Learning by mistakes in memristor networks

Abstract

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.

Article has 11 figures. Builds upon arXiv:adap-org/9707006, arXiv:cond-mat/0009211, and arXiv:1406.2210

References in corpus (1)

• Memristor models for machine learning

Cited by in corpus (2)

• Ergodicity, lack thereof, and the performance of reservoir computing with memristive networks
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