Hsien-Chi Kuo

The paper integrates transcriptomic, morphological, and electrophysiological data to define neuron types in mouse visual cortex and predict their projection targets.

This study integrates molecular and morphological data to classify cortical neurons and map their connectomes, revealing organizational principles of brain-wide networks at the single-cell level.

This study maps the cell-type-specific afferent and efferent projections of the mouse claustrum using viral tracing and single-neuron reconstruction, revealing its widespread connections and regional specificity.

Complete morphologies of 1,708 neurons, sparsely labelled and imaged whole-brain with fMOST, expose region-specific long-range projection rules and finer diversity than current transcriptomic subtypes capture.

DeepNeuron is an open-source deep learning toolbox that improves neuron tracing in light microscopy images by learning features and rules from data to address challenges in detecting, connecting, and classifying neuronal structures.