Presentation 2010-03-09
Reconstruction of input information using transformation from spike train to continuous-time series
Mai SUZUKI, Kaori KURODA, Yutaka SHIMADA, Tohru IKEGUCHI,
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Abstract(in English) Neurons code input information and generate spike trains. The generated spike trains reflect the input information. In this report, we use the Hanning window to transform the spike trains to a continuous instantaneous firing-frequency time series. Then, we reconstruct hidden input information through this transformation. In the numerical simulations, we use periodic time series (sinusoidal wave), quasi-periodic time series generated from the Langford equations, and chaotic time series generated from the Lorenz equations. In addition, we use a real time series of a Japanese vowel /a/. Using the cross-correlation coefficient, we compare reconstructed time series with input time series. As a result, we find that strong correlation exists between the input time series and its reconstructed time series.
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Keyword(in English) neuron / spike train / Hanning window / embedding / time series analysis
Paper # NLP2009-159
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Conference Information
Committee NLP
Conference Date 2010/3/2(1days)
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Paper Information
Registration To Nonlinear Problems (NLP)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Reconstruction of input information using transformation from spike train to continuous-time series
Sub Title (in English)
Keyword(1) neuron
Keyword(2) spike train
Keyword(3) Hanning window
Keyword(4) embedding
Keyword(5) time series analysis
1st Author's Name Mai SUZUKI
1st Author's Affiliation Department of Information and Computer science, school of Engineering, Saitama University()
2nd Author's Name Kaori KURODA
2nd Author's Affiliation Graduate school of Science and Engineering, Saitama University
3rd Author's Name Yutaka SHIMADA
3rd Author's Affiliation Graduate school of Science and Engineering, Saitama University
4th Author's Name Tohru IKEGUCHI
4th Author's Affiliation Graduate school of Science and Engineering, Saitama University:Saitama University, Brain Science Institute
Date 2010-03-09
Paper # NLP2009-159
Volume (vol) vol.109
Number (no) 458
Page pp.pp.-
#Pages 6
Date of Issue