Modeling the diverse effects of divisive normalization on noise correlations.
Modeling the diverse effects of divisive normalization on noise correlations.
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DOI：
10.1371/journal.pcbi.1011667
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发表时间：
2023-11
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
作者：
分裂归一化是一种重要的神经活动描述模型，被许多不同脑区的神经编码理论所采用。然而，正常化与单个神经元以外的神经反应统计之间的关系在很大程度上仍未得到探索。在这里，我们专注于噪声相关性，一个广泛研究的成对统计，因为它的刺激和状态依赖性在神经编码中起着核心作用。现有的协变模型通常忽略正常化，尽管经验证据表明它会影响神经群体的相关性结构。因此，我们提出了一个成对的随机分裂的归一化模型，考虑到归一化和其他因素的影响，协变。我们首先表明，规范化调制噪声相关性在定性上不同的方式取决于是否规范化之间共享神经元，我们讨论如何推断时，规范化信号共享。然后，我们将我们的模型应用于小鼠初级视觉皮层（V1）的钙成像数据，并发现它准确地拟合了数据，通常优于流行的替代相关性模型。我们的分析表明，在这个数据集中，归一化信号通常在V1神经元之间共享。我们的模型将能够量化在广泛的神经系统，这可以提供新的约束电路机制的正常化及其在信息传输和表示的作用之间的关系的正常化和协变性。在相同的实验条件下，皮层反应通常是可变的，这种可变性在神经元之间共享（噪声相关性）。这些噪声相关性已被广泛研究，以了解它们如何影响神经编码以及什么机制决定它们的特性。在这里，我们展示了相关性如何与分裂归一化相关，分裂归一化是一种广泛用于描述神经元的活动如何通过分裂增益控制被其他神经元调制的数学运算。我们介绍这种关系的第一个统计模型。我们广泛地验证了模型，并调查合成数据中的参数推断。我们发现，我们的模型，当应用于从小鼠视觉皮层的数据，优于一个流行的模型的噪声相关性，不包括正常化，它揭示了不同的影响正常化的相关性。我们的工作展示了一个框架来测量噪声相关性和归一化模型的参数之间的关系，这可能成为定量研究噪声相关性的一个不可或缺的工具，在广泛的神经系统，表现出正常化。
Divisive normalization, a prominent descriptive model of neural activity, is employed by theories of neural coding across many different brain areas. Yet, the relationship between normalization and the statistics of neural responses beyond single neurons remains largely unexplored. Here we focus on noise correlations, a widely studied pairwise statistic, because its stimulus and state dependence plays a central role in neural coding. Existing models of covariability typically ignore normalization despite empirical evidence suggesting it affects correlation structure in neural populations. We therefore propose a pairwise stochastic divisive normalization model that accounts for the effects of normalization and other factors on covariability. We first show that normalization modulates noise correlations in qualitatively different ways depending on whether normalization is shared between neurons, and we discuss how to infer when normalization signals are shared. We then apply our model to calcium imaging data from mouse primary visual cortex (V1), and find that it accurately fits the data, often outperforming a popular alternative model of correlations. Our analysis indicates that normalization signals are often shared between V1 neurons in this dataset. Our model will enable quantifying the relation between normalization and covariability in a broad range of neural systems, which could provide new constraints on circuit mechanisms of normalization and their role in information transmission and representation. Cortical responses are often variable across identical experimental conditions, and this variability is shared between neurons (noise correlations). These noise correlations have been extensively studied to understand how they impact neural coding and what mechanisms determine their properties. Here we show how correlations relate to divisive normalization, a mathematical operation widely adopted to describe how the activity of a neuron is modulated by other neurons via divisive gain control. We introduce the first statistical model of this relation. We extensively validate the model and investigate parameter inference in synthetic data. We find that our model, when applied to data from mouse visual cortex, outperforms a popular model of noise correlations that does not include normalization, and it reveals diverse influences of normalization on correlations. Our work demonstrates a framework to measure the relation between noise correlations and the parameters of the normalization model, which could become an indispensable tool for quantitative investigations of noise correlations in the wide range of neural systems that exhibit normalization.
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影响因子：
2.5
作者：
Cavanaugh, JR;Bair, W;Movshon, JA
通讯作者：
Movshon, JA
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