I attended the Neuromatch Academy Summer School in 2020. There was a lot to learn and some real hours of work that I invested there. To put it simply, it was intense. There was a lot I learned and wanted to take something cool and forward-looking back to share with my uni comrades. At the time, I had already made it a hobby to read new comp-neuro and deep learning papers during the weekend, so I made a reading seminar out of it.
Here’s what we read.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Richards, B. A., Lillicrap, T. P., Beaudoin, P., Bengio, Y., Bogacz, R., Christensen, A., Clopath, C., Costa, R. P., de Berker, A., Ganguli, S., Gillon, C. J., Hafner, D., Kepecs, A., Kriegeskorte, N., Latham, P., Lindsay, G. W., Miller, K. D., Naud, R., Pack, C. C., … Kording, K. P. (2019). A deep learning framework for neuroscience. Nature Neuroscience, 22(11), 1761–1770. https://doi.org/10.1038/s41593-019-0520-2
Saxe, A., Nelli, S., & Summerfield, C. (2021). If deep learning is the answer, what is the question? Nature Reviews. Neuroscience, 22(1), 55–67. https://doi.org/10.1038/s41583-020-00395-8
Lillicrap, T. P., & Santoro, A. (2019). Backpropagation through time and the brain. Current Opinion in Neurobiology, 55, 82–89. https://doi.org/10.1016/j.conb.2019.01.011
Lillicrap, T. P., Santoro, A., Marris, L., Akerman, C. J., & Hinton, G. (2020). Backpropagation and the brain. Nature Reviews. Neuroscience, 21(6), 335–346. https://doi.org/10.1038/s41583-020-0277-3
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533–536. https://doi.org/10.1038/323533a0
Cichy, R. M., Pantazis, D., & Oliva, A. (2014). Resolving human object recognition in space and time. Nature Neuroscience, 17(3), 455–462. https://doi.org/10.1038/nn.3635
Kriegeskorte, N., & Douglas, P. K. (2019). Interpreting encoding and decoding models. Current Opinion in Neurobiology, 55, 167–179. https://doi.org/10.1016/j.conb.2019.04.002
Kriegeskorte, N., & Kievit, R. A. (2013). Representational geometry: integrating cognition, computation, and the brain. Trends in Cognitive Sciences, 17(8), 401–412. https://doi.org/10.1016/j.tics.2013.06.007
Bonner, M. F., & Epstein, R. A. (2018). Computational mechanisms underlying cortical responses to the affordance properties of visual scenes. PLoS Computational Biology, 14(4), e1006111. https://doi.org/10.1371/journal.pcbi.1006111
Cichy, R. M., Khosla, A., Pantazis, D., Torralba, A., & Oliva, A. (2016). Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence. Scientific Reports, 6, 27755. https://doi.org/10.1038/srep27755
Khaligh-Razavi, S.-M., & Kriegeskorte, N. (2014). Deep supervised, but not unsupervised, models may explain IT cortical representation. PLoS Computational Biology, 10(11), e1003915. https://doi.org/10.1371/journal.pcbi.1003915
Pinto, N., Cox, D. D., & DiCarlo, J. J. (2008). Why is real-world visual object recognition hard? PLoS Computational Biology, 4(1), e27. https://doi.org/10.1371/journal.pcbi.0040027
DiCarlo, J. J., Zoccolan, D., & Rust, N. C. (2012). How does the brain solve visual object recognition? Neuron, 73(3), 415–434. https://doi.org/10.1016/j.neuron.2012.01.010
Seeliger, K., Fritsche, M., Güçlü, U., Schoenmakers, S., Schoffelen, J.-M., Bosch, S. E., & van Gerven, M. A. J. (2018). Convolutional neural network-based encoding and decoding of visual object recognition in space and time. NeuroImage, 180(Pt A), 253–266. https://doi.org/10.1016/j.neuroimage.2017.07.018
Wen, H., Shi, J., Chen, W., & Liu, Z. (2018). Deep Residual Network Predicts Cortical Representation and Organization of Visual Features for Rapid Categorization. Scientific Reports, 8(1), 3752. https://doi.org/10.1038/s41598-018-22160-9
