research
Resources
RNNs in Neuroscience
In February 2021, I had the honor of hosting the COSYNE (Computational and Systems Neuroscience) Tutorial on recurrent neural network models in neuroscience. Given the virtual format of the meeting, I’m pleased to be able to make the materials accessible to a larger community of learners.
lecture materials
Both lectures are available on the COSYNE YouTube channel (see lecture title links) under a Creative Commons license. To request access to the lecture slides, please email: kanaka_rajan@hms.harvard.edu & kanaka-admin@stellatecomms.com
Foundational elements of recurrent neural network (RNN) models
Dr. Rajan covered the basic design elements (“building blocks”) of neural network models, the role of recurrent connections, linear and non-linear activity, types of time-varying activity (“dynamics”) produced by RNNs, input-driven vs. spontaneous dynamics, etc. The main goal of this lecture is to look at both the tractability and computational power of RNN models, and to appreciate why they have become such a crucial part of the neuroscientific arsenal.
Applications of RNNs in the field of neuroscience
Dr. Rajan reviewed some of the ways in which RNNs have been applied in neuroscience to leverage existing experimental data, to infer mechanisms inaccessible from measurements alone, and to make predictions that guide experimental design. The main goal of this lecture is to appreciate what sorts of insight can be gained by "training RNNs to do something” in a manner consistent with experimental data collected from the biological brain.
problem set
If you’d like to deepen your understanding of recurrent neural networks, I encourage you to complete a problem set created in collaboration with the COSYNE Tutorial TAs. The problem set has detailed instructions and questions to work through. Problems 1 and 2 are intermediate and should be done after watching Lecture 1; Problem 3 is advanced and should be done after watching Lecture 2. Solutions are available in Julia, MATLAB, and Python.
Suggested process for beginners:
Make a personal copy of the Solution Scripts. Read through the script (with solutions) and annotate it with questions and/or your understanding of the process. Attempt to solve Problems 1 and 2 using the solutions as a scaffold.
Suggested process for advanced students or for use in a class setting:
Make a personal copy of the Solution Scripts. Delete the provided solutions and then work through the problems in your own copy. Once you've completed the problems, compare your answers against the provided solutions to check your work.
Solution Scripts
- Julia solution script
- MATLAB solution scripts
- Python:
teaching assistants
Thank you to the fearless group of TAs who helped shape the tutorial with their hard work and quick intellect.
references
Botvinick, M., Wang, J. X., Dabney, W., Miller, K. J., & Kurth-Nelson, Z. (2020). Deep Reinforcement Learning and Its Neuroscientific Implications. Neuron, 107(4), 603–616. doi:10.1016/j.neuron.2020.06.014
Gallego, J. A., Perich, M. G., Chowdhury, R. H., Solla, S. A., & Miller, L. E. (2020). Long-term stability of cortical population dynamics underlying consistent behavior. Nature Neuroscience, 23(2), 260–270. doi:10.1038/s41593-019-0555-4
Lindsay, G. W. (2020). Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future. Journal of Cognitive Neuroscience, 1–15. doi:10.1162/jocn_a_01544
Perich, M. G., & Rajan, K. (2020). Rethinking brain-wide interactions through multi-region “network of networks” models. Current Opinion in Neurobiology, 65, 146–151. doi:10.1016/j.conb.2020.11.003
Perich, M. G., Arlt, C., Soares, S., Young, M. E., Mosher, C. P., Minxha, J., et al. (2020). Inferring brain-wide interactions using data-constrained recurrent neural network models. doi:10.1101/2020.12.18.423348
McClelland, J. L., & Botvinick, M. M. (2020, November 9). Deep Learning: Implications for Human Learning and Memory. doi:10.31234/osf.io/3m5sb
Yang, G. R., & Wang, X.-J. (2020). Artificial Neural Networks for Neuroscientists: A Primer. Neuron, 107(6), 1048–1070. doi:10.1016/j.neuron.2020.09.005
Young, M. E., Spencer-Salmon, C., Mosher, C., Tamang, S., Rajan, K., & Rudebeck, P. H. (2020). Temporally-specific sequences of neural activity across interconnected corticolimbic structures during reward anticipation. doi:10.1101/2020.12.17.423162
Insanally, M. N., Carcea, I., Field, R. E., Rodgers, C. C., DePasquale, B., Rajan, K., … Froemke, R. C. (2019). Spike-timing-dependent ensemble encoding by non-classically responsive cortical neurons. eLife, 8. doi:10.7554/elife.42409
Andalman, A. S., Burns, V. M., Lovett-Barron, M., Broxton, M., Poole, B., Yang, S. J., et al. (2019). Neuronal Dynamics Regulating Brain and Behavioral State Transitions. Cell, 177(4), 970–985.e20. doi:10.1016/j.cell.2019.02.037
Pinto, L., Rajan, K., DePasquale, B., Thiberge, S. Y., Tank, D. W., & Brody, C. D. (2019). Task-Dependent Changes in the Large-Scale Dynamics and Necessity of Cortical Regions. Neuron, 104(4), 810–824.e9. doi:10.1016/j.neuron.2019.08.025
