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Database and deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging

View ORCID ProfilePeter Rupprecht, Stefano Carta, Adrian Hoffmann, Mayumi Echizen, View ORCID ProfileAntonin Blot, View ORCID ProfileAlex C. Kwan, View ORCID ProfileYang Dan, View ORCID ProfileSonja B. Hofer, View ORCID ProfileKazuo Kitamura, View ORCID ProfileFritjof Helmchen, View ORCID ProfileRainer W. Friedrich
doi: https://doi.org/10.1101/2020.08.31.272450
Peter Rupprecht
1Brain Research Institute, University of Zürich, Zürich, Switzerland
2Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
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  • For correspondence: rupprecht@hifo.uzh.ch helmchen@hifo.uzh.ch rainer.friedrich@fmi.ch
Stefano Carta
1Brain Research Institute, University of Zürich, Zürich, Switzerland
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Adrian Hoffmann
1Brain Research Institute, University of Zürich, Zürich, Switzerland
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Mayumi Echizen
4Department of Neurophysiology, The University of Tokyo, Tokyo, Japan
5Department of Anesthesiology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
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Antonin Blot
6Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, United Kingdom
7Biozentrum, University of Basel, Switzerland
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Alex C. Kwan
8Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
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Yang Dan
9Division of Neurobiology, Department of Molecular and Cell Biology, Helen Wills Neuroscience Institute, Howard Hughes Medical Institute, University of California, Berkeley, USA
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Sonja B. Hofer
6Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, United Kingdom
7Biozentrum, University of Basel, Switzerland
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Kazuo Kitamura
4Department of Neurophysiology, The University of Tokyo, Tokyo, Japan
10Department of Neurophysiology, University of Yamanashi, Yamanashi, Japan
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Fritjof Helmchen
1Brain Research Institute, University of Zürich, Zürich, Switzerland
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  • For correspondence: rupprecht@hifo.uzh.ch helmchen@hifo.uzh.ch rainer.friedrich@fmi.ch
Rainer W. Friedrich
2Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
3University of Basel, Basel, Switzerland
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  • ORCID record for Rainer W. Friedrich
  • For correspondence: rupprecht@hifo.uzh.ch helmchen@hifo.uzh.ch rainer.friedrich@fmi.ch
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ABSTRACT

Calcium imaging is a key method to record patterns of neuronal activity across populations of identified neurons. Inference of temporal patterns of action potentials (‘spikes’) from calcium signals is, however, challenging and often limited by the scarcity of ground truth data containing simultaneous measurements of action potentials and calcium signals. To overcome this problem, we compiled a large and diverse ground truth database from publicly available and newly performed recordings. This database covers various types of calcium indicators, cell types, and signal-to-noise ratios and comprises a total of >35 hours from 298 neurons. We then developed a novel algorithm for spike inference (CASCADE) that is based on supervised deep networks, takes advantage of the ground truth database, infers absolute spike rates, and outperforms existing model-based algorithms. To optimize performance for unseen imaging data, CASCADE retrains itself by resampling ground truth data to match the respective sampling rate and noise level. As a consequence, no parameters need to be adjusted by the user. To facilitate routine application of CASCADE we developed systematic performance assessments for unseen data, we openly release all resources, and we provide a user-friendly cloud-based implementation.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵† Contributed equally

  • Addition of new datasets with interneuron data; completely revised comparison with other algorithms (new Fig. 4); comparison with artificial ground truth simulated with NAOMi (see Fig. 3); and various smaller additions: new Fig. S15 to analyze the performance of CASCADE as a function of ground truth dataset size, and Fig. S19 to analyze the performance of CASCADE and other algorithms as a function of temporal precision of predictions.

  • https://github.com/HelmchenLabSoftware/Cascade

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted February 16, 2021.
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Database and deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging
Peter Rupprecht, Stefano Carta, Adrian Hoffmann, Mayumi Echizen, Antonin Blot, Alex C. Kwan, Yang Dan, Sonja B. Hofer, Kazuo Kitamura, Fritjof Helmchen, Rainer W. Friedrich
bioRxiv 2020.08.31.272450; doi: https://doi.org/10.1101/2020.08.31.272450
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Database and deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging
Peter Rupprecht, Stefano Carta, Adrian Hoffmann, Mayumi Echizen, Antonin Blot, Alex C. Kwan, Yang Dan, Sonja B. Hofer, Kazuo Kitamura, Fritjof Helmchen, Rainer W. Friedrich
bioRxiv 2020.08.31.272450; doi: https://doi.org/10.1101/2020.08.31.272450

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