Building functional networks of spiking model neurons

被引：154

作者：

Abbott, L. F. ^{[1
,2
]}

DePasquale, Brian ^{[1
]}

Memmesheimer, Raoul-Martin ^{[1
,3
]}

机构：

[1] Columbia Univ Coll Phys & Surg, Dept Neurosci, 630 W 168th St, New York, NY 10032 USA

[2] Columbia Univ Coll Phys & Surg, Dept Physiol & Cellular Biophys, New York, NY 10032 USA

[3] Radboud Univ Nijmegen, Donders Inst Brain Cognit & Behav, Dept Neuroinformat, NL-6525 ED Nijmegen, Netherlands

来源：

NATURE NEUROSCIENCE | 2016年 / 19卷 / 03期

基金：

美国国家卫生研究院;

关键词：

NEURAL-NETWORKS; MEMORY; INTEGRATION; DYNAMICS; INFORMATION; PROPAGATION; COMPUTATION; PLASTICITY; EXCITATION; FRAMEWORK;

D O I：

10.1038/nn.4241

中图分类号：

Q189 [神经科学];

学科分类号：

071006 ;

摘要：

Most of the networks used by computer scientists and many of those studied by modelers in neuroscience represent unit activities as continuous variables. Neurons, however, communicate primarily through discontinuous spiking. We review methods for transferring our ability to construct interesting networks that perform relevant tasks from the artificial continuous domain to more realistic spiking network models. These methods raise a number of issues that warrant further theoretical and experimental study.

引用

页码：350 / 355

页数：6

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