Training a spiking neuronal network model of visual-motor cortex to play a virtual racket-ball game using reinforcement learning.

ABSTRACT: Recent models of spiking neuronal networks have been trained to perform behaviors in static environments using a variety of learning rules, with varying degrees of biological realism. Most of these models have not been tested in dynamic visual environments where models must make predictions on future states and adjust their behavior accordingly. The models using these learning rules are often treated as black boxes, with little analysis on circuit architectures and learning mechanisms supporting optimal performance. Here we developed visual/motor spiking neuronal network models and trained them to play a virtual racket-ball game using several reinforcement learning algorithms inspired by the dopaminergic reward system. We systematically investigated how different architectures and circuit-

SUBMITTER: Anwar H 

PROVIDER: S-EPMC9094569 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature