Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Rapid decision threshold modulation by reward rate in a neural network
Neural Networks - 2006 Special issue: Neurobiology of decision making
Multihypothesis sequential probability ratio tests .I. Asymptotic optimality
IEEE Transactions on Information Theory
A sequential procedure for multihypothesis testing
IEEE Transactions on Information Theory
Rapid decision threshold modulation by reward rate in a neural network
Neural Networks - 2006 Special issue: Neurobiology of decision making
Journal of Cognitive Neuroscience
Basal Ganglia Models for Autonomous Behavior Learning
Creating Brain-Like Intelligence
Dynamical analysis of bayesian inference models for the eriksen task
Neural Computation
Initiation and termination of integration in a decision process
Neural Networks
Optimal decision making on the basis of evidence represented in spike trains
Neural Computation
Posterior weighted reinforcement learning with state uncertainty
Neural Computation
Reward-modulated hebbian learning of decision making
Neural Computation
Modeling learned categorical perception in human vision
Neural Networks
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Neurophysiological studies have identified a number of brain regions critically involved in solving the problem of action selection or decision making. In the case of highly practiced tasks, these regions include cortical areas hypothesized to integrate evidence supporting alternative actions and the basal ganglia, hypothesized to act as a central switch in gating behavioral requests. However, despite our relatively detailed knowledge of basal ganglia biology and its connectivity with the cortex and numerical simulation studies demonstrating selective function, no formal theoretical framework exists that supplies an algorithmic description of these circuits. This article shows how many aspects of the anatomy and physiology of the circuit involving the cortex and basal ganglia are exactly those required to implement the computation defined by an asymptotically optimal statistical test for decision making: the multihypothesis sequential probability ratio test (MSPRT). The resulting model of basal ganglia provides a new framework for understanding the computation in the basal ganglia during decision making in highly practiced tasks. The predictions of the theory concerning the properties of particular neuronal populations are validated in existing experimental data. Further, we show that this neurobiologically grounded implementation of MSPRT outperforms other candidates for neural decision making, that it is structurally and parametrically robust, and that it can accommodate cortical mechanisms for decision making in a way that complements those in basal ganglia.