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conference paper
Optimal Hebbian Learning: a Probabilistic Point of View
2003
Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP
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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