Experimental demonstration of associative memory with memristive neural networks

By Yuriy V. Pershin, M. Di Ventra. Neural Networks, 2009

1,041 citations recorded by the source corpus.

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.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/1bb490fcd384c4bcda60cd8d1c592b266da5dbd1

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

A Survey of Neuromorphic Computing and Neural Networks in Hardware (2017, neutral): “[1036], [1063]–[1065], [1103], [1149]–[1154], [1177], [1178],”
Pattern classification by memristive crossbar circuits using ex situ and in situ training (2013, result): “For the latter case, the demonstrated circuit functionality relies on the collective operation of 20 variation-prone memristors, which is at least an order of magnitude higher in complexity in comparison with that of previously reported memristor-based ANNs 23–25 .”
Memristor-based neural networks (2013, background): “In 2010, Pershin and Di Ventra described a small system composed of three neurons and two synapses that is fully asynchronous [79, 80].”
Sparse coding with memristor networks. (2017, background): “Additional examples showing the network dynamics in picking a sparse representation for different input patterns are provided in Supplementary Figs 7 – 9.”
Memristor-based memory: The sneak paths problem and solutions (2013, methodology): “In addition to be used as a memory element [2, 17–26], the memristor has found many applications in oscillators [27–30], logic and arithmetic circuits [31, 32], programmable analog circuits [33, 34], and in modeling and emulation of natural phenomena [35, 36].”
Passivity and passification of stochastic impulsive memristor‐based piecewise linear system with mixed delays (2015, background): “More and more attention have been attracted to the memristor because of its potential application [3–15].”
Dynamical Properties and Design Analysis for Nonvolatile Memristor Memories (2011, neutral): “, neural networks and artificial intelligences [39]–[46].”