Main Menu (Mobile)- Block

Main Menu - Block

custom | custom

Search Results

filters_region_cap | custom

Filter

facetapi-Q2b17qCsTdECvJIqZJgYMaGsr8vANl1n | block
facetapi-PV5lg7xuz68EAY8eakJzrcmwtdGEnxR0 | block
general_search_page-panel_pane_1 | views_panes

10 Janelia Publications

Showing 1-10 of 10 results
Your Criteria:
    04/05/22 | Cellpose 2.0: how to train your own model
    Stringer C, Pachitariu M
    bioRxiv. 2022 Apr 05:. doi: 10.1101/2022.04.01.486764

    Generalist models for cellular segmentation, like Cellpose, provide good out-of-the-box results for many types of images. However, such models do not allow users to adapt the segmentation style to their specific needs and may perform sub-optimally for test images that are very different from the training images. Here we introduce Cellpose 2.0, a new package which includes an ensemble of diverse pretrained models as well as a human-in-the-loop pipeline for quickly prototyping new specialist models. We show that specialist models pretrained on the Cellpose dataset can achieve state-of-the-art segmentation on new image categories with very little user-provided training data. Models trained on 500-1000 segmented regions-of-interest (ROIs) performed nearly as well as models trained on entire datasets with up to 200,000 ROIs. A human-in-the-loop approach further reduced the required user annotations to 100-200 ROIs, while maintaining state-of-the-art segmentation performance. This approach enables a new generation of specialist segmentation models that can be trained on new image types with only 1-2 hours of user effort. We provide software tools including an annotation GUI, a model zoo and a human-in-the-loop pipeline to facilitate the adoption of Cellpose 2.0.

    View Publication Page
    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.

    View Publication Page
    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.

    View Publication Page
    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.

    View Publication Page
    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.

    View Publication Page
    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.

    View Publication Page
    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.

    View Publication Page
    04/01/19 | Computational processing of neural recordings from calcium imaging data.
    Stringer C, Pachitariu M
    Current Opinion in Neurobiology. 2019 Apr ;55:22-31. doi: 10.1016/j.conb.2018.11.005

    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.

    View Publication Page
    08/06/18 | Robustness of spike deconvolution for neuronal calcium imaging.
    Pachitariu M, Stringer C, Harris KD
    The Journal of Neuroscience : the official journal of the Society for Neuroscience. 2018 Aug 06;38(37):7976-85. doi: 10.1523/JNEUROSCI.3339-17.2018

    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.

    View Publication Page