Pfister, J.-P.Barber, D.Gerstner, W.2007-11-262007-11-262007-11-26200310.1007/3-540-44989-2_12https://infoscience.epfl.ch/handle/20.500.14299/14919WOS:0001853781000124249Many 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 resultstext::conference output::conference proceedings::conference paper