Noise benefits in spiking retinal and sensory neuron models

This paper presents two new theorems that give sufficient conditions (and necessary in the first case) for a noise benefit or stochastic-resonance effect in popular spiking models of retinal neurons and sensory neurons. Small amounts of additive white noise increase the neuron´s input-output bit count or Shannon mutual information. This stochastic-resonance (SR) effect applies to standard Poisson spiking models of retinal neurons for all possible types of finite-variance noise and for all impulsive or infinite-variance stable noise. A similar SR result holds for several types of sensory spiking neurons such as the Fitzhugh-Nagumo model and the integrate-and-fire model if the additive noise is Gaussian white noise.