Autonomous Neural Information Processing by a Dynamical Memristor Circuit
András Halbritter a c, Dániel Molnár a c, Tímea Nóra Török a d, Roland Kövecs a, László Pósa a d, Péter Balázs a, György Molnár d, Nadia Jimenez Olalla c, Zoltán Balogh a, Juerg Leuthold c, János Volk d, Miklós Csontos c
a Department of Physics, Institute of Physics, Budapest University of Technology and Economics, Muegyetem rkp. 3, H-1111 Budapest, Hungary
b Institute of Electromagnetic Fields, ETH Zurich, Gloriastrasse 35, 8092 Zurich, Switzerland
c HUN-REN-BME Condensed Matter Research Group, Műegyetem rkp. 3., H-1111 Budapest, Hungary.
d Institute of Technical Physics and Materials Science, HUN-REN Centre for Energy Research, Konkoly-Thege M. út 29-33, 1121 Budapest, Hungary.
Proceedings of Neuronics Conference (Neuronics)
València, Spain, 2024 February 21st - 23rd
Organizers: Sabina Spiga and Juan Bisquert
Invited Speaker, András Halbritter, presentation 015
Publication date: 18th December 2023

Analog tunable memristors are widely utilized as artificial synapses in various neural network applications. However, exploiting the dynamical aspects of their conductance change to implement active neurons is still in its infancy, awaiting the realization of efficient neural signal recognition functionalities. Here we experimentally demonstrate an artificial neural information processing unit that can detect a temporal pattern in a very noisy environment, fire an output spike upon successful detection and reset itself in a fully unsupervised, autonomous manner [1]. This circuit relies on the dynamical operation of only two memristive blocks: a non-volatile Ta2O5 device and a volatile VO2 unit. A fading functionality with exponentially tunable memory time constant enables adaptive operation dynamics, which can be tailored for the targeted temporal pattern recognition task. In the trained circuit false input patterns only induce short-term variations. In contrast, the desired signal activates long-term memory operation of the non-volatile component, which triggers a firing output of the volatile block. Possible applications of the presented scheme in larger-scale reservoir computing architectures are also discussed.

We use our own and third party cookies for analysing and measuring usage of our website to improve our services. If you continue browsing, we consider accepting its use. You can check our Cookies Policy in which you will also find how to configure your web browser for the use of cookies. More info