神经表征差异性的流匹配框架
A Flow Matching Framework for Neural Representational Dissimilarity
- The University of Texas at Austin（德克萨斯大学奥斯汀分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出流匹配框架统一神经表征差异性度量，将多种距离视为不同速度约束下的杰弗里斯散度，并支持新度量设计。
AI中文摘要：
神经表征差异性量化了神经响应分布之间的差异，对于比较跨刺激、脑区、任务和模型的神经编码至关重要。常用的距离度量涉及不同的假设，并使用单独的方法进行估计。在此，我们表明多种距离度量可以在深度生成模型中开发的流匹配框架下统一。即，这些距离在不同速度约束下表现为杰弗里斯散度。我们发现流匹配在估计涉及复杂分布和连续变量的距离时具有优势。此外，该框架能够以原则性的方式设计新的距离度量。总之，流匹配为理解、估计和设计神经表征差异性度量提供了一种统一的方法。
英文摘要：
Neural representational dissimilarity quantifies differences between neural response distributions, and is essential for comparing neural codes across stimuli, brain areas, tasks, and models. Commonly used distance metrics involve different assumptions and are estimated with separate methods. Here, we show that a variety of distance metrics can be unified under a flow matching framework developed in deep generative models. That is, these distances arise as Jeffreys divergences under different velocity constraints. We find that flow matching has advantages for estimating distances involving complicated distributions and continuous variables. Furthermore, this framework enables the design of new distance metrics in a principled way. Together, flow matching provides a unified approach for understanding, estimating, and designing neural representational dissimilarity metrics.
