@article {69902, title = {Chromatix: A differentiable, GPU-accelerated wave-optics library}, journal = {bioRxiv}, year = {2025}, month = {2025 May 2}, abstract = {

Modern microscopy methods incorporate computational modeling of optical systems as an integral part of the imaging process, either to solve inverse problems or enable optimization of the optical system design. These methods often depend on differentiable simulations of optical systems, yet no standardized framework exists{\textemdash}forcing computational optics researchers to repeatedly and independently implement simulations that are prone to errors, difficult to reuse in other applications, and often computationally suboptimal. These common problems limit the potential impact of computational optics as a field. We present Chromatix: an open-source, GPU-accelerated differentiable wave optics library. Chromatix builds on JAX to enable fast simulation of diverse optical systems and inverse problem solving, scaling these simulations from single-CPU laptops to multi-GPU servers. The library implements various optical elements (e.g., lenses, polarizers and spatial light modulators) and multiple light propagation models (e.g., Fresnel approximation, angular spectrum and off-axis propagation) that can be flexibly combined to model various computational optics applications such as snapshot microscopy, holography, and phase retrieval of multiple scattering samples. These simulations can be automatically parallelized to scale across multiple GPUs with a single-line change to the modeling code, enabling simulation and optimization of previously impractical optical system designs. We demonstrate Chromatix{\textquoteright}s capacity to substantially accelerate optics simulation and optimization on existing methods in computational optics, speeding up optical simulation and optimization from 2-6{\texttimes} on a single GPU to up to 22{\texttimes} on 8 GPUs (depending on the particular system being modeled) compared to the original implementations. Chromatix establishes a standard for wave optics simulations, democratizing access to and expanding the design space of computational optics.i

}, doi = {10.1101/2025.04.29.651152}, url = {https://www.biorxiv.org/content/early/2025/05/02/2025.04.29.651152}, author = {Deb, Diptodip and Both, Gert-Jan and Bezzam, Eric and Kohli, Amit and Yang, Siqi and Chaware, Amey and Allier, C{\'e}dric and Cai, Changjia and Anderberg, Geneva and Eybposh, M. Hossein and Schneider, Magdalena C. and Heintzmann, Rainer and Rivera-Sanchez, Fabrizio A. and Simmerer, Corey and Meng, Guanghan and Tormes-Vaquerano, Jovan and Han, SeungYun and Shanmugavel, Sibi Chakravarthy and Maruvada, Teja and Yang, Xi and Kim, Yewon and Diederich, Benedict and Joo, Chulmin and Waller, Laura and Durr, Nicholas J. and P{\'e}gard, Nicolas C. and La Rivi{\`e}re, Patrick J. and Horstmeyer, Roarke and Chowdhury, Shwetadwip and Turaga, Srinivas C.} } @article {69833, title = {Glutamate indicators with increased sensitivity and tailored deactivation rates}, journal = {Nat Methods}, year = {2025}, month = {2025 Dec 23}, abstract = {

Understanding how neurons integrate signals from thousands of input synapses requires methods to monitor neurotransmission across many sites simultaneously. The fluorescent protein glutamate indicator iGluSnFR enables visualization of synaptic signaling, but the sensitivity, scale and speed of such measurements are limited by existing variants. Here we developed two highly sensitive fourth-generation iGluSnFR variants with fast activation and tailored deactivation rates: iGluSnFR4f for tracking rapid dynamics, and iGluSnFR4s for recording from large populations of synapses. These indicators detect glutamate with high spatial specificity and single-vesicle sensitivity in vivo. We used them to record natural patterns of synaptic transmission across multiple experimental contexts in mice, including two-photon imaging in cortical layers 1{\textendash}4 and hippocampal CA1, and photometry in the midbrain. The iGluSnFR4 variants extend the speed, sensitivity and scalability of glutamate imaging, enabling direct observation of information flow through neural networks in the intact brain.

