@article {70286, title = {Extracting large-scale neural activity with Suite2p}, journal = {bioRxiv}, year = {2026}, month = {2026 Feb 06}, abstract = {

Neural recordings using optical methods have improved dramatically. For example, we demonstrate here recordings of over 100,000 neurons from the mouse cortex obtained with a standard commercial microscope. To process such large datasets, we developed Suite2p, a collection of efficient algorithms for motion correction, cell detection, activity extraction and quality control. We also developed new approaches to benchmark performance on these tasks. Our GPU-accelerated non-rigid motion correction substantially outperforms alternative methods, while running over five times faster. For cell detection, Suite2p outperforms the CNMF algorithm in Caiman and Fiola, finding more cells and producing fewer false positives, while running in a fraction of the time. We also introduce quality control steps for users to evaluate performance on their own data, while offering alternative algorithms for specialized types of recordings such as those from one-photon and voltage imaging.

}, doi = {10.64898/2026.02.04.703741}, url = {https://www.biorxiv.org/content/early/2026/02/06/2026.02.04.703741}, author = {Stringer, Carsen and Ki, Chris and DelGrosso, Nicholas and LaFosse, Paul and Zhang, Qingqing and Pachitariu, Marius} } @article {70374, title = {Linking neural representations to behavior using generalization}, journal = {bioRxiv}, year = {2026}, month = {2026 Apr 24}, abstract = {

Sensory-guided decisions are the result of sensorimotor transformations across many brain areas. Recent studies have localized the motor- and decision-related components of these transformations using brain-wide neural recordings. It has been more difficult to localize sensory computations in the same way. Here we developed a new approach for linking sensory computations to behavior by training mice to discriminate between two stimuli and testing their responses with new stimuli. In separate animals, we calculated the similarity of neural representations between train and test stimuli, using recordings of up to 73,000 simultaneously-recorded neurons from 9 primary and higher-order visual areas (HVAs) across layers 2 and 3. We found that neural discrimination on test but not train images correlated with behavioral discrimination, and this relation required prior visual experience as it was not present in dark-reared mice. The link between neural and behavioral performance was highest in the medial HVAs, suggesting this region is a critical component of sensory transformations and generalization.

}, doi = {10.64898/2026.04.21.719991}, url = {https://www.biorxiv.org/content/early/2026/04/24/2026.04.21.719991}, author = {N{\'u}{\~n}ez-Ochoa, Miguel Angel and Du, Fengtong and Zhong, Lin and Baptista, Scott and Michaelos, Michalis and Sohn, Alex and Baruchin, Liad and Schr{\"o}der, Sylvia and Stringer, Carsen and Pachitariu, Marius} } @article {70287, title = {Orofacial behaviors, not eye movements, govern neural activity in mouse visual cortex}, journal = {bioRxiv}, year = {2026}, month = {2026 Feb 05}, abstract = {

Neural activity in mouse primary visual cortex (V1) correlates strongly with orofacial movements. Such strong modulation has not been found in the primate visual cortex during eye fixation [1], which led to the suggestion that the modulation may primarily depend on eye movements in both species [2]. Here we examined the influence of eye movements on neural activity in mouse visual cortex both in complete darkness and in the presence of different types of visual input. In all cases, we found that eye movements explain a smaller fraction of neural activity variance compared to orofacial behaviors. Additionally, we found that eye movements were correlated to orofacial movements, such as whisking and sniffing, and thus may be indirectly correlated to neural activity. These results further emphasize the impact of movement signals on mouse visual cortex during free viewing behavior.

}, doi = {10.64898/2026.02.04.703800}, url = {https://www.biorxiv.org/content/early/2026/02/05/2026.02.04.703800}, author = {Syeda, Atika and N{\'u}{\~n}ez-Ochoa, Miguel Angel and Zhong, Lin and Pachitariu, Marius and Stringer, Carsen} } @article {70373, title = {Raster photostimulation of large-scale neural populations}, journal = {bioRxiv}, year = {2026}, month = {2026 Apr 24}, abstract = {

Neural computations are implemented by distributed neural populations that often span multiple brain areas. Causal photo-activation experiments done simultaneously with neural recordings can greatly improve our understanding of these computations, but such methods are typically limited to small subsets of neurons in restricted fields of view. Here we describe a new system called raster photostimulation for photo-activating and recording thousands of neurons, over a short 300 ms time window and over a large 5 mm field-of-view on a two-photon mesoscope. The photo-activation is precisely matched to the neural recording configuration, as it uses the same optical path, although with a different laser that is independently gated. We demonstrate pixel-level precision, frame-by-frame mask updating, and single-frame photostimulation of thousands of neurons. While this method lacks the precise temporal control of alternative methods, it compensates with ease-of-use, spatial precision, cost of implementation and by pushing the limits on the number of near-simultaneously stimulated neurons.

}, doi = {10.64898/2026.04.21.719951}, url = {https://www.biorxiv.org/content/early/2026/04/24/2026.04.21.719951}, author = {LaFosse, Paul K. and Flickinger, Daniel and Jaindl, Georg and Drinnenberg, Antonia and Gr{\o}dem, Sverre and Lensj{\o}, Kristian K. and Ramakrishnan, Charu and Siverts, La{\textquoteright}Akea and Zeng, Hongkui and Tasic, Bosiljka and Daigle, Tanya L. and Fyhn, Marianne and Deisseroth, Karl and Stringer, Carsen and Pachitariu, Marius} } @article {70285, title = {Whole-brain co-mapping of gene expression and neuronal activity at cellular resolution in behaving zebrafish}, journal = {bioRxiv}, year = {2026}, month = {2026 Feb 10}, abstract = {

The brain{\textquoteright}s capabilities rely on both the molecular properties of individual cells and their interactions across brain-wide networks. However, relating gene expression to activity in individual neurons across the entire brain remains elusive. Here we developed an experimental-computational platform, WARP, for whole-brain imaging of neuronal activity during behavior, expansion-assisted spatial transcriptomics, and cellular-level registration of these two modalities. Through joint analysis of whole-brain neuronal activity during multiple behaviors, cellular gene expression, and anatomy, we identified functions of molecularly defined populations {\textemdash} including luminance coding in a cckb-pou4f2 midbrain population and task-structured activity in pvalb7-eomesa hippocampal-like neurons {\textemdash} and defined over 2,000 other function-gene-anatomy subpopulations. Analysis of this unprecedented multimodal dataset also revealed that most gene-matched neurons showed stronger activity correlations, highlighting a brain-wide role for gene expression in functional organization. WARP establishes a foundational platform and open-access dataset for cross-experiment discovery, high-throughput function-to-gene mapping, unification of cell biology and systems neuroscience, and scalable circuit modeling at the whole-brain scale.

