Document Detail


A common framework of signal processing in the induction of cerebellar LTD and cortical STDP.
MedLine Citation:
PMID:  23500505     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Cerebellar long-term depression (LTD) and cortical spike-timing-dependent synaptic plasticity (STDP) are two well-known and well-characterized types of synaptic plasticity. Induction of both types of synaptic plasticity depends on the spike timing, pairing frequency, and pairing numbers of two different sources of spiking. This implies that the induction of synaptic plasticity may share common frameworks in terms of signal processing regardless of the different signaling pathways involved in the two types of synaptic plasticity. Here we propose that both types share common frameworks of signal processing for spike-timing, pairing-frequency, and pairing-numbers detection. We developed system models of both types of synaptic plasticity and analyzed signal processing in the induction of synaptic plasticity. We found that both systems have upstream subsystems for spike-timing detection and downstream subsystems for pairing-frequency and pairing-numbers detection. The upstream systems used multiplication of signals from the feedback filters and nonlinear functions for spike-timing detection. The downstream subsystems used temporal filters with longer time constants for pairing-frequency detection and nonlinear switch-like functions for pairing-numbers detection, indicating that the downstream subsystems serve as a leaky integrate-and-fire system. Thus, our findings suggest that a common conceptual framework for the induction of synaptic plasticity exists despite the differences in molecular species and pathways.
Authors:
Minoru Honda; Hidetoshi Urakubo; Takuya Koumura; Shinya Kuroda
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2013-2-9
Journal Detail:
Title:  Neural networks : the official journal of the International Neural Network Society     Volume:  43C     ISSN:  1879-2782     ISO Abbreviation:  Neural Netw     Publication Date:  2013 Feb 
Date Detail:
Created Date:  2013-3-18     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8805018     Medline TA:  Neural Netw     Country:  -    
Other Details:
Languages:  ENG     Pagination:  114-124     Citation Subset:  -    
Copyright Information:
Copyright © 2013 Elsevier Ltd. All rights reserved.
Affiliation:
Department of Computational Biology, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa 277-8561, Japan.
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