Single and Multiple Change Point Detection in Spike Trains: Comparison of Different CUSUM Methods.
Single and Multiple Change Point Detection in Spike Trains: Comparison of Different CUSUM Methods.
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DOI：
10.3389/fnsys.2016.00051
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发表时间：
2016
影响因子：
3
通讯作者：
Kretzberg J
中科院分区：
文献类型：
--
作者：
Koepcke L;Ashida G;Kretzberg J
在自然环境中，感觉系统面临着不断变化的刺激，这些刺激可能随时发生，消失或改变其属性。为了使动物做出充分的反应，感觉系统必须能够根据其神经元的反应来检测外部刺激的变化。由于神经系统对刺激时机没有先验知识，因此刺激的变化需要从神经元活动的变化来推断，特别是尖峰频率的增加或减少、其变异性和移动的反应潜伏期。从数学的角度来看，这个问题可以重新表述为检测时间序列中统计特性的变化。在神经科学中，累积和（cumulative sum）方法已被应用于记录神经元的反应，以检测单个刺激的变化。在这里，我们调查的适用性的检测单以及多个刺激的变化，诱导增加或减少神经元活动的方法。像神经系统一样，我们的算法完全依赖于以前的神经元群体活动，而不使用有关外部刺激变化的时间或数量的知识。我们将我们的变点检测方法应用于海龟视网膜神经节细胞的多电极记录所获得的实验数据，这些细胞对光刺激的变化做出反应，具有一系列典型的神经元活动模式。我们系统地研究如何变化的数学假设（泊松，高斯和伽玛分布）的算法可能会影响检测未知数量的刺激变化，在我们的数据和比较这些方法与标准的速率变化的方法。我们的研究结果表明，哪些版本的CRACUUM算法可能是有用的不同类型的特定数据集。
In a natural environment, sensory systems are faced with ever-changing stimuli that can occur, disappear or change their properties at any time. For the animal to react adequately the sensory systems must be able to detect changes in external stimuli based on its neuronal responses. Since the nervous system has no prior knowledge of the stimulus timing, changes in stimulus need to be inferred from the changes in neuronal activity, in particular increase or decrease of the spike rate, its variability, and shifted response latencies. From a mathematical point of view, this problem can be rephrased as detecting changes of statistical properties in a time series. In neuroscience, the CUSUM (cumulative sum) method has been applied to recorded neuronal responses for detecting a single stimulus change. Here, we investigate the applicability of the CUSUM approach for detecting single as well as multiple stimulus changes that induce increases or decreases in neuronal activity. Like the nervous system, our algorithm relies exclusively on previous neuronal population activities, without using knowledge about the timing or number of external stimulus changes. We apply our change point detection methods to experimental data obtained by multi-electrode recordings from turtle retinal ganglion cells, which react to changes in light stimulation with a range of typical neuronal activity patterns. We systematically examine how variations of mathematical assumptions (Poisson, Gaussian, and Gamma distributions) used for the algorithms may affect the detection of an unknown number of stimulus changes in our data and compare these CUSUM methods with the standard Rate Change method. Our results suggest which versions of the CUSUM algorithm could be useful for different types of specific data sets.