Theoretical analysis of synchronization phenomena in two pulse-coupled resonate-and-fire neurons

Kazuki Nakada, Keiji Miura, Hatsuo Hayashi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We address the synchronization properties of two pulse-coupled resonate-and-fire neuron (RFN) models. The RFN model is a spiking neuron model that has second-order membrane dynamics with a threshold and a reset value. Due to such dynamics, the RFN model exhibits subthreshold oscillation of the membrane potential, and is sensitive to the timing of stimuli. So far the existence of anti-phase synchronization states and their stability in a system of two pulse-coupled RFN models have been reported. However, the effects of the reset value after firing on such synchronization states have not been considered. The reset value may affect the sensitivity to the input timing, leading to change in synchronization properties in the pulse-coupled RFN models. We newly found out-of-phase burst synchronization states and related bifurcation phenomena depending on the coupling strength in the system as the reset value was changed. Focusing on the symmetry of the system, we analyzed the stability of such phenomena by using a firing time difference map constructed from 1D return maps with respect to firing time difference between two neurons. The analyses revealed the global stability of the out-of-phase synchronization states and the existence of the type I intermittency chaotic behavior.

Original languageEnglish
Title of host publicationThe 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings
Pages950-955
Number of pages6
DOIs
Publication statusPublished - 2007 Dec 1
Event2007 International Joint Conference on Neural Networks, IJCNN 2007 - Orlando, FL, United States
Duration: 2007 Aug 122007 Aug 17

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)1098-7576

Other

Other2007 International Joint Conference on Neural Networks, IJCNN 2007
Country/TerritoryUnited States
CityOrlando, FL
Period07/8/1207/8/17

ASJC Scopus subject areas

  • Software

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