Temporal Coding of Binary Patterns for Learning of Spiking Neuromorphic Systems Based on Nanocomposite Memristors

The metal/nanocomposite/metal (M/NC/M) memristive structures based on (Co 40 Fe 40 B 20 ) x (LiNbO 3 ) 100– x have been studied. It has been shown that such memristors may change their conductance according to the bioinspired spike-timing-dependent plasticity (STDP) rules. Spiking neural network with 4 presynaptic inputs connected by memristor-synapses with a postsynaptic threshold neuron-integrator has been created, in which the images clustering with temporal coding has been implemented using the STDP rule. Thus, the fundamental possibility of using a temporal coding method, which is more effective than population-frequency coding, has been demonstrated for self-learning of spiking neuromorphic systems with synaptic weights based on nanocomposite memristors.