}, doi = {10.64898/2026.02.07.704095}, url = {https://www.biorxiv.org/content/early/2026/02/10/2026.02.07.704095}, author = {Marquez Legorreta, Emmanuel and Fleishman, Greg M. and Hesselink, Luuk W. and Eddison, Mark and Smeets, Kasper and Stringer, Carsen and Keller, Philipp J. and Narayan, Sujatha and Chen, Alex B. and Mensh, Brett D. and Sternson, Scott M. and Englitz, Bernhard and Tillberg, Paul W. and Ahrens, Misha B.} } @article {69864, title = {Abstract 2420: Deep learning enables automated detection of circulating tumor cell-immune cell interactions with prognostic insights in cancer}, journal = {Cancer Research}, volume = {85}, year = {2025}, month = {2025 Apr 21}, pages = {2420-2420}, abstract = {

Circulating tumor cells (CTCs) are critical biomarkers for predicting therapy response and survival in breast cancer patients. Multicellular CTC clusters exhibit enhanced metastatic potential, yet their detection and characterization are constrained by low frequency in blood samples and reliance on labor-intensive manual analysis. Advancing these methods could significantly improve prognostic evaluation and therapeutic strategies.Leveraging FDA-approved CellSearch technology and single-cell sequencing, we analyzed 2, 853 blood specimens, longitudinally collected from 1358 patients with advanced cancer (breast, prostate, etc) and other diseases. Integrating machine learning and deep learning tools, we developed a novel CTCpose platform to automate detection and analysis of CTCs, immune cells, and their interactions. Using artificial intelligence (AI)-driven image analysis, we extracted over 270 cellular and nuclear features including intensity, morphometry, fourier shape, gradient/edge, and haralick of cytokeratin, CD45, and DAPI expression patterns, enabling precise characterization of CTCs, white blood cells (WBCs), CTC clusters, and their interactions with immune cells (WBCs).The CTCpose platform enabled automated identification of CTCs, WBCs, homotypic CTC clusters, heterogenous CTC-WBC clusters, and immune cell clusters, providing comprehensive insights into cell morphology, biomarker expression, and spatial organization. These features correlated with patient survival, disease progression, and treatment response. Our findings highlight the clinical significance of CTC-immune cell interactions and dynamic alterations of CTCs (singles and clusters) and underscore their potential in stratifying patients into distinct risk categories.This study demonstrates the transformative potential of deep learning in overcoming limitations of traditional CTC detection methods and integrating imaging data with large cohorts of patient data. By automating and enhancing the analysis of CTC-immune cell interactions, we present a robust framework for developing predictive models with direct clinical relevance. This work opens avenues for personalized treatment strategies, underscoring the impact of AI in advancing precision oncology.Yuanfei Sun, Joshua R. Squires, Andrew Hoffmann, Youbin Zhang, Allegra Minor, Anmol Singh, David Scholten, Chengsheng Mao, Yuan Luo, Deyu Fang, William J. Gradishar, Massimo Cristofanilli, Carsen Stringer, Huiping Liu. Deep learning enables automated detection of circulating tumor cell-immune cell interactions with prognostic insights in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2420.

}, issn = {0008-5472}, doi = {10.1158/1538-7445.AM2025-2420}, url = {https://doi.org/10.1158/1538-7445.AM2025-2420}, author = {Sun, Yuanfei and Squires, Joshua R. and Hoffmann, Andrew and Zhang, Youbin and Minor, Allegra and Singh, Anmol and Scholten, David and Mao, Chengsheng and Luo, Yuan and Fang, Deyu and Gradishar, William J. and Cristofanilli, Massimo and Stringer, Carsen and Liu, Huiping} } @article {70023, title = {Abstract B049: Deep learning enables identification of cell types and clusters (iCTC) in immune tumor ecosystems for prognostic assessment in cancer}, journal = {Clinical Cancer Research}, volume = {31}, year = {2025}, month = {2025 Jul 10}, pages = {B049-B049}, abstract = {

Spatial multiomic profiling has been transforming the understanding of local tumor ecosystems. Yet, the spatial analyses of tumor-immune interactions at systemic levels, such as in liquid biopsies, are challenging. Within the last 10 years, we have longitudinally collected nearly 3,000 patient blood samples for multiplexing imaging of circulating tumor cells (CTCs) and their interactions with white blood cells (WBCs). Multicellular CTC clusters exhibit enhanced metastatic potential. The detection of CTCs and characterization of tumor immune ecosystems are constrained by (1) low frequency of CTCs in blood samples; (2) specific lineages of immune cells are not recognized by limited channels of current imaging methods, (3) reliance on labor-intensive manual analysis slows down the discovery of biomarkers for predicting therapy response and survival in cancer patients. We hypothesize that an AI-powered platform will accelerate the lineage and spatial characterization of tumor immune ecosystems for prognostic evaluations.

}, issn = {1078-0432}, doi = {10.1158/1557-3265.AIMACHINE-B049}, url = {https://doi.org/10.1158/1557-3265.AIMACHINE-B049}, author = {Squires, Joshua R. and Sun, Yuanfei and Hoffmann, Andrew D. and Zhang, Youbin and Minor, Allegra C. and Singh, Anmol and Scholten, David and Ding, Hannah and Mao, Chengsheng and Platanias, Leonidas C. and Luo, Yuan and Fang, Deyu and Gradishar, William J. and Cristofanilli, Massimo and Stringer, Carsen and Liu, Huiping} } @article {69974, title = {Analysis of deep learning for automated recognition of immune cells interacting with CTCs for prognostic assessment in cancer.}, journal = {Journal of Clinical Oncology}, volume = {43}, year = {2025}, month = {2025 May 28}, pages = {e13028-e13028}, abstract = {

