Schedule

Reading List

1. [Modeling pyramidal neurons]
   Poirazi, P., Brannon, T., & Mel, B. W. (2003). Pyramidal neuron as two-layer neural network. Neuron, 37(6), 989-999.
   Beniaguev, D., Segev, I., & London, M. (2021). Single cortical neurons as deep artificial neural networks. Neuron, 109(17), 2727-2739.
2. [Understanding brains, microprocessors and radios]
   Lazebnik, Y. (2002). Can a biologist fix a radio?—Or, what I learned while studying apoptosis. Cancer cell, 2(3), 179-182.
   Jonas, E., & Kording, K. P. (2017). Could a neuroscientist understand a microprocessor?. PLoS computational biology, 13(1), e1005268.
   Fakhar, K., & Hilgetag, C. C. (2021). Systematic Perturbation of an Artificial Neural Network: A Step Towards Quantifying Causal Contributions in The Brain. bioRxiv.
3. [Learning in the brain without exact error-backpropagation]
   Lillicrap, T. P., Cownden, D., Tweed, D. B., & Akerman, C. J. (2016). Random synaptic feedback weights support error backpropagation for deep learning. Nature communications, 7(1), 1-10.
   Guerguiev, J., Lillicrap, T. P., & Richards, B. A. (2017). Towards deep learning with segregated dendrites. ELife, 6, e22901.
4. [Understanding aspects of Area V4 of the visual cortex via Deep Net models]
   Pospisil, D. A., Pasupathy, A., & Bair, W. (2018). 'Artiphysiology' reveals V4-like shape tuning in a deep network trained for image classification. Elife, 7, e38242.
   Bashivan, P., Kar, K., & DiCarlo, J. J. (2019). Neural population control via deep image synthesis. Science, 364(6439), eaav9436.
5. [Spatial Navigation in Bats]
   Ginosar, G., Aljadeff, J., Burak, Y., Sompolinsky, H., Las, L., & Ulanovsky, N. (2021). Locally ordered representation of 3D space in the entorhinal cortex. Nature, 596(7872), 404-409.
   Eliav, T., Maimon, S. R., Aljadeff, J., Tsodyks, M., Ginosar, G., Las, L., & Ulanovsky, N. (2021). Multiscale representation of very large environments in the hippocampus of flying bats. Science, 372(6545), eabg4020.
6. [Grid Cells & Toroidal manifolds]
   Gardner, R. J., Hermansen, E., Pachitariu, M., Burak, Y., Baas, N. A., Dunn, B. A., ... & Moser, E. I. (2022). Toroidal topology of population activity in grid cells. Nature, 1-6.
   
7. [Neuromodulation in Deep Learning]
   Mei, J., Muller, E., & Ramaswamy, S. (2022). Informing deep neural networks by multiscale principles of neuromodulatory systems. Trends in Neurosciences.
   ¸ð Miconi, T., Rawal, A., Clune, J., & Stanley, K. O. (2020). Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585.
8. [Psychedelics & their neural correlates]
   Kelmendi, B., Kaye, A. P., Pittenger, C., & Kwan, A. C. (2022). Psychedelics. Current Biology, 32(2), R63-R67.
   Carhart-Harris, R. L., Erritzoe, D., Williams, T., Stone, J. M., Reed, L. J., Colasanti, A., ... & Nutt, D. J. (2012). Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin. Proceedings of the National Academy of Sciences, 109(6), 2138-2143.
9. [Hypotheses on correlates of consciousness & dreams]
   Crick, F., & Koch, C. (2003). A framework for consciousness. Nature neuroscience, 6(2), 119-126.
   Aru, J., Suzuki, M., Rutiku, R., Larkum, M. E., & Bachmann, T. (2019). Coupling the state and contents of consciousness. Frontiers in Systems Neuroscience, 43.
   Aru, J., Siclari, F., Phillips, W. A., & Storm, J. F. (2020). Apical drive—A cellular mechanism of dreaming?. Neuroscience & Biobehavioral Reviews, 119, 440-455.
10. [Manifold geometry in Deep Nets & Brains]
    Cohen, U., Chung, S., Lee, D. D., & Sompolinsky, H. (2020). Separability and geometry of object manifolds in deep neural networks. Nature communications, 11(1), 1-13.
    Froudarakis, E., Cohen, U., Diamantaki, M., Walker, E. Y., Reimer, J., Berens, P., ... & Tolias, A. S. (2020). Object manifold geometry across the mouse cortical visual hierarchy. bioRxiv.
11. [Manifolds, Motor learning & Brain-computer interfaces]
    Gallego, J. A., Perich, M. G., Miller, L. E., & Solla, S. A. (2017). Neural manifolds for the control of movement. Neuron, 94(5), 978-984.
    Shenoy, K. V., & Carmena, J. M. (2014). Combining decoder design and neural adaptation in brain-machine interfaces. Neuron, 84(4), 665-680.
    Sadtler, P. T., Quick, K. M., Golub, M. D., Chase, S. M., Ryu, S. I., Tyler-Kabara, E. C., ... & Batista, A. P. (2014). Neural constraints on learning. Nature, 512(7515), 423-426.
12. [Brain-inspired Computer algorithms 1]
    Dasgupta, S., Stevens, C. F., & Navlakha, S. (2017). A neural algorithm for a fundamental computing problem. Science, 358(6364), 793-796.
    Dasgupta, S., Sheehan, T. C., Stevens, C. F., & Navlakha, S. (2018). A neural data structure for novelty detection. Proceedings of the National Academy of Sciences, 115(51), 13093-13098.
13. [Brain-inspired Computer algorithms 2]
    Shen, Y., Dasgupta, S., & Navlakha, S. (2020). Habituation as a neural algorithm for online odor discrimination. Proceedings of the National Academy of Sciences, 117(22), 12402-12410.
    Shen, Y., Dasgupta, S., & Navlakha, S. (2021). Algorithmic insights on continual learning from fruit flies. arXiv preprint arXiv:2107.07617.
    
