标题： 动态原型分析：自主计算 显示英文标题

标题： Dynamical Archetype Analysis: Autonomous Computation

摘要： 研究神经计算旨在将神经系统视为信息处理机器来理解其功能。 神经系统无疑是复杂的，需要有原则和自动化的工具来抽象细节，以组织和逐步建立直觉。 我们认为，具有相同有效行为的系统应由其理想代表，即原型，来抽象，该原型由其渐近动力学结构定义。 我们提出了一组典型的计算库和一种新的差异度量，使我们能够通过显式考虑破坏拓扑共轭的变形以及保持拓扑共轭的微分同胚，根据其有效行为对系统进行分组。 所提出的差异度量可以从观察到的轨迹中估计得出。 数值实验展示了我们方法克服现有（不）相似度量在近似连续吸引子和高维循环神经网络中之前报道的脆弱性的能力。 尽管我们的实验集中在工作记忆系统上，但我们的理论方法自然可以扩展到生物和人工神经系统中循环动力学的一般机制解释。 我们认为，抽象的动力学原型，而不是详细的动力学系统，为描述神经计算提供了更有用的词汇。 显示英文摘要

摘要： The study of neural computation aims to understand the function of a neural system as an information processing machine. Neural systems are undoubtedly complex, necessitating principled and automated tools to abstract away details to organize and incrementally build intuition. We argue that systems with the same effective behavior should be abstracted by their ideal representative, i.e., archetype, defined by its asymptotic dynamical structure. We propose a library of archetypical computations and a new measure of dissimilarity that allows us to group systems based on their effective behavior by explicitly considering both deformations that break topological conjugacy as well as diffeomorphisms that preserve it. The proposed dissimilarity can be estimated from observed trajectories. Numerical experiments demonstrate our method's ability to overcome previously reported fragility of existing (dis)similarity measures for approximate continuous attractors and high-dimensional recurrent neural networks. Although our experiments focus on working memory systems, our theoretical approach naturally extends to general mechanistic interpretation of recurrent dynamics in both biological and artificial neural systems. We argue that abstract dynamical archetypes, rather than detailed dynamical systems, offer a more useful vocabulary for describing neural computation.