Error-backpropagation in temporally encoded networks of spiking neurons

被引:720
作者
Bohte, SM
Kok, JN
La Poutré, H
机构
[1] CWI, NL-1098 SJ Amsterdam, Netherlands
[2] Eindhoven Univ Technol, Sch Technol Management, NL-5600 MB Eindhoven, Netherlands
[3] Leiden Univ, LIACS, NL-2300 RA Leiden, Netherlands
关键词
spiking neurons; temporal coding; error-backpropagation;
D O I
10.1016/S0925-2312(01)00658-0
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
For a network of spiking neurons that encodes information in the timing of individual spike times, we derive a supervised learning rule, SpikeProp, akin to traditional error-backpropagation. With this algorithm, we demonstrate how networks of spiking neurons with biologically reasonable action potentials can perform complex non-linear classification in fast temporal coding just as well as rate-coded networks. We perform experiments for the classical XOR problem, when posed in a temporal setting, as well as for a number of other benchmark datasets. Comparing the (implicit) number of spiking neurons required for the encoding of the interpolated XOR problem, the trained networks demonstrate that temporal coding is a viable code for fast neural information processing, and as such requires less neurons than instantaneous rate-coding. Furthermore, we find that reliable temporal computation in the spiking networks was only accomplished when using spike response functions with a time constant longer than the coding interval, as has been predicted by theoretical considerations. (C) 2002 Elsevier Science B.V. All rights reserved.
引用
收藏
页码:17 / 37
页数:21
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