Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, School of Life Sciences, Peking University, Beijing, 100871, China.
Yuanpei College, Peking University, 100871, Beijing, China.
J Math Biol. 2024 Apr 17;88(6):65. doi: 10.1007/s00285-024-02081-0.
First-principles-based modelings have been extremely successful in providing crucial insights and predictions for complex biological functions and phenomena. However, they can be hard to build and expensive to simulate for complex living systems. On the other hand, modern data-driven methods thrive at modeling many types of high-dimensional and noisy data. Still, the training and interpretation of these data-driven models remain challenging. Here, we combine the two types of methods to model stochastic neuronal network oscillations. Specifically, we develop a class of artificial neural networks to provide faithful surrogates to the high-dimensional, nonlinear oscillatory dynamics produced by a spiking neuronal network model. Furthermore, when the training data set is enlarged within a range of parameter choices, the artificial neural networks become generalizable to these parameters, covering cases in distinctly different dynamical regimes. In all, our work opens a new avenue for modeling complex neuronal network dynamics with artificial neural networks.
基于第一性原理的建模在为复杂的生物功能和现象提供关键的见解和预测方面取得了巨大的成功。然而，对于复杂的生命系统来说，它们可能很难构建，并且模拟成本也很高。另一方面，现代的数据驱动方法在对多种类型的高维噪声数据进行建模方面表现出色。尽管如此，这些数据驱动模型的训练和解释仍然具有挑战性。在这里，我们将这两种类型的方法结合起来，对随机神经元网络振荡进行建模。具体来说，我们开发了一类人工神经网络，为尖峰神经元网络模型产生的高维非线性振荡动力学提供忠实的替代物。此外，当在一定范围的参数选择内扩大训练数据集时，人工神经网络对这些参数变得具有可推广性，涵盖了明显不同动力学状态的情况。总之，我们的工作为使用人工神经网络对复杂的神经元网络动力学进行建模开辟了一条新的途径。
