Neural circuit mechanisms of value-based decision-making and reinforcement learning
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Publication Details
Author list: Soltani A., Chaisangmongkon W., Wang X.-J.
Publisher: Hindawi
Publication year: 2016
Start page: 233
End page: 245
Number of pages: 13
ISBN: 9780128053317; 9780128053089
ISSN: 0146-9428
eISSN: 1745-4557
Languages: English-Great Britain (EN-GB)
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Abstract
Despite groundbreaking progress, currently we still know preciously little about the biophysical and circuit mechanisms of valuation and reward-dependent plasticity underlying adaptive choice behavior. For instance, whereas phasic firing of dopamine neurons has long been ascribed to represent reward-prediction error (RPE), only recently has research begun to uncover the mechanism of how such a signal is computed at the circuit level. In this chapter, we will briefly review neuroscience experiments and mathematical models on reward-dependent adaptive choice behavior and then focus on a biologically plausible, reward-modulated Hebbian synaptic plasticity rule. We will show that a decision-making neural circuit endowed with this learning rule is capable of accounting for behavioral and neurophysiological observations in a variety of value-based decision-making tasks, including foraging, competitive games, and probabilistic inference. Looking forward, an outstanding challenge is to elucidate the distributed nature of reward-dependent processes across a large-scale brain system. ฉ 2017 Elsevier Inc. All rights reserved.
Keywords
Competitive game, Computational principles, Matching law, Neural circuit mechanism, Probabilistic inference, Single-neuron physiology, Valuation computation, Value-based adaptive choice behavior
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