Statistical implications of clipped Hebbian learning of cell assemblies

Authors:
Andreas Knoblauch
Affiliations:
Department of Neural Information Processing, University of Ulm, Oberer Eselsberg, D-89069 Ulm, Germany
Venue:
Neurocomputing
Year:
2005

Citing 3
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Improved bidirectional retrieval of sparse patterns stored by Hebbian learning

Neural Networks
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Optimal matrix compression yields storage capacity 1 for binary Willshaw associative memory

ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing

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Abstract

In this study binary associative networks of the Willshaw type are analyzed with respect to the effect of clipped Hebbian learning on the distribution of postsynaptic potentials when stimulating with random activation patterns. It is shown that the variance in the postsynaptic potentials grows with the square of the stimulation strength if the synapses have been generated by Hebbian learning of many overlapping patterns, but only linearly for independent random synapses. This result bears implications both for analysis of associative memory and the detection of Hebbian cell assemblies in neurophysiological experiments.