e13028Background: Liquid biopsy has emerged as a powerful, minimally invasive tool for predicting treatment response and survival in breast and other advanced cancers. However, the detection and characterization of circulating tumor cells (CTCs) {\textemdash} a key factor in metastatic progression{\textemdash}remain challenging due to their low frequency and reliance on manual, time-intensive validation using only a couple of established methods for immunofluorescence staining, such as CellSearch. Harnessing deep learning for automated CTC detection and characterization of the blood cells interacting with CTCs holds the potential to advance prognostic evaluations and guide more effective therapies significantly. Methods: Leveraging FDA-approved CellSearch technology and sequencing approaches, we analyzed 2,853 blood specimens, longitudinally collected from 1358 patients with advanced cancer (breast, prostate, etc) and additional diseases. We built a novel deep learning platform, CTCpose, which integrates machine learning and AI-driven image analysis to automate the detection and categorization of CTCs, white blood cells (WBCs), and their clustering interactions. We extracted cellular and nuclear features to enable precise evaluation of individual CTCs, WBCs, homotypic CTC clusters, heterotypic CTC{\textendash}WBC clusters, and immune cell aggregates. Results: By employing the CTCpose platform, we achieved fully automated identification of CTCs and immune cells, unraveling the spatial organization and functional characteristics of both homotypic and heterotypic clusters. These highly granular assessments revealed clinically significant correlations with patient survival, disease progression, and therapeutic outcomes. Our data underscore the critical role of CTC{\textendash}immune cell interactions and the dynamic shifts in CTC phenotypes{\textemdash}both as single cells and clusters{\textemdash}in stratifying patients by risk and informing treatment strategies. Conclusions: This work illustrates the transformative power of deep learning in the analysis of liquid biopsy samples. By overcoming the limitations of traditional CTC detection, we have established a robust framework that integrates imaging data with large-scale patient cohorts to deliver predictive models of high clinical relevance. The CTCpose platform not only refines our understanding of CTC{\textendash}immune cell biology but also paves the way for personalized oncology approaches, highlighting the impactful convergence of artificial intelligence and precision medicine.

}, doi = {10.1200/JCO.2025.43.16_suppl.e13028}, url = {https://ascopubs.org/doi/abs/10.1200/JCO.2025.43.16_suppl.e13028}, author = {Liu, Huiping and Squires, Joshua and Sun, Yuanfei and Hoffmann, Andrew Daniel and Zhang, Youbin and Platanias, Leonidas C. and Gradishar, William John and Cristofanilli, Massimo and Stringer, Carsen} } @article {70121, title = {Brainwide hemodynamics predict EEG neural rhythms across sleep and wakefulness in humans.}, journal = {PLoS Comput Biol}, volume = {21}, year = {2025}, month = {2025 Sep 19}, pages = {e1013497}, abstract = {

The brain exhibits rich oscillatory dynamics that play critical roles in vigilance and cognition, such as the neural rhythms that define sleep. These rhythms continuously fluctuate, signaling major changes in vigilance, but the widespread brain dynamics underlying these oscillations are difficult to investigate. Using simultaneous EEG and fast fMRI in humans who fell asleep inside the scanner, we developed a machine learning approach to investigate which fMRI regions and networks predict fluctuations in neural rhythms. We demonstrated that the rise and fall of alpha (8-12 Hz) and delta (1-4 Hz) power-two canonical EEG bands critically involved with cognition and vigilance-can be predicted from fMRI data in subjects that were not present in the training set. This approach also identified predictive information in individual brain regions across the cortex and subcortex. Finally, we developed an approach to identify shared and unique predictive information, and found that information about alpha rhythms was highly separable in two networks linked to arousal and visual systems. Conversely, delta rhythms were diffusely represented on a large spatial scale primarily across the cortex. These results demonstrate that EEG rhythms can be predicted from fMRI data, identify large-scale network patterns that underlie alpha and delta rhythms, and establish a novel framework for investigating multimodal brain dynamics.

}, keywords = {Adult, Brain, Brain Mapping, Computational Biology, Electroencephalography, Female, Hemodynamics, Humans, Machine Learning, Magnetic Resonance Imaging, Male, Sleep, Wakefulness, Young Adult}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1013497}, url = {https://doi.org/10.1371/journal.pcbi.1013497}, author = {Jacob, Leandro P L and Bailes, Sydney M and Williams, Stephanie D and Stringer, Carsen and Lewis, Laura D} } @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 {69732, title = {A critical initialization for biological neural networks}, journal = {bioRxiv}, year = {2025}, month = {01/2025}, abstract = {

Artificial neural networks learn faster if they are initialized well. Good initializations can generate high-dimensional macroscopic dynamics with long timescales. It is not known if biological neural networks have similar properties. Here we show that the eigenvalue spectrum and dynamical properties of large-scale neural recordings in mice (two-photon and electrophysiology) are similar to those produced by linear dynamics governed by a random symmetric matrix that is critically normalized. An exception was hippocampal area CA1: population activity in this area resembled an efficient, uncorrelated neural code, which may be optimized for information storage capacity. Global emergent activity modes persisted in simulations with sparse, clustered or spatial connectivity. We hypothesize that the spontaneous neural activity reflects a critical initialization of whole-brain neural circuits that is optimized for learning time-dependent tasks.

}, doi = {10.1101/2025.01.10.632397}, url = {https://www.biorxiv.org/content/early/2025/01/10/2025.01.10.632397}, author = {Pachitariu, Marius and Zhong, Lin and Gracias, Alexa and Minisi, Amanda and Lopez, Crystall and Stringer, Carsen} } @article {69457, title = {Deep-tissue transcriptomics and subcellular imaging at high spatial resolution}, journal = {Science}, year = {2025}, month = {2025 Feb 20}, abstract = {

Limited color channels in fluorescence microscopy have long constrained spatial analysis in biological specimens. Here, we introduce cycle Hybridization Chain Reaction (HCR), a method that integrates multicycle DNA barcoding with HCR to overcome this limitation. cycleHCR enables highly multiplexed imaging of RNA and proteins using a unified barcode system. Whole-embryo transcriptomics imaging achieved precise three-dimensional gene expression and cell fate mapping across a specimen depth of ~310 μm. When combined with expansion microscopy, cycleHCR revealed an intricate network of 10 subcellular structures in mouse embryonic fibroblasts. In mouse hippocampal slices, multiplex RNA and protein imaging uncovered complex gene expression gradients and cell-type-specific nuclear structural variations. cycleHCR provides a quantitative framework for elucidating spatial regulation in deep tissue contexts for research and potentially diagnostic applications.

