Neural coding in networks of multi-populations of neural oscillators

Rubin Wang, Zhikang Zhang, Chi Kong Tse, Jingyi Qu, Jianting Cao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

6 Citations (Scopus)


The paper studies the dynamical model of motor cognition of neural networks through the theory of stochastic phase resetting dynamics, presents the interaction, phase coding, and the evolution of the time-varying averaged number density in terms of populations of perceptive neurons, inter-neurons, and motor neurons subject to coupling, and probes into the dynamical reaction of neural networks under the condition of spontaneous movement and stimulation, respectively. With numerical simulations, we prove (1) Walter J. Freeman's conjecture that the response of cortex dynamics cannot code external stimulation information; (2) the possession of rhythm coding in the neural coding of serial neural networks; (3) the importance of neural inhibition in the regulation of the central nervous system.
Original languageEnglish
Pages (from-to)52-66
Number of pages15
JournalMathematics and Computers in Simulation
Publication statusPublished - 1 Jan 2012


  • Biological neural networks
  • Inter-neuron
  • Motor neuron
  • Perceptive neuron
  • Phase coding
  • Population of neural oscillators
  • Rhythm coding

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)
  • Numerical Analysis
  • Modelling and Simulation
  • Applied Mathematics


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