Optimal Hebbian Learning: A Probabilistic Point of View

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.

• Type: Conference
• Year: 2003
• Where: ICANN
• Authors: Jean-Pascal Pfister, David Barber, Wulfram Gerstner
• Keywords: Activity Dependent Learning, Dependent Learning Rule, ICANN 2003, Time Dependent Learning