Training deep neural density estimators to identify mechanistic models of neural dynamics.
Training deep neural density estimators to identify mechanistic models of neural dynamics.
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DOI：
10.7554/elife.56261
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发表时间：
2020-09-17
期刊：
影响因子：
7.7
通讯作者：
Macke JH
中科院分区：
文献类型：
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作者：
Gonçalves PJ;Lueckmann JM;Deistler M;Nonnenmacher M;Öcal K;Bassetto G;Chintaluri C;Podlaski WF;Haddad SA;Vogels TP;Greenberg DS;Macke JH
神经科学中的机制建模旨在根据根本原因解释观察到的现象。然而，确定哪些模型参数符合复杂和随机的神经数据提出了一个重大的挑战。我们使用机器学习工具来解决这一挑战，该工具使用深度神经密度估计器（使用模型模拟进行训练）来进行贝叶斯推理，并检索与原始数据或选定数据特征兼容的完整参数空间。我们的方法在参数和数据特征方面具有可扩展性，并且可以在初始训练后快速分析新数据。我们展示了我们的方法在感受野，离子通道和Hodgkin-Huxley模型上的能力和灵活性。我们还描述了在甲壳类动物的口胃神经节中引起节律性活动的电路配置的空间，并使用这些结果来推导潜在的补偿机制的假设。我们的方法将有助于缩小数据驱动和理论驱动的神经动力学模型之间的差距。计算神经科学家使用基于观测数据的数学模型来研究大脑中发生的事情。模型可以模拟大脑活动，从单个神经元的行为到整个神经网络中的集体活动模式。收集实验数据是第一步，接下来的挑战是决定哪种计算机模型最能代表数据，并能解释大脑行为的根本原因。研究人员通常通过试验和错误来为他们的数据找到正确的模型。这涉及调整模型的参数，直到模型可以重现感兴趣的数据。但这一过程是费力的，而且不系统。此外，随着神经科学中数据和计算机模型的日益复杂，建立模型的老派方法开始显示出其局限性。现在，Gonçalves，Lueckmann，Deistler等人设计了一种算法，使研究人员更容易将数学模型拟合到实验数据中。首先，该算法训练人工神经网络来预测哪些模型与模拟数据兼容。在初始训练之后，该方法可以快速地应用于原始实验数据或选定的数据特征。然后，该算法返回生成最佳匹配的模型。这个新开发的机器学习工具能够自动识别模型，这些模型可以从各种神经科学问题中复制观察到的数据。重要的是，进一步的实验表明，这种新方法可以扩展到复杂的机制，例如螃蟹中的神经网络如何保持其活动节奏。该工具可应用于神经科学和生物学其他领域的广泛计算研究，这可能有助于弥合“数据驱动”和“理论驱动”方法之间的差距。
Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators—trained using model simulations—to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin–Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics. Computational neuroscientists use mathematical models built on observational data to investigate what’s happening in the brain. Models can simulate brain activity from the behavior of a single neuron right through to the patterns of collective activity in whole neural networks. Collecting the experimental data is the first step, then the challenge becomes deciding which computer models best represent the data and can explain the underlying causes of how the brain behaves. Researchers usually find the right model for their data through trial and error. This involves tweaking a model’s parameters until the model can reproduce the data of interest. But this process is laborious and not systematic. Moreover, with the ever-increasing complexity of both data and computer models in neuroscience, the old-school approach of building models is starting to show its limitations. Now, Gonçalves, Lueckmann, Deistler et al. have designed an algorithm that makes it easier for researchers to fit mathematical models to experimental data. First, the algorithm trains an artificial neural network to predict which models are compatible with simulated data. After initial training, the method can rapidly be applied to either raw experimental data or selected data features. The algorithm then returns the models that generate the best match. This newly developed machine learning tool was able to automatically identify models which can replicate the observed data from a diverse set of neuroscience problems. Importantly, further experiments showed that this new approach can be scaled up to complex mechanisms, such as how a neural network in crabs maintains its rhythm of activity. This tool could be applied to a wide range of computational investigations in neuroscience and other fields of biology, which may help bridge the gap between ‘data-driven’ and ‘theory-driven’ approaches.