Viren Jain

The paper uses connectomics to map a cerebellum-like circuit in weakly electric fish, revealing structured synaptic connectivity that supports predictive sensory processing and continual learning.

The paper presents the complete connectome of the Drosophila male central nervous system and compares it to the female connectome, identifying sex-specific and dimorphic neuronal types.

This paper introduces CAptVE, a method for fluorescent aptamer-based labeling that enables molecular analysis in ultrastructurally reconstructable electron micrographs.

This paper presents the first vertebrate basal ganglia connectome, revealing complex connectivity patterns that challenge the traditional view of discrete pathways.

The paper presents a connectomic dataset of a larval zebrafish brain, enabling circuit analysis through synaptic and neuronal annotations.

This paper develops LICONN, a light-microscopy based method for synapse-level connectomic reconstruction of brain tissue using hydrogel expansion and deep learning.

The paper characterizes ionocyte subtypes in zebrafish olfactory epithelium, identifying three types with distinct functions and distributions.

The paper presents a connectomic analysis of a human cerebral cortex fragment, revealing structural and connectivity features of neurons and synapses.

SegCLR, a self-supervised learning method, enables classification of cellular subcompartments and cell types from 3D electron microscopy data with minimal labeled data.

The paper presents a vCLEM protocol to map Aβ and pTau in AD mouse hippocampus connectomics data, revealing new molecular and structural alterations.

The paper introduces a vCLEM method using fluorescent scFvs to combine molecular labeling with ssEM, revealing new cell types and subcellular features in mouse cerebellar cortex.

The paper maps PN-to-KC connections in Drosophila MB using EM and shows connectivity biases that enhance discrimination of food-related odor combinations.

The paper develops a compression method for connectomic images using denoising and standard codecs, achieving 17-fold compression with minimal loss in 3D reconstruction and synaptic detection.

Flood-filling networks with local section realignment segment a forty-teravoxel whole-brain Drosophila electron microscopy volume, making circuit reconstruction an order of magnitude faster than manual tracing.

The paper shows how the basal ganglia solve the credit assignment problem through synaptic architecture and biophysical modeling in songbirds.

The paper introduces a 3D convolutional neural network method to classify neuronal subcompartments and correct merge errors in brain reconstructions, achieving high accuracy and error detection rates.

The paper introduces CMNs to automate morphological analysis of neuronal reconstructions, identifying glia cells and resolving reconstruction errors.

The paper introduces SECGAN, a method that improves neuron reconstruction from electron microscopy data by integrating segmentation components, achieving high accuracy without requiring new ground truth annotations.

This paper introduces flood-filling networks for automated neuron reconstruction, achieving high precision in tracing neurites from electron microscopy data.