A supervised spiking time dependant plasticity network based on memristors

Synaptic plasticity has been widely assumed to be the mechanism behind memory and learning, in which, synapse has a critical role. As a newer biologic update rule to hebbian learning, spiking-time dependent plasticity (STDP) concerns on the temporal order of presynaptic spike and postsynaptic spike which will change the strength of, the connection site of neurons, synapse. In this paper a different way is shown to utilise the novel element memristors to implement a supervised STDP. Because the resistance of memristor depends on its past states, researchers are particularly interested in using such functionality to mimic synaptic connection. Furthermore, benefit from the nano size of memristors and its crossbar structure, large scale neural networks could be implemented. In this supervised STDP, each spike arrival will be assumed to leave a trace which decays exponentially and spikes interact under all-to-all interaction. Depending on the temporal order, memristor synapse will weaken or strengthen the connection of presynaptic neuron and postsynaptic neuron. The temporal all-to-all interaction is implemented during the simulation with training samples. We show that, by combining the memristors, a supervised STDP neural network can be built and learn from the temporal order of presynaptic spike and postsynaptic spike of the training samples.