Evolution of biologically plausible neural networks performing a visually guided reaching task

Abstract

An evolutionary strategy (ES) algorithm was utilized to evolve a simulated neural network based on the known anatomy of the posterior parietal cortex (PPC), to perform a visually guided reaching task. In this task, a target remained visible for the duration of a trial, and an agent's goal was to move its hand to the target as rapidly as possible and remain for the duration of that trial. The ES was used to tune the strength of 15609 connections between neural areas and 4 parameters governing the neural dynamics. The model had sensory latencies replicating those found in recording studies with monkeys. The ES ran 100 times and generated very diverse networks that could all perform the task well. The evolved networks 1) showed velocity profiles consistent with biological movements, and 2) found solutions that reflect short-range excitation and long-range, contralateral inhibition similar to neurobiological networks. These results provide theoretical evidence for the important parameters and projections governing sensorimotor transformations in neural systems.