Oby, E. R., Golub, M. D., Hennig, J. A., Degenhart, A. D., Tyler-Kabara, E. C., Yu, B. M. et al. (2019). New neural activity patterns emerge with long-term learning. Proceedings of the National Academy of Sciences, 116(30), 15210–15215. doi:10.1073/pnas.1820296116
Yang, G. R., Cole, M. W., & Rajan, K. (2019). How to study the neural mechanisms of multiple tasks. Current Opinion in Behavioral Sciences, 29, 134–143. doi:10.1016/j.cobeha.2019.07.001
Semedo, J. D., Zandvakili, A., Machens, C. K., Yu, B. M., & Kohn, A. (2019). Cortical Areas Interact through a Communication Subspace. Neuron, 102(1), 249–259.e4. doi:10.1016/j.neuron.2019.01.026
Gallego, J. A., Perich, M. G., Naufel, S. N., Ethier, C., Solla, S. A., & Miller, L. E. (2018). Cortical population activity within a preserved neural manifold underlies multiple motor behaviors. Nature Communications, 9(1). doi:10.1038/s41467-018-06560-z
Perich, M. G., Gallego, J. A., & Miller, L. E. (2018). A Neural Population Mechanism for Rapid Learning. Neuron, 100(4), 964–976.e7. doi:10.1016/j.neuron.2018.09.030
Mastrogiuseppe, F., & Ostojic, S. (2018). Linking Connectivity, Dynamics, and Computations in Low-Rank Recurrent Neural Networks. Neuron, 99(3), 609–623.e29. doi:10.1016/j.neuron.2018.07.003
Schuecker, J., Goedeke, S., & Helias, M. (2018). Optimal Sequence Memory in Driven Random Networks. Physical Review X, 8(4). doi:10.1103/physrevx.8.041029
Barak, O. (2017). Recurrent neural networks as versatile tools of neuroscience research. Current Opinion in Neurobiology, 46, 1–6. doi:10.1016/j.conb.2017.06.003
Marblestone, A. H., Wayne, G., & Kording, K. P. (2016). Toward an Integration of Deep Learning and Neuroscience. Frontiers in Computational Neuroscience. doi:10.3389/fncom.2016.00094
Rajan, K., Harvey, C. D., & Tank, D. W. (2016). Recurrent Network Models of Sequence Generation and Memory. Neuron, 90(1), 128–142. doi:10.1016/j.neuron.2016.02.009
Kriegeskorte, N. (2015). Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing. Annual Review of Vision Science, 1(1), 417–446. doi:10.1146/annurev-vision-082114-035447
Kaufman, M. T., Churchland, M. M., Ryu, S. I., & Shenoy, K. V. (2014). Cortical activity in the null space: permitting preparation without movement. Nature Neuroscience, 17(3), 440–448. doi:10.1038/nn.3643
Sadtler, P. T., Quick, K. M., Golub, M. D., Chase, S. M., Ryu, S. I., Tyler-Kabara, E. C. et al. (2014). Neural constraints on learning. Nature, 512(7515), 423–426. doi:10.1038/nature13665
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
Abbott, L. F., Rajan, K. & Sompolinsky, H. (2011). Interactions between Intrinsic and Stimulus-Evoked Activity in Recurrent Neural Networks. The Dynamic Brain, 65–82.
Rajan, K., Abbott, L. F., & Sompolinsky, H. (2010). Stimulus-dependent suppression of intrinsic variability in recurrent neural networks. BMC Neuroscience, 11(S1). doi:10.1186/1471-2202-11-s1-o17
Rajan, K., Abbott, L. F., & Sompolinsky, H. (2010). Stimulus-dependent suppression of chaos in recurrent neural networks. Physical Review E, 82(1). doi:10.1103/physreve.82.011903
Tao, T., & Vu, V. (2010). Random Matrices: the Distribution of the Smallest Singular Values. Geometric and Functional Analysis, 20(1), 260–297. doi:10.1007/s00039-010-0057-8
Yu, B. M., Cunningham, J. P., Santhanam, G., Ryu, S. I., Shenoy, K. V., & Sahani, M. (2009). Gaussian-Process Factor Analysis for Low-Dimensional Single-Trial Analysis of Neural Population Activity. Journal of Neurophysiology, 102(1), 614–635. doi:10.1152/jn.90941.2008
Kriegeskorte, N. (2008). Representational similarity analysis – connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience. doi:10.3389/neuro.06.004.2008
Rajan, K., & Abbott, L. F. (2006). Eigenvalue Spectra of Random Matrices for Neural Networks. Physical Review Letters, 97(18). doi:10.1103/physrevlett.97.188104
Vogels, T. P., Rajan, K., & Abbott, L. F. (2005). Neural network dynamics. Annual Review of Neuroscience, 28(1), 357–376. doi:10.1146/annurev.neuro.28.061604.135637
Stopfer, M., Jayaraman, V., & Laurent, G. (2003). Intensity versus Identity Coding in an Olfactory System. Neuron, 39(6), 991–1004. doi:10.1016/j.neuron.2003.08.011
Felleman, D.J., Van Essen, D. C. (1991). Distributed hierarchical processing in the primate cerebral cortex. Cereb Cortex, 1(1),1-47. doi: 10.1093/cercor/1.1.1. PMID: 1822724.
Sompolinsky, H., Crisanti, A., & Sommers, H. J. (1988). Chaos in Random Neural Networks. Physical Review Letters, 61(3), 259–262. doi:10.1103/physrevlett.61.259
Sommers, H. J., Crisanti, A., Sompolinsky, H., & Stein, Y. (1988). Spectrum of Large Random Asymmetric Matrices. Physical Review Letters, 60(19), 1895–1898. doi:10.1103/physrevlett.60.1895
Girko, V. L. (1985). Spectral theory of random matrices. Russian Mathematical Surveys, 40(1), 77–120. doi:10.1070/rm1985v040n01abeh003528
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