Beyond the Edge of Chaos: A Bidirectional Critical Reconstructability Corridor Hypothesis for Consciousness-Relevant Neural Dynamics

Abstract

This work introduces the Critical Reconstructability Corridor (CRC) hypothesis, a theoretical, computational, and empirically testable framework for investigating consciousness-relevant neural dynamics beyond the conventional notion of a single “edge of chaos.” The central proposal is that biological dynamics capable of supporting consciousness may occupy a bounded, multidimensional and bidirectionally traversable dynamical corridor. Within this corridor, perturbations can propagate sufficiently to support differentiation and integration while the system retains enough temporal and generative organization to remain reconstructable across observations, moderate perturbations, and state transitions. Rather than identifying consciousness with criticality, the CRC hypothesis treats criticality as only one component of a broader dynamical geometry involving reconstructability, bounded susceptibility, temporal continuity, integration, metastability, non-equilibrium organization, and history-dependent transition dynamics. The hypothesis is formulated initially as a necessary-condition hypothesis, not as a claim that CRC-like dynamics are sufficient for phenomenal consciousness. The study combines analytical arguments, nonlinear dynamical models, synthetic simulations, adversarial model competition, null and countermodels, identifiability analysis, and retrospective empirical stress tests using human neuroimaging and electrophysiological data. Particular attention is given to bidirectional transitions, including anesthesia induction and emergence, because nonlinear consciousness-relevant dynamics need not follow identical trajectories in opposite directions. The empirical program includes multi-subject propofol fMRI analyses, ketamine-related EEG analyses, sleep EEG, disorders-of-consciousness model comparisons, and an accessible-data analysis of the OpenNeuro ds006623 propofol dataset. The latter provides additional end-session state-geometry results but does not contain, through the derivative data retrieved by the executed pipeline, the complete transition-spanning observations required for the intended loss-of-responsiveness (LOR) test. This limitation is explicitly retained rather than replaced by an observationally different surrogate test. Across the executed analyses, multidimensional neural-state differences and cross-subject state information are observed in several datasets. However, the current evidence does not establish that the CRC model specifically outperforms all simpler matched alternatives, does not establish CRC as sufficient for consciousness, and does not identify phenomenal consciousness with criticality or reconstructability. Accordingly, the present research state is: CRC remains a mathematically specified, computationally stress-tested, empirically constrained, and falsifiable hypothesis, but it is not empirically confirmed. The work uses the PFCA framework as a formal and computational research environment for projection-aware reconstruction, residual attribution, model competition, bidirectional inference, and failure analysis. PFCA-generated simulations and internal reconstruction results are explicitly treated as model-relative synthetic evidence rather than independent empirical validation. The paper provides formal definitions, falsification criteria, model comparisons, executable computational methods, empirical analysis protocols, limitations, provenance information, and a review/correction ledger intended to support reproducibility and future independent testing.