Taouali Wahiba, Benvenuti Giacomo, Wallisch Pascal, Chavane Frédéric, Perrinet Laurent U
Institut de Neurosciences de la Timone, Centre National de la Recherche Scientifique, Aix-Marseille Université, Marseille, France; and
Institut de Neurosciences de la Timone, Centre National de la Recherche Scientifique, Aix-Marseille Université, Marseille, France; and.
J Neurophysiol. 2016 Jan 1;115(1):434-44. doi: 10.1152/jn.00194.2015. Epub 2015 Oct 7.
The repeated presentation of an identical visual stimulus in the receptive field of a neuron may evoke different spiking patterns at each trial. Probabilistic methods are essential to understand the functional role of this variance within the neural activity. In that case, a Poisson process is the most common model of trial-to-trial variability. For a Poisson process, the variance of the spike count is constrained to be equal to the mean, irrespective of the duration of measurements. Numerous studies have shown that this relationship does not generally hold. Specifically, a majority of electrophysiological recordings show an "overdispersion" effect: responses that exhibit more intertrial variability than expected from a Poisson process alone. A model that is particularly well suited to quantify overdispersion is the Negative-Binomial distribution model. This model is well-studied and widely used but has only recently been applied to neuroscience. In this article, we address three main issues. First, we describe how the Negative-Binomial distribution provides a model apt to account for overdispersed spike counts. Second, we quantify the significance of this model for any neurophysiological data by proposing a statistical test, which quantifies the odds that overdispersion could be due to the limited number of repetitions (trials). We apply this test to three neurophysiological data sets along the visual pathway. Finally, we compare the performance of this model to the Poisson model on a population decoding task. We show that the decoding accuracy is improved when accounting for overdispersion, especially under the hypothesis of tuned overdispersion.
在神经元的感受野中重复呈现相同的视觉刺激，可能在每次试验中引发不同的放电模式。概率方法对于理解这种神经活动变化的功能作用至关重要。在这种情况下，泊松过程是试验间变异性最常见的模型。对于泊松过程，放电计数的方差被限制为等于均值，而与测量持续时间无关。大量研究表明这种关系通常并不成立。具体而言，大多数电生理记录显示出“过度离散”效应：即反应表现出比仅由泊松过程预期的更多的试验间变异性。一个特别适合量化过度离散的模型是负二项分布模型。这个模型经过了充分研究且被广泛使用，但直到最近才应用于神经科学领域。在本文中，我们解决三个主要问题。首先，我们描述负二项分布如何提供一个适合解释过度离散的放电计数的模型。其次，我们通过提出一种统计检验来量化该模型对任何神经生理数据的重要性，该检验量化了过度离散可能是由于重复次数（试验次数）有限所致的概率。我们将此检验应用于视觉通路中的三个神经生理数据集。最后，我们在群体解码任务中将该模型的性能与泊松模型进行比较。我们表明，在考虑过度离散时，尤其是在调谐过度离散的假设下，解码准确性会提高。
