Modeling of Information Processing in Biomorphic Neuroprocessor

In the present study, we present the results of the modeling of incoming information processing in a neuroprocessor that implements a biomorphic spiking neural network with numerous neurons and trainable synaptic connections between them. Physico-mathematical models of processes of encoding information into biomorphic pulses and their decoding following a neural block into a binary code were developed as well as models of the process of routing the output pulses of neurons by the logic matrix to the synapses of other neurons and the processes of associative self-learning of the memory matrix as part of the hardware spiking neural network with long-term potentiation and with the spike-timing-dependent plasticity of the memristor. The performance of individual devices of the biomorphic neuroprocessor in processing the incoming information is shown based on developed models using numerical simulation.