Dynamical properties of neural network model for working memory with Hodgkin-Huxley neurons

Toshiaki Omori, Tsuyoshi Horiguchi

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

We propose a neural network model of working memory with one-compartmental neurons and investigate its dynamical properties. We assume that the model consists of excitatory neurons and inhibitory neurons; all the neurons are connected to each other. The excitatory neurons are distinguished as several groups of selective neurons and one group of non-selective neurons. The selective neurons are assumed to form subpopulations in which each selective neuron belongs to only one of subpopulations. The non-selective neurons are assumed not to form any subpopulation. Synaptic strengths between neurons within a subpopulation are assumed to be potentiated. By the numerical simulations, persistent firing of neurons in a subpopulation emerges; the persistent firing corresponds to the retention of memory as one of the functions of working memory. We find that the strength of external input and the strength of N-methyl-D-aspartate synapse are important factors for dynamical behaviors of the network; for example, if we enhance the strength of the external input to a subpopulation while the persistent firing is occurring in other subpopulation, the persistent firing occurs in the subpopulation or is sustained against the external input. These results reveal that the neural network as for the function of the working memory is controlled by the neuromodulation and the external stimuli within the proposed model. We also find that the persistent time of firing of the selective neurons shows a kind of phase transition as a function of the degree of potentiation of synapses.

Original languageEnglish
Pages (from-to)600-614
Number of pages15
JournalPhysica A: Statistical Mechanics and its Applications
Volume334
Issue number3-4
DOIs
Publication statusPublished - 2004 Mar 15

Keywords

  • Hodgkin-Huxley neurons
  • Neural network
  • Neuromodulation
  • Phase transition
  • Working memory

ASJC Scopus subject areas

  • Mathematical Physics
  • Statistical and Nonlinear Physics

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