Ying Shi

This paper presents a deep learning framework that classifies neuronal morphologies using topology and graph neural networks, achieving accuracy comparable to human experts.

The paper investigates how human neuronal morphology influences synaptic connectivity complexity and shows that human neuron shapes lead to more complex connectomes compared to rodents.

This study builds a detailed computational model of the human neocortex and compares it to a rat model, revealing differences in cell density, branching complexity, and synaptic connectivity.

The paper creates a detailed computational model of rat somatosensory cortex anatomy, including 4.2 million neurons and 14.2 billion synapses, and identifies limitations in connectivity prediction from morphology.

The paper presents a full-scale in silico model of rat hippocampal CA1 integrating diverse experimental data and validates it through simulations of theta rhythm and other experiments.

The paper compares dendritic architecture in human and mice neuronal networks, finding that human pyramidal cells have greater dendritic complexity, contributing to increased network complexity despite lower neuron density.

The paper develops a computational model of mouse thalamoreticular microcircuitry to study sensory enhancement and sleep spindle properties, revealing mechanisms of network-level activity across brain states.

The paper quantifies neurons and glia in the Drosophila larval CNS, revealing sexual dimorphism with more neurons in females and topological differences.

The paper builds a statistical model of the mouse whole-neocortex micro-connectome using region-to-region connectivity and axon reconstructions, revealing a novel targeting principle for predicting individual axon innervation.

The paper builds biophysically detailed models of thalamocortical neurons constrained by experimental data, showing robustness to parameter changes and morphology variations.

The paper objectively classifies neocortical pyramidal cells using algebraic topology, identifying seventeen types in the rat somatosensory cortex without expert input.