AI's significant recent advances using general-purpose circuit computations offer a potential window into how the neocortex and cerebellum of the brain are able to achieve a diverse range of functions across sensory, cognitive, and motor domains, despite their uniform circuit structures. However, comparing the brain and AI is challenging unless clear similarities exist, and past reviews have been limited to comparison of brain-inspired vision AI and the visual neocortex. Here, to enable comparisons across diverse functional domains, we subdivide circuit computation into three elements -- circuit structure, input/outputs, and the learning algorithm -- and evaluate the similarities for each element. With this novel approach, we identify wide-ranging similarities and convergent evolution in the brain and AI, providing new insights into key concepts in neuroscience. Furthermore, inspired by processing mechanisms of AI, we propose a new theory that integrates established neuroscience theories, particularly the theories of internal models and the mirror neuron system. Both the neocortex and cerebellum predict future world events from past information and learn from prediction errors, thereby acquiring models of the world. These models enable three core processes: (1) Prediction -- generating future information, (2) Understanding -- interpreting the external world via compressed and abstracted sensory information, and (3) Generation -- repurposing the future-information generation mechanism to produce other types of outputs. The universal application of these processes underlies the ability of the neocortex and cerebellum to accomplish diverse functions with uniform circuits. Our systematic approach, insights, and theory promise groundbreaking advances in understanding the brain.

翻译：人工智能近期利用通用回路计算取得的重大进展，为理解大脑新皮层和小脑如何通过统一回路结构实现感觉、认知和运动等多领域功能提供了潜在窗口。然而，除非存在明确相似性，否则比较大脑与人工智能颇具挑战，既往综述多局限于类脑视觉人工智能与视觉新皮层的比较。为实现跨功能域的系统比较，本文将回路计算分解为三个要素——回路结构、输入/输出与学习算法——并分别评估各要素的相似性。通过这一创新方法，我们揭示了大脑与人工智能之间广泛的相似性与趋同演化现象，为神经科学关键概念提供了新见解。进一步地，受人工智能处理机制启发，我们提出整合现有神经科学理论（特别是内部模型理论与镜像神经元系统理论）的新理论框架。新皮层与小脑均能依据历史信息预测未来事件，并通过预测误差进行学习，从而建立世界模型。这些模型支撑着三个核心处理过程：(1) 预测——生成未来信息；(2) 理解——通过压缩抽象的感知信息解释外部世界；(3) 生成——复用未来信息生成机制以产生其他类型输出。这些过程的普适性应用，正是新皮层与小脑能以统一回路实现多样化功能的基础。我们的系统研究方法、创新见解与理论框架，有望推动脑理解取得突破性进展。