Queuing theory for spike driven synaptic dynamics View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

1998

AUTHORS

Mario Annunziato , Stefano Fusi

ABSTRACT

We present a model for spike driven, stochastic dynamics of a synapse with two stable states, suited for aVLSI implementation. The stochastic nature of learning, which allows for optimal storage capacity, is due to the variability in spike emission times of pre- and post-synaptic neurons, and emerges as a result of the collective properties of the whole network. The dynamics of the single synapse is studied with the methods of queuing theory. Numerical results show that LTP and LTD are stochastically induced by the two neurons’ activity states and transition probabilities are in the range required by the theory of stochastic learning. More... »

PAGES

117-122

Book

TITLE

ICANN 98

ISBN

978-3-540-76263-8
978-1-4471-1599-1

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-1-4471-1599-1_13

DOI

http://dx.doi.org/10.1007/978-1-4471-1599-1_13

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1044385719


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