神经群体编码的几何稳定性：区域异质性、行为相关性与环路依赖性

摘要

当前神经群体表征可靠性的模型主要聚焦于时间稳定性：即群体质心在不同实验阶段和天数之间是否得以保持。这一框架留下了一个基本问题悬而未决：在一个实验期内，刺激间成对距离结构在不同独立观测之间复现的可靠性如何？我们主张，这一属性——几何稳定性——构成了现有框架未能捕捉到的表征分析独立维度。我们将几何稳定性形式化为分半表征差异性矩阵之间的斯皮尔曼等级相关系数，并证明它在经验上可分别与时间稳定性和解码准确性相分离。在涵盖视觉辨别任务中229个脑区-实验期观测数据（涉及68个脑区）的分析中（Steinmetz等人，2019），几何稳定性能够预测逐试次的神经-行为耦合（ρ=0.18，p=0.005），而质心漂移则无此预测能力（ρ=0.002，p=0.976）。从区域层级来看，纹状体几何稳定性最高（S=0.44），海马体最低（S=0.19），该顺序大致与时间稳定性的层级排序相反。在方向上一致的嗅觉实验数据（Bolding & Franks, 2018）启发下，我们构建了一个吸引子网络模型，其中循环兴奋性耦合通过从稀疏前馈输入中补全刺激模式，增强了分半RDM的一致性（ρ=+0.64，p=0.010），从而在环路层面解释了几何稳定性如何产生。这些结果表明，几何稳定性是神经群体编码中一种具有功能相关性且依赖于环路的重要属性，与时间漂移测量正交，并与近期关于循环连接如何在海马环路中平衡表征稳定性与序列动态的研究互为补充。

English

Current models of representational reliability in neural populations focus on temporal stability: whether population centroids are preserved across sessions and days. This framing leaves a fundamental question unanswered: how reliably does the pairwise distance structure among stimuli reproduce across independent observations within a session? We argue that this property, geometric stability, constitutes an independent axis of representational analysis that existing frameworks do not capture. We formalize geometric stability as the Spearman rank correlation between split-half representational dissimilarity matrices (Shesha) and show that it is empirically dissociable from both temporal stability and decoding accuracy. Across 229 area-session observations spanning 68 brain regions in a visual discrimination task (Steinmetz et al. 2019), geometric stability predicts trial-by-trial neural-behavioral coupling (ρ= 0.18, p = 0.005) while centroid drift does not (ρ= 0.002, p = 0.976). The regional hierarchy, with striatum most stable (S = 0.44) and hippocampus least (S = 0.19), runs roughly opposite to the temporal stability hierarchy. Directionally consistent olfactory data (Bolding \& Franks 2018) motivate an attractor network model in which recurrent excitatory coupling amplifies split-half RDM consistency by completing stimulus patterns from sparse feedforward input (ρ= +0.64, p = 0.010), providing a circuit-level account of how geometric stability emerges. These results establish geometric stability as a functionally relevant, circuit-dependent property of neural population codes, orthogonal to temporal drift measures and complementary to recent accounts of how recurrent connectivity balances representational stability with sequential dynamics in hippocampal circuits.