Archivio Istituzionale della RicercaChaotic neural networks are able to reproduce chaotic dynamics observable in the brain of various living beings. As a result, study of the dynamical properties of such networks may pave the way towards a better understanding of the memory rules of the brain. In this paper a simple neural circuit employing a theoretical memristive synapse with symmetric charge-flux nonlinearity is found to behave chaotically. After presentation of a novel boundary-condition based model for real memristor nano-structures, conditions under which a suitable arrangement of such nano-structures is dynamically equivalent to the theoretical memristor are derived and validated.
Memristor models for chaotic neural circuits / Corinto, Fernando; Ascoli, Alon; Gilli, Marco. - In: IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE. - ISSN 1556-603X. - (2012), pp. 2967-2974. (Intervento presentato al convegno IJCNN 2012 tenutosi a Brisbane, Australia nel 10-15 June) [10.1109/IJCNN.2012.6252777].
Memristor models for chaotic neural circuits
CORINTO, FERNANDO;ASCOLI, ALON;GILLI, MARCO
2012
Abstract
Chaotic neural networks are able to reproduce chaotic dynamics observable in the brain of various living beings. As a result, study of the dynamical properties of such networks may pave the way towards a better understanding of the memory rules of the brain. In this paper a simple neural circuit employing a theoretical memristive synapse with symmetric charge-flux nonlinearity is found to behave chaotically. After presentation of a novel boundary-condition based model for real memristor nano-structures, conditions under which a suitable arrangement of such nano-structures is dynamically equivalent to the theoretical memristor are derived and validated.
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https://hdl.handle.net/11583/2484779
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