Direct Extraction of Signal and Noise Correlations from Two-Photon Calcium Imaging of Ensemble Neuronal Activity

  1. Anuththara Rupasinghe
  2. Nikolas Francis
  3. Ji Liu
  4. Zac Bowen
  5. Patrick O Kanold
  6. Behtash Babadi  Is a corresponding author
  1. University of Maryland, College Park, United States

Abstract

Neuronal activity correlations are key to understanding how populations of neurons collectively encode information. While two-photon calcium imaging has created a unique opportunity to record the activity of large populations of neurons, existing methods for inferring correlations from these data face several challenges. First, the observations of spiking activity produced by two-photon imaging are temporally blurred and noisy. Secondly, even if the spiking data were perfectly recovered via deconvolution, inferring network-level features from binary spiking data is a challenging task due to the non-linear relation of neuronal spiking to endogenous and exogenous inputs. In this work, we propose a methodology to explicitly model and directly estimate signal and noise correlations from two-photon fluorescence observations, without requiring intermediate spike deconvolution. We provide theoretical guarantees on the performance of the proposed estimator and demonstrate its utility through applications to simulated and experimentally recorded data from the mouse auditory cortex.

Data availability

A MATLAB implementation of the proposed method has been archived in Github at https://github.com/Anuththara-Rupasinghe/Signal-Noise-Correlation. The data used in this work have been deposited in the Digital Repository at the University of Maryland at http://hdl.handle.net/1903/26917.

The following data sets were generated

Article and author information

Author details

  1. Anuththara Rupasinghe

    Department of Electrical and Computer Engineering, University of Maryland, College Park, College Park, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2143-8709
  2. Nikolas Francis

    Department of Biology, University of Maryland, College Park, College Park, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Ji Liu

    Department of Biology, University of Maryland, College Park, College Park, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Zac Bowen

    Department of Biology, University of Maryland, College Park, College Park, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Patrick O Kanold

    Department of Biology, University of Maryland, College Park, College Park, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Behtash Babadi

    Department of Electrical and Computer Engineering, University of Maryland, College Park, College Park, United States
    For correspondence
    behtash@umd.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9856-006X

Funding

National Science Foundation (1807216)

  • Behtash Babadi

National Science Foundation (2032649)

  • Behtash Babadi

National Institutes of Health (1U19NS107464)

  • Behtash Babadi

National Institutes of Health (1U19NS107464)

  • Patrick O Kanold

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: All procedures, under Kanold lab protocol R-JAN-19-06, conformed to the guidelines of the University of Maryland Institutional Animal Care and Use Committee and the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health.

Copyright

© 2021, Rupasinghe et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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