Bayesian spiking neurons I: Inference

被引：188
|
作者：
Deneve, Sophie [1 ]
机构：
[1] Ecole Normale Super, Coll France, Dept Detudes Cognit, Grp Neural Theory, F-75005 Paris, France
来源：
NEURAL COMPUTATION | 2008年 / 20卷 / 01期
关键词：
D O I：
10.1162/neco.2008.20.1.91
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We show that the dynamics of spiking neurons can be interpreted as a form of Bayesian inference in time. Neurons that optimally integrate evidence about events in the external world exhibit properties similar to leaky integrate-and-fire neurons with spike-dependent adaptation and maximally respond to fluctuations of their input. Spikes signal the occurrence of new information-what cannot be predicted from the past activity. As a result, firing statistics are close to Poisson, albeit providing a deterministic representation of probabilities.
引用
收藏
页码：91 / 117
页数：27
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