Resolving the Blow-Up: A Time-Dilated Numerical Framework for Multiple Firing Events in Mean-Field Neuronal Networks

arXiv:2603.18475v1 Announce Type: cross
Abstract: In large-scale excitatory neuronal networks, rapid synchronization manifests as multiple firing events (MFEs), mathematically characterized by a finite-time blow-up of the neuronal firing rate in the mean-field Fokker-Planck equation. Standard numerical methods struggle to resolve this singularity due to the divergent boundary flux and the instantaneous nature of the population voltage reset. In this work, we propose a robust multiscale numerical framework based on time dilation. By transforming the governing equation into a dilated timescale proportional to the firing activity, we desingularize the blow-up, effectively stretching the instantaneous synchronization event into a resolved mesoscopic process. This approach is shown to be physically consistent with the microscopic cascade mechanism underlying MFEs and the system’s inherent fragility. To implement this numerically, we develop a hybrid scheme that utilizes a mesh-independent flux criterion to switch between timescales and a semi-analytical “moving Gaussian” method to accurately evolve the post-blowup Dirac mass. Numerical benchmarks demonstrate that our solver not only captures steady states with high accuracy but also efficiently reproduces periodic MFEs, matching Monte Carlo simulations without the severe time-step restrictions associated with particle cascades.