Abstract
AbstractWhen someone mentions the name of a known person we immediately recall her face and possibly many other traits. This is because we possess the so-called associative memory - the ability to correlate different memories to the same fact or event. Associative memory is such a fundamental and encompassing human ability (and not just human) that the network of neurons in our brain must perform it quite easily. The question is then whether electronic neural networks - electronic schemes that act somewhat similarly to human brains - can be built to perform this type of function. Although the field of neural networks has developed for many years, a key element, namely the synapses between adjacent neurons, has been lacking a satisfactory electronic representation. The reason for this is that a passive circuit element able to reproduce the synapse behaviour 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 behaviour with electronic neural networks.
Highlights
W HEN someone mentions the name of a known person we immediately recall her face and possibly many other traits
The reason is that an electronic circuit that simulates a neural network capable of associative memory needs two important components: neurons and synapses, namely connections between neurons
We have shown that the electronic synapses and neurons we have built can represent important functionalities of their biological counterparts, and when combined together in networks - the one represented in Fig. 1 of this work - they give rise to an important function of the brain, namely associative memory
Summary
W HEN someone mentions the name of a known person we immediately recall her face and possibly many other traits. The reason is that an electronic circuit that simulates a neural network capable of associative memory needs two important components: neurons and synapses, namely connections between neurons. Both components should be of nanoscale dimensions and dissipate little energy so that a scale-up of such circuit to the number density of a typical human brain (consisting of about 1010 synapses/cm2) could be feasible. Memristors can be realized in many ways, ranging from oxide thin films [21], [22], [25] to spin memristive systems [26] All these realizations are limited to the specific material or physical property responsible for memory, and as such they do not allow for tuning of the parameters necessary to implement the different functionalities of electronic neural networks. This process of learning is a realization of the famous Hebbian rule stating, in a simplified form, that ”neurons that fire together, wire together”
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