Turning anatomical maps into predictive models of circuit dynamics.
High-resolution brain imaging can now capture not just synapse locations but their molecular composition, with the cost of such mapping falling exponentially. Yet ultrastructural data has so far told us little about local neuronal physiology — the parameters (e.g., synaptic efficacies, local conductances) that govern neural dynamics.
This preprint proposes to translate molecularly annotated ultrastructure into physiology, introducing the concept of an ultrastructure-to-dynamics compiler: a learned mapping from molecularly annotated ultrastructure to simulator-ready, uncertainty-aware physiological parameters. The requirement is paired training data — jointly acquired ultrastructure from imaging and dynamical responses to perturbations from physiological experiments. Such a compiler would support biophysical simulations by turning anatomical maps into models of circuit dynamics, shifting structure-to-function from a descriptive program to a predictive one.
The preprint (arXiv, March 2026) is by Konrad P. Kording, Anton Arkhipov, Sean Escola, Adam Marblestone, David A. Markowitz, Patrick Mineault, Joanne Peng, Edward S. Boyden, and colleagues.
Read the paper: arXiv:2603.25713