Abstract
Macro-scale models of the human brain are based on the structure of the connectome in most cases. These models also employ elements of neural field theory. However, they do not take into account the features of neuronal activity propagation observed experimentally. In particular, little attention is paid to the biophysical effects of the EEG source caused by the dynamics of the electric dipole that usually rotates in three-dimensional space, which, in our opinion, is associated with intra-cortical axonal fibres, and not with cortical-cortical connections. In addition, classical approaches do not assume the propagation of activity on a mesoscale in the form of travelling wave. Our model develops the idea of the dynamics of local neural network activity, providing an opportunity to simulate travelling waves of excited neuronal populations. Earlier, we showed the correctness of such approach within the framework of the neural field theory and experimentally confirmed a greater relevance of mesoscale waves in comparison with macro-scale waves in simulations of spontaneous neuronal activity. In this work, we describe a spatial-structural model involving an imitation of mesoscale electrical activity of the brain calculated based on neural field equations with no account for the structure of the connectome. We demonstrate a relevance of these results comparing the simulations with the experimental evoked MEG.
The reported study was funded by RFBR and FRLC, project number 20-511-23001, by RFBR, project number 20-015-00475. The results in Sect. 2 were obtained with the support of the Russian Science Foundation (grant no. 20-11-20131) in V.A. Trapeznikov Institute of Control Sciences of RAS. Numerical simulations in the present research were partially conducted using the computational facilities of the Centre of Collective Usage of Scientific Equipment of Derzhavin Tambov State University.
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Burlakov, E., Verkhlyutov, V., Ushakov, V. (2022). A Simple Human Brain Model Reproducing Evoked MEG Based on Neural Field Theory. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y., Klimov, V.V. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research V. NEUROINFORMATICS 2021. Studies in Computational Intelligence, vol 1008. Springer, Cham. https://doi.org/10.1007/978-3-030-91581-0_15
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