Department of Probability and Mathematical Statistics, Faculty of Mathematics and Physics, Charles University in Prague, 18675 Praha 8, Czech Republic.
Neural Comput. 2010 Jul;22(7):1675-97. doi: 10.1162/neco.2010.11-09-1118.
A new statistical method for the estimation of the response latency is proposed. When spontaneous discharge is present, the first spike after the stimulus application may be caused by either the stimulus itself, or it may appear due to the prevailing spontaneous activity. Therefore, an appropriate method to deduce the response latency from the time to the first spike after the stimulus is needed. We develop a nonparametric estimator of the response latency based on repeated stimulations. A simulation study is provided to show how the estimator behaves with an increasing number of observations and for different rates of spontaneous and evoked spikes. Our nonparametric approach requires very few assumptions. For comparison, we also consider a parametric model. The proposed probabilistic model can be used for both single and parallel neuronal spike trains. In the case of simultaneously recorded spike trains in several neurons, the estimators of joint distribution and correlations of response latencies are also introduced. Real data from inferior colliculus auditory neurons obtained from a multielectrode probe are studied to demonstrate the statistical estimators of response latencies and their correlations in space.
提出了一种新的用于估计反应潜伏期的统计方法。当存在自发放电时,刺激后第一个尖峰可能是由刺激本身引起的,也可能是由于当前的自发活动引起的。因此,需要一种从刺激后的第一个尖峰到反应潜伏期的适当方法。我们开发了一种基于重复刺激的反应潜伏期的非参数估计器。提供了一项模拟研究,以展示随着观察次数的增加以及自发和诱发尖峰的不同速率,估计器的行为。我们的非参数方法仅需要很少的假设。作为比较,我们还考虑了一个参数模型。所提出的概率模型可用于单个和并行神经元尖峰列。在同时记录多个神经元的尖峰列的情况下,还引入了反应潜伏期的联合分布和相关性的估计器。从多电极探针获得的下丘听觉神经元的实际数据用于研究空间中反应潜伏期及其相关性的统计估计器。