Soft-bound synaptic plasticity increases storage capacity

Accurate models of synaptic plasticity are essential to understand the adaptive properties of the nervous system and for realistic models of learning and memory. Experiments have shown that synaptic plasticity depends not only on pre- and post-synaptic activity patterns, but also on the strength of...

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Bibliographic Details
Published in:	PLoS computational biology Vol. 8; no. 12; p. e1002836
Main Authors:	van Rossum, Mark C W, Shippi, Maria, Barrett, Adam B
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 01-12-2012 Public Library of Science (PLoS)
Subjects:	Biology Competition Distance learning Experiments Information storage Information Theory Learning Long-Term Potentiation Memory Models, Theoretical Neural networks Neuronal Plasticity Neuroplasticity Neurosciences Physiological aspects Studies Synapses United Kingdom Learning Models, Theoretical Information Theory Neuronal Plasticity Memory Long-Term Potentiation
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Summary:	Accurate models of synaptic plasticity are essential to understand the adaptive properties of the nervous system and for realistic models of learning and memory. Experiments have shown that synaptic plasticity depends not only on pre- and post-synaptic activity patterns, but also on the strength of the connection itself. Namely, weaker synapses are more easily strengthened than already strong ones. This so called soft-bound plasticity automatically constrains the synaptic strengths. It is known that this has important consequences for the dynamics of plasticity and the synaptic weight distribution, but its impact on information storage is unknown. In this modeling study we introduce an information theoretic framework to analyse memory storage in an online learning setting. We show that soft-bound plasticity increases a variety of performance criteria by about 18% over hard-bound plasticity, and likely maximizes the storage capacity of synapses.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Conceived and designed the experiments: MCWvR ABB. Performed the experiments: MS MCWvR. Wrote the paper: ABB MCWvR MS. Current address: School of Informatics, University of Sussex, Brighton, United Kingdom. The authors have declared that no competing interests exist.
ISSN:	1553-7358 1553-734X 1553-7358
DOI:	10.1371/journal.pcbi.1002836