Extending Stochastic Resonance for Neuron Models to General Levy Noise

被引：14

作者：

Applebaum, David ^{[1
]}

机构：

[1] Univ Sheffield, Dept Probabil & Stat, Sheffield S3 7RH, S Yorkshire, England

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2009年 / 20卷 / 12期

关键词：

Levy noise; neuron models; stochastic differential equation (SDE); stochastic resonance (SR);

D O I：

10.1109/TNN.2009.2033183

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A recent paper by Patel and Kosko (2008) demonstrated stochastic resonance (SR) for general feedback continuous and spiking neuron models using additive Levy noise constrained to have finite second moments. In this brief, we drop this constraint and show that their result extends to general Levy noise models. We achieve this by showing that "large jump" discontinuities in the noise can be controlled so as to allow the stochastic model to tend to a deterministic one as the noise dissipates to zero. SR then follows by a "forbidden intervals" theorem as in Patel and Kosko's paper.

引用

页码：1993 / 1995

页数：3

共 5 条

[1]

[Anonymous], 2004, Levy processes and stochastic calculus

[2] Noise-enhanced performance for an optimal Bayesian estimator [J].