@article {69373, title = {Connectome-driven neural inventory of a complete visual system}, journal = {Nature}, year = {2024}, month = {2025 Mar 26}, abstract = {

Vision provides animals with detailed information about their surroundings, conveying diverse features such as color, form, and movement across the visual scene. Computing these parallel spatial features requires a large and diverse network of neurons, such that in animals as distant as flies and humans, visual regions comprise half the brain{\textquoteright}s volume. These visual brain regions often reveal remarkable structure-function relationships, with neurons organized along spatial maps with shapes that directly relate to their roles in visual processing. To unravel the stunning diversity of a complex visual system, a careful mapping of the neural architecture matched to tools for targeted exploration of that circuitry is essential. Here, we report a new connectome of the right optic lobe from a male Drosophila central nervous system FIB-SEM volume and a comprehensive inventory of the fly{\textquoteright}s visual neurons. We developed a computational framework to quantify the anatomy of visual neurons, establishing a basis for interpreting how their shapes relate to spatial vision. By integrating this analysis with connectivity information, neurotransmitter identity, and expert curation, we classified the 53,000 neurons into 727 types, about half of which are systematically described and named for the first time. Finally, we share an extensive collection of split-GAL4 lines matched to our neuron type catalog. Together, this comprehensive set of tools and data unlock new possibilities for systematic investigations of vision in Drosophila, a foundation for a deeper understanding of sensory processing.

}, doi = {10.1038/s41586-025-08746-0}, url = {https://doi.org/10.1038/s41586-025-08746-0}, author = {Aljoscha Nern and Frank Loesche and Shin-ya Takemura and Laura E Burnett and Marisa Dreher and Eyal Gruntman and Judith Hoeller and Gary B Huang and Michal Januszewski and Nathan C Klapoetke and Sanna Koskela and Kit D Longden and Zhiyuan Lu and Stephan Preibisch and Wei Qiu and Edward M Rogers and Pavithraa Seenivasan and Arthur Zhao and John Bogovic and Brandon S Canino and Jody Clements and Michael Cook and Samantha Finley-May and Miriam A Flynn and Imran Hameed and Kenneth J Hayworth and Gary Patrick Hopkins and Philip M Hubbard and William T Katz and Julie Kovalyak and Shirley A Lauchie and Meghan Leonard and Alanna Lohff and Charli A Maldonado and Caroline Mooney and Nneoma Okeoma and Donald J Olbris and Christopher Ordish and Tyler Paterson and Emily M Phillips and Tobias Pietzsch and Jennifer Rivas Salinas and Patricia K Rivlin and Ashley L Scott and Louis A Scuderi and Satoko Takemura and Iris Talebi and Alexander Thomson and Eric T Trautman and Lowell Umayam and Claire Walsh and John J Walsh and C Shan Xu and Emily A Yakal and Tansy Yang and Ting Zhao and Jan Funke and Reed George and Harald F Hess and Gregory S X E Jefferis and Christopher Knecht and Wyatt Korff and Stephen M Plaza and Sandro Romani and Stephan Saalfeld and Louis K Scheffer and Stuart Berg and Gerald M Rubin and Michael B Reiser} } @article {68800, title = {A Connectome of the Male Drosophila Ventral Nerve Cord}, journal = {bioRxiv}, year = {2023}, month = {2023 Jun 06}, abstract = {

Animal behavior is principally expressed through neural control of muscles. Therefore understanding how the brain controls behavior requires mapping neuronal circuits all the way to motor neurons. We have previously established technology to collect large-volume electron microscopy data sets of neural tissue and fully reconstruct the morphology of the neurons and their chemical synaptic connections throughout the volume. Using these tools we generated a dense wiring diagram, or connectome, for a large portion of the\ Drosophila\ central brain. However, in most animals, including the fly, the majority of motor neurons are located outside the brain in a neural center closer to the body, i.e. the mammalian spinal cord or insect ventral nerve cord (VNC). In this paper, we extend our effort to map full neural circuits for behavior by generating a connectome of the VNC of a male fly.

}, doi = {10.1101/2023.06.05.543757}, url = {https://doi.org/10.1101/2023.06.05.543757}, author = {Shin-ya Takemura and Kenneth J Hayworth and Gary B Huang and Michal Januszewski and Zhiyuan Lu and Elizabeth C Marin and Stephan Preibisch and C Shan Xu and John Bogovic and Andrew S Champion and Han S J Cheong and Marta Costa and Katharina Eichler and William Katz and Christopher Knecht and Feng Li and Billy J Morris and Christopher Ordish and Patricia K Rivlin and Philipp Schlegel and Kazunori Shinomiya and Tomke Sturner and Ting Zhao and Griffin Badalamente and Dennis Bailey and Paul Brooks and Brandon S Canino and Jody Clements and Michael Cook and Octave Duclos and Christopher R Dunne and Kelli Fairbanks and Siqi Fang and Samantha Finley-May and Audrey Francis and Reed George and Marina Gkantia and Kyle Harrington and Gary Patrick Hopkins and Joseph Hsu and Philip M Hubbard and Alexandre Javier and Dagmar Kainmueller and Wyatt Korff and Julie Kovalyak and Dominik Krzeminski and Shirley A Lauchie and Alanna Lohff and Charli Maldonado and Emily A Manley and Caroline Mooney and Erika Neace and Matthew Nichols and Omotara Ogundeyi and Nneoma Okeoma and Tyler Paterson and Elliott Phillips and Emily M Phillips and Caitlin Ribeiro and Sean M Ryan and Jon Thomson Rymer and Anne K Scott and Ashley L Scott and David Shepherd and Aya Shinomiya and Claire Smith and Alia Suleiman and Satoko Takemura and Iris Talebi and Imaan F M Tamimi and Eric T Trautman and Lowell Umayam and John J Walsh and Tansy Yang and Gerald M Rubin and Louis K Scheffer and Jan Funke and Stephan Saalfeld and Harald F Hess and Stephen M Plaza and Gwyneth M Card and Gregory S X E Jefferis and Stuart Berg} } @article {68842, title = {Latent Feature Representation via Unsupervised Learning for Pattern Discovery in Massive Electron Microscopy Image Volumes}, journal = {arXiv}, year = {2020}, month = {2020 Dec 22}, abstract = {

We propose a method to facilitate exploration and analysis of new large data sets. In particular, we give an unsupervised deep learning approach to learning a latent representation that captures semantic similarity in the data set. The core idea is to use data augmentations that preserve semantic meaning to generate synthetic examples of elements whose feature representations should be close to one another.
We demonstrate the utility of our method applied to nano-scale electron microscopy data, where even relatively small portions of animal brains can require terabytes of image data. Although supervised methods can be used to predict and identify known patterns of interest, the scale of the data makes it difficult to mine and analyze patterns that are not known a priori. We show the ability of our learned representation to enable query by example, so that if a scientist notices an interesting pattern in the data, they can be presented with other locations with matching patterns. We also demonstrate that clustering of data in the learned space correlates with biologically-meaningful distinctions. Finally, we introduce a visualization tool and software ecosystem to facilitate user-friendly interactive analysis and uncover interesting biological patterns. In short, our work opens possible new avenues in understanding of and discovery in large data sets, arising in domains such as EM analysis.

}, doi = {10.48550/arXiv.2012.12175}, url = {http://doi.org/10.48550/arXiv.2012.12175}, author = {Gary B Huang and Huei-Fang Yang and Shin-ya Takemura and Pat Rivlin and Stephen M Plaza} }