Title

A class of canonical models for weakly connected neural networks

Abstract

In this paper we deduce a class of canonical models for weakly connected neural networks depending on first and second order adaptation conditions. An adaptation condition is a relation involving internal and external network parameters that translates the network's adjustment to environmental stimuli. A qualitative analysis of two dimensional, first order canonical models is presented. Global observations concerning the second order canonical models supporting their potential use as neural simulators is also discussed.

Publication Title

Nonlinear Analysis, Theory, Methods and Applications

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