Preprint: https://doi.org/10.1101/2024.05.17.594641

}, doi = { 10.1126/science.adq2084}, url = {https://www.science.org/doi/10.1126/science.adq2084}, author = {Gandin, Valentina and Kim, Jun and Yang, Liang-Zhong and Lian, Yumin and Kawase, Takashi and Hu, Amy and Rokicki, Konrad and Fleishman, Greg and Tillberg, Paul and Aguilera Castrejon, Alejandro and Stringer, Carsen and Preibisch, Stephan and Liu, Zhe J.} } @article {70157, title = {Functional MRI signals as fast as 1Hz are coupled to brain states and predict spontaneous neural activity}, journal = {bioRxiv}, year = {2025}, month = {2025 Oct 14}, abstract = {

fMRI signals were traditionally seen as slow and sampled in the order of seconds, but recent technological advances have enabled much faster sampling rates. We hypothesized that high-frequency fMRI signals can capture spontaneous neural activity that index brain states. Using fast fMRI (TR=378ms) and simultaneous EEG in 27 humans drifting between sleep and wakefulness, we found that fMRI spectral power increased during NREM sleep (compared to wakefulness) across several frequency ranges as fast as 1Hz. This fast fMRI power was correlated with canonical arousal-linked EEG rhythms (alpha and delta), with spatiotemporal correlation patterns for each rhythm reflecting a combination of shared arousal dynamics and rhythm-specific neural signatures. Using machine learning, we found that alpha and delta EEG rhythms can be decoded from fast fMRI signals, in subjects held-out from the training set, showing that fMRI as fast as 0.9Hz (alpha) and 0.7Hz (delta) contains reliable neurally-coupled information that generalizes across individuals. Finally, we demonstrate that this fast fMRI acquisition allows for EEG rhythms to be decoded from 3.8s windows of fMRI data. These results reveal that high-frequency fMRI signals are coupled to dynamically varying brain states, and that fast fMRI sampling allows for more temporally precise quantification of spontaneous neural activity than previously thought possible.

}, doi = {10.1101/2025.10.13.681720}, url = {https://www.biorxiv.org/content/early/2025/10/14/2025.10.13.681720}, author = {Jacob, Leandro P. L. and Bailes, Sydney M and Stringer, Carsen and Polimeni, Jonathan R and Lewis, Laura D} } @article {68938, title = {Learning produces an orthogonalized state machine in the hippocampus.}, journal = {Nature}, volume = {640}, year = {2025}, month = {2025 February 12}, abstract = {

Cognitive maps confer animals with flexible intelligence by representing spatial, temporal and abstract relationships that can be used to shape thought, planning and behaviour. Cognitive maps have been observed in the hippocampus1, but their algorithmic form and learning mechanisms remain obscure. Here we used large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different linear tracks in virtual reality. Throughout learning, both animal behaviour and hippocampal neural activity progressed through multiple stages, gradually revealing improved task representation that mirrored improved behavioural efficiency. The learning process involved progressive decorrelations in initially similar hippocampal neural activity within and across tracks, ultimately resulting in orthogonalized representations resembling a state machine capturing the inherent structure of the task. This decorrelation process was driven by individual neurons acquiring task-state-specific responses (that is, {\textquoteright}state cells{\textquoteright}). Although various standard artificial neural networks did not naturally capture these dynamics, the clone-structured causal graph, a hidden Markov model variant, uniquely reproduced both the final orthogonalized states and the learning trajectory seen in animals. The observed cellular and population dynamics constrain the mechanisms underlying cognitive map formation in the hippocampus, pointing to hidden state inference as a fundamental computational principle, with implications for both biological and artificial intelligence.

}, doi = {10.1038/s41586-024-08548-w}, url = {https://doi.org/10.1038/s41586-024-08548-w}, author = {Sun, Weinan and Winnubst, Johan and Natrajan, Maanasa and Lai, Chongxi and Kajikawa, Koichiro and Michaelos, Michalis and Gattoni, Rachel and Stringer, Carsen and Flickinger, Daniel and Fitzgerald, James E and Spruston, Nelson} } @article {69514, title = {A simplified minimodel of visual cortical neurons}, journal = {Nat Commun}, year = {2025}, month = {2025 Jul 01}, abstract = {

Artificial neural networks (ANNs) have been shown to predict neural responses in primary visual cortex (V1) better than classical models. However, this performance often comes at the expense of simplicity and interpretability. Here we introduce a new class of simplified ANN models that can predict over 70\% of the response variance of V1 neurons. To achieve this high performance, we first recorded a new dataset of over 29,000 neurons responding to up to 65,000 natural image presentations in mouse V1. We found that ANN models required only two convolutional layers for good performance, with a relatively small first layer. We further found that we could make the second layer small without loss of performance, by fitting individual "minimodels" to each neuron. Similar simplifications applied for models of monkey V1 neurons. We show that the minimodels can be used to gain insight into how stimulus invariance arises in biological neurons.

Preprint:\ https://www.biorxiv.org/content/early/2024/07/02/2024.06.30.601394

}, doi = {10.1038/s41467-025-61171-9}, url = {https://doi.org/10.1038/s41467-025-61171-9}, author = {Du, Fengtong and N{\'u}{\~n}ez-Ochoa, Miguel Angel and Pachitariu, Marius and Stringer, Carsen} } @article {70117, title = {Spatial predictive coding in visual cortical neurons}, journal = {bioRxiv}, year = {2025}, month = {2025 Sep 19}, abstract = {

Predictive coding is a theoretical framework that can explain how animals build internal models of their sensory environments by predicting sensory inputs. Predictive coding may capture either spatial or temporal relationships between sensory objects. While the original theory by Rao and Ballard, 1999 described spatial predictive coding, much of the recent experimental data has been interpreted as evidence for temporal predictive coding. Here we directly tested whether the {\textquotedblleft}mismatch{\textquotedblright} neural responses in sensory cortex are due to a spatial or a temporal internal model. We adopted two common paradigms to study predictive coding: one based on virtual-reality and one based on static images. After training mice with repeated visual stimulation for several days, we performed multiple manipulations, including: 1) we introduced a novel stimulus, 2) we replaced a stimulus with a novel gray wall, 3) we duplicated a trained stimulus, or 4) we altered the order of the stimuli. The first two manipulations induced a substantial mismatch response in neural populations of up to 20,000 neurons recorded across primary and higher-order visual cortex, while the third and fourth ones did not. Thus, a mismatch response only occurred if a new spatial {\textendash} not temporal {\textendash} pattern was introduced.

