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.
The analysis of subjective time scales of the subjects with perspective human-computer interfaces was carried out: neurocomputer (brain-computer), electromyographic, oculografic. It is shown that for all of them it is typical to underestimate the maximum time spent for the execution of one team. In this case, for the electromyographic and oculografic, this feature is also preserved for the indicators of the average time for executing the commands. The results of the assessment demonstrate a unified approach of users to the formation of subjective time when working with various interfaces: the user estimates both the averaged and the best (minimum) with the worst (maximum) time for executing commands on a single scale. Subjects who switched worse from generating one command for the interface to another subjectively rated the interface as slower. The HRV data showed the LF-band relationship with a subjective estimate of the time spent working with the interface. Analysis of the relationship (true time-subjective) / true time has shown that subjective time scales when working with the neurocomputer and oculographic interfaces demonstrate a high correlation with each other as opposed to electromyographic.
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