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 chiₙeuro that bounds the Lⁱnfinity 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.