2001/08/25 by Junji Ito, Kunihiko Kaneko · 2 citations
Computer Science · Neuroscience · Physics and Astronomy · #Neural Networks and Applications #Neural dynamics and brain function #Nonlinear Dynamics and Pattern Formation #cond-mat.dis-nn
paper · pdf · doi:10.1103/physrevlett.88.028701
4 pages, 3 figures, REVTeX
arxiv created 2001/08/25 · openalex publication_date 2001/12/27 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
As a model of temporally evolving networks, we consider a globally coupled logistic map with variable connection weights. The model exhibits self-organization of network structure, reflected by the collective behavior of units. Structural order emerges even without any interunit synchronization of dynamics. Within this structure, units spontaneously separate into two groups whose distinguishing feature is that the first group possesses many outwardly directed connections to the second group, while the second group possesses only a few outwardly directed connections to the first. The relevance of the results to structure formation in neural networks is briefly discussed.