Presentation 2007-04-25
Estimation of neural network structures from multidimensional spike sequences
Tohru ASHIZAWA, Daisuke HARAKI, Tohru IKEGUCHI,
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Abstract(in English) In neural systems, neurons interact each other. Then, they often produce very complicated behavior. To model or predict such complicated behavior, it is important to understand a neural network structure. In the present paper, we proposed new measures, which are based on spike time metrics and a partialization analysis, to estimate such a neural structure by using only observed multidimensional spike sequences. Here, the neural structure means topological connections between neurons. To check the validity of our proposed measures, we applied the proposed measures to multidimensional spike sequences which were produced by a neural network model. As a result, we found that the proposed measures exhibit high performance for estimating the neural network structures.
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Keyword(in English) neural network / coupled oscillator / spike sequence / partial correlation
Paper # NLP2007-2
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Conference Information
Committee NLP
Conference Date 2007/4/18(1days)
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Registration To Nonlinear Problems (NLP)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Estimation of neural network structures from multidimensional spike sequences
Sub Title (in English)
Keyword(1) neural network
Keyword(2) coupled oscillator
Keyword(3) spike sequence
Keyword(4) partial correlation
1st Author's Name Tohru ASHIZAWA
1st Author's Affiliation Graduate School of Science and Engineering, Saitama University()
2nd Author's Name Daisuke HARAKI
2nd Author's Affiliation Graduate School of Science and Engineering, Saitama University
3rd Author's Name Tohru IKEGUCHI
3rd Author's Affiliation Graduate School of Science and Engineering, Saitama University
Date 2007-04-25
Paper # NLP2007-2
Volume (vol) vol.107
Number (no) 21
Page pp.pp.-
#Pages 5
Date of Issue