Diagnostics of brain neural network states from the perspective of chaos

Abstract

Abstract All modern brain sciences are based on the stochastic study of the brain neural networks or individual neurons. At the same time, neuroscience is dominated by the dogma of statistical repetition of any samples of neural network parameters. However, back in 1948, W. Weaver took all living systems beyond stochastics. Currently, the Eskov-Zinchenko effect in biomechanics has been proven, which also extends to the bioelectric activity of the brain. As a result, there is a big problem of accurate evaluation of electroencephalograms, which are also used in the “man-machine” system. Proposes an analog of Heisenberg’s principle in the form of calculation of parameters of pseudoattractors.