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Community-based benchmarking improves spike rate inference from two-photon calcium imaging data.

  • Berens P Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany.
  • Freeman J Chan Zuckerberg Initiative, San Francisco, California, United States of America.
  • Deneux T Unit of Neuroscience Information and Complexity, Centre National de la Recherche Scientifique, Gif-sur-Yvette, France.
  • Chenkov N Bernstein Center for Computational Neuroscience and Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany.
  • McColgan T Bernstein Center for Computational Neuroscience and Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany.
  • Speiser A Research Center Caesar, an associate of the Max Planck Society, Bonn, Germany.
  • Macke JH Research Center Caesar, an associate of the Max Planck Society, Bonn, Germany.
  • Turaga SC Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, United States of America.
  • Mineault P Independent Researcher, San Francisco, California, United States of America.
  • Rupprecht P Friedrich Miescher Institute of Biomedical Research, Basel, Switzerland.
  • Gerhard S Friedrich Miescher Institute of Biomedical Research, Basel, Switzerland.
  • Friedrich RW Friedrich Miescher Institute of Biomedical Research, Basel, Switzerland.
  • Friedrich J Departments of Statistics and Neuroscience, Grossman Center for the Statistics of Mind, and Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America.
  • Paninski L Departments of Statistics and Neuroscience, Grossman Center for the Statistics of Mind, and Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America.
  • Pachitariu M Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, United States of America.
  • Harris KD Institute of Neurology, University College, London, United Kingdom.
  • Bolte B Departments of Mathematics and Computer Science, Emory University, Atlanta, United States of America.
  • Machado TA Departments of Statistics and Neuroscience, Grossman Center for the Statistics of Mind, and Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America.
  • Ringach D Neurobiology and Psychology, Jules Stein Eye Institute, Biomedical Engineering Program, David Geffen School of Medicine, University of California, Los Angeles, California, United States of America.
  • Stone J Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, United States of America.
  • Rogerson LE Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany.
  • Sofroniew NJ Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, United States of America.
  • Reimer J Department of Neuroscience, Baylor College of Medicine, Houston, Texas, United States of America.
  • Froudarakis E Department of Neuroscience, Baylor College of Medicine, Houston, Texas, United States of America.
  • Euler T Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany.
  • Román Rosón M Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany.
  • Theis L Twitter, London, United Kingdom.
  • Tolias AS Department of Neuroscience, Baylor College of Medicine, Houston, Texas, United States of America.
  • Bethge M Center for Integrative Neuroscience, University of Tübingen, Tübingen, Germany.
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  • 2018-05-22
Published in:
  • PLoS computational biology. - 2018
Action Potentials
Algorithms
Animals
Calcium
Computational Biology
Databases, Factual
Mice
Models, Neurological
Molecular Imaging
Optical Imaging
Retina
Retinal Neurons
English 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.
Language
  • English
Open access status
gold
Identifiers
Persistent URL
https://folia.unifr.ch/global/documents/273307
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