Optimal Hebbian Learning: a Probabilistic Point of View

• Presented at: ICANN/ICONIP, Istanbul, Turkey, June 26-29, 2003
• Published in: ICANN/ICONIP
• ICANN/ICONIP: Istanbul, Turkey, 2003

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

Reference

• LCN-CONF-2003-003
• View record in Web of Science

• URL: http://www.informatik.uni-trier.de/~ley/db/conf/icann/icann2003.html

Record created on 2007-11-26, modified on 2012-03-19