@article {69385, title = {A Bayesian Solution to Count the Number of Molecules within a Diffraction Limited Spot}, journal = {bioRxiv}, year = {2024}, month = {2024 Apr 22}, abstract = {

We address the problem of inferring the number of independently blinking fluorescent light emitters, when only their combined intensity contributions can be observed at each timepoint. This problem occurs regularly in light microscopy of objects that are smaller than the diffraction limit, where one wishes to count the number of fluorescently labelled subunits. Our proposed solution directly models the photo-physics of the system, as well as the blinking kinetics of the fluorescent emitters as a fully differentiable hidden Markov model. Given a trace of intensity over time, our model jointly estimates the parameters of the intensity distribution per emitter, their blinking rates, as well as a posterior distribution of the total number of fluorescent emitters. We show that our model is consistently more accurate and increases the range of countable subunits by a factor of two compared to current state-of-the-art methods, which count based on autocorrelation and blinking frequency, Further-more, we demonstrate that our model can be used to investigate the effect of blinking kinetics on counting ability, and therefore can inform experimental conditions that will maximize counting accuracy.

}, doi = {10.1101/2024.04.18.590066}, url = {https://www.biorxiv.org/content/early/2024/04/22/2024.04.18.590066}, author = {Alexander Hillsley and Johannes Stein and Paul W. Tillberg and David L. Stern and Jan Funke} } @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 {68921, title = {Network statistics of the whole-brain connectome of Drosophila}, journal = {Nature}, volume = {634}, year = {2024}, month = {2024 Oct 02}, pages = {153{\textendash}165}, abstract = {

Brains comprise complex networks of neurons and connections, similar to the nodes and edges of artificial networks. Network analysis applied to the wiring diagrams of brains can offer insights into how they support computations and regulate the flow of\ information underlying perception and behaviour. The completion of the first whole-brain connectome of an adult fly, containing over 130,000 neurons and millions of synaptic connections, offers an opportunity to analyse the statistical properties and topological features of a complete brain. Here we computed the prevalence of two- and three-node motifs, examined their strengths, related this information to both neurotransmitter composition and cell type annotations, and compared these metrics with wiring diagrams of other animals. We found that the network of the fly brain displays rich-club organization, with a large population (30\% of the connectome) of highly connected neurons. We identified subsets of rich-club neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions or neuropils. These data products are shared within the FlyWire Codex (https://codex.flywire.ai) and should serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.

}, doi = {10.1038/s41586-024-07968-y}, url = {https://doi.org/10.1038/s41586-024-07968-y}, author = {Albert Lin and Runzhe Yang and Sven Dorkenwald and Arie Matsliah and Amy R. Sterling and Philipp Schlegel and Szi-chieh Yu and Claire E. McKellar and Marta Costa and Katharina Eichler and Alexander Shakeel Bates and Nils Eckstein and Jan Funke and Gregory S.X.E. Jefferis and Mala Murthy} } @article {69045, title = {Neural-circuit basis of song preference learning in fruit flies}, journal = {iScience}, volume = {27}, year = {2024}, month = {2024 Jul 19}, abstract = {

As observed in human language learning and song learning in birds, the fruit fly Drosophila melanogaster changes its auditory behaviors according to prior sound experiences. This phenomenon, known as song preference learning in flies, requires GABAergic input to pC1 neurons in the brain, with these neurons playing a key role in mating behavior. The neural circuit basis of this GABAergic input, however, is not known. Here, we find that GABAergic neurons expressing the sex-determination gene doublesex are necessary for song preference learning. In the brain, only four doublesex-expressing GABAergic neurons exist per hemibrain, identified as pCd-2 neurons. pCd-2 neurons directly, and in many cases mutually, connect with pC1 neurons, suggesting the existence of reciprocal circuits between them. Moreover, GABAergic and dopaminergic inputs to doublesex-expressing GABAergic neurons are necessary for song preference learning. Together, this study provides a neural circuit model that underlies experience-dependent auditory plasticity at a single-cell resolution.

}, doi = {10.1016/j.isci.2024.110266}, url = {https://doi.org/10.1016/j.isci.2024.110266}, author = {Keisuke Imoto and Yuki Ishikawa and Yoshinori Aso and Jan Funke and Ryoya Tanaka and Azusa Kamikouchi} } @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 {68008, title = {Hierarchical architecture of dopaminergic circuits enables second-order conditioning in Drosophila}, journal = {eLife}, year = {2023}, month = {2023 Jan 24}, abstract = {

Dopaminergic neurons with distinct projection patterns and physiological properties compose memory subsystems in a brain. However, it is poorly understood whether or how they interact during complex learning. Here, we identify a feedforward circuit formed between dopamine subsystems and show that it is essential for second-order conditioning, an ethologically important form of higher-order associative learning. The\ Drosophila\ mushroom body comprises a series of dopaminergic compartments, each of which exhibits distinct memory dynamics. We find that a slow and stable memory compartment can serve as an effective {\textquotedblleft}teacher{\textquotedblright} by instructing other faster and transient memory compartments via a single key interneuron, which we identify by connectome analysis and neurotransmitter prediction. This excitatory interneuron acquires enhanced response to reward-predicting odor after first-order conditioning and, upon activation, evokes dopamine release in the {\textquotedblleft}student{\textquotedblright} compartments. These hierarchical connections between dopamine subsystems explain distinct properties of first- and second-order memory long known by behavioral psychologists.

