Neural network model for path-finding problems with the self-recovery property

Kei-Ichi Ueda, Keiichi Kitajo, Yoko Yamaguchi, and Yasumasa Nishiura
Phys. Rev. E 99, 032207 – Published 8 March 2019

Abstract

The large-scale synchronization of neural oscillations is crucial in the functional integration of brain modules, but the combination of modules changes depending on the task. A mathematical description of this flexibility is a key to elucidating the mechanism of such spontaneous neural activity. We present a model that finds the loop structure of a network whose nodes are connected by unidirectional links. Using this model, we propose a path-finding system that spontaneously finds a path connecting two specified nodes. The solution path is represented by phase-synchronized oscillatory solutions. The model has the self-recovery property: that is, it is a system with the ability to find a new path when one of the connections in the existing path is suddenly removed. We show that the model construction procedure is applicable to a wide class of nonlinear systems arising in chemical reactions and neural networks.

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  • Received 31 July 2017
  • Revised 17 December 2018

DOI:https://doi.org/10.1103/PhysRevE.99.032207

©2019 American Physical Society

Physics Subject Headings (PhySH)

Nonlinear Dynamics

Authors & Affiliations

Kei-Ichi Ueda

  • Graduate School of Science and Engineering, University of Toyama, Toyama 930-8555, Japan

Keiichi Kitajo*

  • RIKEN, CBS-TOYOTA Collaboration Center, RIKEN Center for Brain Science, Saitama 351-0198, Japan

Yoko Yamaguchi

  • Neuroinformatics Unit, RIKEN Center for Brain Science, Saitama 351-0198, Japan

Yasumasa Nishiura

  • WPI Advanced Institute for Materials Research, Tohoku University, Miyagi 980-8577, Japan and Mathematics for Advanced Materials-OIL, AIST-Tohoku University, Sendai 980-8577, Japan

  • *Present address: Department of System Neuroscience, National Institute for Physiological Sciences, National Institutes of Natural Sciences, Aichi 444-8585, Japan

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Vol. 99, Iss. 3 — March 2019

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