Signal-to-signal neural networks for improved spike estimation from calcium imaging data.
Signal-to-signal neural networks for improved spike estimation from calcium imaging data.
复制标题
信号到信号神经网络用于钙成像数据的改进尖峰估计。
DOI：
10.1371/journal.pcbi.1007921
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发表时间：
2021-03
影响因子：
4.3
通讯作者：
Magimai-Doss M
中科院分区：
文献类型：
--
作者：
Sebastian J;Sur M;Murthy HA;Magimai-Doss M
单个神经元的放电信息是神经科学研究中进行功能和行为分析的基础。钙成像技术通常用于获得神经元群体的活动。然而，这些技术导致具有低时间分辨率的缓慢变化的荧光信号。从这些信号中估计神经元动作电位的时间位置是一个具有挑战性的问题。在文献中，已经研究了几种基于生成模型和数据驱动的算法，并取得了不同程度的成功。本文提出了一种基于神经网络的信号到信号转换方法，它以原始荧光信号作为输入，并学习以端到端的方式估计尖峰信息。理论上，该方法将尖峰信号的估计归结为一个混合条件未知的单通道信号源分离问题。在输出端估计与较低分辨率下的动作电位对应的源。在spikefinder挑战数据集上的实验研究表明，所提出的信号到信号转换方法在Pearson相关系数、斯皮尔曼秩相关系数方面显著优于最先进的方法，并且在接收器操作特性测量下的区域产生相当的性能。我们还表明，由此产生的系统：（a）具有低复杂性，相对于现有的监督方法，是可重复的;（B）是逐层解释，（c）有能力概括不同的钙指标。利用双光子钙成像技术研究了神经元群体的信息处理。神经元尖峰导致细胞内钙浓度增加。荧光钙指示剂在钙浓度变化时改变其亮度，并且这种变化在成像技术中被捕获。从亮度变化估计实际尖峰位置的任务在形式上被称为尖峰估计。过去已经提出了几种基于信号处理和机器学习的算法来解决这个问题。但是，任务还远未解决。在这里，我们提出了一种新的神经网络为基础的数据驱动的尖峰估计算法。我们的方法以荧光记录作为输入，并合成的锋电位信息信号，这是良好的相关性，与实际的锋电位位置。我们的方法在标准评估框架上优于最先进的方法。我们进一步分析了该模型的不同组成部分，并讨论了它的好处。
Spiking information of individual neurons is essential for functional and behavioral analysis in neuroscience research. Calcium imaging techniques are generally employed to obtain activities of neuronal populations. However, these techniques result in slowly-varying fluorescence signals with low temporal resolution. Estimating the temporal positions of the neuronal action potentials from these signals is a challenging problem. In the literature, several generative model-based and data-driven algorithms have been studied with varied levels of success. This article proposes a neural network-based signal-to-signal conversion approach, where it takes as input raw-fluorescence signal and learns to estimate the spike information in an end-to-end fashion. Theoretically, the proposed approach formulates the spike estimation as a single channel source separation problem with unknown mixing conditions. The source corresponding to the action potentials at a lower resolution is estimated at the output. Experimental studies on the spikefinder challenge dataset show that the proposed signal-to-signal conversion approach significantly outperforms state-of-the-art-methods in terms of Pearson’s correlation coefficient, Spearman’s rank correlation coefficient and yields comparable performance for the area under the receiver operating characteristics measure. We also show that the resulting system: (a) has low complexity with respect to existing supervised approaches and is reproducible; (b) is layer-wise interpretable, and (c) has the capability to generalize across different calcium indicators. Information processing by a population of neurons is studied using two-photon calcium imaging techniques. A neuronal spike results in an increased intracellular calcium concentration. Fluorescent calcium indicators change their brightness upon a change in the calcium concentration, and this change is captured in the imaging technique. The task of estimating the actual spike positions from the brightness variations is formally referred to as spike estimation. Several signal processing and machine learning-based algorithms have been proposed in the past to solve this problem. However, the task is still far from being solved. Here we present a novel neural network-based data-driven algorithm for spike estimation. Our method takes the fluorescence recording as the input and synthesizes the spike information signal, which is well-correlated with the actual spike positions. Our method outperforms state-of-the-art methods on a standard evaluation framework. We further analyze different components of the model and discuss its benefits.
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影响因子：
2.1
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通讯作者：
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