}, doi = {10.1038/s41592-025-02965-z}, url = {https://doi.org/10.1038/s41592-025-02965-z}, author = {Aggarwal, Abhi and Negrean, Adrian and Chen, Yang and Iyer, Rishyashring and Reep, Daniel and Liu, Anyi and Palutla, Anirudh and Xie, Michael E. and MacLennan, Bryan J. and Hagihara, Kenta M. and Kinsey, Lucas W. and Sun, Julianna L. and Yao, Pantong and Zheng, Jihong and Tsang, Arthur and Tsegaye, Getahun and Zhang, Yonghai and Patel, Ronak H. and Arthur, Benjamin J. and Hiblot, Julien and Leippe, Philipp and Tarnawski, Miroslaw and Marvin, Jonathan S. and Vevea, Jason D. and Turaga, Srinivas C. and Tebo, Alison G. and Carandini, Matteo and Rossi, L. F. and Kleinfeld, David and Konnerth, Arthur and Svoboda, Karel and Turner, Glenn C. and Hasseman, Jeremy and Podgorski, Kaspar} } @article {69679, title = {Whole-body simulation of realistic fruit fly locomotion with deep reinforcement learning}, journal = {bioRxiv}, year = {2024}, month = {2024 Nov 22}, abstract = {

The body of an animal influences how the nervous system produces behavior. Therefore, detailed modeling of the neural control of sensorimotor behavior requires a detailed model of the body. Here we contribute an anatomically-detailed biomechanical whole-body model of the fruit fly Drosophila melanogaster in the MuJoCo physics engine. Our model is general-purpose, enabling the simulation of diverse fly behaviors, both on land and in the air. We demonstrate the generality of our model by simulating realistic locomotion, both flight and walking. To support these behaviors, we have extended MuJoCo with phenomenological models of fluid forces and adhesion forces. Through data-driven end-to-end reinforcement learning, we demonstrate that these advances enable the training of neural network controllers capable of realistic locomotion along complex trajectories based on high-level steering control signals. We demonstrate the use of visual sensors and the re-use of a pre-trained general-purpose flight controller by training the model to perform visually guided flight tasks. Our project is an open-source platform for modeling neural control of sensorimotor behavior in an embodied context.Competing Interest StatementThe authors have declared no competing interest.

}, doi = {10.1101/2024.03.11.584515}, url = {https://www.biorxiv.org/content/early/2024/11/22/2024.03.11.584515}, author = {Vaxenburg, Roman and Siwanowicz, Igor and Merel, Josh and Robie, Alice A. and Morrow, Carmen and Novati, Guido and Stefanidi, Zinovia and Both, Gert-Jan and Card, Gwyneth M. and Reiser, Michael B. and Botvinick, Matthew M. and Branson, Kristin M. and Tassa, Yuval and Turaga, Srinivas C.} } @article {67226, title = {Deep learning enables fast and dense single-molecule localization with high accuracy}, journal = {Nature Methods}, volume = {18}, year = {2021}, month = {2021 Sep 03}, abstract = {

Single-molecule localization microscopy (SMLM) has had remarkable success in imaging cellular structures with nanometer resolution, but the need for activating only single isolated emitters limits imaging speed and labeling density. Here, we overcome this major limitation using deep learning. We developed DECODE, a computational tool that can localize single emitters at high density in 3D with highest accuracy for a large range of imaging modalities and conditions. In a public software benchmark competition, it outperformed all other fitters on 12 out of 12 data-sets when comparing both detection accuracy and localization error, often by a substantial margin. DECODE allowed us to take live-cell SMLM data with reduced light exposure in just 3 seconds and to image microtubules at ultra-high labeling density. Packaged for simple installation and use, DECODE will enable many labs to reduce imaging times and increase localization density in SMLM.Competing Interest StatementThe authors have declared no competing interest.

}, doi = {10.1101/2020.10.26.355164}, url = {https://www.biorxiv.org/content/early/2020/10/26/2020.10.26.355164}, author = {Speiser, Artur and M{\"u}ller, Lucas-Raphael and Matti, Ulf and Obara, Christopher J. and Legant, Wesley R. and Kreshuk, Anna and Macke, Jakob H. and Ries, Jonas and Turaga, Srinivas C.} } @booklet {67972, title = {Programmable 3D snapshot microscopy with Fourier convolutional networks}, year = {2021}, abstract = {

3D snapshot microscopy enables fast volumetric imaging by capturing a 3D volume in a single 2D camera image and performing computational reconstruction. Fast volumetric imaging has a variety of biological applications such as whole brain imaging of rapid neural activity in larval zebrafish. The optimal microscope design for this optical 3D-to-2D encoding is both sample- and task-dependent, with no general solution known. Deep learning based decoders can be combined with a differentiable simulation of an optical encoder for end-to-end optimization of both the deep learning decoder and optical encoder. This technique has been used to engineer local optical encoders for other problems such as depth estimation, 3D particle localization, and lensless photography. However, 3D snapshot microscopy is known to require a highly non-local optical encoder which existing UNet-based decoders are not able to engineer. We show that a neural network architecture based on global kernel Fourier convolutional neural networks can efficiently decode information from multiple depths in a volume, globally encoded across a 3D snapshot image. We show in simulation that our proposed networks succeed in engineering and reconstructing optical encoders for 3D snapshot microscopy where the existing state-of-the-art UNet architecture fails. We also show that our networks outperform the state-of-the-art learned reconstruction algorithms for a computational photography dataset collected on a prototype lensless camera which also uses a highly non-local optical encoding.

