| Resum: |
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, 1Hz to 8Hz) 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 λ0 ≈ 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. 6dB2. 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. |
visitant ::
identificació
(Universitat Autònoma de Barcelona. Departament de Filosofia)
