Adaptive learning and decision-making under uncertainty by metaplastic synapses guided by a surprise detection system
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.
Article and author information
Author details
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.
Reviewing Editor
- Naoshige Uchida, Harvard University, United States
Version history
- Received: May 23, 2016
- Accepted: August 8, 2016
- Accepted Manuscript published: August 9, 2016 (version 1)
- Version of Record published: September 1, 2016 (version 2)
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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