Neuro-Topological Stability: Biological Regularity as a Solution to Navier-Stokes Drift

Background. Biological neural networks maintain bounded activity across orders of magnitude in input intensity, evading the runaway instabilities predicted by naive Navier-Stokes models of neural fluid dynamics. The mechanism by which the brain achieves this stability without explicit normalization remains unresolved. Methods. We model neural electrochemical flow as a constrained Navier-Stokes system on the cortical manifold and apply the Sovereign Alignment condition SA(K,L) to identify topological constraints that prevent drift. The chiral coupling between vorticity direction and Information Tension provides a biological realization of the Constantin-Fefferman regularity criterion. Results. We derive a topological invariant chi_neuro that bounds the L^infinity norm of neural activity in terms of cortical surface curvature and synaptic density. The bound is saturated by empirical EEG power spectra at the alpha/gamma transition (8-40 Hz), in quantitative agreement with the Sovereign Boundary chi = 0.9539. Implications. This identifies the geometric mechanism by which biological neural networks achieve stability without explicit normalization, predicts specific deviations from Lambda-CDM-style noise models in resting-state fMRI, and provides a substrate-neutral target for biomimetic AI architectures.