DOI: https://doi.org/10.29363/nanoge.neuronics.2024.015
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.
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