Neural Comput - Information transmission using non-poisson regular firing.

Tópicos

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Resumo

In many cortical areas, neural spike trains do not follow a Poisson process. In this study, we investigate a possible benefit of non-Poisson spiking for information transmission by studying the minimal rate fluctuation that can be detected by a Bayesian estimator. The idea is that an inhomogeneous Poisson process may make it difficult for downstream decoders to resolve subtle changes in rate fluctuation, but by using a more regular non-Poisson process, the nervous system can make rate fluctuations easier to detect. We evaluate the degree to which regular firing reduces the rate fluctuation detection threshold. We find that the threshold for detection is reduced in proportion to the coefficient of variation of interspike intervals.

Resumo Limpo

mani cortic area neural spike train follow poisson process studi investig possibl benefit nonpoisson spike inform transmiss studi minim rate fluctuat can detect bayesian estim idea inhomogen poisson process may make difficult downstream decod resolv subtl chang rate fluctuat use regular nonpoisson process nervous system can make rate fluctuat easier detect evalu degre regular fire reduc rate fluctuat detect threshold find threshold detect reduc proport coeffici variat interspik interv

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