Mechanistic bridges from receptors to whole-brain dynamics: promise and limits of master-equation mean-field models
Abstract
Many pharmacological and pathological perturbations arise at molecular, synaptic, or cellular scales, but are observed through population and whole-brain signals. Cross-scale reductions must preserve relevant mechanisms while remaining tractable. This review asks which microscopic mechanisms remain explicit, interpretable, and testable after reduction, and what claims these models support. Using receptor-aware adaptive mean fields from the master-equation lineage as a worked case, we trace finite-size population statistics and semi-analytical transfer functions into conductance-based adaptive nodes coupled through the connectome. We compare this strategy with phenomenological neural masses, low-dimensional and population-density reductions, large-scale spiking models, and learned or hybrid surrogates, including computational work and memory traffic. Receptor-dependent synaptic kinetics, conductance state, and spike-frequency adaptation can remain manipulable across scales, enabling interpretable interventions and testable mesoscopic and macroscopic consequences. However, this relies on coarse-grained Markovianity, population homogeneity, quasi-stationary transfer functions, moment closure, regional uniformity, and measurement-specific observation models. First-order implementations discard covariance dynamics, while macroscopic agreement cannot identify a unique molecular cause. Node-local biological detail mainly changes prefactors, whereas dense global covariances change the scaling class. Cross-scale models should therefore be judged by the interventions and observables they preserve, validity domain, identifiability, empirical adequacy, and computational burden. Receptor-aware mean fields are not universal, but offer a transparent, tractable strategy for selected mechanistic questions when each reduction step is independently validated.
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BibTeX
@misc{bossard2026mechanistic,
title = {{Mechanistic bridges from receptors to whole-brain dynamics: promise and limits of master-equation mean-field models}},
author = {Yannael Bossard and Lahna Bekri and Alain Destexhe},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2608.00306},
eprint = {2608.00306},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.00306},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Mechanistic bridges from receptors to whole-brain dynamics: promise and limits of master-equation mean-field models
AU - Yannael Bossard
AU - Lahna Bekri
AU - Alain Destexhe
PY - 2026
DA - 2026/10/24
PB - arXiv
DO - 10.48550/arxiv.2608.00306
UR - https://arxiv.org/abs/2608.00306
AB - Many pharmacological and pathological perturbations arise at molecular, synaptic, or cellular scales, but are observed through population and whole-brain signals. Cross-scale reductions must preserve relevant mechanisms while remaining tractable. This review asks which microscopic mechanisms remain explicit, interpretable, and testable after reduction, and what claims these models support. Using receptor-aware adaptive mean fields from the master-equation lineage as a worked case, we trace finite-size population statistics and semi-analytical transfer functions into conductance-based adaptive nodes coupled through the connectome. We compare this strategy with phenomenological neural masses, low-dimensional and population-density reductions, large-scale spiking models, and learned or hybrid surrogates, including computational work and memory traffic. Receptor-dependent synaptic kinetics, conductance state, and spike-frequency adaptation can remain manipulable across scales, enabling interpretable interventions and testable mesoscopic and macroscopic consequences. However, this relies on coarse-grained Markovianity, population homogeneity, quasi-stationary transfer functions, moment closure, regional uniformity, and measurement-specific observation models. First-order implementations discard covariance dynamics, while macroscopic agreement cannot identify a unique molecular cause. Node-local biological detail mainly changes prefactors, whereas dense global covariances change the scaling class. Cross-scale models should therefore be judged by the interventions and observables they preserve, validity domain, identifiability, empirical adequacy, and computational burden. Receptor-aware mean fields are not universal, but offer a transparent, tractable strategy for selected mechanistic questions when each reduction step is independently validated.
ER -