Savaliya, J. H. | bioRxiv Science

bioRxiv2025

Lrrns define a visual circuit underlying brightness and contrast perception

Brightness and contrast are fundamental features of vision, crucial for object detection, environmental navigation, and feeding. Here, we identify a brightness-and contrast-processing circuit in the zebrafish visual system and uncover the role of Leucine-rich repeat neuronal (Lrrn) cell adhesion molecules (CAMs) in regulating its assembly. Deep-projecting retinal ganglion cells (RGCs) serve as the first synaptic relay to the brain requiring Lrrn2 and Lrrn3a for precise axonal targeting and connectivity within the optic tectum. Genetic targeting of these CAMs leads to circuit disorganization and impairments in contrast sensitivity, leading to deficits in visually guided behaviors. Additionally, ultrastructural circuit reconstruction and functional imaging analysis revealed their critical role in luminance processing. These studies define a fundamental visual processing pathway and establish Lrrn CAMs as essential molecular drivers of its assembly.

neuroscience↗

bioRxiv2025

Correlative light and electron microscopy reveals the fine circuit structure underlying evidence accumulation in larval zebrafish

AbstractEvidence accumulation is a fundamental neural computation essential for adaptive behavior, yet its synaptic implementation remains unclear. Addressing this challenge critically depends on linking neural dynamics to circuit structure within the same brain. Here, we combine functional calcium imaging with large-scale ultrastructural electron microscopy (EM) to uncover the wiring logic of visual evidence accumulation in larval zebrafish. In a functionally imaged EM dataset of the anterior hindbrain, we identify conserved morphological cell types whose activity patterns define distinct computational roles. Bilateral inhibition, disinhibition, and recurrent connectivity emerge as key circuit motifs shaping these dynamics. To generalize our findings across animals, we develop a photoconversion-based pipeline to label and reconstruct functionally characterized neurons, enabling us to train a classifier that predicts functional identity from morphology alone. Applying this classifier to a second, whole-brain EM dataset lacking functional data reveals matching connectivity patterns, significantly augmenting its applicability for detailed circuit dissections. Based on these results, we develop and constrain a biophysically realistic neural network model that captures observed dynamics and yields predictions we tested and confirmed experimentally. Our work illustrates how hypothesis-driven connectomics can uncover the synaptic basis of sensory-motor computations and establishes a novel framework for cross-animal circuit dissection in the vertebrate brain.

neuroscience↗