Retinal ganglion cells (RGCs), the retina's output neurons, encode visual information through spiking. The RGC receptive field (RF) represents the basic unit of visual information processing in the retina. RFs are commonly estimated using the spike-triggered average (STA), which is the average of the stimulus patterns to which a given RGC is sensitive. Whereas STA, based on the concept of the average, is simple and intuitive, it leaves more complex structures in the RFs undetected. Alternatively, spike-triggered covariance (STC) analysis provides information on second-order RF statistics. However, STC is computationally cumbersome and difficult to interpret. Thus, the objective of this study was to propose and validate a new computational method, called spike-triggered clustering (STCL), specific for multimodal RFs. Specifically, RFs were fit with a Gaussian mixture model, which provides the means and covariances of multiple RF clusters. The proposed method recovered bipolar stimulus patterns in the RFs of ON-OFF cells, while the STA identified only ON and OFF RGCs, and the remaining RGCs were labeled as unknown types. In contrast, our new STCL analysis distinguished ON-OFF RGCs from the ON, OFF, and unknown RGC types classified by STA. Thus, the proposed method enables us to include ON-OFF RGCs prior to retinal information analysis.

中文翻译：

---

用于视网膜神经节细胞分类的尖峰触发聚类。


视网膜神经节细胞（RGC）是视网膜的输出神经元，通过尖峰编码视觉信息。 RGC 感受野 (RF) 代表视网膜视觉信息处理的基本单位。 RF 通常使用尖峰触发平均值 (STA) 进行估计，STA 是给定 RGC 敏感的刺激模式的平均值。虽然基于平均值概念的 STA 简单直观，但它使 RF 中更复杂的结构未被检测到。或者，尖峰触发协方差 (STC) 分析提供有关二阶 RF 统计信息。然而，STC 计算繁琐且难以解释。因此，本研究的目的是提出并验证一种新的计算方法，称为尖峰触发聚类 (STCL)，专门用于多模态 RF。具体来说，RF 与高斯混合模型相拟合，该模型提供了多个 RF 簇的均值和协方差。所提出的方法恢复了 ON-OFF 细胞 RF 中的双极刺激模式，而 STA 仅识别 ON 和 OFF RGC，其余 RGC 被标记为未知类型。相比之下，我们的新 STCL 分析将 ON-OFF RGC 与 STA 分类的 ON、OFF 和未知 RGC 类型区分开来。因此，所提出的方法使我们能够在视网膜信息分析之前包括开-关 RGC。