NeuroGraphBench: Interacting with Drosophila Connectomes at Scale for Exploring the Functional Logic of Neural Circuits

Drosophila connectomic datasets provide increasingly comprehensive maps of neuronal morphology and synaptic connectivity, offering an unprecedented opportunity to explore the structural organization of its neural circuits. This calls for designing automated tools to interact with connectomic datasets at scale for efficiently exploring structural features embedded in the vast amount of data. Yet the central challenge remains the understanding of the functional logic of neural circuits. In order to understand how elements of the functional logic may emerge from this structural organization, it is critical to (i) characterize the objects in the natural environment in which brain circuits operate, and (ii) formulate how brain circuits represent and process the defined objects in the natural environment. To develop and demonstrate a methodology for these requirements, we focus on the Drosophila looming-evoked escape pathway. We modeled the trajectory of looming objects that are on a collision course (direct-hits) or pass-by the fly (near-misses): their projected images on the retina can be characterized by the solid angle (angular size) and elevation. We then analyzed the pathway's morphology across the OpticLobe, Hemibrain, and FlyWire connectome datasets. By abstracting their sub-neuronal structure and retinotopic organization, we constructed an executable circuit model that maps each structural element to a processing block. We demonstrate that this model separates direct hits from near misses well before the angular size could tell them apart. To accelerate the connectomic analysis step, we developed a Python toolset with an agentic, code-free workspace interface called NeuroGraphBench (NGB). NGB provides four composable morphology-analysis primitives and an AI agent that composes them to interactively respond to natural-language queries aided by visualization on an interactive 3D canvas. Thus, NGB automates tedious and repetitive tasks to enable faster and scalable connectomic exploration, keeping human reasoning, instead of writing code, at the center of an open-ended research inquiry.