Investigation of directed interaction between neural populations using spectral analysis methods

Bulat Batuev, Sergey Sukhov

Abstract


In this work, the mechanisms of directed interaction between two populations of neurons were investigated using spectral analysis methods. The key conclusion of this work is the demonstration that manipulating the background noise level allows the inversion of the direction of information flow between neural populations, opening up new possibilities for controlling functional connectivity in spiking networks. The dynamics of the membrane potentials of two neural populations were modeled based on the “Leaky integrate-and-fire” model. To quantitatively assess the directional information exchange, Granger causality (GC), directional transfer function (DTF), and partial directional coherence (PDC) were used. The results confirm the effectiveness of GC, DTF and PDC for analyzing directed connections in neural networks and justify their use in neurophysiological research. Structural connectivity was generated via an undirected stochastic block model.

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