Noise in Integrate-and-Fire Neurons: From Stochastic Input to Escape Rates
Abstract
We analyze the effect of noise in integrate-and-fire neurons driven by time-dependent input and compare the diffusion approximation for the membrane potential to escape noise. It is shown that for time-dependent subthreshold input, diffusive noise can be replaced by escape noise with a hazard function that has a gaussian dependence on the distance between the (noise-free) membrane voltage and threshold. The approximation is improved if we add to the hazard function a probability current proportional to the derivative of the voltage. Stochastic resonance in response to periodic input occurs in both noise models and exhibits similar characteristics.
Cite
Text
Plesser and Gerstner. "Noise in Integrate-and-Fire Neurons: From Stochastic Input to Escape Rates." Neural Computation, 2000. doi:10.1162/089976600300015835Markdown
[Plesser and Gerstner. "Noise in Integrate-and-Fire Neurons: From Stochastic Input to Escape Rates." Neural Computation, 2000.](https://mlanthology.org/neco/2000/plesser2000neco-noise/) doi:10.1162/089976600300015835BibTeX
@article{plesser2000neco-noise,
title = {{Noise in Integrate-and-Fire Neurons: From Stochastic Input to Escape Rates}},
author = {Plesser, Hans E. and Gerstner, Wulfram},
journal = {Neural Computation},
year = {2000},
pages = {367-384},
doi = {10.1162/089976600300015835},
volume = {12},
url = {https://mlanthology.org/neco/2000/plesser2000neco-noise/}
}