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A deep learning framework for inference of single-trial neural population activity from calcium imaging with sub-frame temporal resolution

View ORCID ProfileFeng Zhu, Harrison A. Grier, Raghav Tandon, View ORCID ProfileChangjia Cai, View ORCID ProfileAndrea Giovannucci, View ORCID ProfileMatthew T. Kaufman, View ORCID ProfileChethan Pandarinath
doi: https://doi.org/10.1101/2021.11.21.469441
Feng Zhu
1Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
2Neuroscience Graduate Program, Graduate Division of Biological and Biomedical Sciences, Emory University, Atlanta, GA, USA
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Harrison A. Grier
3Committee on Computational Neuroscience, The University of Chicago, Chicago, IL, USA
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Raghav Tandon
1Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
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Changjia Cai
4Joint Biomedical Engineering Department University of North Carolina at Chapel Hill and North Carolina State University. Chapel Hill, NC, USA
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Andrea Giovannucci
4Joint Biomedical Engineering Department University of North Carolina at Chapel Hill and North Carolina State University. Chapel Hill, NC, USA
5Neuroscience Center, University of North Carolina at Chapel Hill. Chapel Hill, NC, USA
6Closed-Loop Engineering for Advanced Rehabilitation (CLEAR). North Carolina State University. Raleigh, NC. USA
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  • For correspondence: chethan@gatech.edu agiovann@email.unc.edu mattkaufman@uchicago.edu
Matthew T. Kaufman
7Department of Organismal Biology and Anatomy, The University of Chicago, Chicago, IL, USA
8Neuroscience Institute, The University of Chicago, Chicago, IL, USA
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  • For correspondence: chethan@gatech.edu agiovann@email.unc.edu mattkaufman@uchicago.edu
Chethan Pandarinath
1Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
9Department of Neurosurgery, Emory University, Atlanta, GA, USA
10Center for Machine Learning, Georgia Institute of Technology, Atlanta, GA, USA
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  • For correspondence: chethan@gatech.edu agiovann@email.unc.edu mattkaufman@uchicago.edu
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Abstract

In many brain areas, neural populations act as a coordinated network whose state is tied to behavior on a moment-by-moment basis and millisecond timescale. Two-photon (2p) calcium imaging is a powerful tool to probe network-scale computation, as it can measure the activity of many individual neurons, monitor multiple layers simultaneously, and sample from identified cell types. However, estimating network states and dynamics from 2p measurements has proven challenging because of noise, inherent nonlinearities, and limitations on temporal resolution. Here we describe RADICaL, a deep learning method to overcome these limitations at the population level. RADICaL extends methods that exploit dynamics in spiking activity for application to deconvolved calcium signals, whose statistics and temporal dynamics are quite distinct from electrophysiologically-recorded spikes. It incorporates a novel network training strategy that exploits the timing of 2p sampling to recover network dynamics with high temporal precision. In synthetic tests, RADICaL infers network states more accurately than previous methods, particularly for high-frequency components. In real 2p recordings from sensorimotor areas in mice performing a “water grab” task, RADICaL infers network states with close correspondence to single-trial variations in behavior, and maintains high-quality inference even when neuronal populations are substantially reduced.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • The mouse1 S1 dataset in the 2-photon calcium experiments has been swapped out to correct an error. Figures 3 & 4 and supplementary figures 4, 6 & 7 are updated; The Methods section has also been revised.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted January 25, 2022.
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A deep learning framework for inference of single-trial neural population activity from calcium imaging with sub-frame temporal resolution
Feng Zhu, Harrison A. Grier, Raghav Tandon, Changjia Cai, Andrea Giovannucci, Matthew T. Kaufman, Chethan Pandarinath
bioRxiv 2021.11.21.469441; doi: https://doi.org/10.1101/2021.11.21.469441
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A deep learning framework for inference of single-trial neural population activity from calcium imaging with sub-frame temporal resolution
Feng Zhu, Harrison A. Grier, Raghav Tandon, Changjia Cai, Andrea Giovannucci, Matthew T. Kaufman, Chethan Pandarinath
bioRxiv 2021.11.21.469441; doi: https://doi.org/10.1101/2021.11.21.469441

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