Black Hole-Inspired Horizon Model for Neural Signal Dynamics

Researcher E. Canessa has published a provocative theoretical paper proposing a 'Black Hole-Inspired Horizon Model for Neural Signal Dynamics.' The work, posted to arXiv, applies concepts from theoretical physics to the complex problem of interpreting electroencephalographic (EEG) data. In this framework, the macroscopic EEG signals we can measure are modeled as projections of a deeper, wave-like neural representation. Crucially, this representation is constrained by an effective boundary analogous to the event horizon of a black hole, a surface beyond which information cannot escape.

This analogy leads to a formal mathematical relationship where the signal's amplitude follows a renormalization-group scaling law—a concept from particle physics used to understand how phenomena change across different scales. Within the model, the spectral entropy of an EEG signal (a measure of its complexity or disorder) directly parameterizes which oscillatory 'modes' or patterns are observable from outside this hypothetical horizon. The resulting solutions generate testable predictions about the geometry and spectral signatures of brain oscillations, potentially linking abstract entropy measures to the concrete, scale-dependent dynamics of neural activity. The author suggests this mapping offers a new, physically motivated lens for analyzing brain data and even explores expressing these patterns through sonification.