title: Suprathreshold stochastic resonance in neural processing tuned by correlation.
abstract: Suprathreshold stochastic resonance (SSR) is examined in the context of integrate-and-fire neurons, with an emphasis on the role of correlation in the neuronal firing. We employed a model based on a network of spiking neurons which received synaptic inputs modeled by Poisson processes stimulated by a stepped input signal. The smoothed ensemble firing rate provided an output signal, and the mutual information between this signal and the input was calculated for networks with different noise levels and different numbers of neurons. It was found that an SSR effect was present in this context. We then examined a more biophysically plausible scenario where the noise was not controlled directly, but instead was tuned by the correlation between the inputs. The SSR effect remained present in this scenario with nonzero noise providing improved information transmission, and it was found that negative correlation between the inputs was optimal. Finally, an examination of SSR in the context of this model revealed its connection with more traditional stochastic resonance and showed a trade-off between supratheshold and subthreshold components. We discuss these results in the context of existing empirical evidence concerning correlations in neuronal firing.
authors.affiliation: Department of Informatics, Sussex University, Brighton BN1 9QH, United Kingdom.
authors.forename: Simon
authors.initials: S
authors.lastname: Durrant
authors.forename: Yanmei
authors.initials: Y
authors.lastname: Kang
authors.forename: Nigel
authors.initials: N
authors.lastname: Stocks
authors.forename: Jianfeng
authors.initials: J
authors.lastname: Feng
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000818:Animals; D001703:Biophysics / Q000379:methods; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D009415:Nerve Net / Q000502:physiology; D009474:Neurons / Q000378:metabolism / Q000502:physiology*; D016011:Normal Distribution; D016012:Poisson Distribution; D013269:Stochastic Processes; D009435:Synaptic Transmission / Q000502:physiology; D013997:Time Factors