Direct extraction of signal and noise correlations from two-photon calcium imaging of ensemble neuronal activity

eLife

Behtash Babadi¹  Anuththara Rupasinghe¹  Zac Bowen²  Ji Liu²  Nikolas Francis²  Patrick O Kanold³ 

[1] Department of Electrical and Computer Engineering, University of Maryland, College Park, United States;The Institute for Systems Research, University of Maryland, College Park, United States;Department of Biology, University of Maryland, College Park, United States;The Institute for Systems Research, University of Maryland, College Park, United States;Department of Biology, University of Maryland, College Park, United States;Department of Biomedical Engineering, Johns Hopkins University, Baltimore, United States;

关键词: two-photon imaging;    signal and noise correlations;    bayesian inference;    point process modeling;    mouse auditory cortex;    Mouse;   

DOI  :  10.7554/eLife.68046

来源: eLife Sciences Publications, Ltd

【 摘 要 】

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.

【 授权许可】

CC BY