Electronic learning synapse with spike-timing dependent plasticity using unipolar memory-switching elements

Abstract

According to embodiments of the invention, a system, method and computer program product producing spike-dependent plasticity in an artificial synapse. In an embodiment, a method includes: receiving a pre-synaptic spike in an electronic component; receiving a post-synaptic spike in the electronic component; in response to the pre-synaptic spike, generating a pre-synaptic pulse that occurs a predetermined period of time after the received pre-synaptic spike; in response to the post-synaptic spike, generating a post-synaptic pulse that starts at a baseline value and reaches a first voltage value a first period of time after the post-synaptic spike, followed by a second voltage value a second period of time after the post synaptic spike, followed by a return to the baseline voltage a third period of time after the post-synaptic spike; applying the generated pre-synaptic pulse to a pre-synaptic node of a synaptic device that includes a uni-polar, two-terminal bi-stable device in series with a rectifying element; and applying the generated post-synaptic pulse to a post-synaptic node of the synaptic device, wherein the synaptic device changes from a first conductive state to a second conductive state based on the value of input voltage applied to its pre and post-synaptic nodes, wherein the resultant state of the conductance of the synaptic device after the pre- and post-synaptic pulses are applied thereto depends on the relative timing of the received pre-synaptic spike with respect to the post synaptic spike.

Description

technical field [0001] The present invention generally relates to artificial neural networks. Specifically, e-learning synapses with spike-dependent plasticity are involved. Background technique [0002] The junction between the axon of one neuron and the dendrites of another neuron is called a synapse, and for a synapse, the two neurons are called pre-synaptic and post-synaptic, respectively. The essence of our individual experiences is stored in the conductance of the synapses. Synaptic conductance, according to spike-timing-dependent plasticity (STDP), changes over time as a function of the relative spike times of each pre- and postsynaptic neuron. The STDP rule increases the conductance of a synapse if the postsynaptic neuron fires after its presynaptic neuron fires, and decreases the conductance of the synapse if the order of the two firings is reversed. Again, this change depends on the precise delay between these two events: the greater the delay, the smaller the c...

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Problems solved by technology

Artificial neural networks do not use traditional digital models that manipulate 0s and 1s

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