Artificial neural networks are composed essentially of neurons mutually interconnected by synapses, which are conventionally implemented by digital memories, but which can also be implemented by resistive components whose conductance varies as a function of the voltage applied across their terminals.
A learning rule conventionally used by the spiking neural networks is the STDP (“Spike Timing Dependent Plasticity”) rule. This is a biologically inspired rule whose objective is to reproduce the operation of the learning carried out by biological neurons and synapses. In order for such a rule to be implemented, the conductance of the synapses must vary as a function of the relative instants of arrival of the pre and post synaptic spikes (or pulses) transmitted by the neurons connected respectively at input and at output. According to the STDP rule, the conductance of a synapse is increased if its post synaptic neuron is activated after its pre synaptic neuron and decreased in the converse case. Furthermore, the variation in conductance also depends on the precise lag between the pulses that are generated subsequent to the activation of the neurons. Typically the more significant the lag, the less the conductance will vary.
The STDP learning rule exhibits certain limitations when a real implementation of an artificial neural network is envisaged. Firstly, a biologically inspired learning rule is not necessarily suited to concrete applications; furthermore it turns out to be complex to implement since the conductance variation characteristics of the devices used to embody the artificial synapses are not always compatible with an exact modeling of the biological learning rule.
The state of the art of spiking neural networks essentially comprises solutions which are aimed at proposing an implementation of the STDP learning rule. It is possible to cite for example the publications D. Kuzum, R. G. D. Jeyasingh, B. Lee, and H.-S. P. Wong, “Nanoelectronic Programmable Synapses Based on Phase Change Materials for Brain-Inspired Computing,” Nano Letters, 2011 and Snider, G. S. (2008). “Spike-timing-dependent learning in memristive nanodevices”. IEEE International Symposium on Nanoscale Architectures 2008 NANOARCH 2008.
Patent applications US 2010/0299296 and US 2010/0299297 which describe an implementation of the STDP rule on the basis of unipolar devices of PCM type are also known.
USPTO Backgrounds
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