Optimal Heterogeneity for Coding in Spiking Neural Networks

被引：86

作者：

Mejias, J. F. ^{[1
]}

Longtin, A.

机构：

[1] Univ Ottawa, Dept Phys, Ottawa, ON K1N 6N5, Canada

来源：

PHYSICAL REVIEW LETTERS | 2012年 / 108卷 / 21期

基金：

加拿大自然科学与工程研究理事会;

关键词：

COINCIDENCE DETECTION; STOCHASTIC RESONANCE; STRIATE CORTEX; NEURONS; INFORMATION; SYNCHRONY; DYNAMICS; PLASTICITY; SYSTEMS; TRAINS;

D O I：

10.1103/PhysRevLett.108.228102

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

The effect of cellular heterogeneity on the coding properties of neural populations is studied analytically and numerically. We find that heterogeneity decreases the threshold for synchronization, and its strength is nonlinearly related to the network mean firing rate. In addition, conditions are shown under which heterogeneity optimizes network information transmission for either temporal or rate coding, with high input frequencies leading to different effects for each coding strategy. The results are shown to be robust for more realistic conditions.

引用

页数：5

共 30 条

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[2]

[Anonymous], 2019, SCIENCE, DOI [DOI 10.1126/SCIENCE.275.5297.221, DOI 10.1126/SCIENCE.AAO6640]

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