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2024
2024
CJ Gillon, JE Pina, JA Lecoq, R Ahmed, YN Billeh, S Caldejon, P Groblewski, T M Henley, I Kato, E Lee, J Luviano, K Mace, C Nayan, TV Nguyen, K North, J Perkins, S Seid, MT Valley, A Williford, Y Bengio, TP Lillicrap, BA Richards, and J Zylberberg (2024). “ Responses to pattern-violating visual stimuli evolve differently over days in somata and distal apical dendrites “ https://www.jneurosci.org/content/44/5/e1009232023
M Panniello, CJ Gillon, R Maffulli, M Celotto, BA Richards, S Panzeri, MM Kohl. “Stimulus information guides the emergence of behavior-related signals in primary somatosensory cortex during learning ” (2024)., https://doi.org/10.1523/jneurosci.1009-23.2023. (PDF)
AJ Mocle, AI Ramsaran, AD Jacob, AJ Rashid, A Luchetti, LM Tran, BA Richards, PW Frankland, SA Josselyn. “ Excitability mediates allocation of pre-configured ensembles to a hippocampal engram supporting contextual conditioned threat in mice.” Neuron, 112(9), 1487-1497. (2024)., https://doi.org/10.1016/j.neuron.2024.02.007
R Naud, X Wang, Z Friedenberger, A Payeur, J N Shin, J-C Béïque, BA Richards, M Drüke, ME Larkum, G Doron. “ Fast burst fraction transients convey information independent of the firing rate. ” eLife, 13 (2024) https://doi.org/10.7554/eLife.98290.1
A Ghosh, KK Agrawal, S Sodhani, A Oberman, and BA Richards “ Harnessing small projectors and multiple views for efficient vision pretraining” https://openreview.net/forum?id=Y5DPSJzpra (2024).NeurIPS 37.
Y Zhang, Y Wang, DM Jiménez-Benetó, Z Wang, M Azabou, BA Richards, R Tung, O Winter, EL Dyer, L Paninski, and CL Hurwitz. “ Towards a "Universal Translator" for Neural Dynamics at Single-Cell, Single-Spike Resolution” https://openreview.net/forum?id=nRRJsDahEg NeurIPS 37.(2024)
R Chua, A Ghosh, C Kaplanis, BA Richards, and Doina Precup “ Learning Successor Features the Simple Way” https://openreview.net/forum?id=rI7oZj1WMc (2024) NeurIPS 37.
R Pogodin, J Cornford, A Ghosh, G Gidel, G Lajoie, BA Richards. “ Synaptic weight distributions depend on the geometry of plasticity. ” https://openreview.net/forum?id=x5txICnnjC ICLR 2024 (spotlight)
NH Krishna, C Bredenberg, D Levenstein, BA Richards, G Lajoie. “ Sufficient conditions for offline reactivation in recurrent neural networks” https://openreview.net/forum?id=RVrINT6MT7 ICLR 2024.
