Computer simulation of the biomorphic neuroprocessor
2023 5th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)(2023)
Abstract
In this paper, numerical simulation was carried out and the operability of the input and output devices of a biomorphic neuroprocessor, built using a logic matrix, was shown using the specialized software SPICE (Simulation Program with Integrated Circuit Emphasis) for simulating large electrical circuits with memristor-diode crossbars. The modes of encoding a binary number into frequency and delay of pulses, simultaneously into frequency and delay of pulses by neuron population were investigated. Also, the mode of decoding pulses into a standard binary code for outputting information from the neuroprocessor was investigated. The simulation results of the associative self-learning mechanism in hardware spiking neural networks based on the memory matrix of a neuroprocessor are presented. The learning rules are LTP (Long-Term Potentiation) for a network of four neurons and STDP (Spike-Time Dependent Plasticity) for a hardware spiking perceptron. For the first time, the generation of a new association (new knowledge) in a composite memristor-diode crossbar according to the STDP rule is demonstrated, in opposite to self-learning in existing hardware neural networks with discrete memristors synapses.
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Key words
MDC-SPICE,simulation,biomorphic neuroprocessor,networks
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