基于多维尺度对齐的表征动力学可视化
Visualizing Representational Dynamics with Multidimensional Scaling Alignment
摘要：
表征相似性分析（RSA）已被证明是一个有效的框架，表征脑活动概况和深层神经网络激活作为表征几何，通过计算成对的反应模式的距离作为一个表征相异性矩阵（RDM）。然而，如何正确地分析和可视化的代表性的几何动态的时间过程中，从刺激开始到偏移还没有得到很好的理解。在这项工作中，我们制定了管道，以了解与RDM电影和Procrustes-aligned多维标度（pMDS）的表征动力学，并将其应用于猴子IT皮层的神经记录。我们的研究结果表明，多维缩放对齐可以真正捕捉到动态的类别特定的表示空间与多种可视化的可能性，对象分类可能是分层的，多阶段的，和振荡（或循环）。
英文摘要：
Representational similarity analysis (RSA) has been shown to be an effective framework to characterize brain-activity profiles and deep neural network activations as representational geometry by computing the pairwise distances of the response patterns as a representational dissimilarity matrix (RDM). However, how to properly analyze and visualize the representational geometry as dynamics over the time course from stimulus onset to offset is not well understood. In this work, we formulated the pipeline to understand representational dynamics with RDM movies and Procrustes-aligned Multidimensional Scaling (pMDS), and applied it to neural recording of monkey IT cortex. Our results suggest that the the multidimensional scaling alignment can genuinely capture the dynamics of the category-specific representation spaces with multiple visualization possibilities, and that object categorization may be hierarchical, multi-staged, and oscillatory (or recurrent).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
