Proc. Joint International Conference ICANN/ICONIP 2003, Kaynak et al. (Eds.), Springer, LNCS 2714, pp. 92-98
Optimal Hebbian Learning: a Probabilistic Point of View.
J.-P. Pfister, D. Barber and W. Gerstner
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