Recent technological advances in light-sheet microscopy make it possible to perform whole-brain functional imaging at the cellular level with the use of Ca2+ indicators. The outstanding spatial extent and resolution of this type of data open unique opportunities for understanding the complex organization of neuronal circuits across the brain. However, the analysis of this data remains challenging because the observed variations in fluorescence are, in fact, noisy indirect measures of the neuronal activity. Moreover, measuring over large field-ofview negatively impact temporal resolution and signal-to-noise ratio, which further impedes conventional spike inference. Here we argue that meaningful information can be extracted from large-scale functional imaging data by deconvolving with the calcium response and by modeling moments of sustained neuronal activity instead of individual spikes. Specifically, we characterize the calcium response by a linear system of which the inverse is a differential operator. This operator is then included in a regularization term promoting sparsity of activity transients through generalized total variation. Our results illustrate the numerical performance of the algorithm on simulated signals; i.e., we show the firing rate phase transition at which our model outperforms spike inference. Finally, we apply the proposed algorithm to experimental data from zebrafish larvf. In particular, we show that, when applied to a specific group of neurons, the algorithm retrieves neural activation that matches the locomotor behavior unknown to the method.

中文翻译：

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从大规模钙成像数据反演持续神经活动。

光片显微镜技术的最新技术进步使得可以使用Ca2 +指示剂在细胞水平上进行全脑功能成像。此类数据的出色空间范围和分辨率为理解大脑中神经元回路的复杂组织提供了独特的机会。但是，由于观察到的荧光变化实际上是对神经元活动的嘈杂间接测量，因此对该数据的分析仍然具有挑战性。此外，在大视野范围内进行测量会对时间分辨率和信噪比产生负面影响，这进一步阻碍了常规尖峰推断。在这里，我们认为，可以通过与钙反应去卷积并通过模拟持续神经元活动的时刻而不是单个峰值来从有意义的信息中提取有意义的信息。具体而言，我们通过反演为微分算子的线性系统来表征钙反应。然后将此运算符包含在正则化术语中，以通过广义总变化促进活动瞬态的稀疏性。我们的结果说明了该算法在模拟信号上的数值性能。也就是说，我们展示了模型优于尖峰推断的点火速率相变。最后，我们将提出的算法应用于斑马鱼幼虫的实验数据。特别是，我们表明，当将其应用于特定的神经元组时，