Cortical neuron classes and recursive curvature collapse: a neurobiological model of conscious dynamics

Understanding how conscious cognition remains stable under uncertainty, conflict, and perturbation requires a framework that links neural dynamics to the geometry of evolving representational states. Here we develop Recursive Informational Curvature (RIC), a neurogeometric framework in which conscious access is modeled as a stability regime of trajectories on a stratified informational manifold. In this framework, recursive gain, symbolic entropy dispersion, and loop-level timing coherence jointly determine whether neural activity remains within closure-supporting regimes or approaches collapse. We formalize this balance through an effective curvature index, [Formula: see text], defined relative to a declared critical boundary [Formula: see text], and through circulation-based timing statistics that quantify phase-organized loop stability. The theory integrates three coupled geometric layers: a Fisher layer for precision-weighted discriminability, a Finsler layer for direction-dependent transition cost, and a Hermitian layer for phase-coded recursive coordination. We further propose mechanistic hypotheses linking identifiable cortical neuronal classes, including mirror circuits, von Economo neuron-rich salience territories, TPJ mentalizing ensembles, and prefrontal phase-modulating hubs, to class-specific curvature control. To connect the framework to data, we specify measurement-facing estimators for gain, symbolic entropy structure, loop instability, and effective curvature, and we provide a reduced EEG-based empirical analysis showing that a geometry-sensitive neural state-space proxy is related to moral judgment bias, while broader socially mediated outcomes are not captured by this reduced measure alone. RIC therefore offers a formal and operational framework for studying stability, collapse, and recovery in conscious dynamics across theoretical, empirical, and translational settings.