}, doi = {10.1101/2025.09.17.676794}, url = {https://www.biorxiv.org/content/early/2025/09/19/2025.09.17.676794}, author = {Zhang, Qingqing and Gr{\o}dem, Sverre and Gracias, Alexa and Lensj{\o}, Kristian K. and Fyhn, Marianne and Stringer, Carsen and Pachitariu, Marius} } @article {69490, title = {Spatial transcriptomics reveals human cortical layer and area specification}, journal = {Nature}, year = {2025}, month = {2025 May 14}, abstract = {

The human cerebral cortex, pivotal for advanced cognitive functions, is composed of six distinct layers and dozens of functionally specialized areas. The layers and areas are distinguished both molecularly, by diverse neuronal and glial cell subtypes, and structurally, through intricate spatial organization3,4. While single-cell transcriptomics studies have advanced molecular characterization of human cortical development, a critical gap exists due to the loss of spatial context during cell dissociation. Here, we utilized multiplexed error-robust fluorescence in situ hybridization (MERFISH)9, augmented with deep-learning-based cell segmentation, to examine the molecular, cellular, and cytoarchitectural development of human fetal cortex with spatially resolved single-cell resolution. Our extensive spatial atlas, encompassing 16 million single cells, spans eight cortical areas across four time points in the second and third trimesters. We uncovered an early establishment of the six-layer structure, identifiable in the laminar distribution of excitatory neuronal subtypes by mid-gestation, long before the emergence of cytoarchitectural layers. Notably, while anterior-posterior gradients of neuronal subtypes were generally observed in most cortical areas, a striking exception was the sharp molecular border between primary (V1) and secondary visual cortices (V2) at gestational week 20. Here we discovered an abrupt binary shift in neuronal subtype specification at the earliest stages, challenging the notion that continuous morphogen gradients dictate mid-gestation cortical arealization. Moreover, integrating single-nuclei RNA-sequencing and in situ whole transcriptomics revealed an early upregulation of synaptogenesis in V1-specific Layer 4 neurons, suggesting a role of synaptogenesis in this discrete border formation. Collectively, our findings underscore the crucial role of spatial relationships in determining the molecular specification of cortical layers and areas. This work not only provides a valuable resource for the field, but also establishes a spatially resolved single-cell analysis paradigm that paves the way for a comprehensive developmental atlas of the human brain.

Preprint:\ https://www.biorxiv.org/content/early/2024/06/10/2024.06.05.597673

}, doi = {10.1038/s41586-025-09010-1}, url = {https://doi.org/10.1038/s41586-025-09010-1}, author = {Qian, Xuyu and Coleman, Kyle and Jiang, Shunzhou and Kriz, Andrea J. and Marciano, Jack H. and Luo, Chunyu and Cai, Chunhui and Manam, Monica Devi and Caglayan, Emre and Otani, Aoi and Ghosh, Urmi and Shao, Diane D. and Andersen, Rebecca E. and Neil, Jennifer E. and Johnson, Robert and LeFevre, Alexandra and Hecht, Jonathan L. and Miller, Michael B. and Sun, Liang and Stringer, Carsen and Li, Mingyao and Walsh, Christopher A.} } @article {69668, title = {Analysis methods for large-scale neuronal recordings.}, journal = {Science}, volume = {386}, year = {2024}, month = {2024 Nov 08}, pages = {eadp7429}, abstract = {

Simultaneous recordings from hundreds or thousands of neurons are becoming routine because of innovations in instrumentation, molecular tools, and data processing software. Such recordings can be analyzed with data science methods, but it is not immediately clear what methods to use or how to adapt them for neuroscience applications. We review, categorize, and illustrate diverse analysis methods for neural population recordings and describe how these methods have been used to make progress on longstanding questions in neuroscience. We review a variety of approaches, ranging from the mathematically simple to the complex, from exploratory to hypothesis-driven, and from recently developed to more established methods. We also illustrate some of the common statistical pitfalls in analyzing large-scale neural data.

}, keywords = {Action Potentials, Animals, Humans, Models, Neurological, Neurons, Neurosciences, Software}, issn = {1095-9203}, doi = {10.1126/science.adp7429}, author = {Stringer, Carsen and Pachitariu, Marius} } @article {69622, title = {A modular chemigenetic calcium indicator for multiplexed in vivo functional imaging.}, journal = {Nat Methods}, year = {2024}, month = {2024 Sep 20}, abstract = {

Genetically encoded fluorescent calcium indicators allow cellular-resolution recording of physiology. However, bright, genetically targetable indicators that can be multiplexed with existing tools in vivo are needed for simultaneous imaging of multiple signals. Here we describe WHaloCaMP, a modular chemigenetic calcium indicator built from bright dye-ligands and protein sensor domains. Fluorescence change in WHaloCaMP results from reversible quenching of the bound dye via a strategically placed tryptophan. WHaloCaMP is compatible with rhodamine dye-ligands that fluoresce from green to near-infrared, including several that efficiently label the brain in animals. When bound to a near-infrared dye-ligand, WHaloCaMP shows a 7{\texttimes} increase in fluorescence intensity and a 2.1-ns increase in fluorescence lifetime upon calcium binding. We use WHaloCaMP1a to image Ca responses in vivo in flies and mice, to perform three-color multiplexed functional imaging of hundreds of neurons and astrocytes in zebrafish larvae and to quantify Ca concentration using fluorescence lifetime imaging microscopy (FLIM).

