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Learning by mistakes in memristor networks

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arxiv 2011.07201 v2 pith:PPE4PCQ4 submitted 2020-11-14 cs.ET nlin.AO

Juan Pablo Carbajal , Daniel Alejandro Martin , Dante Renato Chialvo This is my paper
classification cs.ETnlin.AO
keywords devicesimplementationlearningmemristornetworkresultsableadaptive
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Recent results in adaptive matter revived the interest in the implementation of novel devices able to perform brain-like operations. Here we introduce a training algorithm for a memristor network which is inspired in previous work on biological learning. Robust results are obtained from computer simulations of a network of voltage controlled memristive devices. Its implementation in hardware is straightforward, being scalable and requiring very little peripheral computation overhead.

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    The MDP policy linearizes probabilistic QoS constraints and provably achieves near-optimal utility and stability in multi-infrastructure-sharing networks, with error vanishing in the frame size.

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