2023
2023
Doerig, Adrien, et al. “The Neuroconnectionist Research Programme.” Nature Reviews Neuroscience, vol. 24, no. 7, 30 May 2023, pp. 431–450, https://doi.org/10.1038/s41583-023-00705-w. (PDF)
Gillon, Colleen J., Jason E. Pina, et al. “Responses to pattern-violating visual stimuli evolve differently over days in somata and distal apical dendrites.” The Journal of Neuroscience, vol. 44, no. 5, 21 Nov. 2023, https://doi.org/10.1523/jneurosci.1009-23.2023. (PDF)
Gillon, Colleen J., Jérôme A. Lecoq, et al. “Responses of pyramidal cell somata and apical dendrites in mouse visual cortex over multiple days.” Scientific Data, vol. 10, no. 1, 17 May 2023, https://doi.org/10.1038/s41597-023-02214-y. (PDF)
Kalajdzievski, Damjan, et al. Transfer Entropy Bottleneck: Learning Sequence to Sequence Information Transfer, 2023. (Openreview || PDF)
Lin, Dongyan, et al. “Temporal encoding in deep reinforcement learning agents.” Scientific Reports, vol. 13, no. 1, 15 Dec. 2023, https://doi.org/10.1038/s41598-023-49847-y. (PDF)
Richards, Blake Aaron, and Konrad Paul Kording. “The study of plasticity has always been about gradients.” The Journal of Physiology, vol. 601, no. 15, May 2023, pp. 3141–3149, https://doi.org/10.1113/jp282747. (PDF)
Zador, Anthony, et al. “Catalyzing next-generation artificial intelligence through neuroai.” Nature Communications, vol. 14, no. 1, 22 Mar. 2023, https://doi.org/10.1038/s41467-023-37180-x. (PDF)
P Li, J Cornford, A Ghosh, BA Richards. “Learning better with Dale’s Law: A Spectral Perspective” . https://proceedings.neurips.cc/paper_files/paper/2023/hash/02dd0db10c40092de3d9ec2508d12f60-Abstract-Conference.html (2023) NeurIPS 36.
M Azabou, V Arora, V Ganesh, X Mao, S Nachimuthu, M Mendelson, BA Richards, M Perich, G Lajoie, E Dyer. “A unified, scalable framework for neural population decoding". https://proceedings.neurips.cc/paper_files/paper/2023/hash/8ca113d122584f12a6727341aaf58887-Abstract-Conference.html NeurIPS 36. (2023)
C Sun, W Yang, T Jiralerspong, D Malenfant, B Alsbury-Nealy, Y Bengio, BA Richards. "Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RL.". https://proceedings.neurips.cc/paper_files/paper/2023/hash/6357d6d068622c962391081d296bed69-Abstract-Conference.html NeurIPS 36.(2023)
C Bredenberg, E Williams, C Savin, BA Richards, G Lajoie (2023). “ Formalizing locality for normative synaptic plasticity models ”.
https://proceedings.neurips.cc/paper_files/paper/2023/hash/120339238f293d4ae53a7167403abc4b-Abstract-Conference.html NeurIPS 36. (2023)A Ghosh, YH Liu, G Lajoi, KP Körding, and BA Richards. “ How gradient estimator variance and bias impact learning in neural networks.” https://openreview.net/forum?id=EBC60mxBwyw ICLR 2023.
2022
2022
Abramson, J., Ahuja, A., Carnevale, F., Georgiev, P., Goldin, A., Hung, A., Landon, J., Lillicrap, T., Muldal A., Richards, B. A., Santoro, A., Glehn, T von., Wayne, G., Wong, N., & Chen, Y. (2022). Evaluating multimodal interactive agents. Submitted to NeurIPS and available at arXiv. (PDF)
Ernoult, M., Normandin, F., Moudgil, A., Spinney, S., Belilovsky, E., Rish, I., Richards, B. A., & Bengio, Y. (2022). Towards Scaling Difference Target Propagation by Learning Backprop Targets. Submitted to ICML, and available at arXiv. (PDF)
Ghosh, A., Mondal, A. K., Agrawal., & Richards, B. A. (2022). Investigating Power laws in Deep Representation Learning. Submitted to ICML, and available at arXiv. (PDF)
