Liquid computing on and off the edge of chaos with a striatal microcircuit.

ABSTRACT: In reinforcement learning theories of the basal ganglia, there is a need for the expected rewards corresponding to relevant environmental states to be maintained and modified during the learning process. However, the representation of these states that allows them to be associated with reward expectations remains unclear. Previous studies have tended to rely on pre-defined partitioning of states encoded by disjunct neuronal groups or sparse topological drives. A more likely scenario is that striatal neurons are involved in the encoding of multiple different states through their spike patterns, and that an appropriate partitioning of an environment is learned on the basis of task constraints, thus minimizing the number of states involved in solving a particular task. Here we show that stria

SUBMITTER: Toledo-Suarez C 

PROVIDER: S-EPMC4240071 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature