■几十年来,理解神经代码一直是神经科学研究的中心目标之一。尖峰通常被称为信息传递的单位,但是多单位活动(MUA)记录通常以汇总形式进行分析,例如分级尖峰计数,周围刺激时间直方图,射速,或人口代码。各种形式的平均也发生在大脑中,从树突树内尖峰的空间平均到突触动力学的时间平均。然而,这些形式的平均如何彼此相关,或者与神经代码中信息表示的空间和时间单位相关,仍然知之甚少。
■在这项工作中,我们开发了NeuroPixelHD,MUA的符号超维模型,并用它来解码来自大规模MUA记录的AllenInstitute视觉编码-NeuroPixels数据集中的n=9只小鼠显示的静态图像的空间位置和身份。我们参数化地改变了提供给模型的MUA数据的空间和时间分辨率,并比较了其产生的解码精度。
■对于几乎所有科目,我们发现125ms的时间分辨率可以最大化Gabor补丁的空间位置(9×9网格上呈现的补丁为81类)以及整个大脑的自然图像(118类对应于118张图像)的身份的解码精度。然而,这种最佳时间分辨率在不同地区之间差异很大,遵循感官关联的层次结构,并且受到不同区域theta波段振荡的中心频率的显着调制。空间上,对于几乎所有小鼠来说,最佳分辨率是在两个中尺度水平中的任何一个:面积水平,每个大脑区域内所有神经元的尖峰活动结合在一起,和人口水平,其中每个区域内的神经元尖峰在快速尖峰(假定抑制性)和规则尖峰(假定兴奋性)神经元中组合,分别。我们还观察到最佳空间和时间分辨率之间的预期相互作用,通过增加一个维度(空间或时间)的平均量,可以减少另一个维度的最佳平均量,反之亦然。
■我们的发现证实了MUA数据分析中时空分类和平均的现有经验实践,并为优化此类聚合的级别提供严格的计算框架。我们的发现还可以将这些经验实践与大脑中各种生物平均来源的现有知识综合为一种新的神经信息处理理论,其中信息单位根据跨空间和时间的神经元信号和噪声相关性动态变化。
UNASSIGNED: Understanding the neural code has been one of the central aims of neuroscience research for decades. Spikes are commonly referred to as the units of information transfer, but multi-unit activity (MUA) recordings are routinely analyzed in aggregate forms such as binned spike counts, peri-stimulus time histograms, firing rates, or population codes. Various forms of averaging also occur in the brain, from the spatial averaging of spikes within dendritic trees to their temporal averaging through synaptic dynamics. However, how these forms of averaging are related to each other or to the spatial and temporal units of information representation within the neural code has remained poorly understood.
UNASSIGNED: In this work we developed NeuroPixelHD, a symbolic hyperdimensional model of MUA, and used it to decode the spatial location and identity of static images shown to n = 9 mice in the Allen Institute Visual Coding-NeuroPixels dataset from large-scale MUA recordings. We parametrically varied the spatial and temporal resolutions of the MUA data provided to the model, and compared its resulting decoding accuracy.
UNASSIGNED: For almost all subjects, we found 125ms temporal resolution to maximize decoding accuracy for both the spatial location of Gabor patches (81 classes for patches presented over a 9×9 grid) as well as the identity of natural images (118 classes corresponding to 118 images) across the whole brain. This optimal temporal resolution nevertheless varied greatly between different regions, followed a sensory-associate hierarchy, and was significantly modulated by the central frequency of theta-band oscillations across different regions. Spatially, the optimal resolution was at either of two mesoscale levels for almost all mice: the area level, where the spiking activity of all neurons within each brain area are combined, and the population level, where neuronal spikes within each area are combined across fast spiking (putatively inhibitory) and regular spiking (putatively excitatory) neurons, respectively. We also observed an expected interplay between optimal spatial and temporal resolutions, whereby increasing the amount of averaging across one dimension (space or time) decreases the amount of averaging that is optimal across the other dimension, and vice versa.
UNASSIGNED: Our findings corroborate existing empirical practices of spatiotemporal binning and averaging in MUA data analysis, and provide a rigorous computational framework for optimizing the level of such aggregations. Our findings can also synthesize these empirical practices with existing knowledge of the various sources of biological averaging in the brain into a new theory of neural information processing in which the unit of information varies dynamically based on neuronal signal and noise correlations across space and time.