GX-Chen, A., Chelu, V., Richards, B. A., & Pineau, J. (2022). A generalized bootstrap target for value-learning, efficiently combining value and feature predictions. Proceedings of the AAAI Conference on Artificial Intelligence, 36(6), 6829–6837. https://doi.org/10.1609/aaai.v36i6.20639 (PDF)
Kerg, G., Mittal, S., Rolnick, D., Bengio, Y., Richards, B. A., & Lajoie, G. (2022). Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules. Submitted to NeurIPS and available at arXiv. (PDF)
Liu, Y. H., Ghosh, A., Richards, B. A., Shea-Brown, E., & Lajoie, G. (2022). Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules. Submitted to NeurIPS and available at arXiv. (PDF)
Richards, B. A., & Lillicrap, T. P. (2022). The brain-computer metaphor debate is useless: A matter of semantics. Frontiers in Computer Science, 4. https://doi.org/10.3389/fcomp.2022.810358 (PDF)
Richards, B. A., Tsao, D., & Zador, A. (2022). The application of artificial intelligence to biology and neuroscience. Cell, 185(15), 2640–2643. https://doi.org/10.1016/j.cell.2022.06.047 (PDF)
Tran, L. M., Santoro, A., Liu, L., Josselyn, S. A., Richards, B. A., & Frankland, P. W. (2022). Can neurogenesis act as a regularizer? Submitted to PNAS and available at bioRxiv. (PDF)
Yalnizyan-Carson, A., & Richards, B. A. (2022). Forgetting enhances episodic control with Structured Memories. Frontiers in Computational Neuroscience, 16. https://doi.org/10.3389/fncom.2022.757244 (PDF)
2021
2021
Bakhtiari, S., Mineault, P., Lillicrap, T., Pack, C. C., & Richards, B. A. (2021). The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning. NeurIPS. Accepted as a spotlight at NeurIPS. (Available on bioRxiv) (PDF)
Bashivan, P., Bayat, R., Ibrahim, A., Ahuja, K., Faramarzi, M., Laleh, T., Richards, B. A. & Rish, I. (2021). Adversarial feature desensitization. Accepted at NeurIPS. (Available on arXiv) (PDF)
Gillon, C. J., Pina, J. E., Lecoq, J. A., Ahmed, R., Billeh, Y. N., Caldejon, S., Groblewski, P., Henley, T. M., Kato, I., Lee, E., Luviano, J., Mace, K., Nayan, C., Nguyen, T. V., North, K., Perkins, J., Seid, S., Valley, M. T., Williford, A., Bengio, Y., Lillicrap, T. P., Richards, B. A., & Zylberberg, J. (2021). Learning from unexpected events in the neocortical microcircuit. (Available on bioRxiv) (PDF)
Cornford, J., Kalajdzievski, D., Leite, M., Lamarquette, A., Kullmann, D. M., & Richards, B. A. (2021). Learning to live with Dale's principle: ANNs with separate excitatory and inhibitory units. In International Conference on Learning Representations 2021. (Available on Openreview) (PDF)
Eyono, R. H., Boven, E., Ghosh, A., Pemberton, J., Scherr, F., Clopath, C., Ponte Costa, R., Maass, W., Richards, B. A., Savin, C., Wilmes, K., & Prince, L. Y. (2021). Issues with learning in biological recurrent neural networks. NBDT: https://nbdt.scholasticahq.com/article/35302 (PDF)
Harkin, E. F., Shen, P. R., Goel, A., Richards, B. A., & Naud, R. (2021). Parallel and Recurrent Cascade Models as a Unifying Force for Understanding Subcellular Computation. Published on Neuroscience. (HTML) (PDF)
Inayat, M., Cruz-Sanchez, A., Thorpe, H. H., Frie, J. A., Richards, B. A., Khokhar, J. Y., & Arruda-Carvalho, M. (2021). Promoting and optimizing the use of 3D-printed objects in spontaneous recognition memory tasks in rodents: A method for improving rigor and reproducibility. Published on Eneuro, 8(5). (HTML) (PDF)