}, keywords = {Computer Vision and Pattern Recognition (cs.CV), electronic engineering, FOS: Computer and information sciences, FOS: Electrical engineering, Image and Video Processing (eess.IV), information engineering, Machine Learning (cs.LG)}, doi = {10.48550/ARXIV.2104.10611}, url = {https://arxiv.org/abs/2104.10611}, author = {Deb, Diptodip and Jiao, Zhenfei and Chen, Alex B. and Broxton, Michael and Ahrens, Misha B. and Podgorski, Kaspar and Turaga, Srinivas C.} } @article {67229, title = {SongExplorer: A deep learning workflow for discovery and segmentation of animal acoustic communication signals}, journal = {bioRxiv}, year = {2021}, month = {03/2021}, abstract = {

Many animals produce distinct sounds or substrate-borne vibrations, but these signals have proved challenging to segment with automated algorithms. We have developed SongExplorer, a web-browser based interface wrapped around a deep-learning algorithm that supports an interactive workflow for (1) discovery of animal sounds, (2) manual annotation, (3) supervised training of a deep convolutional neural network, and (4) automated segmentation of recordings. Raw data can be explored by simultaneously examining song events, both individually and in the context of the entire recording, watching synced video, and listening to song. We provide a simple way to visualize many song events from large datasets within an interactive low-dimensional visualization, which facilitates detection and correction of incorrectly labelled song events. The machine learning model we implemented displays higher accuracy than existing heuristic algorithms and similar accuracy as two expert human annotators. We show that SongExplorer allows rapid detection of all song types from new species and of novel song types in previously well-studied species.Competing Interest StatementThe authors have declared no competing interest.

}, doi = {10.1101/2021.03.26.437280}, url = {https://www.biorxiv.org/content/early/2021/03/26/2021.03.26.437280}, author = {Arthur, Benjamin J. and Ding, Yun and Sosale, Medhini and Khalif, Faduma and Kim, Elizabeth and Waddell, Peter and Turaga, Srinivas C. and Stern, David L.} } @article {67225, title = {Comparative single-cell transcriptomics of complete insect nervous systems}, journal = {bioRxiv}, year = {2020}, month = {01/2020}, abstract = {

Molecular profiles of neurons influence information processing, but bridging the gap between genes, circuits, and behavior has been very difficult. Furthermore, the behavioral state of an animal continuously changes across development and as a result of sensory experience. How behavioral state influences molecular cell state is poorly understood. Here we present a complete atlas of the Drosophila larval central nervous system composed of over 200,000 single cells across four developmental stages. We develop polyseq, a python package, to perform cell-type analyses. We use single-molecule RNA-FISH to validate our scRNAseq findings. To investigate how internal state affects cell state, we optogentically altered internal state with high-throughput behavior protocols designed to mimic wasp sting and over activation of the memory system. We found nervous system-wide and neuron-specific gene expression changes. This resource is valuable for developmental biology and neuroscience, and it advances our understanding of how genes, neurons, and circuits generate behavior.

}, doi = {10.1101/785931}, url = {https://www.biorxiv.org/content/early/2020/01/16/785931}, author = {Cocanougher, Benjamin T. and Wittenbach, Jason D. and Long, Xi Salina and Kohn, Andrea B. and Norekian, Tigran P. and Yan, Jinyao and Colonell, Jennifer and Masson, Jean-Baptiste and Truman, James W. and Cardona, Albert and Turaga, Srinivas C. and Singer, Robert H. and Moroz, Leonid L. and Zlatic, Marta} } @conference {67227, title = {Learning Guided Electron Microscopy with Active Acquisition}, booktitle = {Medical Image Computing and Computer Assisted Intervention {\textendash} MICCAI 2020}, year = {2020}, month = {10/2020}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, abstract = {

Single-beam scanning electron microscopes (SEM) are widely used to acquire massive datasets for biomedical study, material analysis, and fabrication inspection. Datasets are typically acquired with uniform acquisition: applying the electron beam with the same power and duration to all image pixels, even if there is great variety in the pixels{\textquoteright} importance for eventual use. Many SEMs are now able to move the beam to any pixel in the field of view without delay, enabling them, in principle, to invest their time budget more effectively with non-uniform imaging.

