Xiao-Jing Wang
NYU Shanghai

Many challenging questions about how the brain makes choices require an integrative approach across different levels of investigation, from single neurons to circuits to behavior. Computational modeling provides a powerful tool in this endeavor. Here I will summarize recent work on biological mechanisms and neural network models of choice behavior, and offer an unifying local circuit framework for both perceptual decision and value-based choice behavior in terms of a recurrent neural circuit model endowed with reward-dependent synaptic plasticity. Finally, I will discuss future research directions that aim at understanding learning adaptive choice behavior in a large system of multiple brain modules.