Information processing in biomorphic neuroprocessor

The results of the modeling of incoming information processing in a neuroprocessor that implements a biomorphic spiking neural network with a large number of neurons and trainable synaptic connections between them are presented. Physico-mathematical models of the processes of encoding information into biomorphic impulses and their decoding after a neural block into a binary code have been developed and the processes of associative self-learning of the memory matrix as part of the hardware spiking neural network with the spike-timing-dependent plasticity of the memristor. The performance of individual devices of the biomorphic neuroprocessor in the processing of incoming information is shown on the basis of the developed models with the help of numerical simulation.