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  Adaptive Properties of Stochastic Memristor Networks: A Computational Study

Sigala, R., Smerieri, A., & Erokhin, V. (2011). Adaptive Properties of Stochastic Memristor Networks: A Computational Study. Amsterdam, Netherlands: Elsevier.

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 Creators:
Sigala, R1, 2, Author              
Smerieri, A, Author
Erokhin, V, Author
Affiliations:
1Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: A ‘memristor’ is a passive two-terminal circuit element the electric resistance of which depends on the history of the charge that has passed through it. We implemented a platform to simulate adaptive properties of stochastic memristor networks. We showed that such networks follow a stable behavior that diverges from its initial state depending on the history of stimulation. Additionally, we observed that the connectivity patterns of the networks influence their adaptive properties. These results confirm the adaptive properties of statistical memristor networks and suggest that they can be potentially used as complex and self-assembled ‘learning machines’.

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 Dates: 2011-12
 Publication Status: Published in print
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.1016/j.procs.2011.09.021
BibTex Citekey: SigalaSE2011
 Degree: -

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Title: 2nd European Future Technologies Conference and Exhibition (FET 11)
Place of Event: Budapest, Hungary
Start-/End Date: 2011-05-04 - 2011-05-06

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Title: Procedia Computer Science
Source Genre: Series
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Publ. Info: Amsterdam, Netherlands : Elsevier
Pages: - Volume / Issue: 7 Sequence Number: - Start / End Page: 312 - 313 Identifier: -