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bioRxiv · 10.64898/2026.07.31.742067

BRIDGE: A Computational Workflow from Single Neurons to Network of Mean-Field Models

Carannante, I.·Depannemaecker, D.·Woodman, M.·Purohit, P.·Destexhe, A.
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Abstract

Mean-field models are extensively used in large-scale brain simulations because they provide a wieldy description of population dynamics while preserving key features of neural activity. Despite their widespread adoption, no common and reproducible methodology currently exists to systematically derive and validate mean-field models starting from biologically grounded single neuron dynamics. As a result, implementations are often ad hoc, difficult to reproduce and rarely reusable. Here we introduce BRIDGE, a modular, open-source Python pipeline that enables the bottom-up reconstruction, analysis, validation, and simulation of mean-field models from single neurons. The framework integrates single neurons modelling, network simulations, extraction of population statistics, parameters analysis, quantitative comparisons between spiking neural networks and corresponding mean-field representations, and simulation of network of mean-fields. Its flexible architecture allows users to incorporate different neuron models and to generate region-specific or state-dependent mean-field formulations. BRIDGE provides a reproducible foundation for developing biologically informed mean-field models suitable for large-scale and whole-brain simulations, supporting the transition from generic homogeneous population models toward region-specific ones. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=91 SRC="FIGDIR/small/742067v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@1124404org.highwire.dtl.DTLVardef@2f8b2aorg.highwire.dtl.DTLVardef@1598f37org.highwire.dtl.DTLVardef@c9814b_HPS_FORMAT_FIGEXP M_FIG C_FIG

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Carannante, I., Depannemaecker, D., Woodman, M., Purohit, P., Destexhe, A.. 2026-08-06. BRIDGE: A Computational Workflow from Single Neurons to Network of Mean-Field Models. https://doi.org/10.64898/2026.07.31.742067

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