Noise in Integrate-and-Fire Neurons: From Stochastic Input to Escape Rates
- 1 February 2000
- journal article
- Published by MIT Press in Neural Computation
- Vol. 12 (2) , 367-384
- https://doi.org/10.1162/089976600300015835
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.Keywords
This publication has 26 references indexed in Scilit:
- Fast Global Oscillations in Networks of Integrate-and-Fire Neurons with Low Firing RatesNeural Computation, 1999
- Stochastic resonanceReviews of Modern Physics, 1998
- Aperiodic stochastic resonancePhysical Review E, 1996
- Noise in human muscle spindlesNature, 1996
- Threshold detection of wideband signals: A noise-induced maximum in the mutual informationPhysical Review E, 1996
- Time structure of the activity in neural network modelsPhysical Review E, 1995
- Noise enhancement of information transfer in crayfish mechanoreceptors by stochastic resonanceNature, 1993
- Associative memory in a network of ‘spiking’ neuronsNetwork: Computation in Neural Systems, 1992
- Probability density function of successive intervals of a nonhomogeneous Poisson process under low-frequency conditionsBiological Cybernetics, 1991
- Spike initiation by transmembrane current: a white‐noise analysis.The Journal of Physiology, 1976