David H. Feng

3D-MAESTRO automates processing of large-scale volumetric brain microscopy data, enabling scalable and reproducible analysis of mouse brain structures.

The paper presents scalable spike sorting pipelines and benchmarking tools for large-scale electrophysiology data, showing Kilosort4 outperforms Kilosort2.5 and 7x compression has minimal impact on performance.

The paper introduces ExA-SPIM, a microscope that enables high-resolution imaging of large tissue samples without sectioning, demonstrating its use on mouse brains and human white matter.

HortaCloud provides a cloud-based platform for collaborative whole-brain neuronal reconstruction from light microscopy data, enabling efficient analysis of large datasets without local hardware.

This paper evaluates compression strategies for electrophysiology data, finding that lossy compression can significantly reduce file sizes without affecting spike sorting accuracy.

The paper identifies diverse functional neuron types in the visual cortex that process novel and familiar stimuli in mice.

The paper presents a synaptic physiology dataset and analysis platform to study cortical microcircuit structure and synaptic properties in mouse and human neocortex, revealing layer-dependent connectivity and subclass-specific dynamics.

The paper presents a dataset and analysis of spiking activity across multiple mouse visual areas, revealing that functional connectivity mirrors anatomical hierarchy.

This study identifies the anatomical composition and connectivity patterns of the mouse default mode network at the regional, layer, and cell-class levels.

The paper characterizes transcriptomic and physiological properties of GABAergic cortical neurons in mice, identifying 20 interneuron met-types with consistent features across modalities.

The paper identifies transcriptomic diversity in human supragranular neurons and links it to functional specialization and disease vulnerability.

The paper presents a large-scale dataset of neural activity in the mouse visual cortex and shows that responses to different stimuli are largely independent, suggesting multi-layer hierarchical computation.

The paper expands the Allen Mouse Brain Connectivity Atlas by analyzing intracortical connections at the layer and cell class level, revealing modular organization and distinct projection patterns.

The paper compares cell types in human and mouse cortex using single nucleus RNA-sequencing, finding conserved architecture with species-specific differences.

The paper classifies neurons in mouse visual cortex into 17 electrophysiological and 35 morphological types using unsupervised methods and links them to transcriptomic types.

The paper quantifies biological variation in the sizes, shapes, and locations of 11 visual cortical areas in mice, finding substantial size variation across the population.

NeuroInfo automatically registers experimental mouse brain sections with a 3D atlas to accurately identify brain regions, improving navigation and reducing observer error.

The paper develops a biophysically detailed in silico model of layer 4 of the mouse primary visual cortex, showing how functional synaptic connectivity reproduces experimental observations of neuronal responses to visual stimuli.

The paper develops generalized leaky integrate-and-fire models to classify neuron types in the mammalian neocortex using electrophysiological data.