Asymptotic Behavior and Synchronizability Characteristics of a Class of Recurrent Neural Networks

Authors:
Christof Cebulla
Affiliations:
Institute for Applied Mathematics, Universität Bonn, D-53115 Bonn, Germany cebulla@wiener.iam.uni-bonn.de
Venue:
Neural Computation
Year:
2007

Citing 7
Cited 0

Introduction to the theory of neural computation

Introduction to the theory of neural computation
Fixed-point attractor analysis for a class of neurodynamics

Neural Computation
Mean-field Theory and Synchronization in Random Recurrent Neural Networks

Neural Processing Letters
Spiking Neuron Models: An Introduction

Spiking Neuron Models: An Introduction
Synchronization in networks of excitatory and inhibitory Neurons with sparse, random connectivity

Neural Computation
Synchronization of the Neural Response to Noisy Periodic Synaptic Input

Neural Computation
The Number of Synaptic Inputs and the Synchrony of Large, Sparse Neuronal Networks

Neural Computation

Quantified Score

Hi-index	0.01

Visualization

Abstract

We propose an approach to the analysis of the influence of the topology of a neural network on its synchronizability in the sense of equal output activity rates given by a particular neural network model. The model we introduce is a variation of the Zhang model. We investigate the time-asymptotic behavior of the corresponding dynamical system (in particular, the conditions for the existence of an invariant compact asymptotic set) and apply the results of the synchronizability analysis on a class of random scale free networks and to the classical random networks with Poisson connectivity distribution.