This preprint presents the updated and expanded formulation of the RCNNG framework, a geometric–resonant theory proposing a unified foundation for perception, cognition, and conscious experience. In RCNNG, the fundamental units of mental processing are Closed Recurrent Geometries (CRGs)—stable, self‑reinforcing geometric structures formed through recurrent neural activity. The resonance,...
This preprint introduces the Resonance Of Closed Neural Network Geometry (RCNNG) hypothesis, a theoretical model proposing that perception and conscious experience arise from resonance within closed geometrical structures formed in adaptive neural networks. The hypothesis suggests that repeated frequency‑based stimulation can generate stable closed attractors in neural activity, and that these...