Two Notes on Continuous-Time Neurodynamical Systems

Abstract

The neurodynamical model of recurrent networks in this paper is approached from an engineering perspective, i.e., to make networks efficient in terms of topology and capture dynamics of time-varying systems. Neural dynamics in that case can be considered from two aspects, convergence of state variables (memory recall) and the number, position, local stability and domains of attraction of equilibrium states (memory capacity). The purpose of this work is to investigate some relationship between Lyapunov exponents and the recurrent neural network model described by the concrete system of delay-differential equations.