2005 Special Issue: Stochastic resonance in noisy spiking retinal and sensory neuron models

Authors:
Ashok Patel;Bart Kosko
Affiliations:
Department of Electrical Engineering, Signal and Image Processing Institute, University of Southern California, Los Angeles, CA 90089-2564, USA;Department of Electrical Engineering, Signal and Image Processing Institute, University of Southern California, Los Angeles, CA 90089-2564, USA
Venue:
Neural Networks - 2005 Special issue: IJCNN 2005
Year:
2005

Citing 7
Cited 3

The identification of nonlinear biological systems: LNL cascade models

Biological Cybernetics
Stochastic resonance in the mutual information between input and output spike trains of noisy central neurons

Physica D
Dynamics of membrane excitability determine interspike interval variability: a link between spike generation mechanisms and cortical spike train statistics

Neural Computation
Ion channel stochasticity may be critical in determining the reliability and precision of spike timing

Neural Computation
Detecting and estimating signals in noisy cable structures, I: neuronal noise sources

Neural Computation
Analytic expressions for rate and CV of a type I Neuron driven by white Gaussian noise

Neural Computation
Stochastic resonance in noisy threshold neurons

Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03

2009 Special Issue: Error-probability noise benefits in threshold neural signal detection

Neural Networks
Optimal noise benefits in Neyman-Pearson and inequality-constrained statistical signal detection

IEEE Transactions on Signal Processing
Neural signal-detection noise benefits based on error probability

IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Two new theorems show that small amounts of additive white noise can improve the bit count or mutual information of several popular models of spiking retinal neurons and spiking sensory neurons. The first theorem gives necessary and sufficient conditions for this noise benefit or stochastic resonance (SR) effect for subthreshold signals in a standard family of Poisson spiking models of retinal neurons. The result holds for all types of finite-variance noise and for all types of infinite-variance stable noise: SR occurs if and only if a sum of noise means or location parameters falls outside a 'forbidden interval' of values. The second theorem gives a similar forbidden-interval sufficient condition for the SR effect for several types of spiking sensory neurons that include the Fitzhugh-Nagumo neuron, the leaky integrate-and-fire neuron, and the reduced Type I neuron model if the additive noise is Gaussian white noise. Simulations show that neither the forbidden-interval condition nor Gaussianity is necessary for the SR effect.