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. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Pfister, J. P., Barber, D., & Gerstner, W. (2003). Optimal hebbian learning: A probabilistic point of view. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. https://doi.org/10.1007/3-540-44989-2_12
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