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Blake Richards

Appointment

Fellow

Canada CIFAR AI Chair

Learning in Machines & Brains

Pan-Canadian AI Strategy

Connect

McGill University

Google Scholar

Website

About

Appointed Canada CIFAR AI Chair – 2019

Renewed Canada CIFAR AI Chair – 2024

Blake Richards’ research is at the intersection of neuroscience and AI. His laboratory investigates universal principles of intelligence that apply to both natural and artificial agents. This includes work on predictive learning, memory systems, and bio-inspired networks.

Awards

  • Arthur B. McDonald Fellowship, NSERC, 2022
  • NSERC Discovery Accelerator Supplement, 2020
  • Young Investigator Award, Canadian Association for Neuroscience, 2019
  • Ontario Early Researcher Award, 2018
  • Google Faculty Research, 2016

Relevant Publications

  • Azabou, M., et al. (2024). A unified, scalable framework for neural population decoding. Advances in Neural Information Processing Systems, 36.
  • Kalajdzievski, D., Mao, X., Fortier-Poisson, P., Lajoie, G., & Richards, B. (2023). Transfer Entropy Bottleneck: Learning Sequence to Sequence Information Transfer. Transactions in Machine Learning Research.
  • Zadort, A., et al. (2023). Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution. Nature Communications, 14: 1597.
  • Pogodin, R., Cornford, J., Ghosh, A., Gidel, G., Lajoie, G., & Richards, B.A. (2023). Synaptic Weight Distributions Depend on the Geometry of Plasticity" In The Twelfth International Conference on Learning Representations.
  • Agrawal, K.K., Mondal, A.K., Ghosh, A., & Richards, B. (2022). $\alpha $-ReQ: Assessing Representation Quality in Self-Supervised Learning by measuring eigenspectrum decay. Advances in Neural Information Processing Systems, 35, 17626-17638.
  • Richards, B. A., Lillicrap, T. P., Beaudoin, P., Bengio, Y., Bogacz, R., Christensen, A., ... & Kording, K. P. (2019). A deep learning framework for neuroscience. Nature neuroscience, 22(11), 1761-1770.
  • Bartunov, S., A. Santoro, B.A. Richards, G.E. Hinton and T.P. Lillicrap (2018) “Assessing the scalability of biologically-motivated deep learning algorithms and architectures.” Neural Information Processing Systems.

Institution

McGill University

Mila

Department

School of Computer Science and Department of Neurology & Neurosurgery

Education

  • DPhil (Neuroscience), University of Oxford
  • MSc (Neuroscience), University of Oxford
  • Hon BSc (Cognitive Science & Artificial Intelligence), University of Toronto

Country

Canada

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