Awesome NeuroAI Papers

A curated list of Papers & Reviews from the intersection of deep learning and neuroscience

This list is providing an overview of recent publications connecting neuroscience & computer science research. As both fields are growing rapidly this list is only presenting a small subset of relevant papers. In case important papers are missing please send a pull request.

Papers

Veerabadran, V., Goldman, J., Shankar, S., Cheung, B., Papernot, N., Kurakin, A., ... & Elsayed, G. F. Subtle adversarial image manipulations influence both human and machine perception Nature Communications (2023)

Bricken, T., Templeton, A., Batson, J., Chen, B., Jermyn, A., Conerly, T., ... & Olah, C. Towards monosemanticity: Decomposing language models with dictionary learning Transformer Circuits Thread (2023)

Spieler, A., Rahaman, N., Martius, G., Schölkopf, B., & Levina, A. The ELM Neuron: an Efficient and Expressive Cortical Neuron Model Can Solve Long-Horizon Tasks arXiv (2023)

Schneider, S., Lee, J. H., & Mathis, M. W. Learnable latent embeddings for joint behavioral and neural analysis arXiv (2022)

Raju, R. V., Guntupalli, J. S., Zhou, G., Lázaro-Gredilla, M., & George, D. Space is a latent sequence: Structured sequence learning as a unified theory of representation in the hippocampus arXiv (2022)

Millet, J., Caucheteux, C., Orhan, P., Boubenec, Y., Gramfort, A., Dunbar, E., ... & King, J. R. Toward a realistic model of speech processing in the brain with self-supervised learning arXiv (2022)

Ivanova, A. A., Schrimpf, M., Anzellotti, S., Zaslavsky, N., Fedorenko, E., & Isik, L. Beyond linear regression: mapping models in cognitive neuroscience should align with research goals arXiv (2022)

Sucevic, J., & Schapiro, A. C. A neural network model of hippocampal contributions to category learning bioRxiv (2022)

Schmidgall, Samuel, and Joe Hays. Learning to learn online with neuromodulated synaptic plasticity in spiking neural networks . bioRxiv (2022)

Adolfi, F., Bowers, J. S., & Poeppel, D. Successes and critical failures of neural networks in capturing human-like speech recognition arXiv (2022)

Bakhtiari, S., Mineault, P., Lillicrap, T., Pack, C., & Richards, B. The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning NeurIPS (2021)

Conwell, C., Mayo, D., Barbu, A., Buice, M., Alvarez, G., & Katz, B. Neural regression, representational similarity, model zoology & neural taskonomy at scale in rodent visual cortex NeurIPS (2021)

Krotov, Dmitry. Hierarchical associative memory arXiv (2021)

Krotov, Dmitry, and John Hopfield. Large associative memory problem in neurobiology and machine learning ICLR (2021)

Whittington, J. C., Warren, J., & Behrens, T. E. Relating transformers to models and neural representations of the hippocampal formation arXiv (2021)

Nonaka, S., Majima, K., Aoki, S. C., & Kamitani, Y. Brain hierarchy score: Which deep neural networks are hierarchically brain-like? IScience (2021)

Schrimpf, M., Blank, I. A., Tuckute, G., Kauf, C., Hosseini, E. A., Kanwisher, N., ... & Fedorenko, E. The neural architecture of language: Integrative modeling converges on predictive processing PNAS (2021)

Liang, Yuchen, Chaitanya K. Ryali, Benjamin Hoover, Leopold Grinberg, Saket Navlakha, Mohammed J. Zaki, and Dmitry Krotov. Can a Fruit Fly Learn Word Embeddings? ICLR (2021)

Liu, Helena Y., Stephen Smith, Stefan Mihalas, Eric Shea-Brown, and Uygar Sümbül Cell-type–specific neuromodulation guides synaptic credit assignment in a spiking neural network. Proceedings of the National Academy of Sciences of the United States of America (2021)

George, D., Rikhye, R. V., Gothoskar, N., Guntupalli, J. S., Dedieu, A., & Lázaro-Gredilla, M. Clone-structured graph representations enable flexible learning and vicarious evaluation of cognitive maps Nature communications (2021)

Whittington, J. C., Muller, T. H., Mark, S., Chen, G., Barry, C., Burgess, N., & Behrens, T. E. The Tolman-Eichenbaum machine: Unifying space and relational memory through generalization in the hippocampal formation Cell (2020)

