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  Pattern storage and processing in attractor networks with short-time synaptic dynamics

Bibitchkov, D., Herrmann, J. M., & Geisel, T. (2002). Pattern storage and processing in attractor networks with short-time synaptic dynamics. Network: Computation in Neural Systems, 13(1), 115-129.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0029-179F-6 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0029-17A0-D
Genre: Journal Article

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 Creators:
Bibitchkov, D., Author
Herrmann, J. M.1, Author              
Geisel, T.1, Author              
Affiliations:
1Department of Nonlinear Dynamics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2063286              

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 Abstract: Neurophysiological experiments show that the strength of synaptic connections can undergo substantial changes on a short time scale. These changes depend on the history of the presynaptic input. Using mean-field techniques, we study how short-time dynamics of synaptic connections influence the performance of attractor neural networks in terms of their memory capacity and capability to process external signals. For binary discrete-time as well as for firing rate continuous-time neural networks, the fixed points of the network dynamics are shown to be unaffected by synaptic dynamics. However, the stability of patterns changes considerably. Synaptic depression turns out to reduce the storage capacity. On the other hand, synaptic depression is shown to be advantageous for processing of pattern sequences. The analytical results on stability, size of the basins of attraction and on the switching between patterns are complemented by numerical simulations.

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Language(s): eng - English
 Dates: 2002-02
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: eDoc: 15163
ISI: 000174237400004
 Degree: -

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Title: Network: Computation in Neural Systems
  Alternative Title : Netw.-Comput. Neural Syst.
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 13 (1) Sequence Number: - Start / End Page: 115 - 129 Identifier: ISSN: 0954-898X