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Spiking Sequence Learning using Maximum Likelihood: Hopfield Networks

Summary: Spiking Sequence Learning using Maximum
Likelihood: Hopfield Networks
David Barber # Felix Agakov
Division of Informatics, University of Edinburgh, Edinburgh EH1 2QL, UK
d.barber@anc.ed.ac.uk felixa@dai.ed.ac.uk http://anc.ed.ac.uk
July 15, 2002
We consider the learning of correlated temporal sequences from a statistical
viewpoint, using Maximum Likelihood to derive a simple local learning rule for
an idealized spiking neuron model. The rule is capable of robustly storing mul­
tiple sequences of correlated patterns in a recurrent network model, unlike other
prescriptions based on pattern correlations alone.
1 Introduction
Simple models of temporal sequence storage devices are typically based on recurrent net­
works that model the hippocampal region CA3 with training sequence inputs represented
# Corresponding author.

by projections from entorhinal cortex and dentate gyrus (Shon et al., 2002). Mechanisms
by which a network can learn to store temporal sequences are usually based on local


Source: Agakov, Felix - Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh


Collections: Computer Technologies and Information Sciences