| A neural circuit model forming semantic network with exception using spike-timing-dependent plasticity of inhibitory synapses. | |
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MedLine Citation:
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PMID: 17643738 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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We propose a neural circuit model forming a semantic network with exceptions using the spike-timing-dependent plasticity (STDP) of inhibitory synapses. To evaluate the proposed model, we conducted nine types of computer simulation by combining the three STDP rules for inhibitory synapses and the three spike pairing rules. The simulation results obtained with the STDP rule for inhibitory synapses by Haas et al. [Haas, J.S., Nowotny, T., Abarbanel, H.D.I., 2006, Spike-timing-dependent plasticity of inhibitory synapses in the entorhinal cortex. J. Neurophysiol. 96, 3305-3313] are successful, whereas, the other results are unsuccessful. The results and examinations suggested that the inhibitory connection from the concept linked with an exceptional feature to the general feature is necessary for forming a semantic network with an exception. |
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Authors:
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Kazushi Murakoshi; Kyoji Suganuma |
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Publication Detail:
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Type: Journal Article Date: 2007-06-13 |
Journal Detail:
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Title: Bio Systems Volume: 90 ISSN: 0303-2647 ISO Abbreviation: BioSystems Publication Date: 2007 Nov-Dec |
Date Detail:
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Created Date: 2007-11-06 Completed Date: 2008-01-22 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 0430773 Medline TA: Biosystems Country: Ireland |
Other Details:
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Languages: eng Pagination: 903-10 Citation Subset: IM |
Affiliation:
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Department of Knowledge-Based Information Engineering, Toyohashi University of Technology, 1-1 Hibarigaoka, Tenpaku-cho, Toyohashi 441-8580, Japan. mura@tutkie.tut.ac.jp |
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| MeSH Terms | |
Descriptor/Qualifier:
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Action Potentials
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physiology Computer Simulation Humans Memory / physiology Models, Neurological* Nerve Net / physiology* Neural Networks (Computer) Neuronal Plasticity / physiology Semantics Synapses / physiology Systems Biology |
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