A spiking neuron model of theta phase precession

  • Authors:

  • Enhua Shen;Rubin Wang;Zhikang Zhang;Jianhua Peng

  • Affiliations:

  • Institute for Brain Information Processing and Cognitive Neurodynamics, School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China;Institute for Brain Information Processing and Cognitive Neurodynamics, School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China;Institute for Brain Information Processing and Cognitive Neurodynamics, School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China;Institute for Brain Information Processing and Cognitive Neurodynamics, School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China

  • Venue:
  • ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

Theta phase precession is an interesting phenomenon in hippocampus and may enhance learning and memory. Based on Harris KD et al. and Magee JC's electrophysiology experiments, a biology plausible spiking neuron model for theta phase precession was proposed. The model is both simple enough for constructing large scale network and realistic enough to match the biology context. The numerical results of our model were shown in this paper. The model can capture the main attributes of experimental result. The results of a simple neuron network were also showed in the paper, and were compared with single neuron result. The influence of network connections on theta phase precession was discussed. The relationship of phase shift with place shift in experiment was well repeated in our model. Such a model can mimic the biological phenomenon of theta phase precession, and preserve the main physiology factors underline theta phase precession.