Neuronal responses to complex stimuli and tasks can encompass a wide range of time scales. Understanding these responses requires measures that characterize how the information on these response patterns are represented across multiple temporal resolutions. In this paper we propose a metric – which we call multiscale relevance (MSR) – to capture the dynamical variability of the activity of single neurons across different time scales. The MSR is a non-parametric, fully featureless indicator in that it uses only the time stamps of the firing activity without resorting to any a priori covariate or invoking any specific structure in the tuning curve for neural activity. When applied to neural data from the mEC and from the ADn and PoS regions of freely-behaving rodents, we found that neurons having low MSR tend to have low mutual information and low firing sparsity across the correlates that are believed to be encoded by the region of the brain where the recordings were made. In addition, neurons with high MSR contain significant information on spatial navigation and allow to decode spatial position or head direction as efficiently as those neurons whose firing activity has high mutual information with the covariate to be decoded and significantly better than the set of neurons with high local variations in their interspike intervals. Given these results, we propose that the MSR can be used as a measure to rank and select neurons for their information content without the need to appeal to any a priori covariate.

中文翻译：

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神经元尖峰序列中的多尺度相关性和信息编码。

神经元对复杂刺激和任务的反应可以涵盖广泛的时间范围。了解这些响应需要采取措施来表征跨多个时间分辨率如何表示这些响应模式的信息。在本文中，我们提出了一个度量标准-我们称其为多尺度相关性（MSR）-以捕获不同时间范围内单个神经元活动的动态变化。MSR是一种非参数，完全没有特征的指标，因为它仅使用触发活动的时间戳，而无需借助任何先验对神经活动进行调整或在调整曲线中调用任何特定结构。当将它们应用于来自mEC以及行为自由的啮齿动物的ADn和PoS区域的神经数据时，我们发现具有低MSR的神经元在相关性（据认为由该区域编码）中具有较低的相互信息和较低的发射稀疏性记录录音的大脑。此外，具有较高MSR的神经元包含有关空间导航的大量信息，并允许对空间位置或头部方向进行有效解码，其激发活动与要解码的协变量具有较高的互信息，并且比具有较高的神经元集的神经元具有更高的交互作用。间钉间隔的局部变化。鉴于这些结果，先验协变量。