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<title>Abstract</title> <p>We introduce BMIM II (Biophysical Multiscale Integration Model), a dynamical framework proposing that cognitive transduction emerges from the dynamics of coupled Stuart-Landau oscillators subject to multiplicative neural gains and thermodynamic noise. The model unifies endogenous, exogenous, and executive gain channels into a single multiplicative manifold whose product determines the system’s bifurcation parameter. When the total gain surpasses a critical threshold, the network undergoes a Hopf bifurcation—a phase transition we term “cognitive ignition”—yielding a macroscopic order parameter that represents integrated thought. We formalize the biological ontogeny of thought, the solidification and stochastic reactivation of latent memories, and the clinical extremes of cognitive variability. Furthermore, we outline the stochastic etiopathogenesis of severe cognitive alterations (psychosis, autism spectrum disorder, major depression) as critical deviations in metabolic noise intensity. We detail a clinical validation methodology employing continuous electroencephalography and infrared pupillometry to extract gains and noise in vivo. We discuss theoretical limitations, engage in comparison with contemporary theories including gain-mediated transitions and whole-brain turbulent dynamics, and conclude by addressing our primary objective: delineating a physical mechanism for macroscopic neural integration—a necessary correlate of conscious access—using tools already available at the clinical bedside. We explicitly distinguish this dynamical modeling of neural integration from the explanation of subjective phenomenal experience (qualia), which remains outside the scope of this framework. A fully functional computational prototype—Luz de Noche—has been developed implementing the BMIM II equations in real-time, demonstrating the framework's capacity for gain-driven phase transitions, stochastic resonance, and consciousness-state classification in a live artificial system (available at https://bmimai-fosgpp3g.manus.space).</p>

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Keywords

cognitive neural noise stochastic clinical

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