时间扭曲的脉冲时间刺激-响应分布几何结构的全维表征
Full-dimensional characterisation of time-warped spike-time stimulus-response distribution geometries
AI总结：
本文研究神经脉冲时间表示的全维几何，提出大型细胞集群的精确脉冲模式在试次间存在时间扭曲，并表明扭曲程度依赖单次试次的皮层背景活动水平。
摘要：
表征大脑中感觉刺激的表示是一项基础性科学探索，它可以阐明信息编码的原理。大多数表征通过将脉冲序列转换为发放率或分箱脉冲计数、应用明确命名的“降维”方法，或压缩试次间变异性，来降低神经数据的维度。对基于时间的表示进行全维几何表征，可能会为复杂高维信息如何编码带来意想不到的洞见。近期研究表明，在单个刺激的多个试次中引发的表示分布，可以在不应用降维的情况下进行几何表征，从而保留细胞集群中单个神经元的时间脉冲信息，并揭示丰富的几何结构。我们扩展了这些结果，表明更大细胞集群的精确脉冲时间模式在每个试次上都会发生时间扭曲（即被拉伸或压缩）。此外，通过对大型脉冲时间模式的分布进行几何表征，我们的分析支持了以下假说：脉冲时间模式被时间扭曲的程度，取决于单次试次中皮层区域的背景活动水平。最后，我们认为，大型电生理数据集的激增以及“神经几何学家”的日益集中，为表征全维脉冲时间表示创造了理想条件，可作为降维方法的补充。
英文摘要：
Characterising the representation of sensory stimuli in the brain is a fundamental scientific endeavor, which can illuminate principles of information coding. Most characterizations reduce the dimensionality of neural data by converting spike trains to firing rates or binned spike counts, applying explicitly named methods of ``dimensionality reduction'', or collapsing trial-to-trial variability. Characterisation of the full-dimensional geometry of timing-based representations may provide unexpected insights into how complex high-dimensional information is encoded. Recent research shows that the distribution of representations elicited over trials of a single stimulus can be geometrically characterized without the application of dimensionality reduction, maintaining the temporal spiking information of individual neurons in a cell assembly and illuminating rich geometric structure. We extend these results, showing that precise spike time patterns for larger cell assemblies are time-warped (i.e. stretched or compressed) on each trial. Moreover, by geometrically characterizing distributions of large spike time patterns, our analysis supports the hypothesis that the degree to which a spike time pattern is time-warped depends on the cortical area's background activity level on a single trial. Finally, we suggest that the proliferation of large electrophysiology datasets and the increasing concentration of ``neural geometrists'', creates ideal conditions for characterization of full-dimensional spike time representations, in complement to dimensionality reduction approaches.
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