Traveling Waves in Locally Connected Chaotic Neural Networks and Their Phenomenological Modeling

Conference paper

Abstract

The emergence of traveling waves is a universal property of nervous systems. However, mechanisms of these waves and their functional roles have not yet been fully elucidated. Here, we numerically investigate traveling waves in a locally connected large-scale chaotic neural network (CNN) consisting of more than one million units. We simulate it by parallel computing and visualize the network output by using color images. If the refractoriness of neurons is sufficiently large, many local cell assemblies are generated and the boundaries between them move as traveling waves. We also develop a simplified phenomenological model for the CNN by adding a negative self-feedback mechanism to the Potts model. The proposed meso-scopic model can qualitatively reproduce complex wave patterns in the CNN. Because the model requires less computational resources, it may serve as a useful tool for investigating traveling waves in nervous systems.

Keywords

Potts Model Phenomenological Model Spiral Wave Chaotic Neural Network Moore Neighborhood 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This research is partially supported by the Japan Society for the Promotion of Science, a Grant-in-Aid for JSPS Fellows (21 ⋅937) and the Aihara Project, the FIRST program from JSPS, initiated by CSTP.

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Graduate School of Information Science and TechnologyThe University of TokyoTokyoJapan
  2. 2.Institute of Industrial ScienceThe University of TokyoTokyoJapan

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