Connectome-constrained modeling identifies neurons and synapses that sustain spontaneous activity in Drosophila

Abstract

Synapse-resolution connectomes specify a brain's wiring; brain-wide recordings capture its activity. Neither alone identifies the cells and synapses that generate the activity. We bridge them by fitting a FlyWire connectome-constrained whole-brain model to calcium recordings of spontaneous activity in head-fixed Drosophila, then probing it in silico at cellular and synaptic resolution. The fitted model reproduces three features it was never trained on: lognormal synaptic weights, scale-free neuronal avalanches, and short intrinsic time constants in visual cells, each consistent with experiment. Systematic perturbations of the digital whole-brain model show that spontaneous activity is not distributed uniformly across the connectome, but is organized by a compact neuropil core. Within this core, a highly sparse, brain-spanning ensemble of inhibitory hub neurons and their reciprocal synapses with excitatory partners are necessary and sufficient to sustain whole-brain resting-state dynamics. Connectome-constrained modeling therefore converts wiring diagrams and recordings into a perturbable digital platform that identifies the cells and synapses sustaining resting-state dynamics.