NDLI: Stochastic resonance can enhance information transmission of supra-threshold neural signals

Content Provider	IEEE Xplore Digital Library
Author	Kawaguchi, M. Mino, H. Momose, K. Durand, D.M.
Copyright Year	2009
Description	Author affiliation: Collage of Human Sciences, Waseda University, 2-579-15 Mikajima, Tokorozawa 359-1192, Japan (Momose, K.) \|\| Graduate School of Human Sciences, Wa-seda University, 2-579-15 Mikajima, Tokorozawa 359-1192, Japan (Kawaguchi, M.) \|\| Neural Engineering Center, Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave., Cleveland, OH 44106-7207, U.S.A (Durand, D.M.) \|\| Department of Electrical and Computer Engineering, Kanto Gakuin University, 1-50-1 Mut-suura E., Kanazawa-ku, Yokohama 236-8501, Japan (Mino, H.)
Abstract	Stochastic resonance (SR) has been shown to improve detection of sub-threshold signals with additive uncor-related background noise, not only in a single hippocampal CA1 neuron model, but in a population of hippocampal CA1 neuron models (Array-Enhanced Stochastic Resonance; AESR). However, most of the information in the CNS is transmitted through supra-threshold signals and the effect of stochastic resonance in neurons on these signals is unknown. Therefore, we investigate through computer simulations whether information transmission of supra-threshold input signal can be improved by uncorrelated noise in a population of hippocampal CA1 neuron models by supra-threshold stochastic resonance (SSR). The mutual information was estimated as an index of information transmission via total and noise entropies from the inter-spike interval (ISI) histograms of the spike trains generated by gathering each of spike trains in a population of hippocampal CA1 neuron models at N = 3D1, 2, 4, 10, 20 and 50. It was shown that the mutual information was maximized at a specific amplitude of uncorrelated noise, i.e., a typical curve of SR was observed when the number of neurons was greater than 10 with SSR. However, SSR did not affect the information transfer with a small number of neurons. In conclusion, SSR may play an important role in processing information such as memory formation in a population of hippocampal neurons.
Starting Page	6806
Ending Page	6809
File Size	383750
Page Count	4
File Format	PDF
ISBN	9781424432967
ISSN	1557170X
DOI	10.1109/IEMBS.2009.5333973
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stochastic resonance Neurons Strontium Mutual information Signal detection Additive noise Background noise Computer simulation Noise generators Entropy monte carlo simulation Action Potential supra-threshold stochastic resonance hodgkin-huxley model homogeneous poisson process synaptic noise information-theoretic analysis numerical method
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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