Banino, A., Badia, A. P., Köster, R., Chadwick, M. J., Zambaldi, V., Hassabis, D. & Blundell, C. Memo: A deep network for flexible combination of episodic memories arXiv (2020)

Chengxu Zhuang, Siming Yan, Aran Nayebi, Martin Schrimpf, Michael C. Frank, James J. DiCarlo, Daniel L. K. Yamins Unsupervised Neural Network Models of the Ventral Visual Stream bioRxiv (2020)

Tyler Bonnen, Daniel L.K. Yaminsa, Anthony D. Wagner When the ventral visual stream is not enough: A deep learning account of medial temporal lobe involvement in perception bioRxiv (2020)

Van de Ven, G. M., Siegelmann, H. T., & Tolias, A. S. Brain-inspired replay for continual learning with artificial neural networks Nature communications (2020)

Kim, K., Sano, M., De Freitas, J., Haber, N., & Yamins, D. Active World Model Learning with Progress Curiosity arXiv (2020)

Guangyu Robert Yang, Xiao-Jing Wang Artificial Neural Networks for Neuroscientists: A Primer Neuron (2020)

Glaser G.I., Benjamin, S.A., Chowdhury, H.R., Perich G.M., Miller, L.E., Kording, K.P. Machine Learning for Neural Decoding eNeuro (2020)

Jones, I. S., & Kording, K. P. Can Single Neurons Solve MNIST? The Computational Power of Biological Dendritic Trees arXiv (2020)

Rolnick, D., & Kording, K. Reverse-engineering deep ReLU networks ICML (2020)

Geirhos, R., Narayanappa, K., Mitzkus, B., Bethge, M., Wichmann, F. A., & Brendel, W. On the surprising similarities between supervised and self-supervised models arXiv (2020)

Storrs, K. R., Kietzmann, T. C., Walther, A., Mehrer, J., & Kriegeskorte, N. Diverse deep neural networks all predict human IT well, after training and fitting bioRxiv (2020)

Yonatan Sanz Perl, Hernán Boccacio, Ignacio Pérez-Ipiña, Federico Zamberlán, Helmut Laufs, Morten Kringelbach, Gustavo Deco, Enzo Tagliazucchi Generative embeddings of brain collective dynamics using variational autoencoders arXiv (2020)

George, D., Lazaro-Gredilla, M., Lehrach, W., Dedieu, A., & Zhou, G. A detailed mathematical theory of thalamic and cortical microcircuits based on inference in a generative vision model bioRxiv (2020)

van Bergen, R. S., & Kriegeskorte, N. Going in circles is the way forward: the role of recurrence in visual inference arXiv (2020)

Joseph G. Makin, David A. Moses, Edward F. Chang Machine translation of cortical activity to text with an encoder–decoder framework Nature Neuroscience (2020)

Richards, B. A., & Lillicrap, T. P. Dendritic solutions to the credit assignment problem Current opinion in neurobiology (2019)

Sinz, F. H., Pitkow, X., Reimer, J., Bethge, M., & Tolias, A. S. Engineering a less artificial intelligence Neuron (2019)

Kubilius, J., Schrimpf, M., Kar, K., Rajalingham, R., Hong, H., Majaj, N. & DiCarlo, J. J. Brain-like object recognition with high-performing shallow recurrent ANNs Advances in Neural Information Processing Systems (2019)

Barrett, D. G., Morcos, A. S., & Macke, J. H. Analyzing biological and artificial neural networks: challenges with opportunities for synergy? Current opinion in neurobiology (2019)

Lillicrap, T. P., & Kording, K. P. What does it mean to understand a neural network? arXiv (2019)

Stringer, C., Pachitariu, M., Steinmetz, N., Carandini, M., & Harris, K. D. High-dimensional geometry of population responses in visual cortex Nature (2019)

Beniaguev David, Segev Idan, London Michael Single Cortical Neurons as Deep Artificial Neural Networks bioRxiv (2019)

Krotov, D. & Hopfield, J.J. Unsupervised learning by competing hidden units PNAS (2019)

Guillaume Bellec, Franz Scherr, Anand Subramoney, Elias Hajek, Darjan Salaj, Robert Legenstein, Wolfgang Maass A solution to the learning dilemma for recurrent 2 networks of spiking neurons bioRxiv (2019)

