Which Experiment Would Change Your Mind? Bayesian Optimal Experimental Design for Connectome-Constrained Models

Connectome-constrained models combine maps of synaptic wiring with task-driven learning to predict neural activity. These networks can solve the same task while making incompatible predictions about neural activity. However, it’s imperative to ask the question: what recording would distinguish them? We formulate the joint choice of cell type and stimulus as Bayesian experimental design over trained networks, accounting for what calcium imaging can measure. The framework integrates out recording-specific gain and offset analytically, marginalizes uncertain voltage-to-calcium transforms at inference, and searches 38,870 candidate recordings subject to genetic targeting constraints. In a benchmark of 91 networks calibrated using 313 published calcium recordings, one selected recording identifies the generating network in 63.7% of single-trial and 80.8% of five trial measurements. A conventional T5b motion experiment reaches 50.8% and 63.9%, and the first recording of a standard battery reaches 37.1% and 55.4%. For single-trial measurements, stimulus optimization on the selected target adds 12.3 percentage points over a generic edge, demonstrating the value of joint design. A protocol fixed on the original ensemble reaches 90% identification within 65 seconds of scheduled recording in two retrained ensembles. Retrospective tests on published recordings improve held-out prediction over random conditions. These results turn disagreement among connectome-constrained models into concrete recording choices, with identification evaluated within an explicit finite hypothesis set.