Modeling and Implementation of Firing-Rate Neuromorphic-Network Classifiers with Bilayer Pt / Al 2 O 3 / TiO 2x / Pt Memristors

Neuromorphic pattern classifiers were implemented, for the first time, using transistor-free integrated crossbar circuits with bilayer metal-oxide memristors. 10×6and 10×8crosspoint 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.

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