EEGs disclose significant brain activity correlated with synaptic fickleness

TL;DR

This study models neural networks to explain EEG brain wave oscillations and abrupt variations, linking synaptic dynamics to observable EEG patterns and phase transitions in brain activity.

Contribution

It introduces a neural network model that reproduces EEG-like oscillations and phase transitions, explaining delta-gamma and theta-gamma modulations observed in brain data.

Findings

EEG-like oscillations emerge from synaptic interactions in the model.

Abrupt changes in EEG signals are linked to synaptic restrictions and phase transitions.

The model reproduces delta-gamma and theta-gamma modulation phenomena.

Abstract

We here study a network of synaptic relations mingling excitatory and inhibitory neuron nodes that displays oscillations quite similar to electroencephalogram (EEG) brain waves, and identify abrupt variations brought about by swift synaptic mediations. We thus conclude that corresponding changes in EEG series surely come from the slowdown of the activity in neuron populations due to synaptic restrictions. The latter happens to generate an imbalance between excitation and inhibition causing a quick explosive increase of excitatory activity, which turns out to be a (first-order) transition among dynamic mental phases. Besides, near this phase transition, our model system exhibits waves with a strong component in the so-called \textit{delta-theta domain} that coexist with fast oscillations. These findings provide a simple explanation for the observed \textit{delta-gamma} and \textit{theta-gamma modulation} in actual brains, and open a serious and versatile path to understand deeply large amounts of apparently erratic, easily accessible brain data.