Ramezanian-Panahi Mahta, Abrevaya Germán, Gagnon-Audet Jean-Christophe, Voleti Vikram, Rish Irina, Dumas Guillaume
Mila-Quebec AI Institute, Montréal, QC, Canada.
Université de Montréal, Montréal, QC, Canada.
Front Artif Intell. 2022 Jul 15;5:807406. doi: 10.3389/frai.2022.807406. eCollection 2022.
This review article gives a high-level overview of the approaches across different scales of organization and levels of abstraction. The studies covered in this paper include fundamental models in computational neuroscience, nonlinear dynamics, data-driven methods, as well as emergent practices. While not all of these models span the intersection of neuroscience, AI, and system dynamics, all of them do or can work in tandem as generative models, which, as we argue, provide superior properties for the analysis of neuroscientific data. We discuss the limitations and unique dynamical traits of brain data and the complementary need for hypothesis- and data-driven modeling. By way of conclusion, we present several hybrid generative models from recent literature in scientific machine learning, which can be efficiently deployed to yield interpretable models of neural dynamics.
这篇综述文章对不同组织规模和抽象层次的方法进行了高层次概述。本文涵盖的研究包括计算神经科学的基本模型、非线性动力学、数据驱动方法以及新兴实践。虽然并非所有这些模型都跨越神经科学、人工智能和系统动力学的交叉领域，但它们都可以或能够作为生成模型协同工作，正如我们所论证的，这些生成模型为神经科学数据的分析提供了卓越的特性。我们讨论了脑数据的局限性和独特的动力学特征，以及对假设驱动和数据驱动建模的互补需求。作为结论，我们展示了科学机器学习近期文献中的几个混合生成模型，这些模型可以有效地部署以生成神经动力学的可解释模型。
