An Innovative Perspective to Profound Functions of the Brain: Hypothesis of Resonance of Closed Neural Network Geometries
Figshare July 17, 2026 Hasan Niazi
A new hypothesis, the Resonance Of Closed Neural Network Geometry (RCNNG), proposes that perception and conscious experience arise from resonance within closed geometrical structures formed in adaptive neural networks. Repeated frequency-based stimulation can generate stable closed attractors in neural activity, and these resonant geometries serve as the basis of perceptual states. The manuscript develops mathematical and dynamical foundations, outlines computational approaches for simulating resonance-driven attractor formation, and proposes experimental paradigms using EEG/MEG and optogenetic stimulation for empirical testing. It defines falsifiability criteria based on the relationship between resonant geometries and subjective perceptual reports, integrating nonlinear dynamics, network theory, and computational neuroscience.