Resonance in a Stochastic Neuron Model with Delayed Interaction
Abstract
We study here a simple stochastic single neuron model with delayed self-feedback capable of generating spike trains. Simulations show that its spike trains exhibit resonant behavior between "noise" and "delay". In order to gain insight into this resonance, we simplify the model and study a stochastic binary element whose transition probability depends on its state at a fixed interval in the past. With this simplified model we can analytically compute interspike interval histograms, and show how the resonance between noise and delay arises. The resonance is also observed when such elements are coupled through delayed interaction.
Cite
Text
Ohira et al. "Resonance in a Stochastic Neuron Model with Delayed Interaction." Neural Information Processing Systems, 1999.Markdown
[Ohira et al. "Resonance in a Stochastic Neuron Model with Delayed Interaction." Neural Information Processing Systems, 1999.](https://mlanthology.org/neurips/1999/ohira1999neurips-resonance/)BibTeX
@inproceedings{ohira1999neurips-resonance,
title = {{Resonance in a Stochastic Neuron Model with Delayed Interaction}},
author = {Ohira, Toru and Sato, Yuzuru and Cowan, Jack D.},
booktitle = {Neural Information Processing Systems},
year = {1999},
pages = {314-320},
url = {https://mlanthology.org/neurips/1999/ohira1999neurips-resonance/}
}