}, issn = {1548-7105}, doi = {10.1038/s41592-024-02411-6}, url = {https://doi.org/10.1038/s41592-024-02411-6}, author = {Farrants, Helen and Shuai, Yichun and Lemon, William C and Monroy Hernandez, Christian and Zhang, Deng and Yang, Shang and Patel, Ronak and Qiao, Guanda and Frei, Michelle S and Plutkis, Sarah E and Grimm, Jonathan B and Hanson, Timothy L and Tomaska, Filip and Turner, Glenn C and Stringer, Carsen and Keller, Philipp J and Beyene, Abraham G and Chen, Yao and Liang, Yajie and Lavis, Luke D and Schreiter, Eric R} } @article {69352, title = {Spike sorting with Kilosort4}, journal = {Nat Methods}, year = {2024}, month = {2024 Apr 08}, abstract = {

Spike sorting is the computational process of extracting the firing times of single neurons from recordings of local electrical fields. This is an important but hard problem in neuroscience, made complicated by the nonstationarity of the recordings and the dense overlap in electrical fields between nearby neurons. To address the spike-sorting problem, we have been openly developing the Kilosort framework. Here we describe the various algorithmic steps introduced in different versions of Kilosort. We also report the development of Kilosort4, a version with substantially improved performance due to clustering algorithms inspired by graph-based approaches. To test the performance of Kilosort, we developed a realistic simulation framework that uses densely sampled electrical fields from real experiments to generate nonstationary spike waveforms and realistic noise. We found that nearly all versions of Kilosort outperformed other algorithms on a variety of simulated conditions and that Kilosort4 performed best in all cases, correctly identifying even neurons with low amplitudes and small spatial extents in high drift conditions.

}, issn = {1548-7105}, doi = {10.1038/s41592-024-02232-7}, author = {Pachitariu, Marius and Sridhar, Shashwat and Pennington, Jacob and Stringer, Carsen} } @article {69074, title = {Vagal sensory neurons mediate the Bezold-Jarisch reflex and induce syncope.}, journal = {Nature}, volume = {623}, year = {2023}, month = {2023 Nov 01}, pages = {387-396}, abstract = {

Visceral sensory pathways mediate homeostatic reflexes, the dysfunction of which leads to many neurological disorders. The Bezold-Jarisch reflex (BJR), first described in 1867, is a cardioinhibitory reflex that is speculated to be mediated by vagal sensory neurons (VSNs) that also triggers syncope. However, the molecular identity, anatomical organization, physiological characteristics and behavioural influence of cardiac VSNs remain mostly unknown. Here we leveraged single-cell RNA-sequencing data and HYBRiD tissue clearing to show that VSNs that express neuropeptide Y receptor Y2 (NPY2R) predominately connect the heart ventricular wall to the area postrema. Optogenetic activation of NPY2R VSNs elicits the classic triad of BJR responses-hypotension, bradycardia and suppressed respiration-and causes an animal to faint. Photostimulation during high-resolution echocardiography and laser Doppler flowmetry with behavioural observation revealed a range of phenotypes reflected in clinical syncope, including reduced cardiac output, cerebral hypoperfusion, pupil dilation and eye-roll. Large-scale Neuropixels brain recordings and machine-learning-based modelling showed that this manipulation causes the suppression of activity across a large distributed neuronal population that is not explained by changes in spontaneous behavioural movements. Additionally, bidirectional manipulation of the periventricular zone had a push-pull effect, with inhibition leading to longer syncope periods and activation inducing arousal. Finally, ablating NPY2R VSNs specifically abolished the BJR. Combined, these results demonstrate a genetically defined cardiac reflex that recapitulates characteristics of human syncope at physiological, behavioural and neural network levels.

}, keywords = {Area Postrema, Bradycardia, Cardiac Output, Low, Echocardiography, Heart, Heart Rate, Humans, Hypotension, Laser-Doppler Flowmetry, Nerve Net, Reflex, Sensory Receptor Cells, Single-Cell Gene Expression Analysis, Syncope, vagus nerve}, issn = {1476-4687}, doi = {10.1038/s41586-023-06680-7}, author = {Lovelace, Jonathan W and Ma, Jingrui and Yadav, Saurabh and Chhabria, Karishma and Shen, Hanbing and Pang, Zhengyuan and Qi, Tianbo and Sehgal, Ruchi and Zhang, Yunxiao and Bali, Tushar and Vaissiere, Thomas and Tan, Shawn and Liu, Yuejia and Rumbaugh, Gavin and Ye, Li and Kleinfeld, David and Stringer, Carsen and Augustine, Vineet} } @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 {68253, title = {Not so spontaneous: Multi-dimensional representations of behaviors and context in sensory areas.}, journal = {Neuron}, volume = {110}, year = {2022}, month = {2022 Oct 05}, pages = {3064}, abstract = {

Sensory areas are spontaneously active in the absence of sensory stimuli. This spontaneous activity has long been studied; however, its functional role remains largely unknown. Recent advances in technology, allowing large-scale neural recordings in the awake and behaving animal, have transformed our understanding of spontaneous activity. Studies using these recordings have discovered high-dimensional spontaneous activity patterns, correlation between spontaneous activity and behavior, and dissimilarity between spontaneous and sensory-driven activity patterns. These findings are supported by evidence from developing animals, where a transition toward these characteristics is observed as the circuit matures, as well as by evidence from mature animals across species. These newly revealed characteristics call for the formulation of a new role for spontaneous activity in neural sensory computation.

}, issn = {1097-4199}, doi = {10.1016/j.neuron.2022.06.019}, author = {Avitan, Lilach and Stringer, Carsen} } @article {68410, title = {Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation.}, journal = {Nature Methods}, year = {2022}, month = {2022 Oct 17}, abstract = {

Advances in microscopy hold great promise for allowing quantitative and precise measurement of morphological and molecular phenomena at the single-cell level in bacteria; however, the potential of this approach is ultimately limited by the availability of methods to faithfully segment cells independent of their morphological or optical characteristics. Here, we present Omnipose, a deep neural network image-segmentation algorithm. Unique network outputs such as the gradient of the distance field allow Omnipose to accurately segment cells on which current algorithms, including its predecessor, Cellpose, produce errors. We show that Omnipose achieves unprecedented segmentation performance on mixed bacterial cultures, antibiotic-treated cells and cells of elongated or branched morphology. Furthermore, the benefits of Omnipose extend to non-bacterial subjects, varied imaging modalities and three-dimensional objects. Finally, we demonstrate the utility of Omnipose in the characterization of extreme morphological phenotypes that arise during interbacterial antagonism. Our results distinguish Omnipose as a powerful tool for characterizing diverse and arbitrarily shaped cell types from imaging data.

