Presentation 2011-12-20
A Study of Pulsed Neuron Model for Learning of the Order Relation
Kaname IWASA, Mauricio KUGLER, Susumu KUROYANAGI, Akira IWATA,
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Abstract(in English) Pulsed neuron model is suitable for processing time series data, like sound signals, and can be easily implemented in hardware. However, this model recognizes frequency vector pattern in current time, and cannot consider of the order relation in series of input data. In this paper, we propose the learning method for time sequence of input data using pulsed neuron model. The proposed model adds the sets of connection weights to the pulsed neuron model and change them by inner potential value. Therefore, the inner potential when input data has in a specific order. Experimental results showed datasets coreesponding to different order relation, could be successfully identified, a the number of units and size of circuit of the proposed model is smaller than other method.
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Keyword(in English) pulsed neuron model / order relation / set of weights / inner potential
Paper # NC2011-93
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Committee NC
Conference Date 2011/12/13(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A Study of Pulsed Neuron Model for Learning of the Order Relation
Sub Title (in English)
Keyword(1) pulsed neuron model
Keyword(2) order relation
Keyword(3) set of weights
Keyword(4) inner potential
1st Author's Name Kaname IWASA
1st Author's Affiliation Department of Scienctific and Engineering Simulation, Nagoya Institute of Technology()
2nd Author's Name Mauricio KUGLER
2nd Author's Affiliation Department of Scienctific and Engineering Simulation, Nagoya Institute of Technology
3rd Author's Name Susumu KUROYANAGI
3rd Author's Affiliation Department of Scienctific and Engineering Simulation, Nagoya Institute of Technology
4th Author's Name Akira IWATA
4th Author's Affiliation Department of Scienctific and Engineering Simulation, Nagoya Institute of Technology
Date 2011-12-20
Paper # NC2011-93
Volume (vol) vol.111
Number (no) 368
Page pp.pp.-
#Pages 5
Date of Issue