Responses of Ensemble Neurons to Spike-Train Signals with Independent Noises: Stochastic Resonance and Spike Variability

Details Responses of Ensemble Neurons to Spike-Train Signals with Independent Noises: Stochastic Resonance and Spike Variability Responses of Ensemble Neurons to Spike-Train Signals with Independent Noises: Stochastic Resonance and Spike Variability

2002-02-15

arxiv.org/abs/cond-mat/0202252v1

Bull. Tokyo Gakugei Univ. Sect. IV, 55 (2003) 5

Physics

Condensed Matter

Disordered Systems and Neural Networks

15 pages, 3 figures

Scientific paper

Responses have been numerically studied of an ensemble of $N$ (=1, 10, and 100) Hodgkin-Huxley (HH) neurons to coherent spike-train inputs applied with independent Poisson spike-train (ST) noise and Gaussian white noise. Three interrelated issues have been investigated: (1) the difference and the similarity between the effects of the two noises, (2) the size effect of a neuron ensemble on the signal-to-noise ratio (SNR), and (3) the compatibility of a large firing variability with fairly good information transmission. (1) The property of stochastic resonance (SR) for ST noise is shown to be rather different from that for white noise. When SNR for sub-threshold inputs obtained in our simulation is analyzed by the expression given by $SNR=10 {\rm log_{10}} [(A/X^{\alpha}) {\rm exp}(-B/X)]$ where $X$ expresses the noise intensity and $A$ and $B$ are constants, the index $\alpha$ is $\alpha=3$ for the ST noise and $\alpha=2$ for the white noise: the former is different from the conventional value of $\alpha=2$ realized in many non-linear systems. ST noise works less effectively for SR than white noise. (2) The transmission fidelity evaluated by SNR is much improved by increasing $N$, the size of ensemble neurons. In a large-scale neuron ensemble, SNR for supra-threshold inputs is shown to be not significantly degraded by weak noises responsible to SR for sub-threshold inputs. (3) Interspike intervals (ISIs) of output spikes for sub-threshold inputs have a large variability ($c_{v} \siml 0.8$), which is comparable to the data observed in cortical neurons. Despite variable firings of individual neurons, output signals summed over an ensemble may carry information with a fairly good SNR by the aid of SR and a pooling effect.

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