Johansson Birger, Tjøstheim Trond A, Balkenius Christian
Lund University Cognitive Science, Department of Philosophy, Lund University, Lund, Sweden.
Front Comput Neurosci. 2025 Jul 16;19:1607239. doi: 10.3389/fncom.2025.1607239. eCollection 2025.
System-level brain modeling is a powerful method for building computational models of the brain and allows biologically motivated models to produce measurable behavior that can be tested against empirical data. System-level brain models occupy an intermediate position between detailed neuronal circuit models and abstract cognitive models. They are distinguished by their structural and functional resemblance to the brain, while also allowing for thorough testing and evaluation. In designing system-level brain models, several questions need to be addressed. What are the components of the system? At what level should these components be modeled? How are the components connected-that is, what is the structure of the system? What is the function of each component? What kind of information flows between the components, and how is that information coded? We mainly address models of cognitive abilities or subsystems that produce measurable behavior rather than models that to reproduce internal states, signals or activation patterns. In this method paper, we argue that system-level modeling is an excellent method for addressing complex cognitive and behavioral phenomena.
系统级大脑建模是构建大脑计算模型的一种强大方法，它能使具有生物学动机的模型产生可测量的行为，这些行为可与实证数据进行对比测试。系统级大脑模型处于详细神经元回路模型和抽象认知模型之间的中间位置。它们的特点是在结构和功能上与大脑相似，同时也便于进行全面的测试和评估。在设计系统级大脑模型时，需要解决几个问题。系统的组成部分有哪些？这些组成部分应在何种层面进行建模？各组成部分是如何连接的，即系统的结构是怎样的？每个组成部分的功能是什么？各组成部分之间流动何种信息，以及这些信息是如何编码的？我们主要关注产生可测量行为的认知能力或子系统的模型，而非旨在重现内部状态、信号或激活模式的模型。在这篇方法论文中，我们认为系统级建模是解决复杂认知和行为现象的一种出色方法。
