The Syncytial Mesh Model: A Biophysical Framework for Scale-Dependent Coherence in the Brain

Large-scale neural coherence and distributed plasticity are fundamental to brain function, yet traditional circuit- and connectome-based models fail to capture stable phase gradients, harmonic resonances, and non-local reorganization observed across spatially disconnected regions. We introduce the Syncytial Mesh Model: a unified, three-layered framework in which a mesh-like substrate--grounded in astrocytic syncytia physiology--operates alongside local circuit and structural connectivity layers. The Syncytial Mesh layer, implemented via a damped wave equation on a small-world astrocytic network, generates traveling waves, interference-driven resonance, and distributed co-activation signals that underpin rare, scale-dependent phase coherence (delta/theta, 1 Hz to 8 Hz) and diffuse plasticity. Numerical simulations--using a 9-point isotropic Laplacian, perfectly matched layer (PML) boundaries, and unified RK4 integration--produce artifact-free amplitude snapshots, radial phase gradients, and precise spectral peaks matching human MEG and LFP data. An analytic two-mode model, fitted to empirical phase-gradient coherence across N = 43 subjects, yields a decoherence rate{lambda} 0 {approx} 1.5903/s, explaining why coherence is negligible at micrometer scales yet plateaus at [~] 4.65 % for millimeter-scale patches. Quantitative comparison with individual spectra shows median Pearson correlation r = 0.917 and median MSE = 26.6 dB2. By embedding the mesh in astrocytic physiology, the Syncytial Mesh Model provides a falsifiable, mechanistically grounded alternative to connectome-centric theories, unifying neural synchrony, resonance, and distributed plasticity across scales.