@article {69732, title = {A critical initialization for biological neural networks}, journal = {Nature}, year = {2026}, month = {2026 May 20}, abstract = {

Intrinsically generated, brainwide neural activity displays macroscopic coordination among large populations of neurons that persists beyond the biophysical timescales of individual neurons1-3. It is not well understood how these macroscopic behaviours arise from microscopic, short-lived interactions between pairs of neurons. Here we show that the eigenvalue spectrum and dynamical properties of large-scale neural recordings in mice are similar to those produced by linear dynamics governed by a random symmetric matrix that is critically normalized. An exception was population activity in hippocampal area CA1, which resembled an efficient, uncorrelated neural code that may be optimized for information storage capacity. High-dimensional, global activity modes emerged in critically normalized artificial networks and persisted under sparse, clustered or spatial connectivity. These dynamics were useful for solving time-dependent tasks such as a zero-shot working memory task.

}, doi = {10.1038/s41586-026-10528-1}, url = {https://doi.org/10.1038/s41586-026-10528-1}, author = {Pachitariu, Marius and Zhong, Lin and Gracias, Alexa and Minisi, Amanda and Lopez, Crystall and Stringer, Carsen} } @article {69207, title = {Cellpose3: One-click image restoration for improved cellular segmentation.}, journal = {Nat Methods}, year = {2025}, month = {2025 Feb 12}, abstract = {

Generalist methods for cellular segmentation have good out-of-the-box performance on a variety of image types; however, existing methods struggle for images that are degraded by noise, blurring or undersampling, all of which are common in microscopy. We focused the development of Cellpose3 on addressing these cases and here we demonstrate substantial out-of-the-box gains in segmentation and image quality for noisy, blurry and undersampled images. Unlike previous approaches that train models to restore pixel values, we trained Cellpose3 to output images that are well segmented by a generalist segmentation model, while maintaining perceptual similarity to the target images. Furthermore, we trained the restoration models on a large, varied collection of datasets, thus ensuring good generalization to user images. We provide these tools as {\textquoteright}one-click{\textquoteright} buttons inside the graphical interface of Cellpose as well as in the Cellpose API.

}, doi = {10.1038/s41592-025-02595-5}, url = {https://doi.org/10.1038/s41592-025-02595-5}, author = {Stringer, Carsen and Pachitariu, Marius} } @article {69901, title = {Cellpose-SAM: Superhuman generalization for cellular segmentation}, journal = {bioRxiv}, year = {2025}, month = {2025 May 1}, abstract = {

Modern algorithms for biological segmentation can match inter-human agreement in annotation quality. This however is not a performance bound: a hypothetical human-consensus segmentation could reduce error rates in half. To obtain a model that generalizes better we adapted the pretrained transformer backbone of a foundation model (SAM) to the Cellpose framework. The resulting Cellpose-SAM model substantially outperforms inter-human agreement and approaches the human-consensus bound. We increase generalization performance further by making the model robust to channel shuffling, cell size, shot noise, downsampling, isotropic and anisotropic blur. The new model can be readily adopted into the Cellpose ecosystem which includes finetuning, human-in-the-loop training, image restoration and 3D segmentation approaches. These properties establish Cellpose-SAM as a foundation model for biological segmentation.

}, doi = {10.1101/2025.04.28.651001}, url = {https://www.biorxiv.org/content/early/2025/05/01/2025.04.28.651001}, author = {Pachitariu, Marius and Rariden, Michael and Stringer, Carsen} } @article {69783, title = {Cortical control of innate behavior from subcortical demonstration}, journal = {bioRxiv}, year = {2025}, month = {2025 Feb 13}, abstract = {

Motor control in mammals is traditionally viewed as a hierarchy of descending spinal-targeting pathways, with frontal cortex at the top 1{\textendash}3. Many redundant muscle patterns can solve a given task, and this high dimensionality allows flexibility but poses a problem for efficient learning 4. Although a feasible solution invokes subcortical innate motor patterns, or primitives, to reduce the dimensionality of the control problem, how cortex learns to utilize such primitives remains an open question 5{\textendash}7. To address this, we studied cortical and subcortical interactions as head-fixed mice learned contextual control of innate hindlimb extension behavior. Na{\"\i}ve mice performed reactive extensions to turn off a cold air stimulus within seconds and, using predictive cues, learned to avoid the stimulus altogether in tens of trials. Optogenetic inhibition of large areas of rostral cortex completely prevented avoidance behavior, but did not impair hindlimb extensions in reaction to the cold air stimulus. Remarkably, mice covertly learned to avoid the cold stimulus even without any prior experience of successful, cortically-mediated avoidance. These findings support a dynamic, heterarchical model in which the dominant locus of control can change, on the order of seconds, between cortical and subcortical brain areas. We propose that cortex can leverage periods when subcortex predominates as demonstrations, to learn parameterized control of innate behavioral primitives.

