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24 Publications

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    10/05/22 | Not so spontaneous: Multi-dimensional representations of behaviors and context in sensory areas.
    Avitan L, Stringer C
    Neuron. 2022 Oct 05;110(19):3064. doi: 10.1016/j.neuron.2022.06.019

    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.

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    07/27/22 | Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation
    Kevin J. Cutler , Carsen Stringer , Paul A. Wiggins , Joseph D. Mougous
    bioRxiv. 2022 Jul 27:. doi: 10.1101/2021.11.03.467199

    Advances in microscopy hold great promise for allowing quantitative and precise readouts 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 perform unbiased cell segmentation, defined as the ability to faithfully identify cells independent of their morphology or optical characteristics. In this study, we present a new algorithm, Omnipose, which accurately segments samples that present significant challenges to current algorithms, including mixed bacterial cultures, antibiotic-treated cells, and cells of extended or branched morphology. We show that Omnipose achieves generality and performance beyond leading algorithms and its predecessor, Cellpose, by virtue of unique neural network outputs such as the gradient of the distance field. Finally, we demonstrate the utility of Omnipose in the characterization of extreme morphological phenotypes that arise during interbacterial antagonism and on the segmentation of non-bacterial objects. Our results distinguish Omnipose as a uniquely powerful tool for answering diverse questions in bacterial cell biology.

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    02/23/22 | The importance of accounting for movement when relating neuronal activity to sensory and cognitive processes.
    Edward Zagha , Jeffrey C Erlich , Soohyun Lee , Gyorgy Lur , Daniel H O'Connor , Nicholas A Steinmetz , Carsen Stringer , Hongdian Yang
    Journal of Neuroscience: The Official Journal of the Society for Neuroscience. 2022 Feb 23;42(8):1375-1382. doi: 10.1523/JNEUROSCI.1919-21.2021

    A surprising finding of recent studies in mouse is the dominance of widespread movement-related activity throughout the brain, including in early sensory areas. In awake subjects, failing to account for movement risks misattributing movement-related activity to other (e.g., sensory or cognitive) processes. In this article, we 1) review task designs for separating task-related and movement-related activity, 2) review three 'case studies' in which not considering movement would have resulted in critically different interpretations of neuronal function, and 3) discuss functional couplings that may prevent us from ever fully isolating sensory, motor, and cognitive-related activity. Our main thesis is that neural signals related to movement are ubiquitous, and therefore ought to be considered first and foremost when attempting to correlate neuronal activity with task-related processes.

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    05/13/21 | High-precision coding in visual cortex.
    Stringer C, Michaelos M, Tsyboulski D, Lindo SE, Pachitariu M
    Cell. 2021 May 13;184(10):2767-78. doi: 10.1016/j.cell.2021.03.042

    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° and 0.37°, 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.

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    01/07/21 | Cellpose: a generalist algorithm for cellular segmentation.
    Stringer C, Wang T, Michaelos M, Pachitariu M
    Nature Methods. 2021 Jan 07;18(1):100-106. doi: 10.1038/s41592-020-01018-x

    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.

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    10/06/20 | Simultaneous computation of dynamical and equilibrium information using a weighted ensemble of trajectories
    Suarez E, Lettieri S, Stringer CA, Zwier MC, Subramanian SR, Chong LT, Zuckerman DM
    Journal of chemical theory and computation;10:2658–2667
    02/03/20 | Cellpose: a generalist algorithm for cellular segmentation
    Stringer C, Michaelos M, Pachitariu M
    bioRxiv. 2020 Feb 03:. doi: 10.1101/2020.02.02.931238

    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.

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    06/26/19 | High-dimensional geometry of population responses in visual cortex.
    Stringer C, Pachitariu M, Steinmetz NA, Carandini M, Harris KD
    Nature. 2019 Jun 26;571(7765):361-65. doi: 10.1038/s41586-019-1346-5

    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.

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    06/21/19 | High precision coding in visual cortex
    Stringer C, Michaelos M, Pachitariu M
    BioRxiv. 06/2019:679324. doi: https://doi.org/10.1101/679324

    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 “noise” 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° 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.

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    04/19/19 | Spontaneous behaviors drive multidimensional, brain-wide population activity.
    Stringer C, Pachitariu M, Steinmetz NA, Reddy CB, Carandini M, Harris KD
    Science. 2019 Apr 18;364(6437):255. doi: 10.1101/306019

    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.

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