}, issn = {1548-7105}, doi = {10.1038/s41592-022-01639-4}, author = {Cutler, Kevin J and Stringer, Carsen and Lo, Teresa W and Rappez, Luca and Stroustrup, Nicholas and Brook Peterson, S and Wiggins, Paul A and Mougous, Joseph D} } @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 {67234, title = {High-precision coding in visual cortex.}, journal = {Cell}, volume = {184}, year = {2021}, month = {2021 May 13}, pages = {2767-78}, abstract = {

Individual neurons in visual cortex provide the brain with unreliable estimates of visual features. It is not known whether the single-neuron variability is correlated across large neural populations, thus impairing the global encoding of stimuli. We recorded simultaneously from up to 50,000 neurons in mouse primary visual cortex (V1) and in higher order visual areas and measured stimulus discrimination thresholds of 0.35{\textdegree} and 0.37{\textdegree}, respectively, in an orientation decoding task. These neural thresholds were almost 100 times smaller than the behavioral discrimination thresholds reported in mice. This discrepancy could not be explained by stimulus properties or arousal states. Furthermore, behavioral variability during a sensory discrimination task could not be explained by neural variability in V1. Instead, behavior-related neural activity arose dynamically across a network of non-sensory brain areas. These results imply that perceptual discrimination in mice is limited by downstream decoders, not by neural noise in sensory representations.

}, issn = {1097-4172}, doi = {10.1016/j.cell.2021.03.042}, author = {Stringer, Carsen and Michaelos, Michalis and Tsyboulski, Dmitri and Lindo, Sarah E 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 {65752, title = {High precision coding in visual cortex}, journal = {BioRxiv}, year = {2019}, month = {06/2019}, pages = {679324}, abstract = {

Single neurons in visual cortex provide unreliable measurements of visual features due to their high trial-to-trial variability. It is not known if this {\textquotedblleft}noise{\textquotedblright} extends its effects over large neural populations to impair the global encoding of sensory stimuli. We recorded simultaneously from \~{}20,000 neurons in mouse visual cortex and found that the neural population had discrimination thresholds of 0.3{\textdegree} in an orientation decoding task. These thresholds are \~{}100 times smaller than those reported behaviorally in mice. The discrepancy between neural and behavioral discrimination could not be explained by the types of stimuli we used, by behavioral states or by the sequential nature of trial-by-trial perceptual learning tasks. These results imply that the limits of sensory perception in mice are not set by neural noise in sensory cortex, but by the limitations of downstream decoders.

}, doi = {https://doi.org/10.1101/679324}, author = {Stringer, Carsen and Michaelos, Michalis and Pachitariu, Marius} } @article {49534, title = {High-dimensional geometry of population responses in visual cortex.}, journal = {Nature}, volume = {571}, year = {2019}, month = {2019 Jun 26}, pages = {361-65}, abstract = {

A neuronal population encodes information most efficiently when its activity is uncorrelated and high-dimensional, and most robustly when its activity is correlated and lower-dimensional. Here, we analyzed the correlation structure of natural image coding, in large visual cortical populations recorded from awake mice. Evoked population activity was high dimensional, with correlations obeying an unexpected power-law: the n-th principal component variance scaled as 1/n. This was not inherited from the 1/f spectrum of natural images, because it persisted after stimulus whitening. We proved mathematically that the variance spectrum must decay at least this fast if a population code is smooth, i.e. if small changes in input cannot dominate population activity. The theory also predicts larger power-law exponents for lower-dimensional stimulus ensembles, which we validated experimentally. These results suggest that coding smoothness represents a fundamental constraint governing correlations in neural population codes.

}, doi = {10.1038/s41586-019-1346-5}, author = {Stringer, Carsen and Pachitariu, Marius and Steinmetz, Nicholas A and Carandini, Matteo and Harris, Kenneth D} } @article {49187, title = {Spontaneous behaviors drive multidimensional, brain-wide population activity.}, journal = {Science}, volume = {364}, year = {2019}, month = {2019 Apr 18}, pages = {255}, abstract = {

Sensory cortices are active in the absence of external sensory stimuli. To understand the nature of this ongoing activity, we used two-photon calcium imaging to record from over 10,000 neurons in the visual cortex of mice awake in darkness while monitoring their behavior videographically. Ongoing population activity was multidimensional, exhibiting at least 100 significant dimensions, some of which were related to the spontaneous behaviors of the mice. The largest single dimension was correlated with the running speed and pupil area, while a 16-dimensional summary of orofacial behaviors could predict ~45\% of the explainable neural variance. Electrophysiological recordings with 8 simultaneous Neuropixels probes revealed a similar encoding of high-dimensional orofacial behaviors across multiple forebrain regions. Representation of motor variables continued uninterrupted during visual stimulus presentation, occupying dimensions nearly orthogonal to the stimulus responses. Our results show that a multidimensional representation of motor state is encoded across the forebrain, and is integrated with visual input by neuronal populations in primary visual cortex.

}, doi = {10.1101/306019}, author = {Stringer, Carsen and Pachitariu, Marius and Steinmetz, Nicholas A and Reddy, Charu Bai and Carandini, Matteo and Harris, Kenneth D} } @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 {49345, title = {Robustness of spike deconvolution for neuronal calcium imaging.}, journal = {The Journal of Neuroscience : the official journal of the Society for Neuroscience}, volume = {38}, year = {2018}, month = {2018 Aug 06}, pages = {7976-85}, abstract = {

