The signaling capacity of a neural population depends on the scale and orientation of its covariance across trials. Estimating this "noise" covariance is challenging and is thought to require a large number of stereotyped trials. New approaches are therefore needed to interrogate the structure of neural noise across rich, naturalistic behaviors and sensory experiences, with few trials per condition. Here, we exploit the fact that conditions are smoothly parameterized in many experiments and leverage Wishart process models to pool statistical power from trials in neighboring conditions. We demonstrate that these models perform favorably on experimental data from the mouse visual cortex and monkey motor cortex relative to standard covariance estimators. Moreover, they produce smooth estimates of covariance as a function of stimulus parameters, enabling estimates of noise correlations in entirely unseen conditions as well as continuous estimates of Fisher information--a commonly used measure of signal fidelity. Together, our results suggest that Wishart processes are broadly applicable tools for quantification and uncertainty estimation of noise correlations in trial-limited regimes, paving the way toward understanding the role of noise in complex neural computations and behavior.

翻译：神经群体的信号编码能力取决于其协方差在试验间（trial-to-trial）的尺度与方向。估计这种"噪声"协方差极具挑战性，通常需要大量重复的固定刺激试验。因此，亟需发展新方法以在自然化行为与感官体验的丰富情境中，通过每个条件有限的试验次数探究神经噪声的结构。本文利用许多实验中条件参数连续平滑的特性，引入Wishart过程模型来整合相邻条件试验的统计效能。我们证明，相较于传统协方差估计器，该模型在小鼠视觉皮层与猴运动皮层的实验数据中表现更优。此外，该模型能生成随刺激参数平滑变化的协方差估计，不仅可对完全未观测条件下的噪声相关性进行估计，还能实现费希尔信息（衡量信号保真度的常用指标）的连续评估。综上，我们的结果表明Wishart过程是量化与不确定性评估试验有限条件下噪声相关性的通用工具，为理解复杂神经计算与行为中噪声的作用开辟了新路径。