A deep learning framework for inference of single-trial neural population dynamics from calcium imaging with subframe temporal resolution.

Journal: Nature neuroscience
PMID: 36424431

Abstract

In many areas of the brain, neural populations act as a coordinated network whose state is tied to behavior on a millisecond timescale. Two-photon (2p) calcium imaging is a powerful tool to probe such network-scale phenomena. However, estimating the network state and dynamics from 2p measurements has proven challenging because of noise, inherent nonlinearities and limitations on temporal resolution. Here we describe Recurrent Autoencoder for Discovering Imaged Calcium Latents (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 new network training strategy that capitalizes on the timing of 2p sampling to recover network dynamics with high temporal precision. In synthetic tests, RADICaL infers the network state more accurately than previous methods, particularly for high-frequency components. In 2p recordings from sensorimotor areas in mice performing a forelimb reach task, RADICaL infers network state with close correspondence to single-trial variations in behavior and maintains high-quality inference even when neuronal populations are substantially reduced.

Authors

  • Feng Zhu
    Department of Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, 200120, People's Republic of China.
  • Harrison A Grier
    Committee on Computational Neuroscience, The University of Chicago, Chicago, IL, USA.
  • Raghav Tandon
    Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA.
  • Changjia Cai
    Joint Biomedical Engineering Department, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC, USA.
  • Anjali Agarwal
    , Uttar Pradesh, India.
  • Andrea Giovannucci
    Joint Biomedical Engineering Department, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC, USA. agiovann@email.unc.edu.
  • Matthew T Kaufman
    Department of Electrical Engineering and Neurosciences Program, Stanford University, Stanford, California, USA.
  • Chethan Pandarinath
    Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, United States of America.

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