Applied ITT — CyberSynapse I: Neural Field Coupling

Applied ITT — CyberSynapse I: Neural Field Coupling Armstrong Knight (Sensei Intent Tensor) · intent-tensor-theory.com Every BCI performs signal decoding: neural signals dimensionality-reduced to a 1D feature vector, classified by a linear decoder. We propose direct neural field coupling: spatial correlation structure of multi-electrode neural recordings sets ITT Allen-Cahn graph weights directly. Field equilibrium — ZETA topology, shell densities, i₀ position — encodes cognitive state. No dimensionality reduction. No linear classifier. No labeled training data. Key results: FitzHugh-Nagumo reduces to Allen-Cahn — brain already runs these equations. Wilson-Cowan reduces to Allen-Cahn in strong-inhibition limit. Proposition 8.1: W_ij = max(0, C_ij)^1.35 is the functional connectivity graph. ZETA self-classifies 5 cognitive states without labels. Array mismatch problem: Neuralink N1 discards 3D correlation structure. CyberSynapse preserves it. Implementation: CyberSynapse.jsx. Validation protocol: PhysioNet EEG Motor Imagery dataset. Repository: https://gitlab.com/intent-tensor-theory.com-group/git-0-0-applied-intent-tensor-theoryWebsite: https://intent-tensor-theory.com