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Accumulating information is a critical component of most circuit computations in the brain across species, yet its precise implementation at the synaptic level remains poorly understood. Dissecting such neural circuits in vertebrates requires precise knowledge of functional neural properties and the ability to directly correlate neural dynamics with the underlying wiring diagram in the same animal. Here we combine functional calcium imaging with ultrastructural circuit reconstruction, using a visual motion accumulation paradigm in larval zebrafish. Using connectomic analyses of functionally identified cells and computational modeling, we show that bilateral inhibition, disinhibition, and recurrent connectivity are prominent motifs for sensory accumulation within the anterior hindbrain. We also demonstrate that similar insights about the structure-function relationship within this circuit can be obtained through complementary methods involving cell-specific morphological labeling via photo-conversion of functionally identified neuronal response types. We used our unique ground truth datasets to train and test a novel classifier algorithm, allowing us to assign functional labels to neurons from morphological libraries where functional information is lacking. The resulting feature-rich library of neuronal identities and connectomes enabled us to constrain a biophysically realistic network model of the anterior hindbrain that can reproduce observed neuronal dynamics and make testable predictions for future experiments. Our work exemplifies the power of hypothesis-driven electron microscopy paired with functional recordings to gain mechanistic insights into signal processing and provides a framework for dissecting neural computations across vertebrates.
Understanding how neural circuits give rise to behavior requires comprehensive knowledge of neuronal morphology, connectivity, and function. Atlas platforms play a critical role in enabling the visualization, exploration, and dissemination of such information. Here, we present FishExplorer, an interactive and expandable community platform designed to integrate and analyze multimodal brain data from larval zebrafish. FishExplorer supports datasets acquired through light microscopy (LM), electron microscopy (EM), and X-ray imaging, all co-registered within a unified spatial coordinate system which enables seamless comparison of neuronal morphologies and synaptic connections. To further assist circuit analysis, FishExplorer includes a suite of tools for querying and visualizing connectivity at the whole-brain scale. By integrating data from recent large-scale EM reconstructions (presented in companion studies), FishExplorer enables researchers to validate circuit models, explore wiring principles, and generate new hypotheses. As a continuously evolving resource, FishExplorer is designed to facilitate collaborative discovery and serve the growing needs of the teleost neuroscience community.