STOCHASTIC MODEL OF THE SPIKING NEUROMORPHIC NETWORK

The work is devoted to the simulation of an analog neural network based on memristive elements, taking into account the stochastic dynamics of their functioning.

Modeling the restoration of biological and biotechnical systems using hardware analog and software artificial neural networks

A hardware analog model of an artificial neural network was developed, based on a specially trained software artificial neural network, for modeling the process of recovering damaged biological and biotechnical systems using neurochips based on the evolutionary method of training. A series of 12 computational experiments on the restoration of a damaged hardware analog artificial neural network with the help of a software artificial neural network was carried out. To restore a damaged network, an evolutionary approach is used. In most cases, it is possible to restore a damaged hardware analog neural network to 100% accuracy. The obtained results confirm the efficiency of the proposed approach in the framework of modeling the restoration of damaged biological and biotechnical systems using a neurochipon the basis of the evolutionary method using the "isolation" mechanism. The proposed recovery method opens up prospects for such areas as neuroprosthetics, self-learning and self-adapting systems; reverse-engineering; restoration of damaged data banks, image restoration; decision making and management, and so on.