Neurodynamics analysis of brain information transmission

Ru Bin Wang, Zhi Kang Zhang, Chi Kong Tse

Research output: Journal article publicationJournal articleAcademic researchpeer-review

10 Citations (Scopus)

Abstract

This paper proposes a model of neural networks consisting of populations of perceptive neurons, inter-neurons, and motor neurons according to the theory of stochastic phase resetting dynamics. According to this model, the dynamical characteristics of neural networks are studied in three coupling cases, namely, series and parallel coupling, series coupling, and unilateral coupling. The results show that the indentified structure of neural networks enables the basic characteristics of neural information processing to be described in terms of the actions of both the optional motor and the reflected motor. The excitation of local neural networks is caused by the action of the optional motor. In particular, the excitation of the neural population caused by the action of the optional motor in the motor cortex is larger than that caused by the action of the reflected motor. This phenomenon indicates that there are more neurons participating in the neural information processing and the excited synchronization motion under the action of the optional motor.
Original languageEnglish
Pages (from-to)1415-1428
Number of pages14
JournalApplied Mathematics and Mechanics (English Edition)
Volume30
Issue number11
DOIs
Publication statusPublished - 1 Nov 2009

Keywords

  • Inter-neuron
  • Motor neuron
  • Perception neuron
  • Phase coding
  • Population of neural oscillators
  • Serial and parallel model of neural networks
  • Synchronous motion

ASJC Scopus subject areas

  • Applied Mathematics
  • Mechanics of Materials
  • Mechanical Engineering

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