Dan Kapner

NEURD offers automated proofreading and feature extraction for connectomics

NEURD automates proofreading and feature extraction from connectomics data to improve accuracy and accessibility of neural morphology analyses.

Functional connectomics reveals general wiring rule in mouse visual cortex

Functionally similar neurons in mouse visual cortex connect preferentially across layers and areas, including feedback, and feature tuning, not receptive-field position, predicts synapses beyond axon-dendrite proximity.

An unsupervised map of excitatory neurons' dendritic morphology in the mouse visual cortex

The paper uses graph-based machine learning to analyze dendritic morphology of excitatory neurons in mouse visual cortex, finding a continuous morphological landscape rather than discrete types.

Integrating EM and Patch-seq data: Synaptic connectivity and target specificity of predicted Sst transcriptomic types

The paper predicts MET-type identities from EM data and shows they have distinct connectivity patterns and synapse targets.

Perisomatic Features Enable Efficient and Dataset Wide Cell-Type Classifications Across Large-Scale Electron Microscopy Volumes

The paper uses electron microscopy data to classify cell types in the neocortex based on perisomatic features, showing that these features are sufficient for accurate classification even in large datasets.