Optimal Hebbian learning: A Probabilistic point of view

被引：0

作者：

Pfister, JP ^{[1
]}

Barber, D

Gerstner, W

机构：

[1] EPFL, Lab Computat Neurosci, CH-1005 Lausanne, Switzerland

[2] Univ Edinburgh, Inst Adapt & Neural Computat, Edinburgh EH1 2QL, Midlothian, Scotland

来源：

ARTIFICAIL NEURAL NETWORKS AND NEURAL INFORMATION PROCESSING - ICAN/ICONIP 2003 | 2003年 / 2714卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Many activity dependent learning rules have been proposed in order to model long-term potentiation (LTP). Our aim is to derive a spike time dependent learning rule from a probabilistic optimality criterion. Our approach allows us to obtain quantitative results in terms of a learning window. This is done by maximising a given likelihood function with respect to the synaptic weights. The resulting weight adaptation is compared with experimental results.

引用

页码：92 / 98

页数：7

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