{"ID":2833135,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.10982","arxiv_id":"2512.10982","title":"Rosetta Stone of Neural Mass Models","abstract":"Brain dynamics dominate every level of neural organization -- from single-neuron spiking to the macroscopic waves captured by fMRI, MEG, and EEG -- yet the mathematical tools used to interrogate those dynamics remain scattered across a patchwork of traditions. Neural mass models (NMMs) (aggregate neural models) provide one of the most popular gateways into this landscape, but their sheer variety -- spanning lumped parameter models, firing-rate equations, and multi-layer generators -- demands a unifying framework that situates diverse architectures along a continuum of abstraction and biological detail. Here, we start from the idea that oscillations originate from a simple push-pull interaction between two or more neural populations. We build from the undamped harmonic oscillator and, guided by a simple push-pull motif between excitatory and inhibitory populations, climb a systematic ladder of detail. Each rung is presented first in isolation, next under forcing, and then within a coupled network, reflecting the progression from single-node to whole-brain modeling. By transforming a repertoire of disparate formalisms into a navigable ladder, we hope to turn NMM choice from a subjective act into a principled design decision, helping both theorists and experimentalists translate between scales, modalities, and interventions. In doing so, we offer a \\emph{Rosetta Stone} for brain oscillation models -- one that lets the field speak a common dynamical language while preserving the dialectical richness that fuels discovery.","short_abstract":"Brain dynamics dominate every level of neural organization -- from single-neuron spiking to the macroscopic waves captured by fMRI, MEG, and EEG -- yet the mathematical tools used to interrogate those dynamics remain scattered across a patchwork of traditions. Neural mass models (NMMs) (aggregate neural models) provide...","url_abs":"https://arxiv.org/abs/2512.10982","url_pdf":"https://arxiv.org/pdf/2512.10982v1","authors":"[\"Francesca Castaldo\",\"Raul de Palma Aristides\",\"Pau Clusella\",\"Jordi Garcia-Ojalvo\",\"Giulio Ruffini\"]","published":"2025-12-04T18:37:14Z","proceeding":"q-bio.NC","tasks":"[\"q-bio.NC\",\"nlin.CD\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}