2007/10/18 by Takahiro Omi, Shigeru Shinomoto
Computer Science · Neuroscience · Physics and Astronomy · #Neural Networks Stability and Synchronization #Neural dynamics and brain function #cond-mat.dis-nn #nlin.AO #stochastic dynamics and bifurcation
paper · pdf · doi:10.1103/physreve.77.046214
published as PHYSICAL REVIEW E 77, 046214 (2008) · 4pages 5figures
arxiv created 2007/10/18 · openalex publication_date 2008/04/17 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Signal transmission delays tend to destabilize dynamical networks leading to oscillation, but their dispersion contributes oppositely toward stabilization. We analyze an integrodifferential equation that describes the collective dynamics of a neural network with distributed signal delays. With the Gamma distributed delays less dispersed than exponential distribution, the system exhibits reentrant phenomena, in which the stability is once lost but then recovered as the mean delay is increased. With delays dispersed more highly than exponential, the system never destabilizes.