Hardware elementary perceptron based on polyaniline memristive devices

• Published in: Organic electronics Vol. 25; pp. 16 - 20
• Main Authors: Demin, V.A., Erokhin, V.V., Emelyanov, A.V., Battistoni, S., Baldi, G., Iannotta, S., Kashkarov, P.K., Kovalchuk, M.V.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2015
• Subjects: Machine learning; Memristor; Neuromorphic computing; Pattern classification; Perceptron; Polyaniline; Memristor; Perceptron; Polyaniline; Pattern classification; Machine learning; Neuromorphic computing
• ISSN: 1566-1199; 1878-5530
• DOI: 10.1016/j.orgel.2015.06.015

[Display omitted] •Elementary perceptron is realized on polyaniline-based memristive devices.•Training procedure is demonstrated for perceptron to perform NAND and NOR functions.•Possibility of realization of neural network with organic memristive links is shown. Elementary perceptron is an artificial neural network with a single layer of adaptive links and one output neuron that can solve simple linearly separable tasks such as invariant pattern recognition, linear approximation, prediction and others. We report on the hardware realization of the elementary perceptron with the use of polyaniline-based memristive devices as the analog link weights. An error correction algorithm was used to get the perceptron to learn the implementation of the NAND and NOR logic functions as examples of linearly separable tasks. The physical realization of an elementary perceptron demonstrates the ability to form the hardware-based neuromorphic networks with the use of organic memristive devices. The results provide a great promise toward new approaches for very compact, low-volatile and high-performance neurochips that could be made for a huge number of intellectual products and applications.