Adaptive learning and decision-making under uncertainty by metaplastic synapses guided by a surprise detection system

  1. Kiyohito Iigaya  Is a corresponding author
  1. University College London, United Kingdom

Abstract

Recent experiments have shown that animals and humans have a remarkable ability to adapt their learning rate according to the volatility of the environment. Yet the neural mechanism responsible for such adaptive learning has remained unclear. To fill this gap, we investigated a biophysically inspired, metaplastic synaptic model within the context of a well-studied decision-making network, in which synapses can change their rate of plasticity in addition to their efficacy according to a reward-based learning rule. We found that our model, which assumes that synaptic plasticity is guided by a novel surprise detection system, captures a wide range of key experimental findings and performs as well as a Bayes optimal model, with remarkably little parameter tuning. Our results further demonstrate the computational power of synaptic plasticity, and provide insights into the circuit-level computation which underlies adaptive decision-making.

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Author details

  1. Kiyohito Iigaya

    Gatsby Computational Neuroscience Unit, University College London, London, United Kingdom
    For correspondence
    kiigaya@gatsby.ucl.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4748-8432

Funding

Schwartz foundation

  • Kiyohito Iigaya

Gatsby Charitable Foundation

  • Kiyohito Iigaya

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2016, Iigaya

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Kiyohito Iigaya
(2016)
Adaptive learning and decision-making under uncertainty by metaplastic synapses guided by a surprise detection system
eLife 5:e18073.
https://doi.org/10.7554/eLife.18073

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https://doi.org/10.7554/eLife.18073