Kar, K., Kubilius, J., Schmidt, K., Issa, E. B., & DiCarlo, J. J. (2019). Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior. Nature Neuroscience, 22(6), 974–983. https://doi.org/10.1038/s41593-019-0392-5
Kietzmann, T. C., Spoerer, C. J., Sörensen, L. K. A., Cichy, R. M., Hauk, O., & Kriegeskorte, N. (2019). Recurrence is required to capture the representational dynamics of the human visual system. Proceedings of the National Academy of Sciences of the United States of America, 116(43), 21854–21863. https://doi.org/10.1073/pnas.1905544116
Orhan, A. E., & Ma, W. J. (2019). A diverse range of factors affect the nature of neural representations underlying short-term memory. Nature Neuroscience, 22(2), 275–283. https://doi.org/10.1038/s41593-018-0314-y
Remington, E. D., Narain, D., Hosseini, E. A., & Jazayeri, M. (2018). Flexible Sensorimotor Computations through Rapid Reconfiguration of Cortical Dynamics. Neuron, 98(5), 1005–1019.e5. https://doi.org/10.1016/j.neuron.2018.05.020
Güçlü, U., & van Gerven, M. A. J. (2015). Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 35(27), 10005–10014. https://doi.org/10.1523/JNEUROSCI.5023-14.2015
VanRullen, R., & Reddy, L. (2019). Reconstructing faces from fMRI patterns using deep generative neural networks. Communications Biology, 2, 193.
Wen, H., Shi, J., Zhang, Y., Lu, K.-H., Cao, J., & Liu, Z. (2018). Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision. Cerebral Cortex , 28(12), 4136–4160. https://doi.org/10.1093/cercor/bhx268
Yang, G. R., Joglekar, M. R., Song, H. F., Newsome, W. T., & Wang, X.-J. (2019). Task representations in neural networks trained to perform many cognitive tasks. Nature Neuroscience, 22(2), 297–306. https://doi.org/10.1038/s41593-018-0310-2
Han, K., Wen, H., Shi, J., Lu, K.-H., Zhang, Y., Fu, D., & Liu, Z. (2019). Variational autoencoder: An unsupervised model for encoding and decoding fMRI activity in visual cortex. NeuroImage, 198, 125–136. https://doi.org/10.1016/j.neuroimage.2019.05.039
Mehrer, J., Spoerer, C. J., Kriegeskorte, N., & Kietzmann, T. C. (2020). Individual differences among deep neural network models. Nature Communications, 11(1), 5725. https://doi.org/10.1038/s41467-020-19632-w
Sinz, F. H., Pitkow, X., Reimer, J., Bethge, M., & Tolias, A. S. (2019). Engineering a Less Artificial Intelligence. Neuron, 103(6), 967–979. https://doi.org/10.1016/j.neuron.2019.08.034
Hebart, M. N., Bankson, B. B., Harel, A., Baker, C. I., & Cichy, R. M. (2018). The representational dynamics of task and object processing in humans. eLife, 7. https://doi.org/10.7554/eLife.32816
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, 13276. https://doi.org/10.1038/ncomms13276
Spoerer, C. J., Kietzmann, T. C., Mehrer, J., Charest, I., & Kriegeskorte, N. (2020). Recurrent neural networks can explain flexible trading of speed and accuracy in biological vision. PLoS Computational Biology, 16(10), e1008215. https://doi.org/10.1371/journal.pcbi.1008215
Botvinick, M., Ritter, S., Wang, J. X., Kurth-Nelson, Z., Blundell, C., & Hassabis, D. (2019). Reinforcement Learning, Fast and Slow. Trends in Cognitive Sciences, 23(5), 408–422. https://doi.org/10.1016/j.tics.2019.02.006
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. https://doi.org/10.1016/j.neuron.2020.06.014
Urbanczik, R., & Senn, W. (2009). Reinforcement learning in populations of spiking neurons. Nature Neuroscience, 12(3), 250–252. https://doi.org/10.1038/nn.2264
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