Stochastic resonance with a mixture of sub-and supra-threshold stimuli in a population of neuron models
| Autor: | Keiko Momose, Minato Kawaguchi, Dominique Durand, Hiroyuki Mino |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
Time Factors
Computer science Models Neurological Population Action Potentials Signal-To-Noise Ratio Stimulus (physiology) Synaptic Transmission Membrane Potentials medicine Humans Computer Simulation Poisson Distribution education Simulation Neurons Stochastic Processes education.field_of_study Artificial neural network Noise (signal processing) Stochastic process Reproducibility of Results Signal Processing Computer-Assisted Stochastic resonance (sensory neurobiology) Mutual information Neurophysiology Hodgkin–Huxley model medicine.anatomical_structure Neuron Biological system Monte Carlo Method Algorithms |
| Zdroj: | EMBC Scopus-Elsevier |
| DOI: | 10.1109/iembs.2011.6091709 |
| Popis: | This paper presents a novel type of stochastic resonance (SR) with a mixture of sub- and supra-threshold stimuli in a population of neuron models beyond regular SR and Supra-threshold SR (SSR) phenomena. We investigate through computer simulations if the novel type of SR can be observed or not, using the mutual information (MI) estimated from a population of neural spike trains as an index of information transmission. Computer simulations showed that the MI had a typical type of SR curves, even when the balance between sub-and supra-threshold stimuli was varied, suggesting the novel type of SR. Moreover, the peak of MI increased as the balance of supra-threshold stimuli got stronger, i.e., as the situation was getting close to the SSR from the regular SR. This finding could accelerate our understanding about how fluctuations play a role in processing information carried by a mixture of sub-and supra-threshold stimuli. |
| Databáze: | OpenAIRE |
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