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.
Elife. 2021 Jun 28;10:e68046. doi: 10.7554/eLife.68046.
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.
神经元活动相关性是理解神经元群体如何集体编码信息的关键。虽然双光子钙成像为记录大量神经元的活动创造了独特的机会，但从这些数据中推断相关性的现有方法面临着几个挑战。首先，双光子成像产生的尖峰活动观测具有时间模糊和噪声。其次，即使通过反卷积完美地恢复了尖峰数据，由于神经元尖峰与内源性和外源性输入的非线性关系，从二进制尖峰数据推断网络级特征仍然是一项具有挑战性的任务。在这项工作中，我们提出了一种从双光子荧光观测中显式建模和直接估计信号和噪声相关性的方法，而无需中间的尖峰反卷积。我们对所提出的估计器的性能提供了理论保证，并通过应用于来自小鼠听觉皮层的模拟和实验记录数据来证明其有效性。
