Journal
NEURAL NETWORKS
Volume 19, Issue 8, Pages 1075-1090Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2006.05.044
Keywords
decision making; reward-dependent stochastic Hebbian learning rule; reinforcement learning; meta-learning; synaptic plasticity; game theory
Funding
- NIMH NIH HHS [MH073246, R01 MH073246] Funding Source: Medline
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Previous studies have shown that non-human primates can generate highly stochastic choice behaviour, especially when this is required during a competitive interaction with another agent. To understand the neural mechanism of such dynamic choice behaviour, we propose a biologically plausible model of decision making endowed with synaptic plasticity that follows a reward-dependent stochastic Hebbian learning rule. This model constitutes a biophysical implementation of reinforcement learning, and it reproduces salient features of behavioural data from an experiment with monkeys playing a matching pennies game. Due to interaction with an opponent and learning dynamics, the model generates quasi-random behaviour robustly in spite of intrinsic biases. Furthermore, non-random choice behaviour can also emerge when the model plays against a non-interactive opponent, as observed in the monkey experiment. Finally, when combined with a meta-learning algorithm, our model accounts for the slow drift in the animal's strategy based on a process of reward maximization. (c) 2006 Elsevier Ltd. All rights reserved.
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