• 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. (PDF) (Openreview)

  • 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. (PDF) (bioRxiv)

  • Payeur, A., Guerguiev, J., Zenke, F. et al. Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits. Nat Neurosci (2021). (PDF) (Nature)

  • 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. (bioRxiv)

  • Prince, L. Y., Tran, M. M., Gray. D, Saad, L., Chasiotis, H., Kwag, J., Kohl, M. M. & Richards, B. A. (2021). Neocortical inhibitory interneuron subtypes display distinct responses to synchrony and rate of activity. Communications Biology. (Comms. Bio.)

  • Suarez, L. E., Richards, B. A., Lajoie, G., & Misic , B. (2021). Learning function from structure in neuromorphic networks. Accepted at Nature Machine Intelligence. (bioRxiv )

  • 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. (IEEE Explore)


  • Bashivan, P., Richards, B.A., & Rish, I. (2020). Adversarial Feature Desensitization. (PDF) (arXiv)

  • Berens, S. C., Richards, B. A., & Horner, A. J. (2020). Dissociating memory accessibility and precision in forgetting. Nature Human Behaviour, 1-12. (PDF) (HTML)

  • 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. Science Advances, 6(17), eaay5333. (PDF) (HTML)

  • Park, K., Lee, J., Jang, H. J., Richards, B. A., Kohl, M. M., & Kwag, J. (2020). 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 Biology, 18(1), 1-20. (PDF) (HTML)

  • Sathiyakumar, S., Scromne Carrasco, S., Saad., L., & Richards, B. A. (2020). Systems consolidation impairs behavioral flexibility. Learning and Memory. (PDF)(HTML)

  • Schulz, M. A., Yeo, B. T., Vogelstein, J. T., Mourao-Miranada, J., Kather, J. N., Kording, K., Richards, B. A., & Bzdok, D. (2020). Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets. Nature Communications, 11(1), 1-15. (PDF) (HTML)


  • Guerguiev, J., Kording, K. P., & Richards, B. A. (2019). Spike-based causal inference for weight alignment. In International Conference on Learning Representations. (PDF) (OpenReview)

  • Richards, B. A. (2019). Moving beyond reward prediction errors. Nature Machine Intelligence, 1(5), 204-205. (PDF) (HTML)

  • Richards, B. A., & Lillicrap, T. P. (2019). Dendritic solutions to the credit assignment problem. Current Opinion in Neurobiology, 54, 28-36. (PDF) (HTML)

  • Richards, B. A., Lillicrap, T. P., Beaudoin, P., Bengio, Y., Bogacz, R., Christensen, A., ... Gillon, C. J. ... & Körding, K. P. (2019). A deep learning framework for neuroscience. Nature Neuroscience, 22(11), 1761-1770. (PDF) (HTML)

  • Tran, L. M., Josselyn, S. A., Richards, B. A., & Frankland, P. W. (2019). Forgetting at biologically realistic levels of neurogenesis in a large-scale hippocampal model. Behavioural Brain Research, 376, 112180. (PDF) (HTML)


  • 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. In Advances in Neural Information Processing Systems (pp. 9368-9378). (PDF) (HTML)

  • Insel, N., Guerguiev, J., & Richards, B. A. (2018). Irrelevance by inhibition: Learning, computation, and implications for schizophrenia. PLoS Computational Biology, 14(8), e1006315. (PDF) (HTML)

  • Richards, B. A., & Lillicrap, T. P. (2018). Can neocortical feedback alter the sign of plasticity?. Nature Reviews Neuroscience, 19(10), 636-636. (PDF) (HTML)


  • 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)