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.
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页码：92 / 98
页数：7
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