Nicholas L. Turner

CAVE enables reproducible connectome analysis in large-scale datasets during ongoing annotation and proofreading.

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

The paper presents a functional connectomics dataset of mouse visual cortex, combining calcium imaging with EM reconstruction to study neural circuits and their connectivity.

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.

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.

The paper uses a larval zebrafish connectome to predict neural coding of behavior and shows statistical matches with calcium imaging data.

The paper investigates the synaptic connectivity of thick tufted layer 5 pyramidal neurons in the mouse visual cortex, revealing their local inhibitory and long-range excitatory connections.

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

Electron microscopy of 1352 neurons in a mouse visual cortex column reveals widespread cell-type-specific inhibition, a basket-cell-targeting disinhibitory class, and interneuron motif groups sharing excitatory targets.

The paper maps synaptic connectivity between cortical pyramidal neurons and shows synapse size is modeled by a combination of binary and analog variables.

The paper identifies novel nuclear layer contacts between starburst amacrine cells in the mouse retina through detailed morphological analysis of electron microscopic data.

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.

The paper reconstructs 3D cell nuclei in a Drosophila brain, identifying over 133,000 nuclei, mostly neurons, and highlights differences in nuclear size and cell density across brain regions.

Cloud-run convolutional nets aligned 19,939 serial EM sections of mouse visual cortex despite cracks and folds, segmented neurons and detected 523 million synapses; code and the reconstruction are public.

The paper identifies oligodendrocyte precursor cells (OPCs) as major contributors to axon pruning in the mouse neocortex through phagocytic activity.

The paper presents a semi-automated reconstruction of mouse primary visual cortex from electron microscopy data, revealing structural and functional relationships in cortical circuits.

The paper maps chandelier cell synapses onto pyramidal neurons and finds variability in synaptic input correlated with target neuron features, while showing functional correlation with arousal state.

The paper presents a digital resource of mouse retinal ganglion cell dendritic arbors and visual responses, identifies sublaminae, clusters cells, and finds sustained responses in one sublamina.