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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.
摘要：
神经元活动相关性是理解神经元群体如何共同编码信息的关键。虽然双光子钙成像创造了一个独特的机会来记录大量神经元的活动，从这些数据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的观察在时间上是模糊和嘈杂的。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是一项具有挑战性的任务。在这项工作中,我们提出了一种方法来明确地建模和直接估计信号和噪声相关性从双光子荧光观测，不需要中间尖峰去卷积。我们为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实用性。
