Xin Tang

This study uses flexible electrodes to track and model representational drift in mouse visual cortex, enabling a stable decoder for brain-machine interfaces.

BEND-BCI benchmarks 23 neural decoding methods across multiple tasks and recordings, showing that accuracy alone does not determine the best decoder for practical use.

GIRAFI improves sparse single-cell proteomics by imputing missing values and revealing cell states using graph-informed statistical learning, outperforming existing methods and recovering biological structure.

This paper introduces a stretchable microelectrode array that integrates into the developing brain, enabling long-term, stable neural activity tracking without disrupting development.

This study shows that swimming motions in larval zebrafish trigger Ca2+ events in vascular endothelial cells through mechanical activation of Piezo1, independent of neural activity or blood flow.

The paper creates a high-resolution spatial atlas of the mouse CNS by combining in situ sequencing with scRNA-seq data to map cell types and gene expression across the brain and spinal cord.

This paper introduces TEMPOmap, a method to profile subcellular RNA kinetics in single cells, revealing kinetic gene clusters with distinct functional roles.

This paper introduces in situ electro-sequencing to map gene expression and electrophysiology in 3D tissues, improving cell type analysis and developmental trajectory reconstruction.

This study develops a stable neural recording method for mice, enabling long-term tracking of single-neuron activity and analysis of aging effects using machine learning.

This paper introduces UnitedNet, a multi-task deep learning model for analyzing single-cell multi-modality data, showing improved performance in joint group identification and cross-modal prediction.

ClusterMap is a framework for analyzing spatial gene expression data, clustering RNAs into subcellular structures and tissue regions across multiple dimensions.