}, isbn = {978-3-030-59722-1}, url = {https://link.springer.com/chapter/10.1007/978-3-030-59722-1_8}, author = {Mi, Lu and Wang, Hao and Meirovitch, Yaron and Schalek, Richard and Turaga, Srinivas C. and Lichtman, Jeff W. and Samuel, Aravinthan D. T. and Shavit, Nir}, editor = {Martel, Anne L. and Abolmaesumi, Purang and Stoyanov, Danail and Mateus, Diana and Zuluaga, Maria A. and Zhou, S. Kevin and Racoceanu, Daniel and Joskowicz, Leo} } @article {67222, title = {Teaching deep neural networks to localize single molecules for super-resolution microscopy}, journal = {arXiv e-prints}, year = {2019}, month = {06/2019}, pages = {arXiv:1907.00770}, abstract = {

Single-molecule localization fluorescence microscopy constructs super-resolution images by sequential imaging and computational localization of sparsely activated fluorophores. Accurate and efficient fluorophore localization algorithms are key to the success of this computational microscopy method. We present a novel localization algorithm based on deep learning which significantly improves upon the state of the art. Our contributions are a novel network architecture for simultaneous detection and localization, and new loss function which phrases detection and localization as a Bayesian inference problem, and thus allows the network to provide uncertainty-estimates. In contrast to standard methods which independently process imaging frames, our network architecture uses temporal context from multiple sequentially imaged frames to detect and localize molecules. We demonstrate the power of our method across a variety of datasets, imaging modalities, signal to noise ratios, and fluorophore densities. While existing localization algorithms can achieve optimal localization accuracy at low fluorophore densities, they are confounded by high densities. Our method is the first deep-learning based approach which achieves state-of-the-art on the SMLM2016 challenge. It achieves the best scores on 12 out of 12 data-sets when comparing both detection accuracy and precision, and excels at high densities. Finally, we investigate how unsupervised learning can be used to make the network robust against mismatch between simulated and real data. The lessons learned here are more generally relevant for the training of deep networks to solve challenging Bayesian inverse problems on spatially extended domains in biology and physics.

}, keywords = {Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Statistics - Machine Learning}, url = {https://arxiv.org/abs/1907.00770}, author = {Speiser, Artur and M{\"u}ller, Lucas-Raphael and Matti, Ulf and Obara, Christopher J. and Legant, Wesley R. and Ries, Jonas and Macke, Jakob H. and Turaga, Srinivas C.} } @article {67219, title = {Community-based benchmarking improves spike rate inference from two-photon calcium imaging data}, journal = {PLOS Computational Biology}, volume = {14}, year = {2018}, month = {Sep-05-2019}, pages = {e1006157}, abstract = {

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators. Different algorithms for estimating spike rates from noisy calcium measurements have been proposed in the past, but it is an open question how far performance can be improved. Here, we report the results of the\ spikefinder\ challenge, launched to catalyze the development of new spike rate inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

}, doi = {10.1371/journal.pcbi.1006157}, url = {https://journals.plos.org/ploscompbiol/article?rev=2\&id=10.1371/journal.pcbi.1006157}, author = {Berens, Philipp and Freeman, Jeremy and Deneux, Thomas and Chenkov, Nikolay and McColgan, Thomas and Speiser, Artur and Macke, Jakob H. and Turaga, Srinivas C. and Mineault, Patrick and Rupprecht, Peter and Gerhard, Stephan and Friedrich, Rainer W. and Friedrich, Johannes and Paninski, Liam and Pachitariu, Marius and Harris, Kenneth D. and Bolte, Ben and Machado, Timothy A. and Ringach, Dario and Stone, Jasmine and Rogerson, Luke E. and Sofroniew, Nicolas J. and Reimer, Jacob and Froudarakis, Emmanouil and Euler, Thomas and Rom{\'a}n Ros{\'o}n, Miroslav and Theis, Lucas and Tolias, Andreas S. and Bethge, Matthias}, editor = {Bush, Daniel} }