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Adaptive Properties of Stochastic Memristor Networks: A Computational Study

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84221

Sigala,  R
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Sigala, R., Smerieri, A., & Erokhin, V. (2011). Adaptive Properties of Stochastic Memristor Networks: A Computational Study. In Procedia Computer Science Volume 7 (pp. 312-313). Amsterdam, Netherlands: Elsevier.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-BBC2-5
Zusammenfassung
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’.