A Tractable Method for Describing Complex Couplings between Neurons and Population Rate
A Tractable Method for Describing Complex Couplings between Neurons and Population Rate
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DOI：
10.1523/eneuro.0160-15.2016
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发表时间：
2016-07-01
期刊：
影响因子：
3.4
通讯作者：
Mora, Thierry
中科院分区：
文献类型：
--
作者：
Gardella, Christophe;Marre, Olivier;Mora, Thierry
群体中的神经元是强相关的，但如何简单地捕捉这些相关性仍然是一个争论的问题。最近的研究表明，每个细胞的活性受到群体速率的影响，群体速率被定义为群体中所有神经元的总活性。然而，一个明确的，易于处理的模型，这些相互作用仍然缺乏。在这里，我们建立了一个概率模型的人口活动，重现每个细胞的发射率，人口率的分布，以及它们之间的线性耦合。这个模型是易于处理的，这意味着它的参数可以在标准计算机上在几秒钟内学习，即使是大人口记录。我们推断我们的模型为蝾螈视网膜中的160个神经元。在这个群体中，单细胞放电率以意想不到的方式依赖于群体速率。特别是，一些细胞有一个优选的群体率，在这个群体率下，它们最有可能发射。这些复杂的依赖关系不能用细胞和种群率之间的线性耦合来解释。我们设计了一个更通用、更易于处理的模型，可以完全解释这些非线性依赖关系。因此，我们提供了一种简单且计算上易于处理的方法来学习模型，该模型再现了每个神经元对种群速率的依赖性。
Neurons within a population are strongly correlated, but how to simply capture these correlations is still a matter of debate. Recent studies have shown that the activity of each cell is influenced by the population rate, defined as the summed activity of all neurons in the population. However, an explicit, tractable model for these interactions is still lacking. Here we build a probabilistic model of population activity that reproduces the firing rate of each cell, the distribution of the population rate, and the linear coupling between them. This model is tractable, meaning that its parameters can be learned in a few seconds on a standard computer even for large population recordings. We inferred our model for a population of 160 neurons in the salamander retina. In this population, single-cell firing rates depended in unexpected ways on the population rate. In particular, some cells had a preferred population rate at which they were most likely to fire. These complex dependencies could not be explained by a linear coupling between the cell and the population rate. We designed a more general, still tractable model that could fully account for these nonlinear dependencies. We thus provide a simple and computationally tractable way to learn models that reproduce the dependence of each neuron on the population rate.