Periodic and non-periodic brainwaves emerging via random syncronization of closed loops of firing neurons

TL;DR

This paper models brainwaves as synchronized sequences of neuron firing loops, explaining both periodic and non-periodic signals, and links these to observed EEG and MEG features through analytical relationships.

Contribution

It introduces a novel model of brainwaves based on synchronized neuron loops and their fluctuations, capturing both periodic and broadband components.

Findings

Fundamental and harmonic components appear as spectral lines.

Fluctuations in loop synchronization cause broadband spectral features.

Model aligns with empirical EEG and MEG nonstationary signals.

Abstract

Periodic and nonperiodic components of electrophysiological signals are modelled in terms of syncronized sequences of closed loops of firing neurons correlated in Markov chains. Single closed loops of firing neurons reproduce fundamental and harmonic components, appearing as lines in the power spectra at frequencies ranging about from $0.5 Hz$ to $ 100 Hz$. Further interesting features of the brainwave signals emerge by considering multiple syncronized sequences of closed loops. In particular, we show that the fluctuations of the number of syncronized loops leads to the onset of broadband power spectral components. By effect of the fluctuations of the number of synchronized loops and the emergence of the related broadband component, highly distorted waveform and nonstationarity of the signal are observed, consistently with empirical EEG and MEG signals. The analytical relationships of the periodic and aperiodic components are evaluated by using typical firing neuron pulse amplitudes and durations.