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A deep learning framework for neuroscience

Nature Neuroscience volume 22, pages 1761–1770 (2019)Cite this article

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Abstract

Systems neuroscience seeks explanations for how the brain implements a wide variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to design computational systems based on the tasks they will have to solve. In artificial neural networks, the three components specified by design are the objective functions, the learning rules and the architectures. With the growing success of deep learning, which utilizes brain-inspired architectures, these three designed components have increasingly become central to how we model, engineer and optimize complex artificial learning systems. Here we argue that a greater focus on these components would also benefit systems neuroscience. We give examples of how this optimization-based framework can drive theoretical and experimental progress in neuroscience. We contend that this principled perspective on systems neuroscience will help to generate more rapid progress.

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Fig. 1: The three core components of ANN design.
Fig. 2: Bias and variance in learning rules.
Fig. 3: Learning rules that don’t follow gradients.
Fig. 4: Comparing deep ANN models and the brain.
Fig. 5: Biological models of credit assignment.

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Acknowledgements

This article emerged from a workshop on optimization in the brain that took place February 24–28, 2019 at the Bellairs Research Institute of McGill University. We thank Element AI and Bellairs Research Institute for their critical support in organizing this workshop. This work was also supported by the Canadian Institute for Advanced Research Learning in Machines and Brains Program.

Author information

Author notes

  1. These authors contributed equally: Blake A. Richards, Timothy P. Lillicrap, Denis Therien, Konrad P Kording.

Authors and Affiliations

  1. Mila, Montréal, Quebec, Canada

    Blake A. Richards, Yoshua Bengio & Benjamin Scellier

  2. School of Computer Science, McGill University, Montréal, Quebec, Canada

    Blake A. Richards

  3. Department of Neurology & Neurosurgery, McGill University, Montréal, Quebec, Canada

    Blake A. Richards & Christopher C. Pack

  4. Canadian Institute for Advanced Research, Toronto, Ontario, Canada

    Blake A. Richards, Yoshua Bengio, Joel Zylberberg & Konrad P. Kording

  5. DeepMind, Inc., London, UK

    Timothy P. Lillicrap & Greg Wayne

  6. Centre for Computation, Mathematics and Physics in the Life Sciences and Experimental Biology, University College London, London, UK

    Timothy P. Lillicrap

  7. Element AI, Montréal, QC, Canada

    Philippe Beaudoin, Archy de Berker & Denis Therien

  8. Université de Montréal, Montréal, Quebec, Canada

    Yoshua Bengio & Benjamin Scellier

  9. MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK

    Rafal Bogacz

  10. Department of Electrical Engineering, Stanford University, Stanford, CA, USA

    Amelia Christensen

  11. Department of Bioengineering, Imperial College London, London, UK

    Claudia Clopath

  12. Computational Neuroscience Unit, School of Computer Science, Electrical and Electronic Engineering, and Engineering Maths, University of Bristol, Bristol, UK

    Rui Ponte Costa

  13. Department of Physiology, Universität Bern, Bern, Switzerland

    Rui Ponte Costa & Walter Senn

  14. Department of Applied Physics, Stanford University, Stanford, CA, USA

    Surya Ganguli

  15. Google Brain, Mountain View, CA, USA

    Surya Ganguli & Danijar Hafner

  16. Department of Biological Sciences, University of Toronto Scarborough, Toronto, Ontario, Canada

    Colleen J. Gillon

  17. Department of Cell & Systems Biology, University of Toronto, Toronto, Ontario, Canada

    Colleen J. Gillon

  18. Department of Computer Science, University of Toronto, Toronto, Ontario, Canada

    Danijar Hafner

  19. Vector Institute, Toronto, Ontario, Canada

    Danijar Hafner

  20. Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA

    Adam Kepecs

  21. Department of Psychology and Neuroscience, Columbia University, New York, NY, USA

    Nikolaus Kriegeskorte

  22. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, USA

    Nikolaus Kriegeskorte, Grace W. Lindsay & Kenneth D. Miller

  23. Gatsby Computational Neuroscience Unit, University College London, London, UK

    Peter Latham

  24. Center for Theoretical Neuroscience, Columbia University, New York, NY, USA

    Grace W. Lindsay & Kenneth D. Miller

  25. Department of Neuroscience, College of Physicians and Surgeons, Columbia University, New York, NY, USA

    Kenneth D. Miller

  26. University of Ottawa Brain and Mind Institute, Ottawa, Ontario, Canada

    Richard Naud

  27. Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Ontario, Canada

    Richard Naud

  28. Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece

    Panayiota Poirazi

  29. Department of Vision & Cognition, Netherlands Institute for Neuroscience, Amsterdam, Netherlands

    Pieter Roelfsema

  30. Institute of Neuroinformatics, ETH Zürich and University of Zürich, Zürich, Switzerland

    João Sacramento

  31. Department of Experimental Psychology, University of Oxford, Oxford, UK

    Andrew Saxe

  32. Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA

    Anna C. Schapiro

  33. Department of Psychology, Stanford University, Stanford, CA, USA

    Daniel Yamins

  34. Department of Computer Science, Stanford University, Stanford, CA, USA

    Daniel Yamins

  35. Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA

    Daniel Yamins

  36. Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland

    Friedemann Zenke

  37. Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK

    Friedemann Zenke

  38. Department of Physics and Astronomy York University, Toronto, Ontario, Canada

    Joel Zylberberg

  39. Center for Vision Research, York University, Toronto, Ontario, Canada

    Joel Zylberberg

  40. Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA

    Konrad P. Kording

  41. Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA

    Konrad P. Kording

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Corresponding author

Correspondence to Blake A. Richards.

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The authors declare no competing interests.

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Peer Review Information Nature Neuroscience thanks Gabriel Kreiman and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Richards, B.A., Lillicrap, T.P., Beaudoin, P. et al. A deep learning framework for neuroscience. Nat Neurosci 22, 1761–1770 (2019). https://doi.org/10.1038/s41593-019-0520-2

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