Lin, D., Huang, A. & Richards, B. A. (2021). Time cell encoding in deep reinforcement learning agents depends on mnemonic demands. Submitted to Journal of Neuroscience. (Available on bioRxiv) (PDF)
Mineault, P. J., Bakhtiari, S., Richards, B. A., & Pack, C. C. (2021). Your head is there to move you around: Goal-driven models of the primate dorsal pathway. Accepted as a spotlight at NeurIPS. (Available on BioRxiv) (PDF)
Payeur, A., Guerguiev, J., Zenke, F. et al. Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits. Published in Nature Neuroscience (2021). (HTML) (PDF)
Prince, L. Y., Bakhtiari, S., Gillon, C. J., & Richards, B. A. (2021). Parallel inference of hierarchical latent dynamics in two-photon calcium imaging of neuronal populations. Invited for resubmission to Patterns. (Available on BioRxiv) (PDF)
Prince, L. Y., Boven, E., Eyono, R. H., Ghosh, A., Pemberton, J., Scherr, F., Clopath, C., Costa, R. P., Maass, W., Richards, B. A., Savin, C. & Wilmes & K. A.(2021) Issues with learning in biological recurrent neural networks. Accepted at NBDT. (Available at arXiv) (PDF)
Prince, L. Y., Tran, M. M., Grey, D., Saad, L., Chasiotis, H., Kwag, J., Kohl, M. M., & Richards, B. A. (2021). Neocortical inhibitory interneuron subtypes are differentially attuned to synchrony- and rate-coded information. Published on Communications Biology, 4(1). (HTML) (PDF)
Roy, N., Posner, I., Barfoot, Beaudoin, P., Bengio, Y., Bohg, J., Brock, O., Depatie, I., Fox, D., Koditschek, D., Lozano-Perez, T., Mansinghka, V., Pal, C., Richards, B. A., Sadigh, D., Schaal, S., Sukhatme, G., Therien, D., Toussaint, M., & Van de Panne, M., (2021). From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence. Submitted to Journal of Machine Learning Research and available at arXiv. (PDF)
Suárez, L. E., Richards, B. A., Lajoie, G., & Misic, B. (2021). Learning function from structure in neuromorphic networks. Published on Nature Machine Intelligence, 3(9), 771–786. (HTML) (PDF)
Tessier-Larivière, O., Prince, L. Y., Fortier-Poisson, P., Wernisch, L., Armitage, O., Hewage, E., Lajoie, G., & Richards, B. A. (2021). PNS-GAN: Conditional generation of peripheral nerve signals in the wavelet domain via adversarial networks. In 10th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 778-782. IEEE, 2021. (HTML) (PDF)
2020
2020
Berens, S.C., Richards, B.A. & Horner, A.J. Dissociating memory accessibility and precision in forgetting. Published on Nature Human Behavior 4, 866–877 (2020). (HTML) (PDF)
Guerguiev, J., Körding, K.P., & Richards, B.A. (2020). Spike-based causal inference for weight alignment. ICLR 2020: https://openreview.net/forum?id=rJxWxxSYvB. (Available on arXiv) (PDF)
Jang, H. J., Chung, H., Rowland, J. M., Richards, B. A., Kohl, M. M., & Kwag, J. (2020). Distinct roles of parvalbumin and somatostatin interneurons in gating the synchronization of spike times in the neocortex. Published on Science advances, 6(17), eaay5333. (HTML) (PDF)
Sathiyakumar, S., Carrasco, S. S., Saad, L., & Richards, B. A. (2020). Systems consolidation impairs behavioral flexibility. Published on Learning & Memory, 27(5), 201–208. (HTML) (PDF)
Schulz, M. A., Yeo, B. T. T., Vogelstein, J. T., Mourao-Miranada, J., Kather, J. N., Kording, K., Richards, B., & Bzdok, D. (2020). Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets. Published on Nature Communications, 11(1). (HTML) (PDF)
2019
2019
