An Innovative Perspective to Profound Functions of the Brain: Hypothesis of Resonance of Closed Neural Network Geometries

Abstract

This work presents the Resonance of Closed Neural Network Geometries (RCNNG) hypothesis, proposing that perception and consciousness arise from the formation and stabilization of Closed Resonant Geometries (CRGs) within adaptive recurrent neural networks. Structured and repeated frequency-based inputs drive the network toward stable closed-loop attractors that encode perceptual identity, associative relationships, and multi-feature binding. Analytical arguments and simulations demonstrate how specific spectral components selectively strengthen corresponding resonant geometries, leading to propagation, merging, and multiplication of CRGs during repeated or behaviorally relevant stimulation. Fourier-based signal decomposition reveals how resonance profiles emerge from the temporal–spectral structure of inputs, while topological analysis using Gradient Persistence Diagrams confirms the stability and significance of long-lived H1 features corresponding to robust CRGs. Short-lived components reflect transient, non-perceptual geometries, and the persistence landscape shows that repeated stimulation enhances the durability of resonant loops, providing a topological signature for perceptual consolidation. Overall, RCNNG offers a unified geometric and dynamical framework for understanding how stable perceptual states, memory formation, and resonance-based neural coding emerge from complex recurrent activity. The work outlines theoretical foundations, experimental predictions, and falsifiability criteria linking resonant geometries to subjective perceptual reports, aiming to stimulate further investigation into resonance-driven models of perception and consciousness.