Inferring and validating mechanistic models of neural microcircuits based on spike-train data
Inferring and validating mechanistic models of neural microcircuits based on spike-train data
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DOI：
10.1038/s41467-019-12572-0
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发表时间：
2019-10-30
影响因子：
16.6
通讯作者：
Ostojic, Srdjan
中科院分区：
文献类型：
--
作者：
Ladenbauer, Josef;McKenzie, Sam;Ostojic, Srdjan
神经元尖峰序列记录的解释通常依赖于抽象的统计模型，这些模型允许原则上的参数估计和模型选择，但只能提供对底层微电路的有限见解。相比之下，机制模型对于解释微电路动力学是有用的，但由于方法上的挑战，很少与实验数据定量匹配。本文提出了有效拟合单次试验尖峰串的尖峰电路模型的分析方法。利用推导出的似然函数，我们统计地推断出隐藏输入的均值和方差、神经元自适应特性和耦合的积分-火神经元的连通性。对合成数据的综合评估，使用体外和体内记录的地面真实值进行验证，以及与现有技术的比较表明，参数估计非常准确和有效，即使对于高度次采样的网络也是如此。我们的方法将统计、数据驱动和理论、基于模型的神经科学结合在一起，在尖峰电路的水平上，对记录的神经元群体活动进行定量、机械的解释。
The interpretation of neuronal spike train recordings often relies on abstract statistical models that allow for principled parameter estimation and model selection but provide only limited insights into underlying microcircuits. In contrast, mechanistic models are useful to interpret microcircuit dynamics, but are rarely quantitatively matched to experimental data due to methodological challenges. Here we present analytical methods to efficiently fit spiking circuit models to single-trial spike trains. Using derived likelihood functions, we statistically infer the mean and variance of hidden inputs, neuronal adaptation properties and connectivity for coupled integrate-and-fire neurons. Comprehensive evaluations on synthetic data, validations using ground truth in-vitro and in-vivo recordings, and comparisons with existing techniques demonstrate that parameter estimation is very accurate and efficient, even for highly subsampled networks. Our methods bridge statistical, data-driven and theoretical, model-based neurosciences at the level of spiking circuits, for the purpose of a quantitative, mechanistic interpretation of recorded neuronal population activity.