Suggested further readings

Further reading on linear dynamical systems models in neuroscience:

Billeh, Y. N., Cai, B., Gratiy, S. L., Dai, K., Iyer, R., Gouwens, N. W., Abbasi-Asl, R., Jia, X., Siegle, J. H., Olsen, S. R., Koch, C., Mihalas, S., & Arkhipov, A. (2020). Systematic Integration of Structural and Functional Data into Multi-scale Models of Mouse Primary Visual Cortex. Neuron, 106(3), 388-403.e18. https://doi.org/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. https://doi.org/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. https://doi.org/10.1016/j.jneumeth.2015.10.010

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. https://doi.org/10.1073/pnas.1813476116

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. https://doi.org/10.1007/s00422-009-0319-4

Harris, K. D., Aravkin, A., Rao, R., & Brunton, B. W. (2020). Time-varying autoregression with low rank tensors. ArXiv:1905.08389 Cs, Stat. http://arxiv.org/abs/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. https://doi.org/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. https://doi.org/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:1610.08466 Stat. http://arxiv.org/abs/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. https://doi.org/10.1038/nature12742

Morrison, K., & Curto, C. (2018). Predicting neural network dynamics via graphical analysis. ArXiv:1804.01487 Math, q-Bio. http://arxiv.org/abs/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 https://doi.org/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. https://doi.org/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. https://doi.org/10.1038/nature07140

Usher, M., & McClelland, J. L. (2001). The time course of perceptual choice: The leaky, competing accumulator model. Psychological Review, 108(3), 550–592. https://doi.org/10.1037/0033-295X.108.3.550

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

Further reading from Outro lecture:

Vyas S, Golub MD, Sussillo D, Shenoy KV (2020) Computation through neural population dynamics. Annual Review of Neuroscience. 43:249-275.

Willett FR, Deo DR, Avansino DT, Rezaii Paymon, Hochberg LR, Henderson JM, Shenoy KV (2020) Hand knob area of motor cortex in people with tetraplegia represents the whole body in a compositional way. Cell. 181:396–409.

Vyas S, O’Shea DJ, Ryu SI, Shenoy KV (2020) Causal role of motor preparation during error-driven learning. Neuron. 106:329-339.

Stavisky SD, Willett FR, Wilson GH, Murphy BA, Rezaii P, Avansino D, Memberg WD, Miller JP, Kirsch RF, Hochberg LR, Ajiboye AB, Druckmann S, Shenoy KV, Henderson JM (2019) Neural ensemble dynamics in dorsal motor cortex during speech in people with paralysis. eLife. 8:e46015.

Trautmann EM, Stavisky SD, Lahiri S, Ames KC, Kaufman MT, O’Shea DJ, Vyas S, Sun X, Ryu SI, Ganguli S, Shenoy KV (2019) Accurate estimation of neural population dynamics without spike sorting. Neuron. 103:1-17.

Ames KC, Ryu SI, Shenoy KV (2019) Simultaneous movement preparation and execution in a last-moment reach correction task. Nature Communications. 10(1):2718.

Nuyujukian P, Sanabria JA, Saab J, Pandarinath C, Jarosiewicz B, Blabe C, Franco B, Mernoff ST, Eskandar EN, Simeral JD, Hochberg LR, Shenoy KV, Henderson JM (2018) Cortical control of a tablet computer by people with paralysis. PLoS One. 13:e0204566.

Pandarinath C, O’Shea DJ, Collins J, Jozefowicz R, Stavisky SD, Kao JC, Trautmann EM, Kaufman MT, Ryu SI, Hochberg LR, Henderson JM, Shenoy KV, Abbott LF, Sussillo D (2018) Inferring single-trial neural population dynamics using sequential auto-encoders. Nature Methods.15:805-815.

Williams AH, Kim TH, Wang F, Vyas S, Ryu SI, Shenoy KV, Schnitzer M, Kolda TG, Ganguli S (2018) Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor components analysis. Neuron.98:1-17.

Vyas S, Even-Chen N, Stavisky SD, Ryu SI, Nuyujukian P, Shenoy KV (2018) Neural population dynamics underlying motor learning transfer. Neuron. 97: 1-10.

Stavisky SD, Kao JC, Ryu SI, Shenoy KV (2017) Motor cortical visuomotor feedback activity is initially isolated from downstream targets in output-null neural state space dimensions. Neuron. 95:195-208.

Pandarinath C, Nuyujukian P, Blabe CH, Sorice B, Saab J, Willett F, Hochberg LR, Shenoy KV, Henderson JM (2017) High performance communication by people with paralysis using an intracortical brain-computer interface. eLife. 6:e18554.

Kao JC, Nuyujukian P, Ryu SI, Shenoy KV (2017) A high-performance neural prosthesis incorporating discrete state selection with hidden Markov models. IEEE Transactions on Biomedical Engineering. 64:935-945.

Nuyujukian P, Kao JC, Ryu SI, Shenoy KV (2017) A non-human primate brain computer typing interface. Proceedings of the IEEE.105:66-72.

Gilja V, Pandarinath C, Blabe CH, Nuyujukian P, Simeral JD, Sarma AA, Sorice BL, Perge JA, Jarosiewicz B, Hochberg LR, Shenoy KV, Henderson JM (2015) Clinical translation of a high performance neural prosthesis. Nature Medicine. 21:1142-1145.

Kao JC, Nuyujukian P, Cunningham JP, Churchland MM, Ryu SI, Shenoy KV (2015) Single-trial dynamics of motor cortex and their applications to brain-machine interfaces. Nature Communications. 6:7759. Kaufman MT, Churchland MM, Ryu SI, Shenoy KV (2014) Cortical activity in the null space: permitting preparation without movement. Nature Neuroscience. 17:440-448.

Shenoy KV, Sahani M, Churchland MM (2013) Cortical control of arm movements: A dynamical systems perspective. Annual Review of Neuroscience. 36:337-359.

Churchland MM, Cunningham JP, Kaufman MT, Foster JD, Nuyujukian P, Ryu SI, Shenoy KV (2012) Neural population dynamics during reaching. Nature. 487:51-56.

Santhanam G, Yu BM, Gilja V, Afshar A, Ryu SI, Sahani M, Shenoy KV (2009) Factor-analysis methods for higher-performance neural prostheses. Journal of Neurophysiology. 102:1315-1330.

Yu BM, Cunningham JP, Santhanam G, Ryu SI, Shenoy KV, Sahani M (2009) Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity. Journal of Neurophysiology. 102:614-635.