Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany.
Center for Integrative Neuroscience, University of Tübingen, Tübingen, Germany.
PLoS Comput Biol. 2019 Aug 2;15(8):e1007205. doi: 10.1371/journal.pcbi.1007205. eCollection 2019 Aug.
Variability, stochastic or otherwise, is a central feature of neural activity. Yet the means by which estimates of variation and uncertainty are derived from noisy observations of neural activity is often heuristic, with more weight given to numerical convenience than statistical rigour. For two-photon imaging data, composed of fundamentally probabilistic streams of photon detections, the problem is particularly acute. Here, we present a statistical pipeline for the inference and analysis of neural activity using Gaussian Process regression, applied to two-photon recordings of light-driven activity in ex vivo mouse retina. We demonstrate the flexibility and extensibility of these models, considering cases with non-stationary statistics, driven by complex parametric stimuli, in signal discrimination, hierarchical clustering and other inference tasks. Sparse approximation methods allow these models to be fitted rapidly, permitting them to actively guide the design of light stimulation in the midst of ongoing two-photon experiments.
变异性，无论是随机的还是其他的，是神经活动的一个核心特征。然而，从神经活动的噪声观测中得出变异性和不确定性估计的方法通常是启发式的，更注重数值上的方便，而不是统计上的严谨性。对于由光子探测的基本概率流组成的双光子成像数据，这个问题尤其突出。在这里，我们提出了一种使用高斯过程回归对神经活动进行推断和分析的统计管道，该方法应用于离体小鼠视网膜光驱动活动的双光子记录。我们展示了这些模型的灵活性和可扩展性，考虑了具有非平稳统计数据的情况，这些数据由复杂的参数刺激驱动，应用于信号判别、层次聚类和其他推理任务。稀疏逼近方法允许这些模型快速拟合，从而能够在双光子实验进行的过程中主动指导光刺激的设计。
