输入对称性会误导神经表征相似性分析结果
Stimulus symmetries can confound representational similarity analyses
- 利用对称性破坏表征几何，导致不同表示产生差异RSM
- 随机梯度下降可生成稀疏漂移编码，引发RSM漂移
- 适用于研究神经编码对比与表征分析的学者
表征相似性矩阵（RSM）能揭示神经编码的哪些信息？随着这类统计量日益流行，对其特性的完整刻画愈发重要。本文表明，网络输入中的对称性会干扰基于RSM的分析。刺激对称性使多种表征功能等价，但这些不同配置可能产生不同的RSM，反映质性不同的表征几何。我们证明，随机梯度下降或能量正则化可生成稀疏、漂移的编码，进而导致RSM漂移。此外，我们在训练编码图像数据的网络中发现此类现象，其中对称性为隐含存在。结果说明，在功能等价表示不通过简单旋转关联时，比较非线性神经编码存在根本挑战。
原文摘要 · Abstract (English)
What can representational similarity matrices (RSMs) tell us about a neural code? As the popularity of these summary statistics grows, so too does the need for a more complete characterization of their properties. Here, we show that symmetries in network inputs can confound RSM-based analyses. Stimulus symmetries render many representations functionally equivalent, but these different configurations can lead to different RSMs. These different RSMs reflect qualitatively different representational geometries. We show that stochastic gradient descent or energetic regularization can generate sparse, drifting codes, leading in turn to drifting RSMs. Moreover, we demonstrate that these phenomena are present in networks trained to encode image data, where the symmetry is latent. Our results illustrate the challenges inherent in comparing nonlinear neural codes, when functionally-equivalent representations are not related by a simple rotation.
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