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


Published in:
ICANN/ICONIP
Presented at:
ICANN/ICONIP, Istanbul, Turkey, June 26-29, 2003
Year:
2003
Publisher:
ICANN/ICONIP, Istanbul, Turkey
Other identifiers:
Laboratories:




 Record created 2007-11-26, last modified 2018-01-28

External links:
Download fulltextURL
Download fulltextn/a
Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)