Park, K., Lee, J., Jang, H.J. et al. (2019). Optogenetic activation of parvalbumin and somatostatin interneurons selectively restores theta-nested gamma oscillations and oscillation-induced spike timing-dependent long-term potentiation impaired by amyloid β oligomers. BMC Biol 18, 7 (2020). (HTML) (PDF)
Richards B.A., Lillicrap T.P.. Dendritic solutions to the credit assignment problem. Current Opinion Neurobiology. 2019 Feb;54:28-36. (HTML) (PDF)
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. (HTML) (PDF)
Tran LM, Josselyn SA, Richards BA, Frankland PW. (2019). Forgetting at biologically realistic levels of neurogenesis in a large-scale hippocampal model. Behavioural Brain Research. 2019 Dec 30;376:112180. (HTML) (PDF)
2018
2018
Bartunov, S., Santoro, A., Richards, B. A., Marris, L., Hinton, G. E., & Lillicrap, T. (2018). Assessing the scalability of biologically-motivated deep learning algorithms and architectures. Neural Information Processing Systems (pp. 9368-9378). (HTML) (PDF)
Insel, N., Guerguiev, J., & Richards, B. A. (2018). Irrelevance by inhibition: Learning, computation, and implications for schizophrenia. PLOS Computational Biology, 14(8), e1006315. (HTML) (PDF)
Richards, B.A., Lillicrap, T.P. Can neocortical feedback alter the sign of plasticity?. Nature Reviews Neuroscience 19, 636 (2018). (HTML) (PDF)
2017
2017
Guerguiev, J., Lillicrap, T. P., & Richards, B. A. (2017). Towards deep learning with segregated dendrites. ELife, 6, e22901. (PDF) (HTML)
Parfitt, G. M., Nguyen, R., Bang, J. Y., Aqrabawi, A.J., Tran, M. M., Seo, D. K., Richards, B. A., & Kim, J. C. (2017). Bidirectional control of anxiety-related behaviors in mice: role of inputs arising from the ventral hippocampus to the lateral septum and medial prefrontal cortex. Neuropsychopharmacology, 42(8), 1715-1728. (PDF) (HTML)
Raimondo, J. V., Richards, B. A., & Woodin, M. A. (2017). Neuronal chloride and excitability—the big impact of small changes. Current Opinion in Neurobiology, 43, 35-42. (PDF) (HTML)
Richards, B. A., & Frankland, P. W. (2017). The persistence and transience of memory. Neuron, 94(6), 1071-1084. (PDF) (HTML)
Xia, F., Richards, B. A., Tran, M. M., Josselyn, S. A., Takehara-Nishiuchi, K., & Frankland, P. W. (2017). Parvalbumin-positive interneurons mediate neocortical-hippocampal interactions that are necessary for memory consolidation. Elife, 6, e27868. (PDF) (HTML)
2014
2014
Akers, K. G., Martinez-Canabal, A., Restivo, L., Yiu, A. P., De Cristofaro, A., Hsiang, H. L. L., Ohira, K., Richards, B. A., Miyakawa, T., Josselyn, S. A., & Frankland, P. W. (2014). Hippocampal neurogenesis regulates forgetting during adulthood and infancy. Science, 344(6184), 598-602. (PDF) (HTML)
Muldal, A. M., Lillicrap, T. P., Richards, B. A., & Akerman, C. J. (2014). Clonal relationships impact neuronal tuning within a phylogenetically ancient vertebrate brain structure. Current Biology, 24(16), 1929-1933. (PDF) (HTML)
Richards, B. A., Xia, F., Santoro, A., Husse, J., Woodin, M. A., Josselyn, S. A., & Frankland, P. W. (2014). Patterns across multiple memories are identified over time. Nature Neuroscience, 17(7), 981-986. (PDF) (HTML)
Yiu, A. P., Mercaldo, V., Yan, C., Richards, B. A., Rashid, A. J., Hsiang, H. L. L., Pressey, J., Mahadevan, V., Tran, M. M., Kushner, S. A., Woodin, M. A., Frankland P. W., Josselyn, S. A. (2014). Neurons are recruited to a memory trace based on relative neuronal excitability immediately before training. Neuron, 83(3), 722-735. (PDF) (HTML)
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