• 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. (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. (PDF)

  • Richards, B. A., Tsao, D., & Zador, A. (2022). The application of artificial intelligence to biology and neuroscience. Cell, 185(15), 2640–2643. (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. (PDF)


  • 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: (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)


  • 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: (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)


  • 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)


  • 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)


  • 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)


  • Santoro, A., Frankland, P. W., & Richards, B. A. (2016). Memory transformation enhances reinforcement learning in dynamic environments. Journal of Neuroscience, 36(48), 12228-12242. (PDF) (HTML)


  • Van Rheede, J. J., Richards, B. A., & Akerman, C. J. (2015). Sensory-evoked spiking behavior emerges via an experience-dependent plasticity mechanism. Neuron, 87(5), 1050-1062. (PDF) (HTML)


  • 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)