数据增强：建模海马体对泛化贡献的框架
Data augmentation as a framework for modeling hippocampal contributions to generalization
浏览论文内容
中文总结 AI 辅助
本文提出数据增强可作为框架建模海马体对泛化的贡献，其离线、在线策略对应海马体功能，可构建连接函数以预测依赖海马体的多样行为，助力相关理论的正式化与评估。
中文摘要 AI 辅助
海马体在泛化中发挥关键作用，使我们能灵活复用过往经验完成新任务。本文提出，数据增强——一种通过重构过往经验提升泛化能力的机器学习策略——为概念化和建模海马体功能提供了有用框架。我们先概述数据增强在两个时间尺度上的运作方式：传统“离线”场景中，重构训练数据可生成更具泛化性的表征；“在线”场景中，检索到的经验可在测试时灵活重构，以支持零样本推理。我们认为这些“离线”和“在线”计算策略对应海马体支持的功能。关键在于，这些计算工具可用于构建实验证据与理论主张间的正式“连接函数”，从而能用统一建模方法预测依赖海马体的多样行为，包括高维感知环境中的导航及更抽象的推理。我们希望这一视角及可用的建模策略，能助力正式化和评估海马体功能理论的新工作。
英文摘要
The hippocampus plays a critical role in generalization, enabling us to flexibly repurpose prior experiences to perform novel tasks. Here we suggest that data augmentation---a machine learning strategy to improve generalization by refactoring prior experience---offers a useful framework to conceptualize and model hippocampal function. We begin by outlining how data augmentation operates across two timescales: the traditional ``offline'' setting, where refactoring training data yields more general representations, and an ``online'' setting, where retrieved experiences can be flexibly refactored at test time to support zero-shot inference. We suggest that these `offline' and `online' computational strategies map onto functions supported by the hippocampus. Critically, we argue that these computational tools can be leveraged to develop formal `linking functions' between experimental evidence and theoretical claims, such that a unified modeling approach can be used to predict the diverse behaviors that depend on the hippocampus---from navigating in high-dimensional sensory environments to more abstract inferences. We hope this perspective, and the modeling strategies it makes available, will support new efforts to formalize and evaluate theories of hippocampal function.