14. [Industry-driven Brain-Computer Interface efforts]
    Musk, E., Neuralink (2019). An integrated brain-machine interface platform with thousands of channels. Journal of medical Internet research, 21(10), e16194.
    Makin, J. G., Moses, D. A., & Chang, E. F. (2020). Machine translation of cortical activity to text with an encoder–decoder framework. Nature neuroscience, 23(4), 575-582. See also this Facebook Reality Labs blog post.
15. [Drift in representations of stimuli/percepts and how the brain might operate properly in spite of them]
    Marks, T. D., & Goard, M. J. (2021). Stimulus-dependent representational drift in primary visual cortex. Nature communications, 12(1), 1-16.
    Michael E. Rule, Timothy O’Leary (2022) Self-healing codes: How stable neural populations can track continually reconfiguring neural representations. Proceedings of the National Academy of Sciences Feb 2022, 119 (7) e2106692119; DOI: 10.1073/pnas.2106692119.
16. [Locomotion and vision]
    Christensen, A. J., & Pillow, J. W. (2017). Running reduces firing but improves coding in rodent higher-order visual cortex. bioRxiv, 214007.
    Dipoppa, M., Ranson, A., Krumin, M., Pachitariu, M., Carandini, M., & Harris, K. D. (2018). Vision and locomotion shape the interactions between neuron types in mouse visual cortex. Neuron, 98(3), 602-615.
17. [Retina, deep net models and understanding]
    Gollisch, T., & Meister, M. (2010). Eye smarter than scientists believed: neural computations in circuits of the retina. Neuron, 65(2), 150-164.
    Tanaka, H., Nayebi, A., Maheswaranathan, N., McIntosh, L., Baccus, S., & Ganguli, S. (2019). From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction. Advances in neural information processing systems, 32.
18. [On face recognition in the primate brain]
    Chang, L., & Tsao, D. Y. (2017). The code for facial identity in the primate brain. Cell, 169(6), 1013-1028.
    Higgins, I., Chang, L., Langston, V., Hassabis, D., Summerfield, C., Tsao, D., & Botvinick, M. (2021). Unsupervised deep learning identifies semantic disentanglement in single inferotemporal face patch neurons. Nature communications, 12(1), 1-14.
19. [Sound localization in owls, humans and models]
    Carr, C. E., & Konishi, M. (1990). A circuit for detection of interaural time differences in the brain stem of the barn owl. Journal of Neuroscience, 10(10), 3227-3246.
    Rayleigh, L. (1907). XII. On our perception of sound direction. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 13(74), 214-232.
    Francl, A., & McDermott, J. H. (2022). Deep neural network models of sound localization reveal how perception is adapted to real-world environments. Nature Human Behaviour, 6(1), 111-133.