Albert Gidon, Timothy Adam Zolnik, Pawel Fidzinski, Felix Bolduan, Athanasia Papoutsi, Panayiota Poirazi, Martin Holtkamp, Imre Vida, Matthew Evan Larkum Dendritic action potentials and computation in human layer 2/3 cortical neurons Science (2019)

Adam Gaier, David Ha Weight Agnostic Neural Networks arXiv (2019)

Ben Sorscher, Gabriel C. Mel, Surya Ganguli, Samuel A. Ocko A unified theory for the origin of grid cells through the lens of pattern formation NeurIPS (2019)

Sara Hooker, Aaron Courville, Yann Dauphin, Andrea Frome Selective Brain Damage: Measuring the Disparate Impact of Model Pruning arXiv (2019)

Walker, E. Y., Sinz, F. H., Cobos, E., Muhammad, T., Froudarakis, E., Fahey, P. G. & Tolias, A. S. Inception loops discover what excites neurons most using deep predictive models Nature neuroscience (2019)

Alessio Ansuini, Alessandro Laio, Jakob H. Macke, Davide Zoccolan Intrinsic dimension of data representations in deep neural networks arXiv (2019)

Josh Merel, Diego Aldarondo, Jesse Marshall, Yuval Tassa, Greg Wayne, Bence Ölveczky Deep neuroethology of a virtual rodent arXiv (2019)

Zhe Li, Wieland Brendel, Edgar Y. Walker, Erick Cobos, Taliah Muhammad, Jacob Reimer, Matthias Bethge, Fabian H. Sinz, Xaq Pitkow, Andreas S. Tolias Learning From Brains How to Regularize Machines arXiv (2019)

Hidenori Tanaka, Aran Nayebi, Niru Maheswaranathan, Lane McIntosh, Stephen Baccus, Surya Ganguli From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction NeurIPS (2019)

Stefano Recanatesi, Matthew Farrell ,Guillaume Lajoie, Sophie Deneve, Mattia Rigotti, and Eric Shea-Brown Predictive learning extracts latent space representations from sensory observations BiorXiv (2019)

Nasr, Khaled, Pooja Viswanathan, and Andreas Nieder. Number detectors spontaneously emerge in a deep neural network designed for visual object recognition. Science Advances (2019)

Bashivan, Pouya, Kohitij Kar, and James J. DiCarlo. Neural population control via deep image synthesis. Science (2019)

Ponce, Carlos R., Will Xiao, Peter F. Schade, Till S. Hartmann, Gabriel Kreiman, and Margaret S. Livingstone. Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences Cell (2019)

Kar, Kohitij, Jonas Kubilius, Kailyn M. Schmidt, Elias B. Issa, and James J. DiCarlo. Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior. Nature Neuroscience (2019)

Russin, Jake, Jason Jo, and Randall C. O'Reilly. Compositional generalization in a deep seq2seq model by separating syntax and semantics. arXiv (2019)

Rajalingham, Rishi, Elias B. Issa, Pouya Bashivan, Kohitij Kar, Kailyn Schmidt, and James J. DiCarlo. Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks. Journal of Neuroscience (2018)

Eslami, SM Ali, Danilo Jimenez Rezende, Frederic Besse, Fabio Viola, Ari S. Morcos, Marta Garnelo, Avraham Ruderman et al. Neural scene representation and rendering. Science (2018)

Banino, Andrea, Caswell Barry, Benigno Uria, Charles Blundell, Timothy Lillicrap, Piotr Mirowski, Alexander Pritzel et al. Vector-based navigation using grid-like representations in artificial agents. Nature (2018)

Schrimpf, Martin, Kubilius, Jonas, Hong, Ha, Majaj, Najib J., Rajalingham, Rishi, Issa, Elias B., Kar, Kohitij, Bashivan, Pouya, Prescott-Roy, Jonathan, Geiger, Franziska, Schmidt, Kailyn, Yamins, Daniel L. K., and DiCarlo, James J. Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like? bioRxiv (2018)

Kell, A. J., Yamins, D. L., Shook, E. N., Norman-Haignere, S. V., & McDermott, J. H. A task-optimized neural network replicates human auditory behavior, predicts brain responses, and reveals a cortical processing hierarchy Neuron (2018)

Guerguiev, Jordan, Timothy P. Lillicrap, and Blake A. Richards. Towards deep learning with segregated dendrites. ELife (2017).

