Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland, United States of America.
Institute for Systems Research, University of Maryland, College Park, Maryland, United States of America.
PLoS Comput Biol. 2024 May 28;20(5):e1011605. doi: 10.1371/journal.pcbi.1011605. eCollection 2024 May.
Central in the study of population codes, coordinated ensemble spiking activity is widely observable in neural recordings with hypothesized roles in robust stimulus representation, interareal communication, and learning and memory formation. Model-free measures of synchrony characterize coherent pairwise activity but not higher-order interactions, a limitation transcended by statistical models of ensemble spiking activity. However, existing model-based analyses often impose assumptions about the relevance of higher-order interactions and require repeated trials to characterize dynamics in the correlational structure of ensemble activity. To address these shortcomings, we propose an adaptive greedy filtering algorithm based on a discretized mark point-process model of ensemble spiking and a corresponding statistical inference framework to identify significant higher-order coordination. In the course of developing a precise statistical test, we show that confidence intervals can be constructed for greedily estimated parameters. We demonstrate the utility of our proposed methods on simulated neuronal assemblies. Applied to multi-electrode recordings from human and rat cortical assemblies, our proposed methods provide new insights into the dynamics underlying localized population activity during transitions between brain states.
在群体编码的研究中，协调的整体爆发活动在神经记录中广泛可见，其被假设在稳健的刺激表示、区域间通信以及学习和记忆形成中发挥作用。同步的无模型度量可以描述相干的成对活动，但不能描述更高阶的相互作用，这一局限性被整体爆发活动的统计模型所超越。然而，现有的基于模型的分析方法通常对高阶相互作用的相关性做出假设，并且需要重复试验来描述整体活动相关结构中的动态。为了解决这些缺点，我们提出了一种基于整体爆发离散标记点过程模型的自适应贪婪滤波算法和相应的统计推断框架，以识别显著的高阶协调。在开发精确的统计测试的过程中，我们证明了可以为贪婪估计的参数构建置信区间。我们在模拟神经元集合上验证了我们所提出方法的有效性。将我们提出的方法应用于来自人和大鼠皮质集合的多电极记录，为大脑状态之间的过渡期间局部群体活动的动态提供了新的见解。
