Neural Field Models for Latent State Inference: Application to Large-Scale Neuronal Recordings
Neural Field Models for Latent State Inference: Application to Large-Scale Neuronal Recordings
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用于潜在状态推理的神经场模型：在大规模神经元记录中的应用
DOI：
10.1101/543769
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Rule M
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文献类型：
--
作者：
Rule M
现在，大规模的神经记录方法使我们能够同时观察大量已识别的单个神经元，从而打开了一扇了解活着生物体中神经种群动态的窗口。然而，提炼如此大规模的录音来构建新兴集体动力学理论，仍然是一个根本性的统计学挑战。威尔逊、考恩和他的同事的神经场模型由于其可解释性、力学参数和对数学分析的适应性而仍然是数学人口模型的支柱。受生物化学建模最新进展的启发，我们发展了一种基于矩闭合的方法，将神经场模型解释为潜在的状态空间点过程模型，使其易于统计推理。使用这种方法，我们可以仅从大种群中的尖峰活动来推断神经元的内在状态，如活跃和难治。在用合成数据验证了这种方法后，我们将其应用于高密度记录发育中的小鼠视网膜的尖峰活动。这证实了长期的不稳定状态在形成新生儿视网膜波的时空特性中的重要作用。这一概念和方法的进步开辟了神经数据分析中数学理论和点过程状态空间模型之间的新的理论联系。
Large-scale neural recording methods now allow us to observe large populations of identified single neurons simultaneously, opening a window into neural population dynamics in living organisms. However, distilling such large-scale recordings to build theories of emergent collective dynamics remains a fundamental statistical challenge. The neural field models of Wilson, Cowan, and colleagues remain the mainstay of mathematical population modeling owing to their interpretable, mechanistic parameters and amenability to mathematical analysis. Inspired by recent advances in biochemical modeling, we develop a method based on moment closure to interpret neural field models as latent state-space point-process models, making them amenable to statistical inference. With this approach we can infer the intrinsic states of neurons, such as active and refractory, solely from spiking activity in large populations. After validating this approach with synthetic data, we apply it to high-density recordings of spiking activity in the developing mouse retina. This confirms the essential role of a long lasting refractory state in shaping spatiotemporal properties of neonatal retinal waves. This conceptual and methodological advance opens up new theoretical connections between mathematical theory and point-process state-space models in neural data analysis.