Jan Funke

This paper introduces sOrganellomics, a method to map cell state diversity and metabolic adaptation in tissues using spatial imaging and machine learning, revealing new insights into hepatocyte organization and metabolic changes under stress.

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 presents a multi-resolution imaging and analysis pipeline for circuit reconstruction in insects, revealing conserved and specialized neural circuits across six species.

The paper presents a spatial omics framework combining protein and RNA imaging with machine learning to classify brain cell types and discover structural features at tissue scale.

The paper introduces PRISM, a platform that uses combinatorial protein barcodes to enable self-correcting neuron tracing with molecular context, improving tracing accuracy and handling spatial gaps.

This paper introduces a Bayesian method to estimate the number of fluorescent emitters in diffraction-limited spots, showing improved accuracy over existing methods.

A FIB-SEM connectome of the male Drosophila right optic lobe sorts ∼53,000 neurons into 727 types, with neurotransmitter predictions and 577 matched split-GAL4 lines.

This paper introduces QuAC, a method for explaining deep neural network decisions on images by generating counterfactuals and attributing class differences, and evaluates it on synthetic and biological datasets.

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 creates a computational model of the Drosophila brain connectome to study sensorimotor processing and validates predictions through experiments.

The paper analyzes the network properties of the Drosophila whole-brain connectome, identifying rich club organization and signal integration/broadcasting neurons.

This study identifies reciprocal neural circuits between pC1 and pCd-2 neurons in Drosophila that mediate song preference learning through GABAergic and dopaminergic inputs.

The paper uses artificial neural networks to classify neurotransmitters from electron microscopy images of Drosophila melanogaster synapses, achieving high accuracy and revealing ultrastructural differences.

Roughly 23 thousand traced neurons and 10 million TBars of the male Drosophila nerve cord, reconstructed from electron microscopy, typed, and queryable in neuPrint down to motor neurons.

The paper identifies a feedforward circuit between dopamine subsystems in Drosophila that is essential for second-order conditioning, mediated by a key interneuron.

This paper introduces an auxiliary learning task using Local Shape Descriptors to improve neuron segmentation in electron microscopy volumes, achieving performance comparable to state-of-the-art methods with greater efficiency.

The paper maps cerebellar connectivity in mice and shows how structured, non-random motifs improve pattern separation while maintaining encoding capacity.

This paper introduces a method for automated lineage reconstruction in developing embryos using sparse annotations, achieving 75.8% accuracy on mouse embryos compared to 31.8% previously.

The paper maps the auditory pathway in Drosophila, identifies new cell types, and reveals a network of neurons with continuous song mode preferences and non-hierarchical connectivity.

The paper develops a method to map synaptic activity in vivo using FIBSEM and machine learning, revealing distribution patterns of vesicles in synapses of awake mice.

The paper presents a high-resolution EM dataset of Drosophila ventral nerve cord and develops GridTape for automated EM data collection, revealing a unique feedback pathway in motor control.

This paper introduces X-ray holographic nano-tomography for imaging dense neuronal structures in Drosophila and mammals, using convolutional neural networks for automated segmentation.

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.