V. (2019). Accurate estimation of neural population dynamics without spike sorting. Neuron, 103(2), 292-308. doi: 10.1016/j.neuron.2019.05.003 .

Vyas, S., Even-Chen, N., Stavisky, S. D., Ryu, S. I., Nuyujukian, P., & Shenoy, K. V. (2018). Neural population dynamics underlying motor learning transfer. Neuron, 97(5), 1177-1186. doi: 10.1016/j.neuron.2018.01.040 .

Vyas, S., Golub, M. D., Sussillo, D., & Shenoy, K. V. (2020). Computation through neural population dynamics. Annual Review of Neuroscience, 43, 249-275. doi: 10.1146/annurev-neuro-092619-094115 (postprint: www.ncbi.nlm.nih.gov/pmc/articles/PMC7402639 ).

Vyas, S., O’Shea, D. J., Ryu, S. I., & Shenoy, K. V. (2020). Causal role of motor preparation during error-driven learning. Neuron, 106(2), 329-339. doi: 10.1016/j.neuron.2020.01.019 .

Willett, F. R., Deo, D. R., Avansino, D. T., Rezaii, P., Hochberg, L. R., Henderson, J. M., & Shenoy, K. V. (2020). Hand knob area of premotor cortex represents the whole body in a compositional way. Cell, 181(2), 396-409. doi: 10.1016/j.cell.2020.02.043 .

Williams, A. H., Kim, T. H., Wang, F., Vyas, S., Ryu, S. I., Shenoy, K. V., … & Ganguli, S. (2018). Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor component analysis. Neuron, 98(6), 1099-1115. doi: 10.1016/j.neuron.2018.05.015 .

Yu, B. M., Cunningham, J. P., Santhanam, G., Ryu, S., Shenoy, K. V., & Sahani, M. (2008). Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity. Advances in neural information processing systems, 21. URL: proceedings.neurips.cc/paper/2008/hash/ad972f10e0800b49d76fed33a21f6698-Abstract.html.