title: Experimental demonstration of associative memory with memristive neural networks.
abstract: Synapses are essential elements for computation and information storage in both real and artificial neural systems. An artificial synapse needs to remember its past dynamical history, store a continuous set of states, and be "plastic" according to the pre-synaptic and post-synaptic neuronal activity. Here we show that all this can be accomplished by a memory-resistor (memristor for short). In particular, by using simple and inexpensive off-the-shelf components we have built a memristor emulator which realizes all required synaptic properties. Most importantly, we have demonstrated experimentally the formation of associative memory in a simple neural network consisting of three electronic neurons connected by two memristor-emulator synapses. This experimental demonstration opens up new possibilities in the understanding of neural processes using memory devices, an important step forward to reproduce complex learning, adaptive and spontaneous behavior with electronic neural networks.
authors.affiliation: Department of Physics and Astronomy and USC Nanocenter, University of South Carolina, Columbia, SC 29208, USA. pershin@physics.sc.edu
authors.forename: Yuriy V
authors.initials: YV
authors.lastname: Pershin
authors.forename: Massimiliano
authors.initials: M
authors.lastname: Di Ventra
mesh_terms: D001185:Artificial Intelligence; D001245:Association Learning; D008959:Models, Neurological; D009415:Nerve Net; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity; D009474:Neurons; D013569:Synapses