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  Benchmarking Spike Rate Inference in Population Calcium Imaging

Theis, L., Berens, P., Froudarakis, E., Reimer, J., Román Rosón, M., Baden, T., et al. (2016). Benchmarking Spike Rate Inference in Population Calcium Imaging. Neuron, 90(3), 471-482. doi:10.1016/j.neuron.2016.04.014.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0000-79CE-1 Version Permalink: http://hdl.handle.net/21.11116/0000-0000-79CF-0
Genre: Journal Article

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Theis, L1, 2, Author              
Berens, P1, 2, Author              
Froudarakis, E, Author
Reimer, J, Author
Román Rosón, M, Author
Baden, T, Author
Euler, T, Author
Tolias, AS2, 3, Author              
Bethge, M1, 2, Author              
Affiliations:
1Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497805              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
3Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              

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 Abstract: A fundamental challenge in calcium imaging has been to infer spike rates of neurons from the measured noisy fluorescence traces. We systematically evaluate different 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). In addition, we introduce a new algorithm based on supervised learning in flexible probabilistic models and find that it performs better than other published techniques. Importantly, it outperforms other algorithms even when applied to entirely new datasets for which no simultaneously recorded data is available. Future data acquired in new experimental conditions can be used to further improve the spike prediction accuracy and generalization performance of the model. Finally, we show that comparing algorithms on artificial data is not informative about performance on real data, suggesting that benchmarking different methods with real-world datasets may greatly facilitate future algorithmic developments in neuroscience.

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 Dates: 2016-05
 Publication Status: Published in print
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 Identifiers: DOI: 10.1016/j.neuron.2016.04.014
BibTex Citekey: TheisBFRRBETB2016
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Title: Neuron
Source Genre: Journal
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Pages: - Volume / Issue: 90 (3) Sequence Number: - Start / End Page: 471 - 482 Identifier: -