Explaining Cognitive and Perceptual Phenomena Through Resonance of Closed Neural Network Geometries (RCNNG): A Unified Framework

This article presents a unified theoretical framework for explaining cognitive and perceptual phenomena through the Resonance of Closed Neural Network Geometries (RCNNG) hypothesis. The work extends the RCNNG hypothesis by demonstrating how diverse mental states—including perception, imagination, conceptual combination, creativity, innovation, doubt, hallucination, and self-perception—emerge from the intensification and interaction of closed geometrical structures within the neural network. By organizing these phenomena under a single geometric–dynamical mechanism, the article provides a coherent and integrative model for understanding how moment-to-moment experience arises from RCNNG resonance patterns, offering a unified explanation for the architecture of perception, cognition, and conscious experience.