Bayesian inference of neuronal assemblies
Bayesian inference of neuronal assemblies
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DOI：
10.1371/journal.pcbi.1007481
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发表时间：
2019-10-01
影响因子：
4.3
通讯作者：
Meyer, Martin P.
中科院分区：
文献类型：
--
作者：
Diana, Giovanni;Sainsbury, Thomas T. J.;Meyer, Martin P.
作者总结对种群活动的结构和动态的表征可以提供对计算如何在神经网络中分布的洞察。在这里，我们开发了一种新的统计方法来描述神经种群记录中的同步活动模式。我们的方法可以准确地描述神经元是如何组织成协同活动群体(集合)的，并揭示了集合的动态特征，如它们的放电模式、放电持续时间以及组成神经元的同步和异步放电的程度。我们演示了如何使用我们的技术来解剖复杂的神经元群体，将数据记录到其行为相关的组件中。在大脑的许多区域，自发和刺激诱发的活动都可以表现为神经元组件的同步激活。组装结构和动力学的特征为大脑计算如何在神经网络中分布提供了重要的见解。记录神经元集合体活动的实验技术的激增需要一种全面的统计方法来描述、分析和表征这些高维数据集。现有的定义组件的方法的性能对神经元放电模式中的噪声和随机性以及组件的异质性很敏感。为了解决这些问题，我们引入了同步活动的生成性分层模型来描述神经元组织成集合的过程。与现有方法不同，我们的分析提供了对装配组成、动力学和装配内统计特征的同时估计，例如活动水平、噪声和装配同步性。我们已经使用我们的方法来描述斑马鱼幼体顶盖整个顶盖的种群活动，使我们能够对顶盖集合的时空组织、它们的组成以及它们相互作用的逻辑做出统计推断。我们还将我们的方法应用于小鼠的功能成像和神经像素记录，使我们能够将识别出的组件的活动与特定行为联系起来，如奔跑或瞳孔直径的变化。
Author summary Characterization of the structure and dynamics of population activity can provide insight into how computations are distributed within neural networks. Here we develop a new statistical method to describe patterns of synchronous activity in neural population recordings. Our method can accurately describe how neurons are organized into co-active populations (assemblies) and reveals dynamic features of assemblies such as their firing pattern, firing duration, and the degree of synchronous and asynchronous firing of their constituent neurons. We demonstrate how our technique can be used to dissect complex neuronal population recording data into its behaviorally relevant components.In many areas of the brain, both spontaneous and stimulus-evoked activity can manifest as synchronous activation of neuronal assemblies. The characterization of assembly structure and dynamics provides important insights into how brain computations are distributed across neural networks. The proliferation of experimental techniques for recording the activity of neuronal assemblies calls for a comprehensive statistical method to describe, analyze and characterize these high dimensional datasets. The performance of existing methods for defining assemblies is sensitive to noise and stochasticity in neuronal firing patterns and assembly heterogeneity. To address these problems, we introduce a generative hierarchical model of synchronous activity to describe the organization of neurons into assemblies. Unlike existing methods, our analysis provides a simultaneous estimation of assembly composition, dynamics and within-assembly statistical features, such as the levels of activity, noise and assembly synchrony. We have used our method to characterize population activity throughout the tectum of larval zebrafish, allowing us to make statistical inference on the spatiotemporal organization of tectal assemblies, their composition and the logic of their interactions. We have also applied our method to functional imaging and neuropixels recordings from the mouse, allowing us to relate the activity of identified assemblies to specific behaviours such as running or changes in pupil diameter.