Publication
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
Eilif Benjamin Muller, Michael Reimann, James Gonzalo King, Marwan Muhammad Ahmed Abdellah, Pramod Shivaji Kumbhar, András Ecker, Sirio Bolaños Puchet, James Bryden Isbister, Daniela Egas Santander, Jorge Blanco Alonso, Giuseppe Chindemi, Ioannis Magkanaris