标题： 通用大脑模块：基于微型柱假设的神经网络中的模块重复 显示英文标题

标题： The Generalist Brain Module: Module Repetition in Neural Networks in Light of the Minicolumn Hypothesis

摘要： 虽然现代人工智能持续进步，但生物大脑在鲁棒性、适应性和效率方面仍然是神经网络的巅峰。 本综述探讨了一种受大脑结构启发的人工智能架构路径，特别是微型柱假设，该假设将新皮层视为一个由重复模块组成的分布式系统——我们将其与集体智能（CI）联系起来。 尽管已有相关研究，但缺乏将皮层柱与重复神经模块架构相连接的全面综述。 本综述旨在通过综合历史、理论和方法论视角来填补这一空白。 我们区分了架构重复——即结构的重复使用——和参数共享模块重复，其中相同的功能单元在网络中被重复使用。 后者表现出集体智能的关键特性，如鲁棒性、适应性和泛化能力。 证据表明，重复模块倾向于收敛为一种通用模块：简单、灵活的问题解决者，能够处理集合中的多种角色。 这种通用倾向可能为现代人工智能的长期挑战提供解决方案：通过简单性和可扩展性提高训练期间的能量效率，以及通过泛化实现鲁棒的身体控制。 虽然实证结果表明此类系统可以泛化到分布外问题，但理论结果仍显不足。 总体而言，具有模块重复特征的架构仍是一种新兴且未被充分探索的架构策略，对于效率、鲁棒性和适应性具有巨大的未开发潜力。 我们认为，一个采用集体智能优势，同时遵循微型柱的架构和功能原则的系统，可能会挑战现代人工智能在可扩展性、能耗和普及方面的难题。 显示英文摘要

摘要： While modern AI continues to advance, the biological brain remains the pinnacle of neural networks in its robustness, adaptability, and efficiency. This review explores an AI architectural path inspired by the brain's structure, particularly the minicolumn hypothesis, which views the neocortex as a distributed system of repeated modules - a structure we connect to collective intelligence (CI). Despite existing work, there is a lack of comprehensive reviews connecting the cortical column to the architectures of repeated neural modules. This review aims to fill that gap by synthesizing historical, theoretical, and methodological perspectives on neural module repetition. We distinguish between architectural repetition - reusing structure - and parameter-shared module repetition, where the same functional unit is repeated across a network. The latter exhibits key CI properties such as robustness, adaptability, and generalization. Evidence suggests that the repeated module tends to converge toward a generalist module: simple, flexible problem solvers capable of handling many roles in the ensemble. This generalist tendency may offer solutions to longstanding challenges in modern AI: improved energy efficiency during training through simplicity and scalability, and robust embodied control via generalization. While empirical results suggest such systems can generalize to out-of-distribution problems, theoretical results are still lacking. Overall, architectures featuring module repetition remain an emerging and unexplored architectural strategy, with significant untapped potential for both efficiency, robustness, and adaptiveness. We believe that a system that adopts the benefits of CI, while adhering to architectural and functional principles of the minicolumns, could challenge the modern AI problems of scalability, energy consumption, and democratization.