Pfister, Jean-Pascal; Barber, David; Gerstner, Wulfram Optimal Hebbian learning: A probabilistic point of view. (English) Zbl 1037.68707 Kaynak, Okyay (ed.) et al., Artificial neural networks and neural information processing — ICANN/ICONIP 2003. Joint international conference ICANN/ICONIP 2003, Istanbul, Turkey, 26–29, 2003. Proceedings. Berlin: Springer (ISBN 3-540-40408-2/pbk). Lect. Notes Comput. Sci. 2714, 92-98 (2003). Summary: Many activity dependent learning rules have been proposed in order to model long-term potentiation. 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.For the entire collection see [Zbl 1029.00055]. Cited in 2 Documents MSC: 68T05 Learning and adaptive systems in artificial intelligence 92B20 Neural networks for/in biological studies, artificial life and related topics PDFBibTeX XMLCite \textit{J.-P. Pfister} et al., Lect. Notes Comput. Sci. 2714, 92--98 (2003; Zbl 1037.68707) Full Text: Link