Testing the odds of inherent vs. observed overdispersion in neural spike counts.
Testing the odds of inherent vs. observed overdispersion in neural spike counts.
复制标题
测试神经尖峰计数固有与观察到的过度分散的几率。
DOI:
10.1152/jn.00194.2015
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发表时间:
2016
影响因子:
2.5
通讯作者:
Perrinet,LaurentU
中科院分区:
文献类型:
--
作者:
Taouali,Wahiba;Benvenuti,Giacomo;Wallisch,Pascal;Chavane,Frédéric;Perrinet,LaurentU
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.
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影响因子:
2.7
作者:
Robert D. Grey;Jerry L. Hedrick
通讯作者:
Jerry L. Hedrick
影响因子:
2.7
作者:
J. Stewart;Robert D. Grey
通讯作者:
Robert D. Grey
影响因子:
2.7
作者:
Robert D. Grey;Don P. Wolf;J. L. Hedrick
通讯作者:
J. L. Hedrick
DOI:
10.1002/mrd.1120030106
发表时间:
1980-01-01
期刊:
GAMETE RESEARCH
影响因子:
--
作者:
HOLLINGER, TG;CORTON, GL
通讯作者:
CORTON, GL
DOI:
--
发表时间:
1974
影响因子:
11.1
作者:
R. E. Wyrick;T. Nishihara;J. L. Hedrick
通讯作者:
J. L. Hedrick