Experimental demonstration of associative memory with memristive neural networks

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...

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Bibliographic Details
Published in:Neural networks 2010-09, Vol.23 (7), p.881-886
Main Authors: Pershin, Yuriy V., Di Ventra, Massimiliano
Format: Article
Language:English
Subjects:
Applied sciences
Artificial Intelligence
Association Learning
Computer science
control theory
systems
Connectionism. Neural networks
Electronics
Exact sciences and technology
Integrated circuits
Integrated circuits by function (including memories and processors)
Memory
Miscellaneous
Models, Neurological
Nerve Net
Neural network hardware
Neural networks
Neural Networks (Computer)
Neuronal Plasticity
Neurons
Resistance
Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices
Storage and reproduction of information
Synapses
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Summary: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.
ISSN:0893-6080
1879-2782
DOI:10.1016/j.neunet.2010.05.001