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Supervised learning sets benchmark for robust spike rate inference from calcium imaging signals

Lucas Theis, Philipp Berens, Emmanouil Froudarakis, Jacob Reimer, Miroslav Román Rosón, Tom Baden, Thomas Euler, Andreas Tolias, Matthias Bethge
doi: https://doi.org/10.1101/010777
Lucas Theis
1Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany
2Institute of Theoretical Physics, University of Tübingen, Tübingen, Germany
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Philipp Berens
1Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany
2Institute of Theoretical Physics, University of Tübingen, Tübingen, Germany
3Bernstein Center for Computational Neuroscience, University of Tübingen, Tübingen, Germany
4Department of Neuroscience, Baylor College of Medicine, Houston, USA
5Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
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  • For correspondence: philipp.berens@uni-tuebingen.de matthias.bethge@uni-tuebingen.de
Emmanouil Froudarakis
4Department of Neuroscience, Baylor College of Medicine, Houston, USA
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Jacob Reimer
4Department of Neuroscience, Baylor College of Medicine, Houston, USA
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Miroslav Román Rosón
1Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany
5Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
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Tom Baden
1Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany
3Bernstein Center for Computational Neuroscience, University of Tübingen, Tübingen, Germany
5Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
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Thomas Euler
1Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany
3Bernstein Center for Computational Neuroscience, University of Tübingen, Tübingen, Germany
5Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
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Andreas Tolias
3Bernstein Center for Computational Neuroscience, University of Tübingen, Tübingen, Germany
4Department of Neuroscience, Baylor College of Medicine, Houston, USA
6Department of Computational and Applied Mathematics, Rice University, Houston, USA
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Matthias Bethge
1Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany
2Institute of Theoretical Physics, University of Tübingen, Tübingen, Germany
3Bernstein Center for Computational Neuroscience, University of Tübingen, Tübingen, Germany
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  • For correspondence: philipp.berens@uni-tuebingen.de matthias.bethge@uni-tuebingen.de
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Summary

A fundamental challenge in calcium imaging has been to infer spike rates of neurons from the measured noisy calcium fluorescence traces. We systematically evaluate a range of spike inference algorithms on a large benchmark dataset (>100.000 spikes) recorded from varying neural tissue (V1 and retina) using different calcium indicators (OGB-1 and GCaMP6). We introduce a new algorithm based on supervised learning in flexible probabilistic models and show that it outperforms all previously published techniques. Importantly, it even performs better than other algorithms when applied to entirely new datasets for which no simultaneously recorded data is available. Future data acquired in new experimental conditions can easily be used to further improve its spike prediction accuracy and generalization performance. Finally, we show that comparing algorithms on artificial data is not informative about performance on real data, suggesting that benchmark datasets such as the one we provide may greatly facilitate future algorithmic developments.

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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 August 03, 2015.
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Supervised learning sets benchmark for robust spike rate inference from calcium imaging signals
Lucas Theis, Philipp Berens, Emmanouil Froudarakis, Jacob Reimer, Miroslav Román Rosón, Tom Baden, Thomas Euler, Andreas Tolias, Matthias Bethge
bioRxiv 010777; doi: https://doi.org/10.1101/010777
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Supervised learning sets benchmark for robust spike rate inference from calcium imaging signals
Lucas Theis, Philipp Berens, Emmanouil Froudarakis, Jacob Reimer, Miroslav Román Rosón, Tom Baden, Thomas Euler, Andreas Tolias, Matthias Bethge
bioRxiv 010777; doi: https://doi.org/10.1101/010777

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