Enhancing reinforcement learning models by including direct and indirect pathways improves performance on striatal dependent tasks.

ABSTRACT: A major advance in understanding learning behavior stems from experiments showing that reward learning requires dopamine inputs to striatal neurons and arises from synaptic plasticity of cortico-striatal synapses. Numerous reinforcement learning models mimic this dopamine-dependent synaptic plasticity by using the reward prediction error, which resembles dopamine neuron firing, to learn the best action in response to a set of cues. Though these models can explain many facets of behavior, reproducing some types of goal-directed behavior, such as renewal and reversal, require additional model components. Here we present a reinforcement learning model, TD2Q, which better corresponds to the basal ganglia with two Q matrices, one representing direct pathway neurons (G) and another representing

SUBMITTER: Blackwell KT 

PROVIDER: S-EPMC10479916 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature