Features of Simulation of a Biomorphic Neural Network on Electronic Device with Non-Volatile Memory and Low Power Consumption

About the authors:

Alexander N. Busygin, Cand. Sci. (Phys.-Math.), Associate Professor, Department of Applied and Technical Physics, School of Natural Sciences, University of Tyumen, Tyumen, Russia; Senior Researcher, Memristive Materials Laboratory, Center for Nature-Inspired Engineering, University of Tyumen, Tyumen, Russia; a.n.busygin@utmn.ru, https://orcid.org/0000-0002-3439-8067

Aleksey Yu. Kuzmenko, Postgraduate Student, Department of Micro- and Nanotechnologies, Tyumen State University; alekslock@yandex.ru

Alexander D. Pisarev, Cand. Sci. (Tech.), Associate Professor, Department of Applied and Technical Physics, School of Natural Sciences, University of Tyumen, Tyumen, Russia; Senior Researcher, Memristive Materials Laboratory, Center for Nature-Inspired Engineering, University of Tyumen, Tyumen, Russia; spcb.doc@utmn.ru, https://orcid.org/0000-0002-5602-3880

Vadim A. Filippov, Cand. Sci. (Soc.), Head of the Center for Advanced Studies “Artificial Cognitive Systems”, Deputy Rector for the Program “5-100”, Tyumen State University; filippov-vadim@yandex.ru

Abstract:

The article discusses the electronic device for the simulation of biomorphic neural networks which combines programmable microcontrollers and a non-volatile memristor memory, and it is compatible with a personal computer. The device is positioned as a research platform to develop the most effective architecture for a corticomorphic processor. A single-layer perceptron for the primary association of the input data and the biomorphic neural network are chosen as its initial architecture. The article discusses the features of biomorphic neural networks and their adaptation to the device.

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