Stephan Gerhard

The paper classifies neuron types and their connectivity in the zebrafish olfactory bulb using ultrastructural features from electron microscopy.

The paper identifies and analyzes sexually dimorphic neurons in the Drosophila brain using connectome data to understand their connectivity and functional roles in behavior.

The paper presents a connectome of the human foveal retina using deep learning, revealing novel synaptic pathways and distinct neural circuits unique to humans.

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 presents an automatic method to identify synaptic partners in a whole-brain Drosophila EM dataset using convolutional neural networks, achieving high accuracy in predicting synaptic connections.

The paper benchmarks spike rate inference algorithms for two-photon calcium imaging data and finds that top-performing algorithms show high correlation despite diverse approaches.

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

The paper introduces methods to improve neuronal circuit reconstruction in Drosophila by leveraging anatomical features and synaptic connectivity patterns to reduce reconstruction errors.