Forrest Collman

The paper integrates transcriptomic, morphological, and electrophysiological data to define neuron types in mouse visual cortex and predict their projection targets.

The paper presents the first densely reconstructed adult fruit fly connectome integrating the brain and ventral nerve cord, revealing distributed control circuits involving local and long-range neural connections.

The paper maps connectivity in mouse primary visual cortex, revealing cell-type-specific parallel pathways for top-down and bottom-up processing.

The paper identifies a somatotopic map in the fly leg that guides targeted grooming through a four-layer neural circuit.

The paper presents a computational pipeline for classifying postsynaptic targets in neuronal meshes, achieving high accuracy on mouse visual cortex data and demonstrating generalizability to other connectomes.

The paper develops a cross-species enhancer-AAV toolkit for targeting specific cell types in the basal ganglia across species, validated in mouse and macaque.

The paper maps large-scale neuronal connections in mouse hippocampal CA3, revealing spatially graded mossy fiber inputs and a feedforward inhibitory circuit targeting specific pyramidal cells.

The paper develops a toolkit of transgenic mouse lines and enhancer AAVs for selective targeting of cortical cell populations using transcriptomic and epigenomic data.

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 presents the first neuronal wiring diagram of a whole adult Drosophila brain, revealing synaptic connections and circuit mechanisms underlying sensorimotor behaviors.

The paper reconstructs and analyzes the female Drosophila ventral nerve cord connectome, identifying neural circuits that coordinate wings and legs during escape takeoff.

The paper analyzes premotor circuits in Drosophila to compare leg and wing motor control networks, finding modular organization and differences in synaptic connectivity.

The paper introduces a computational pipeline for automated tracing and assembly of axons across serial sections, validated on centimeter-scale data for orientation analysis and proofreading.

The paper presents a connectomic analysis of a human cerebral cortex fragment, revealing structural and connectivity features of neurons and synapses.

The paper quantifies the efficiency of retrograde monosynaptic rabies virus tracing in mouse primary visual cortex, finding about 40% of first-order presynaptic excitatory inputs are labeled.

SegCLR, a self-supervised learning method, enables classification of cellular subcompartments and cell types from 3D electron microscopy data with minimal labeled data.

The paper develops a screening strategy and database of antibodies validated for array tomography-based imaging of brain synapses, improving antibody selection for this technique.

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.

This paper trains a foundation model on neural activity data to predict responses to new stimuli and anatomical features, showing strong generalization across mice and tasks.

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.

NeuVue is a framework for managing and prioritizing proofreading tasks in connectomic datasets, enabling scalable and coordinated error correction by over forty proofreaders.