标题： ReBeCCA-ss：相关性平衡连续相关分析，采用平滑和替代方法，用于量化群体尖峰活动之间的相似性 显示英文标题

标题： ReBaCCA-ss: Relevance-Balanced Continuum Correlation Analysis with Smoothing and Surrogating for Quantifying Similarity Between Population Spiking Activities

摘要： 量化种群尖峰模式之间的相似性对于理解神经动力学如何编码信息至关重要。 传统方法结合了核平滑、主成分分析（PCA）和典型相关分析（CCA），但存在局限性：核平滑的带宽通常通过经验选择，CCA在最大化模式之间对齐时未考虑模式内部解释的方差，并且来自随机尖峰的基础相关性很少被修正。 我们引入了ReBaCCA-ss（具有平滑和替代的关联连续相关分析），这是一种新颖的框架，通过三项创新解决了这些挑战：（1）通过连续典型相关平衡对齐和方差解释；（2）使用替代尖峰序列纠正噪声；以及（3）通过最大化真实与替代相关性之间的差异来选择最优核带宽。 ReBaCCA-ss在模拟数据和大鼠执行延迟非匹配到样本任务时的海马记录上得到了验证。 它能够可靠地识别尖峰模式之间的时空相似性。 结合多维尺度分析，ReBaCCA-ss揭示了跨试验、事件、会话和动物的结构化神经表示，为神经群体分析提供了一个强大的工具。 显示英文摘要

摘要： Quantifying similarity between population spike patterns is essential for understanding how neural dynamics encode information. Traditional approaches, which combine kernel smoothing, PCA, and CCA, have limitations: smoothing kernel bandwidths are often empirically chosen, CCA maximizes alignment between patterns without considering the variance explained within patterns, and baseline correlations from stochastic spiking are rarely corrected. We introduce ReBaCCA-ss (Relevance-Balanced Continuum Correlation Analysis with smoothing and surrogating), a novel framework that addresses these challenges through three innovations: (1) balancing alignment and variance explanation via continuum canonical correlation; (2) correcting for noise using surrogate spike trains; and (3) selecting the optimal kernel bandwidth by maximizing the difference between true and surrogate correlations. ReBaCCA-ss is validated on both simulated data and hippocampal recordings from rats performing a Delayed Nonmatch-to-Sample task. It reliably identifies spatio-temporal similarities between spike patterns. Combined with Multidimensional Scaling, ReBaCCA-ss reveals structured neural representations across trials, events, sessions, and animals, offering a powerful tool for neural population analysis.