title: Competition model for aperiodic stochastic resonance in a Fitzhugh-Nagumo model of cardiac sensory neurons.
abstract: Regional cardiac control depends upon feedback of the status of the heart from afferent neurons responding to chemical and mechanical stimuli as transduced by an array of sensory neurites. Emerging experimental evidence shows that neural control in the heart may be partially exerted using subthreshold inputs that are amplified by noisy mechanical fluctuations. This amplification is known as aperiodic stochastic resonance (ASR). Neural control in the noisy, subthreshold regime is difficult to see since there is a near absence of any correlation between input and the output, the latter being the average firing (spiking) rate of the neuron. This lack of correlation is unresolved by traditional energy models of ASR since these models are unsuitable for identifying "cause and effect" between such inputs and outputs. In this paper, the "competition between averages" model is used to determine what portion of a noisy, subthreshold input is responsible, on average, for the output of sensory neurons as represented by the Fitzhugh-Nagumo equations. A physiologically relevant conclusion of this analysis is that a nearly constant amount of input is responsible for a spike, on average, and this amount is approximately independent of the firing rate. Hence, correlation measures are generally reduced as the firing rate is lowered even though neural control under this model is actually unaffected.
authors: Department of Engineering Mathematics, Dalhousie University, P. O. Box 1000, Halifax, Nova Scotia, Canada B3J 2X4.
authors: G C
authors: GC
authors: Kember
authors: G A
authors: GA
authors: Fenton
authors: J A
authors: JA
authors: Armour
authors: N
authors: N
authors: Kalyaniwalla
mesh_terms: D000818:Animals; D055592:Biophysical Phenomena; D001703:Biophysics; D006321:Heart / Q000294:innervation*; D008959:Models, Neurological; D015233:Models, Statistical; D009415:Nerve Net; D009474:Neurons / Q000473:pathology*; D009475:Neurons, Afferent / Q000502:physiology*; D013269:Stochastic Processes; D009435:Synaptic Transmission; D013997:Time Factors