}, doi = {10.7554/eLife.79042}, url = {https://doi.org/10.7554/eLife.79042}, author = {Daichi Yamada and Daniel Bushey and Li Feng and Karen Hibbard and Megan Sammons and Jan Funke and Ashok Litwin-Kumar and Toshihide Hige and Yoshinori Aso} } @article {68774, title = {A leaky integrate-and-fire computational model based on the connectome of the entire adult Drosophila brain reveals insights into sensorimotor processing}, journal = {bioRxiv}, year = {2023}, month = {2023 May 02}, abstract = {

The forthcoming assembly of the adult\ Drosophila melanogaster\ central brain connectome, containing over 125,000 neurons and 50 million synaptic connections, provides a template for examining sensory processing throughout the brain. Here, we create a leaky integrate-and-fire computational model of the entire\ Drosophila\ brain, based on neural connectivity and neurotransmitter identity, to study circuit properties of feeding and grooming behaviors. We show that activation of sugar-sensing or water-sensing gustatory neurons in the computational model accurately predicts neurons that respond to tastes and are required for feeding initiation. Computational activation of neurons in the feeding region of the\ Drosophila\ brain predicts those that elicit motor neuron firing, a testable hypothesis that we validate by optogenetic activation and behavioral studies. Moreover, computational activation of different classes of gustatory neurons makes accurate predictions of how multiple taste modalities interact, providing circuit-level insight into aversive and appetitive taste processing. Our computational model predicts that the sugar and water pathways form a partially shared appetitive feeding initiation pathway, which our calcium imaging and behavioral experiments confirm. Additionally, we applied this model to mechanosensory circuits and found that computational activation of mechanosensory neurons predicts activation of a small set of neurons comprising the antennal grooming circuit that do not overlap with gustatory circuits, and accurately describes the circuit response upon activation of different mechanosensory subtypes. Our results demonstrate that modeling brain circuits purely from connectivity and predicted neurotransmitter identity generates experimentally testable hypotheses and can accurately describe complete sensorimotor transformations.

}, doi = {10.1101/2023.05.02.539144}, url = {https://doi.org/10.1101/2023.05.02.539144}, author = {Philip K. Shiu and Gabriella R. Sterne and Nico Spiller and Romain Franconville and Andrea Sandoval and Joie Zhou and Neha Simha and Chan Hyuk Kang and Seongbong Yu and Jinseop S. Kim and Sven Dorkenwald and Arie Matsliah and Philipp Schlegel and Szi-chieh Yu and Claire E. McKellar and Amy Sterling and Marta Costa and Katharina Eichler and Gregory S.X.E. Jefferis and Mala Murthy and Alexander Shakeel Bates and Nils Eckstein and Jan Funke and Salil S. Bidaye and Stefanie Hampel and Andrew M. Seeds and Kristin Scott} } @article {69127, title = {Model-Based Inference of Synaptic Plasticity Rules}, journal = {bioRxiv}, year = {2023}, month = {2023 Dec 12}, abstract = {

Understanding learning through synaptic plasticity rules in the brain is a grand challenge for neuroscience. Here we introduce a novel computational framework for inferring plasticity rules from experimental data on neural activity trajectories and behavioral learning dynamics. Our methodology parameterizes the plasticity function to provide theoretical interpretability and facilitate gradient-based optimization. For instance, we use Taylor series expansions or multilayer perceptrons to approximate plasticity rules, and we adjust their parameters via gradient descent over entire trajectories to closely match observed neural activity and behavioral data. Notably, our approach can learn intricate rules that induce long nonlinear time-dependencies, such as those incorporating postsynaptic activity and current synaptic weights. We validate our method through simulations, accurately recovering established rules, like Oja{\textquoteright}s, as well as more complex hypothetical rules incorporating reward-modulated terms. We assess the resilience of our technique to noise and, as a tangible application, apply it to behavioral data from\ Drosophila\ during a probabilistic reward-learning experiment. Remarkably, we identify an active forgetting component of reward learning in flies that enhances the predictive accuracy of previous models. Overall, our modeling framework provides an exciting new avenue to elucidate the computational principles governing synaptic plasticity and learning in the brain.

}, doi = {10.1101/2023.12.11.571103}, url = {https://doi.org/10.1101/2023.12.11.571103}, author = {Yash Mehta and Danil Tyulmankov and Adithya E. Rajagopalan and Glenn C. Turner and James E. Fitzgerald and Jan Funke} } @conference {65837, title = {Microtubule Tracking in Electron Microscopy Volumes}, booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention}, year = {2020}, month = {2020 Sep 17}, abstract = {

We present a method for microtubule tracking in electron microscopy volumes. Our method first identifies a sparse set of voxels that likely belong to microtubules. Similar to prior work, we then enumerate potential edges between these voxels, which we represent in a candidate graph. Tracks of microtubules are found by selecting nodes and edges in the candidate graph by solving a constrained optimization problem incorporating biological priors on microtubule structure. For this, we present a novel integer linear programming formulation, which results in speed-ups of three orders of magnitude and an increase of 53\% in accuracy compared to prior art (evaluated on three 1 . 2 {\texttimes} 4 {\texttimes} 4{\textmu}m volumes of Drosophila neural tissue). We also propose a scheme to solve the optimization problem in a block-wise fashion, which allows distributed tracking and is necessary to process very large electron microscopy volumes. Finally, we release a benchmark dataset for microtubule tracking, here used for training, testing and validation, consisting of eight 30 x 1000 x 1000 voxel blocks (1 . 2 {\texttimes} 4 {\texttimes} 4{\textmu}m) of densely annotated microtubules in the CREMI data set (https://github.com/nilsec/micron).

}, author = {Nils Eckstein and Julia Buhmann and Matthew Cook and Jan Funke} }