Kanitscheider, I., & Fiete, I. Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems arXiv (2017)

George, D., Lehrach, W., Kansky, K., Lázaro-Gredilla, M., Laan, C., Marthi, B., ... & Phoenix, D. S. A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs Science (2017)

DeWolf, T., Stewart, T. C., Slotine, J. J., & Eliasmith, C. A spiking neural model of adaptive arm control Proceedings of the Royal Society B: Biological Sciences, (2016)

Bengio, Yoshua, Dong-Hyun Lee, Jorg Bornschein, Thomas Mesnard, and Zhouhan Lin. Towards biologically plausible deep learning. arXiv (2015).

Güçlü, Umut, and Marcel AJ van Gerven. Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream. Journal of Neuroscience (2015)

Cadieu, Charles F., Ha Hong, Daniel LK Yamins, Nicolas Pinto, Diego Ardila, Ethan A. Solomon, Najib J. Majaj, and James J. DiCarlo. Deep neural networks rival the representation of primate IT cortex for core visual object recognition. PLoS computational biology (2014)

Reviews

Gebicke-Haerter, P. J. The computational power of the human brain Frontiers in Cellular Neuroscience (2023)

Zhao, L., Zhang, L., Wu, Z., Chen, Y., Dai, H., Yu, X., ... & Liu, T. When brain-inspired AI meets AGI arXiv (2023)

Zador, A., Richards, B., Ölveczky, B., Escola, S., Bengio, Y., Boahen, K., ... & Tsao, D. Toward next-generation artificial intelligence: catalyzing the NeuroAI revolution arXiv (2022)

Doerig, A., Sommers, R., Seeliger, K., Richards, B., Ismael, J., Lindsay, G., ... & Kietzmann, T. C. The neuroconnectionist research programme arXiv (2022)

Lindsay, G. W. Convolutional neural networks as a model of the visual system: Past, present, and future arXiv (2021)

Hasselmo, M. E., Alexander, A. S., Hoyland, A., Robinson, J. C., Bezaire, M. J., Chapman, G. W., ... & Dannenberg, H. The Unexplored Territory of Neural Models: Potential Guides for Exploring the Function of Metabotropic Neuromodulation Neuroscience (2021)

Hadsell, R., Rao, D., Rusu, A. A., & Pascanu, R. Embracing change: Continual learning in deep neural networks Trends in cognitive sciences, (2020)

Bermudez-Contreras, E., Clark, B.J., Wilber, A. The Neuroscience of Spatial Navigation and the Relationship to Artificial Intelligence Front. Comput. Neurosci. (2020)

Botvinick, M., Wang, J.X., Dabney, W., Miller, K.J., Kurth-Nelson, Z. Deep Reinforcement Learning and Its Neuroscientific Implications Neuron (2020)

Lillicrap, T.P., Santoro, A., Marris, L., Akerman, C.J. & Hinton, G. Backpropagation and the brain Nature Reviews Neuroscience, (2020)

Saxe, A., Nelli, S. & Summerfield, C. If deep learning is the answer, then what is the question? arXiv, (2020)

Hasson, U., Nastase, S. A., & Goldstein, A. Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks. Neuron (2020)

Schrimpf, M., Kubilius, J., Lee, M. J., Ratan Murty, N. A., Ajemian, R., & DiCarlo, J. J. Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence. Neuron (2020)

Merel, J., Botvinick, M., & Wayne, G. Hierarchical motor control in mammals and machines Nature communications (2019)

Storrs, K. R., & Kriegeskorte, N. Deep learning for cognitive neuroscience. arXiv (2019)

Zador, M.Z. A critique of pure learning and what artificial neural networks can learn from animal brains, Nature Communications, (2019)

Richards, Blake A., Timothy P. Lillicrap, Philippe Beaudoin, Yoshua Bengio, Rafal Bogacz, Amelia Christensen, Claudia Clopath et al. A deep learning framework for neuroscience. Nature neuroscience (2019)

Kietzmann, T. C., McClure, P., & Kriegeskorte, N. (2018). Deep neural networks in computational neuroscience BioRxiv, (2018)

Hassabis, Demis, Dharshan Kumaran, Christopher Summerfield, and Matthew Botvinick. Neuroscience-inspired artificial intelligence. Neuron (2017)

Lake, Brenden M., Tomer D. Ullman, Joshua B. Tenenbaum, and Samuel J. Gershman. Building machines that learn and think like people. Behavioral and brain sciences (2017).

Marblestone, Adam H., Greg Wayne, and Konrad P. Kording. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience (2016)