Information transformation from a spatiotemporal pattern to synchrony through STDP network

Ryosuke Hosaka, Tohru Ikeguchi, Hikoichiro Nakamura, Osamu Araki

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

1 Citation (Scopus)

Abstract

Recent experimental results show the sensitivity of synaptic plasticity to the timing of synaptic events and postsynaptic firings (spike timing dependent plasticity: STDP). Although a number of studies have been made on STDP, little is known about the effect of STDP on the relation between external input patterns to the recurrent neural network and its output spikes. In this study, we examine this relation by computer simulations of a spiking neural network with STDP. We have found that STDP organizes the neural network to transform an external spatiotemporal input pattern into a synchronous firing, and the synchrony is much dependent on the spatiotemporal structure of the external input. This result suggests that the original of the synchrony propagate through feed-forward network may be generated by the recurrent neural network respond to the external input.

Original languageEnglish
Title of host publication2004 IEEE International Joint Conference on Neural Networks - Proceedings
Pages1475-1480
Number of pages6
DOIs
Publication statusPublished - 2004
Externally publishedYes
Event2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary
Duration: 2004 Jul 252004 Jul 29

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
Volume2
ISSN (Print)1098-7576

Conference

Conference2004 IEEE International Joint Conference on Neural Networks - Proceedings
Country/TerritoryHungary
CityBudapest
Period04/7/2504/7/29

ASJC Scopus subject areas

  • Software

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