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Effective and Efficient Neural Networks for Spike Inference from In Vivo Calcium Imaging

View ORCID ProfileZhanhong Zhou, Hei Matthew Yip, Katya Tsimring, Mriganka Sur, Jacque Pak Kan Ip, View ORCID ProfileChung Tin
doi: https://doi.org/10.1101/2021.08.30.458217
Zhanhong Zhou
1Department of Biomedical Engineering, City University of Hong Kong, Hong Kong SAR
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Hei Matthew Yip
2School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong SAR
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Katya Tsimring
3Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139 USA
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Mriganka Sur
3Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139 USA
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Jacque Pak Kan Ip
2School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong SAR
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  • For correspondence: chungtin@cityu.edu.hk jacqueip@cuhk.edu.hk
Chung Tin
1Department of Biomedical Engineering, City University of Hong Kong, Hong Kong SAR
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  • For correspondence: chungtin@cityu.edu.hk jacqueip@cuhk.edu.hk
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Abstract

Calcium imaging technique provides the advantages in monitoring large population of neuronal activities simultaneously. However, it lacks the signal quality provided by neural spike recording in traditional electrophysiology. To address this issue, we developed a supervised data-driven approach to extract spike information from calcium signals. We propose the ENS2 (effective and efficient neural networks for spike inference from calcium signals) system for spike-rate and spike-event predictions using raw calcium inputs based on U-Net deep neural network. When testing on a large, ground truth public database, it consistently outperformed state-of-the-arts algorithms in both spike-rate and spike-event predictions with reduced computational load. We further demonstrated that ENS2 would improve analyses of orientation selectivity in primary visual cortex neurons. We concluded that optimizing our system for spike-event prediction would produce a versatile inference system that benefits diverse neuroscience studies.

Competing Interest Statement

The authors have declared no competing interest.

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 April 04, 2022.
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Effective and Efficient Neural Networks for Spike Inference from In Vivo Calcium Imaging
Zhanhong Zhou, Hei Matthew Yip, Katya Tsimring, Mriganka Sur, Jacque Pak Kan Ip, Chung Tin
bioRxiv 2021.08.30.458217; doi: https://doi.org/10.1101/2021.08.30.458217
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Effective and Efficient Neural Networks for Spike Inference from In Vivo Calcium Imaging
Zhanhong Zhou, Hei Matthew Yip, Katya Tsimring, Mriganka Sur, Jacque Pak Kan Ip, Chung Tin
bioRxiv 2021.08.30.458217; doi: https://doi.org/10.1101/2021.08.30.458217

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