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.
Highlights
26 Neuronal activity correlations are essential in understanding how populations of neurons encode27 information
Two-photon calcium imaging has become increasingly popular in recent years to record in vivo neural activity simultaneously from hundreds of neurons (Ahrens et al, 2013; Romano et al, 2017; Stosiek et al, 2003; Svoboda and Yasuda, 2006)
Before presenting the results, we will give an overview of the proposed signal and noise correlation inference framework, and outline our contributions and their relationship to existing work
Summary
26 Neuronal activity correlations are essential in understanding how populations of neurons encode27 information. Signal correlation quantifies the similarity of 34 neural responses that are time-locked to a repeated stimulus across trials, whereas noise correlation. Two-photon calcium imaging has become increasingly popular in recent years to record in vivo neural activity simultaneously from hundreds of neurons (Ahrens et al, 2013; Romano et al, 2017; Stosiek et al, 2003; Svoboda and Yasuda, 2006).
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