Modeling the Role of Striatum in Stochastic Multi Context Tasks
. 2017-09-30; :
DOI: 10.1101/196543
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Decision making in non-stationary and stochastic environments can be interpreted as a variant of non-stationary multi armed bandit task where the optimal decision requires identification of the current context. We formalize the problem using a Bayesian approach taking biological constraints into account (limited memory) that allow us to define a sub-optimal theoretical model. From this theoretical model, we derive a biological model of the striatum based on its micro-anatomy that is able to learn state and action representations. We show that this model matches the theoretical model for low stochasticity in the environment and could be considered as a neural implementation of the theoretical model. Both models are tested on non-stationary multi-armed bandit task and compared to animal performances.