Bifurcation in a Discrete-Time Piecewise Constant Dynamical System

Abstract EN

The study of recurrent neural networks with piecewise constant transition or control functions has attracted much attention recently because they can be used to simulate many physical phenomena.

A recurrent and discontinuous two-state dynamical system involving a nonnegative bifurcation parameter is studied.

By elementary but novel arguments, we are able to give a complete analysis on its asymptotic behavior when the parameter varies from 0 to ∞.

It is hoped that our analysis will provide motivation for further results on large-scale recurrent McCulloch-Pitts-type neural networks and piecewise continuous discrete-time dynamical systems.