Calcium imaging is a powerful method to record the activity of neural populations in many species, but inferring spike times from calcium signals is a challenging problem. We compared multiple approaches using multiple datasets with ground truth electrophysiology, and found that simple non-negative deconvolution (NND) outperformed all other algorithms on out-of-sample test data. We introduce a novel benchmark applicable to recordings without electrophysiological ground truth, based on the correlation of responses to two stimulus repeats, and used this to show that unconstrained NND also outperformed the other algorithms when run on "zoomed out" datasets of \~{}10,000 cell recordings from the visual cortex of mice of either sex. Finally, we show that NND-based methods match the performance of a supervised method based on convolutional neural networks, while avoiding some of the biases of such methods, and at much faster running times. We therefore recommend that spikes be inferred from calcium traces using simple NND, due to its simplicity, efficiency and accuracy.The experimental method that currently allows for recordings of the largest numbers of cells simultaneously is two-photon calcium imaging. However, use of this powerful method requires that neuronal firing times be inferred correctly from the large resulting datasets. Previous studies have claimed that complex supervised learning algorithms outperform simple deconvolution methods at this task. Unfortunately, these studies suffered from several problems and biases. When we repeated the analysis, using the same data and correcting these problems, we found that simpler spike inference methods perform better. Even more importantly, we found that supervised learning methods can introduce artifactual structure into spike trains, that can in turn lead to erroneous scientific conclusions. Of the algorithms we evaluated, we found that an extremely simple method performed best in all circumstances tested, was much faster to run, and was insensitive to parameter choices, making incorrect scientific conclusions much less likely.

}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.3339-17.2018}, author = {Pachitariu, Marius and Stringer, Carsen and Harris, Kenneth D} } @article {48837, title = {Robustness of spike deconvolution for calcium imaging of neural spiking.}, journal = {bioRxiv}, year = {2017}, month = {2017 Jun 27}, pages = {156786}, abstract = {

Calcium imaging is a powerful method to record the activity of neural populations, but inferring spike times from calcium signals is a challenging problem. We compared multiple approaches using multiple datasets with ground truth electrophysiology, and found that simple non-negative deconvolution (NND) outperformed all other algorithms. We introduce a novel benchmark applicable to recordings without electrophysiological ground truth, based on the correlation of responses to two stimulus repeats, and used this to show that unconstrained NND also outperformed the other algorithms when run on {\textquoteright}zoomed out{\textquoteright} datasets of ~10,000 cell recordings. Finally, we show that NND-based methods match the performance of a supervised method based on convolutional neural networks, while avoiding some of the biases of such methods, and at much faster running times. We therefore recommend that spikes be inferred from calcium traces using simple NND, due to its simplicity, efficiency and accuracy.

}, doi = {https://doi.org/10.1101/156786}, author = {Pachitariu, Marius and Stringer, Carsen and Harris, Kenneth D} } @article {48840, title = {Suite2p: beyond 10,000 neurons with standard two-photon microscopy.}, journal = {bioRxiv}, year = {2017}, month = {2017 Jul 20}, pages = {061507}, abstract = {

Two-photon microscopy of calcium-dependent sensors has enabled unprecedented recordings from vast populations of neurons. While the sensors and microscopes have matured over several generations of development, computational methods to process the resulting movies remain inefficient and can give results that are hard to interpret. Here we introduce Suite2p: a fast, accurate and complete pipeline that registers raw movies, detects active cells, extracts their calcium traces and infers their spike times. Suite2p runs on standard workstations, operates faster than real time, and recovers ~2 times more cells than the previous state-of-the-art method. Its low computational load allows routine detection of ~10,000 cells simultaneously with standard two-photon resonant-scanning microscopes. Recordings at this scale promise to reveal the fine structure of activity in large populations of neurons or large populations of subcellular structures such as synaptic boutons.

}, doi = {https://doi.org/10.1101/061507}, author = {Pachitariu, Marius and Stringer, Carsen and Dipoppa, Mario and Schr{\"o}der, Sylvia and Rossi, L Federico and Dalgleish, Henry and Carandini, Matteo and Harris, Kenneth D} } @article {48842, title = {Inhibitory control of correlated intrinsic variability in cortical networks.}, journal = {eLife}, volume = {5}, year = {2016}, month = {2016 Dec 07}, abstract = {

Cortical networks exhibit intrinsic dynamics that drive coordinated, large-scale fluctuations across neuronal populations and create noise correlations that impact sensory coding. To investigate the network-level mechanisms that underlie these dynamics, we developed novel computational techniques to fit a deterministic spiking network model directly to multi-neuron recordings from different rodent species, sensory modalities, and behavioral states. The model generated correlated variability without external noise and accurately reproduced the diverse activity patterns in our recordings. Analysis of the model parameters suggested that differences in noise correlations across recordings were due primarily to differences in the strength of feedback inhibition. Further analysis of our recordings confirmed that putative inhibitory neurons were indeed more active during desynchronized cortical states with weak noise correlations. Our results demonstrate that network models with intrinsically-generated variability can accurately reproduce the activity patterns observed in multi-neuron recordings and suggest that inhibition modulates the interactions between intrinsic dynamics and sensory inputs to control the strength of noise correlations.

}, issn = {2050-084X}, doi = {10.7554/eLife.19695}, author = {Stringer, Carsen and Pachitariu, Marius and Steinmetz, Nicholas A and Okun, Michael and Bartho, Peter and Harris, Kenneth D and Sahani, Maneesh and Lesica, Nicholas A} } @article {65751, title = {Inhibitory control of correlated intrinsic variability in cortical networks}, journal = {Elife}, volume = {5}, year = {2016}, month = {12/2016}, pages = {e19695}, abstract = {

Cortical networks exhibit intrinsic dynamics that drive coordinated, large-scale fluctuations across neuronal populations and create noise correlations that impact sensory coding. To investigate the network-level mechanisms that underlie these dynamics, we developed novel computational techniques to fit a deterministic spiking network model directly to multi-neuron recordings from different rodent species, sensory modalities, and behavioral states. The model generated correlated variability without external noise and accurately reproduced the diverse activity patterns in our recordings. Analysis of the model parameters suggested that differences in noise correlations across recordings were due primarily to differences in the strength of feedback inhibition. Further analysis of our recordings confirmed that putative inhibitory neurons were indeed more active during desynchronized cortical states with weak noise correlations. Our results demonstrate that network models with intrinsically-generated variability can accurately reproduce the activity patterns observed in multi-neuron recordings and suggest that inhibition modulates the interactions between intrinsic dynamics and sensory inputs to control the strength of noise correlations.

}, doi = {https://doi.org/10.7554/eLife.19695}, author = {Stringer, Carsen and Pachitariu, Marius and Steinmetz, Nicholas A and Okun, Michael and Bartho, Peter and Harris, Kenneth D and Sahani, Maneesh and Lesica, Nicholas A} }