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Bioinspired learning

Moving beyond reward prediction errors

Nature Machine Intelligencevolume 1pages204205 (2019) | Download Citation

Classic theories of reinforcement learning and neuromodulation rely on reward prediction errors. A new machine learning technique relies on neuromodulatory signals that are optimized for specific tasks, which may lead to better AI and better explanations of neuroscience data.

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Author information

Affiliations

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

    • Blake A. Richards
  2. Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada

    • Blake A. Richards
  3. Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada

    • Blake A. Richards
  4. Canadian Institute for Advanced Research, Toronto, Ontario, Canada

    • Blake A. Richards

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The author declares no competing interests.

Corresponding author

Correspondence to Blake A. Richards.

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https://doi.org/10.1038/s42256-019-0053-0

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