Simulation of working memory using neural field equations

Resumo

Neural field equations are intended to model the synaptic interactions between neurons in a continuous neural network, called a neural field .This kind of integro-differential equations proved to be a useful tool for the spatiotemporal modeling of the neuronal activity from a macroscopic point of view, allowing the study of a wide variety of neurobiological phenomena, such as the processing of sensory stimuli. In particular, they are a perfect tool for the simulation of working memory, which makes them very useful in Robotics.
The aim of the present talk is to study the effects of additive noise in one- and two-dimensional neural fields, while taking into account finite signal transmission speed.
A Galerkin-type method to approximate such models is presented, which applies the Fast Fourier Transformation to optimise the computational effort required to solve this type of equations. Numerical simulations obtained by this algorithm are presented and discussed.