NDLI: Enhancement of Information Transmission with Stochastic Resonance in Hippocampal CA1 Neuron Models

Content Provider	IEEE Xplore Digital Library
Author	Mino, H. Durand, D.M. Kawaguchi, M.
Copyright Year	2006
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Kanto Gakuin Univ., Yokohama (Mino, H.)
Abstract	Stochastic resonance (SR) has been shown to enhance the signal to noise ratio or detection of signals in neurons. It is not yet clear how this effect of SR on the signal to noise ratio affects signal processing in neural networks. In this paper, we test the hypothesis that SR can improve information transmission in the hippocampus. From spike firing times recorded at the soma, the inter spike intervals were generated and then "total" and "noise" entropies were estimated to obtain the mutual information and information rate of the spike trains. The results show that the information rate reached a maximum value at a specific amplitude of the background noise, implying that the stochastic resonance can improve the information transmission in the CA1 neuron model. Furthermore, the results also show that the effect of stochastic resonance tended to decrease as the intensity of the random sub-threshold spike trains (signal) (more than 20 l/s) approached to that of the background noise (100 l/s). In conclusion, the computation results that the stochastic resonance can improve information processing in the hippocampal CA1 neuron model in which the intensity of the random sub-threshold spike trains was set at 5-20 l/s
Sponsorship	IEEE EMB
Starting Page	4957
Ending Page	4960
File Size	392969
Page Count	4
File Format	PDF
ISBN	1424400325
ISSN	1557170X
DOI	10.1109/IEMBS.2006.260133
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stochastic resonance Neurons Strontium Signal to noise ratio Information rates Background noise Signal detection Signal processing Neural networks Testing Monte Carlo Simulation Synaptic Noise Shot Noise Action Potential Stochastic Resonance Hodgkin-Huxley model Homogeneous Poisson Process Numerical Method
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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