Computational models in neuroscience usually take the form of systems of differential equations. The behaviour of such systems is the subject of dynamical systems theory. Dynamical systems theory provides a powerful mathematical toolbox for analysing neurobiological processes and has been a mainstay of computational neuroscience for decades. Recently, recurrent neural networks (RNNs) have become a popular machine learning tool for studying the non-linear dynamics of neural and behavioural processes by emulating an underlying system of differential equations. RNNs have been routinely trained on similar behavioural tasks to those used for animal subjects to generate hypotheses about the underlying computational mechanisms. By contrast, RNNs can also be trained on the measured physiological and behavioural data, thereby directly inheriting their temporal and geometrical properties. In this way they become a formal surrogate for the experimentally probed system that can be further analysed, perturbed and simulated. This powerful approach is called dynamical system reconstruction. In this Perspective, we focus on recent trends in artificial intelligence and machine learning in this exciting and rapidly expanding field, which may be less well known in neuroscience. We discuss formal prerequisites, different model architectures and training approaches for RNN-based dynamical system reconstructions, ways to evaluate and validate model performance, how to interpret trained models in a neuroscience context, and current challenges.

神经科学中的计算模型通常采用微分方程组的形式。此类系统的行为是动力系统理论的主题。动力系统理论为分析神经生物学过程提供了强大的数学工具箱，几十年来一直是计算神经科学的支柱。最近，循环神经网络（RNN）已成为一种流行的机器学习工具，用于通过模拟底层微分方程系统来研究神经和行为过程的非线性动力学。RNN 经常接受与动物受试者类似的行为任务训练，以生成有关底层计算机制的假设。相比之下，RNN 还可以根据测量的生理和行为数据进行训练，从而直接继承它们的时间和几何特性。通过这种方式，它们成为实验探测系统的正式替代品，可以进一步分析、扰动和模拟。这种强大的方法称为动态系统重建。在本视角中，我们重点关注人工智能和机器学习这个令人兴奋且迅速发展的领域的最新趋势，而该领域在神经科学中可能不太为人所知。我们讨论基于 RNN 的动态系统重建的形式先决条件、不同的模型架构和训练方法、评估和验证模型性能的方法、如何在神经科学背景下解释训练模型以及当前的挑战。