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由长程相关噪声驱动的神经元模型的放电统计

Firing statistics of a neuron model driven by long-range correlated noise.

作者信息

Middleton J W, Chacron M J, Lindner B, Longtin A

机构信息

Department of Physics, University of Ottawa, 150 Louis Pasteur, Ottawa, Ontario, Canada K1N 6N5.

出版信息

Phys Rev E Stat Nonlin Soft Matter Phys. 2003 Aug;68(2 Pt 1):021920. doi: 10.1103/PhysRevE.68.021920. Epub 2003 Aug 28.

Abstract

We study the statistics of the firing patterns of a perfect integrate and fire neuron model driven by additive long-range correlated Ornstein-Uhlenbeck noise. Using a quasistatic weak noise approximation we obtain expressions for the interspike interval (ISI) probability density, the power spectral density, and the spike count Fano factor. We find unimodal, long-tailed ISI densities, Lorenzian power spectra at low frequencies, and a minimum in the Fano factor as a function of counting time. The implications of these results for signal detection are discussed.

摘要

我们研究了由加性长程相关奥恩斯坦 - 乌伦贝克噪声驱动的完美积分发放神经元模型的放电模式统计特性。利用准静态弱噪声近似,我们得到了峰峰间隔(ISI)概率密度、功率谱密度和脉冲计数法诺因子的表达式。我们发现了单峰、长尾的ISI密度,低频处的洛伦兹功率谱,以及作为计数时间函数的法诺因子最小值。讨论了这些结果对信号检测的意义。

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