Black Hole-Inspired Horizon Model for Neural Signal Dynamics

Abstract

Electroencephalographic (EEG) signals provide macroscopic observables of complex neural dynamics. We introduce a horizon-inspired framework in which measured EEG signals are modeled as projections of a complex wave-like representation constrained by an effective boundary analogous to an event horizon. In this formulation the signal amplitude obeys a renormalization-group scaling relation while EEG spectral entropy parameterizes the accessibility of observable modes. The resulting solutions generate oscillatory structures whose geometry and spectral signatures can be explored through signal analysis and sonification. This mapping between entropy-based neural observables and wave-like signal representations provides a physically motivated framework linking entropy measures, scale-dependent dynamics, and observable neural oscillations, and suggests testable connections between spectral entropy and the amplitude scaling of EEG modes.

Figures (2)

• Figure 1: Schematic representation of the horizon-inspired model. Externally measured EEG observables correspond to signals accessible outside an effective horizon characterized by the accessibility parameter $\Gamma_{r}$, measuring the distance from the effective boundary $r_{s}$. Internal neural processes remain hidden inside the boundary with an inaccessible core. Observable signals emerge as wave-like modes $\psi(t)$ that can be analyzed and sonified.
• Figure 2: Wavefunction trajectories and corresponding spectrograms produced by the model using EEG recordings in a female skull (Fpz-Cz channel) EEGLab. Top panels show single-helical trajectories of the complex wavefunction while bottom panels show double-helix regime obtained for larger angular frequency $\omega$.