Daniel J. Bumbarger

The paper compares the nervous systems of two nematode species to identify patterns of evolutionary change in their connectomes.

The paper identifies neural assemblies consistent with Hebb's theory using a novel dataset linking optical physiology to connectivity, finding they are structurally integrated but not defined by strong monosynaptic connections.

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 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 presents a scalable pipeline for assembling large electron microscopy datasets into 3D volumes, achieving high throughput and modular design for efficient processing.

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.

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 describes a high-throughput transmission electron microscopy pipeline for imaging neuronal circuits, achieving petascale data acquisition of mouse neocortex.

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 compares the neuroanatomy and connectivity of the chemosensory system in two nematode species, finding conservation in neuronal number and position but divergence in ciliary morphology and synaptic wiring.