Casey M Schneider-Mizell

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

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 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 describes an automated ultra-microtome for TEM sectioning, achieving high success rates in cutting and picking up sections from various biological samples.

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 develops a method to analyze synaptic features of cortical interneurons, revealing synaptic heterogeneity and distinct strategies in inhibitory coverage.

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 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 maps a neuronal circuit in Drosophila that uses serotonergic feedback to stabilize swallowing rhythms through mechanosensory input and motor-sensory-modulatory loops.

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 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.

FlyWire enables collaborative proofreading of whole-brain connectomes through an online platform, demonstrating its utility in reconstructing and analyzing mechanosensory neuron circuits.

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 uses electron microscopy to map sensory and interneuronal pathways to neuroendocrine cells in Drosophila, identifying a CO2-responsive network involving Corazonin and Diuretic hormone 44 neurons.

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 maps the synaptic connectome of the Drosophila larva mushroom body and identifies feedback pathways that enable modulatory neurons to compute teaching signals for reinforcement learning.

The paper maps sensory input pathways and their connections to motor outputs in Drosophila larvae, revealing monosynaptic and polysynaptic routes that converge on common motor targets.

The paper shows that neural circuit structure in Drosophila larvae remains consistent despite growth, with compensatory changes in neuronal morphology and connectivity.