Mathematical model for controlling brain neuroplasticity during neurofeedback

The purpose of this work is to apply a model of interaction between thalamocortical system modules to control brain neuroplasticity. Methods. Psychophysiological experiments on neurofeedback are being carried out, which consist of light stimulation of the eyes with monofrequency light pulses in the range of 4...20 Hz and recording the bioelectrical activity of the brain. As a characteristic of maturity, brain rhythms use the combination of the presence or absence in the bioelectrical activity of the brain of a dominant peak frequency in the alpha range of the EEG, the effect of assimilation of the rhythms imposed by stimulation, and the presence of a multiplying effect from the rhythms imposed by stimulation. Solutions to the model of an elementary thalamocortical cell, which is described by a system of differential equations, corresponding to a psychophysiological experiment are considered. The model is implemented using the Python. Results. The model parameters are selected in such a way as to achieve a qualitative correspondence of the spectral characteristics of the obtained solutions with the bioelectrical activity of the subject’s brain. Rhythmic maturity is assessed based on the parameters of the thalamocortical cell model. The brightness and frequency characteristics of light stimuli are selected based on the prediction of the model, the input of which is supplied with various variants of pulse sequences. Conclusion. A method has been developed for digital diagnostics of the level of brain rhythm maturity based on a comparison of modeling results and data from a psychophysiological experiment on neurofeedback. The evolution of model solutions depending on its parameters simulates the process of biocontrol of brain neuroplasticity, taking into account the initial level of rhythmic maturity and stress-induced distortions of neurodynamics. Experiments on the model with different parameters of the model and external signal can be used in the development of new neurofeedback protocols.