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PLoS Comput Biol. 2018 May 21;14(5):e1006157. doi: 10.1371/journal.pcbi.1006157. eCollection 2018 May.

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

Author information

1
Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany.
2
Center for Integrative Neuroscience, University of Tübingen, Tübingen, Germany.
3
Bernstein Center for Computational Neuroscience, University of Tübingen, Tübingen, Germany.
4
Chan Zuckerberg Initiative, San Francisco, California, United States of America.
5
Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, United States of America.
6
Unit of Neuroscience Information and Complexity, Centre National de la Recherche Scientifique, Gif-sur-Yvette, France.
7
Bernstein Center for Computational Neuroscience and Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany.
8
Research Center Caesar, an associate of the Max Planck Society, Bonn, Germany.
9
Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germany.
10
Independent Researcher, San Francisco, California, United States of America.
11
Friedrich Miescher Institute of Biomedical Research, Basel, Switzerland.
12
University of Basel, Basel, Switzerland.
13
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.
14
Institute of Neurology, University College, London, United Kingdom.
15
Departments of Mathematics and Computer Science, Emory University, Atlanta, United States of America.
16
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.
17
Departement of Computer Science, Yale University, New Haven, Connecticut, United States of America.
18
Department of Neuroscience, Baylor College of Medicine, Houston, Texas, United States of America.
19
Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, Texas, United States of America.
20
Division of Neurobiology, Department Biology II, LMU Munich, Munich, Germany.
21
Twitter, London, United Kingdom.
22
Department of Electrical and Computer Engineering, Rice University, Houston, Texas, United States of America.
23
Institute of Theoretical Physics, University of Tübingen, Tübingen, Germany.

Abstract

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators. Different algorithms for estimating spike rates from noisy calcium measurements have been proposed in the past, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike rate inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

PMID:
29782491
PMCID:
PMC5997358
DOI:
10.1371/journal.pcbi.1006157
[Indexed for MEDLINE]
Free PMC Article

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