视网膜神经节细胞(RGCs),视网膜的输出神经元,通过尖峰编码视觉信息。RGC感受野(RF)代表视网膜中视觉信息处理的基本单位。RF通常使用尖峰触发平均值(STA)来估计,这是给定RGC敏感的刺激模式的平均值。而STA,基于平均数的概念,简单直观,它在RF中留下更复杂的结构而未被发现。或者,尖峰触发协方差(STC)分析提供有关二阶射频统计的信息。然而,STC在计算上是麻烦的并且难以解释。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),特定于多模态RFs。具体来说,RF与高斯混合模型拟合,它提供了多个RF集群的均值和协方差。所提出的方法恢复了ON-OFF细胞的RF中的双极刺激模式,而STA只识别开和关RGC,其余RGC被标记为未知类型。相比之下,我们新的STCL分析区分开-关RGC和开,关闭,和由STA分类的未知RGC类型。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.
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.