Novel memristive STDP approach and neural clusters formation

Spike-timing-dependent plasticity (STDP) is the most common mechanism of long-term synaptic plasticity and the main approach for neural networks learning. The traditional memristive community is dominated by the approach in which the memristor changes its conductance (and thus its coupling weight) due to the action of neuron signals applied directly to the memristor contacts. We propose a system for controlling the state of the memristor, in which the spikes of pre- and postsynaptic neurons do not change the state of the memristor due to their small amplitude but trigger an additional block that affects the memristor. We studied the proposed novel STDP approach in numerical simulation with second order memristor model. The magnitude of change in the memristor state parameter turns out to be very similar to the classical STDP rules of synaptic plasticity We showed the possibility of cluster formation in spiking neural network by memristive STDP under external stimulation.