}, doi = {10.1101/2025.02.12.637930}, url = {https://www.biorxiv.org/content/early/2025/02/13/2025.02.12.637930}, author = {Keller, Jason A. and Kwak, Iljung S. and Stark, Alyssa K. and Pachitariu, Marius and Branson, Kristin and Dudman, Joshua T.} } @article {68464, title = {Cellpose 2.0: how to train your own model.}, journal = {Nature Methods}, volume = {19}, year = {2022}, month = {2022 Nov 07}, pages = {1634-41}, abstract = {

Pretrained neural network models for biological segmentation can provide good out-of-the-box results for many image types. However, such models do not allow users to adapt the segmentation style to their specific needs and can perform suboptimally for test images that are very different from the training images. Here we introduce Cellpose 2.0, a new package that includes an ensemble of diverse pretrained models as well as a human-in-the-loop pipeline for rapid prototyping of new custom models. We show that models pretrained on the Cellpose dataset can be fine-tuned with only 500-1,000 user-annotated regions of interest (ROI) to perform nearly as well as models trained on entire datasets with up to 200,000 ROI. A human-in-the-loop approach further reduced the required user annotation to 100-200 ROI, while maintaining high-quality segmentations. We provide software tools such as an annotation graphical user interface, a model zoo and a human-in-the-loop pipeline to facilitate the adoption of Cellpose 2.0.

}, issn = {1548-7105}, doi = {10.1038/s41592-022-01663-4}, author = {Pachitariu, Marius and Stringer, Carsen} } @article {65897, title = {Cellpose: a generalist algorithm for cellular segmentation.}, journal = {Nature Methods}, volume = {18}, year = {2021}, month = {2021 Jan 07}, pages = {100-106}, abstract = {

Many biological applications require the segmentation of cell bodies, membranes and nuclei from microscopy images. Deep learning has enabled great progress on this problem, but current methods are specialized for images that have large training datasets. Here we introduce a generalist, deep learning-based segmentation method called Cellpose, which can precisely segment cells from a wide range of image types and does not require model retraining or parameter adjustments. Cellpose was trained on a new dataset of highly varied images of cells, containing over 70,000 segmented objects. We also demonstrate a three-dimensional (3D) extension of Cellpose that reuses the two-dimensional (2D) model and does not require 3D-labeled data. To support community contributions to the training data, we developed software for manual labeling and for curation of the automated results. Periodically retraining the model on the community-contributed data will ensure that Cellpose improves constantly.

}, issn = {1548-7105}, doi = {10.1038/s41592-020-01018-x}, author = {Stringer, Carsen and Wang, Tim and Michaelos, Michalis and Pachitariu, Marius} } @article {65294, title = {Cellpose: a generalist algorithm for cellular segmentation}, journal = {bioRxiv}, year = {2020}, month = {2020 Feb 03}, abstract = {

Many biological applications require the segmentation of cell bodies, membranes and nuclei from microscopy images. Deep learning has enabled great progress on this problem, but current methods are specialized for images that have large training datasets. Here we introduce a generalist, deep learning-based segmentation algorithm called Cellpose, which can very precisely segment a wide range of image types out-of-the-box and does not require model retraining or parameter adjustments. We trained Cellpose on a new dataset of highly-varied images of cells, containing over 70,000 segmented objects. To support community contributions to the training data, we developed software for manual labelling and for curation of the automated results, with optional direct upload to our data repository. Periodically retraining the model on the community-contributed data will ensure that Cellpose improves constantly.

}, doi = {10.1101/2020.02.02.931238}, author = {Stringer, Carsen and Michaelos, Michalis and Pachitariu, Marius} } @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} } @article {49580, title = {Computational processing of neural recordings from calcium imaging data.}, journal = {Current Opinion in Neurobiology}, volume = {55}, year = {2018}, month = {2019 Apr }, pages = {22-31}, abstract = {

Electrophysiology has long been the workhorse of neuroscience, allowing scientists to record with millisecond precision the action potentials generated by neurons in vivo. Recently, calcium imaging of fluorescent indicators has emerged as a powerful alternative. This technique has its own strengths and weaknesses and unique data processing problems and interpretation confounds. Here we review the computational methods that convert raw calcium movies to estimates of single neuron spike times with minimal human supervision. By computationally addressing the weaknesses of calcium imaging, these methods hold the promise of significantly improving data quality. We also introduce a new metric to evaluate the output of these processing pipelines, which is based on the cluster isolation distance routinely used in electrophysiology.

}, issn = {1873-6882}, doi = {10.1016/j.conb.2018.11.005}, author = {Stringer, Carsen and Pachitariu, Marius} } @article {48836, title = {Community-based benchmarking improves spike inference from two-photon calcium imaging data.}, journal = {PLoS Computational Biology}, volume = {14}, year = {2017}, month = {2018 May 21}, 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}, author = {Berens, Philipp and Freeman, Jeremy and Deneux, Thomas and Chenkov, Nicolay 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 etal} }