标题： 一种数学方法来约束神经抽象以及扩展到更高阶认知所需的机制 显示英文标题

标题： A Mathematical Approach to Constraining Neural Abstraction and the Mechanisms Needed to Scale to Higher-Order Cognition

摘要： 人工智能在过去十年中取得了巨大进展，但仍无法达到人脑的水平，人脑是已知最著名的智能例子。 对大脑如何在有限的能力下实现如此多的飞跃的神经过程了解不多，除了其能够创建可以灵活和动态组合、重新组合并以新颖方式应用的知识结构。 本文提出了一种基于图论和谱图理论的数学方法，假设如何根据特征关系来限制这些信息的神经簇。 这一假设被分层应用于从最小到最大的知识簇，最终导致模型构建和推理。 显示英文摘要

摘要： Artificial intelligence has made great strides in the last decade but still falls short of the human brain, the best-known example of intelligence. Not much is known of the neural processes that allow the brain to make the leap to achieve so much from so little beyond its ability to create knowledge structures that can be flexibly and dynamically combined, recombined, and applied in new and novel ways. This paper proposes a mathematical approach using graph theory and spectral graph theory, to hypothesize how to constrain these neural clusters of information based on eigen-relationships. This same hypothesis is hierarchically applied to scale up from the smallest to the largest clusters of knowledge that eventually lead to model building and reasoning.