Modeling and implementation of firing-rate neuromorphic-network classifiers with bilayer Pt/Al2O3/TiO2−x/Pt Memristors

Abstract

Neuromorphic pattern classifiers were implemented, for the first time, using transistor-free integrated crossbar circuits with bilayer metal-oxide memristors. 10×6- and 10×8-crosspoint neuromorphic networks were trained in-situ using a Manhattan-Rule algorithm to separate a set of 3×3 binary images: into 3 classes using the batch-mode training, and into 4 classes using the stochastic-mode training, respectively. Simulation of much larger, multilayer neural network classifiers based on such technology has sown that their fidelity may be on a par with the state-of-the-art results for software-implemented networks.