Dimitrov Alexander G, Gedeon Tomás
Center for Computational Biology, Montana State University, Bozeman, Montana, USA.
J Comput Neurosci. 2006 Jun;20(3):265-83. doi: 10.1007/s10827-006-6357-1. Epub 2006 Apr 22.
Stimulus selectivity of sensory systems is often characterized by analyzing response-conditioned stimulus ensembles. However, in many cases these response-triggered stimulus sets have structure that is more complex than assumed. If not taken into account, when present it will bias the estimates of many simple statistics, and distort the estimated stimulus selectivity of a neural sensory system. We present an approach that mitigates these problems by modeling some of the response-conditioned stimulus structure as being generated by a set of transformations acting on a simple stimulus distribution. This approach corrects the estimates of key statistics and counters biases introduced by the transformations. In cases involving temporal spike jitter or spatial jitter of images, the main observed effects of transformations are blurring of the conditional mean and introduction of artefacts in the spectral decomposition of the conditional covariance matrix. We illustrate this approach by analyzing and correcting a set of model stimuli perturbed by temporal and spatial jitter. We apply the approach to neurophysiological data from the cricket cercal sensory system to correct the effects of temporal jitter.
感觉系统的刺激选择性通常通过分析响应条件刺激集合来表征。然而，在许多情况下，这些响应触发的刺激集具有比假设更复杂的结构。如果不加以考虑，当它存在时会使许多简单统计量的估计产生偏差，并扭曲神经感觉系统估计的刺激选择性。我们提出了一种方法，通过将一些响应条件刺激结构建模为由作用于简单刺激分布的一组变换生成，来减轻这些问题。这种方法校正了关键统计量的估计，并抵消了变换引入的偏差。在涉及时间尖峰抖动或图像空间抖动的情况下，变换的主要观察到的影响是条件均值的模糊以及条件协方差矩阵谱分解中伪影的引入。我们通过分析和校正一组受时间和空间抖动干扰的模型刺激来说明这种方法。我们将该方法应用于蟋蟀尾须感觉系统的神经生理学数据，以校正时间抖动的影响。
