Tri Nguyen

The paper presents tools to reconstruct spinal cord circuits and identifies modality-specific inhibitory synapses between sensory neurons and inhibitory neurons.

The paper maps synaptic connections in the mouse spinal dorsal horn to show how different sensory modalities are routed through distinct pathways to the brain.

The paper investigates how climbing fiber inputs selectively excite disinhibitory interneurons to enhance dendritic calcium signaling in Purkinje cells, supporting cerebellar learning.

The paper presents a connectome of the human foveal retina using deep learning, revealing novel synaptic pathways and distinct neural circuits unique to humans.

This study uses 3D reconstructions of mechanosensory end organs to propose a unified mechanism for Aβ RA-LTMR activation through axon protrusions and Piezo2 distribution.

The paper identifies two MLI subtypes with distinct connectivity and functional roles in cerebellar circuits, showing how disinhibition gates cerebellar-dependent learning.

This paper introduces an auxiliary learning task using Local Shape Descriptors to improve neuron segmentation in electron microscopy volumes, achieving performance comparable to state-of-the-art methods with greater efficiency.

The paper maps cerebellar connectivity in mice and shows how structured, non-random motifs improve pattern separation while maintaining encoding capacity.

The paper identifies candelabrum cells as abundant, GABAergic interneurons in the cerebellar cortex with distinct molecular properties and a unique role in circuit function.

The paper presents a high-resolution EM dataset of Drosophila ventral nerve cord and develops GridTape for automated EM data collection, revealing a unique feedback pathway in motor control.

This paper introduces X-ray holographic nano-tomography for imaging dense neuronal structures in Drosophila and mammals, using convolutional neural networks for automated segmentation.

The paper presents an automatic method to identify synaptic partners in a whole-brain Drosophila EM dataset using convolutional neural networks, achieving high accuracy in predicting synaptic connections.