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Asynchronous Cellular Automaton Based Modeling of Nonlinear Dynamics of Neuron

  • Hiroyuki Torikai
  • Takashi Matsubara
Chapter
Part of the Understanding Complex Systems book series (UCS)

Abstract

A modeling approach of nonlinear dynamics of neurons by an asynchronous cellular automaton is introduced. It is shown that an asynchronous cellular automaton neuron model can realize not only typical nonlinear response characteristics of neurons but also their underlying occurrence mechanisms (i.e., bifurcation scenarios). The model can be implemented as an asynchronous sequential logic circuit, whose control parameter is the pattern of wires that can be dynamically updated in a dynamic reconfigurable FPGA. An on-FPGA learning algorithm (i.e., on-FPGA rewiring algorithm) is presented and is used to tune the model so that it reproduces nonlinear response characteristics of a neuron.

Keywords

Neuron Model Logic Gate Synaptic Weight FPGA Device Vector Field Unit 
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.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Graduate School of Engineering ScienceOsaka UniversityToyonakaJapan

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