Marked point process models have recently been used to capture the coding properties of neural populations from multiunit electrophysiological recordings without spike sorting. These clusterless models have been shown in some instances to better describe the firing properties of neural populations than collections of receptive field models for sorted neurons and to lead to better decoding results. To assess their quality, we previously proposed a goodness-of-fit technique for marked point process models based on time rescaling, which for a correct model produces a set of uniform samples over a random region of space. However, assessing uniformity over such a region can be challenging, especially in high dimensions. Here, we propose a set of new transformations in both time and the space of spike waveform features, which generate events that are uniformly distributed in the new mark and time spaces. These transformations are scalable to multidimensional mark spaces and provide uniformly distributed samples in hypercubes, which are well suited for uniformity tests. We discuss the properties of these transformations and demonstrate aspects of model fit captured by each transformation. We also compare multiple uniformity tests to determine their power to identify lack-of-fit in the rescaled data. We demonstrate an application of these transformations and uniformity tests in a simulation study. Proofs for each transformation are provided in the appendix.

中文翻译：

---

通过时间和速率重新缩放评估神经种群编码的标记点过程模型的拟合优度

标记点过程模型最近已用于从多单元电生理记录中捕获神经群体的编码特性，而无需进行尖峰排序。在某些情况下，这些无聚类模型已被证明比分类神经元的感受野模型集合更好地描述神经群体的放电特性，并导致更好的解码结果。为了评估它们的质量，我们之前提出了一种基于时间重新缩放的标记点过程模型的拟合优度技术，对于正确的模型，该技术在随机空间区域上生成一组均匀样本。然而，评估这样一个区域的均匀性可能具有挑战性，尤其是在高维度上。在这里，我们提出了一组新的尖峰波形特征的时间和空间变换，生成均匀分布在新标记和时间空间中的事件。这些转换可扩展到多维标记空间，并在超立方体中提供均匀分布的样本，非常适合均匀性测试。我们讨论这些转换的属性并演示每个转换捕获的模型拟合的各个方面。我们还比较了多个均匀性测试，以确定它们在重新调整后的数据中识别失配的能力。我们展示了这些转换和均匀性测试在模拟研究中的应用。附录中提供了每个变换的证明。非常适合均匀性测试。我们讨论这些转换的属性并演示每个转换捕获的模型拟合的各个方面。我们还比较了多个均匀性测试，以确定它们在重新调整后的数据中识别失配的能力。我们展示了这些转换和均匀性测试在模拟研究中的应用。附录中提供了每个变换的证明。非常适合于均匀性测试。我们讨论这些转换的属性并演示每个转换捕获的模型拟合的各个方面。我们还比较了多个均匀性测试，以确定它们在重新调整后的数据中识别失配的能力。我们展示了这些转换和均匀性测试在模拟研究中的应用。附录中提供了每个变换的证明。