Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania.
Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania.
J Neurophysiol. 2022 Dec 1;128(6):1578-1592. doi: 10.1152/jn.00066.2022. Epub 2022 Nov 2.
For many perceptual and behavioral tasks, a prominent feature of neural spike trains involves high firing rates across relatively short intervals of time. We call these events "population bursts." Because during a population burst information is, presumably, transmitted from one part of the brain to another, burst timing should reveal activity related to the flow of information across neural circuits. We developed a statistical method (based on a point process model) of determining, accurately, the time of the maximum (peak) population firing rate on a trial-by-trial basis and used it to characterize burst propagation across areas. We then examined the tendency of peak firing rates in distinct brain areas to shift earlier or later in time, together, across repeated trials, and found this trial-to-trial coupling of peak times to be a sensitive indicator of interaction across populations. In the data we examined, from the Allen Brain Observatory, we found many very strong correlations (95% confidence intervals above 0.75) in cases where standard methods were unable to demonstrate cross-area correlation. The statistical model introduced cross-area covariation only through population-level trial-dependent time shifts and gain constants (values of which were learned from the data), yet it provided very good fits to data histograms, including histograms of spike count correlations within and across visual areas. Our results demonstrate the utility of carefully assessing timing and propagation, across brain regions, of transient bursts in neural population activity, based on multiple spike train recordings. We developed a novel statistical method for identifying coordinated propagation of activity across populations of spiking neurons, with high temporal accuracy. Using simultaneous recordings from three visual areas we document precise timing relationships on a trial-by-trial basis, and we show how previously existing techniques can fail to discover coordinated activity in cases where the new approach finds very strong cross-area correlation.
对于许多感知和行为任务，神经尖峰序列的一个突出特征涉及在相对较短的时间间隔内具有较高的发射率。我们称这些事件为“群体爆发”。因为在群体爆发期间，信息可能是从大脑的一部分传递到另一部分，所以爆发时间应该揭示与信息在神经回路中流动相关的活动。我们开发了一种统计方法（基于点过程模型），能够准确地确定每次试验中最大（峰值）群体发射率的时间，并使用它来描述跨区域的爆发传播。然后，我们检查了不同大脑区域的峰值发射率在重复试验中一起提前或延迟的趋势，并发现这种峰值时间的试验到试验耦合是群体间相互作用的敏感指标。在我们从艾伦脑观测站检查的数据中，我们发现了许多非常强的相关性（置信区间在 95%以上为 0.75），而标准方法无法证明跨区域相关性。该统计模型仅通过群体水平的试验依赖性时间移位和增益常数（其值从数据中学习）引入跨区域协变，但其对数据直方图，包括视觉区域内和跨区域的尖峰计数相关性的直方图，提供了非常好的拟合。我们的结果表明，基于多个尖峰序列记录，仔细评估大脑区域之间的瞬态爆发的时间和传播是有用的。我们开发了一种新的统计方法，用于以高精度识别尖峰神经元群体活动的协调传播。我们使用来自三个视觉区域的同步记录，在每次试验的基础上记录精确的时间关系，并展示了在新方法发现非常强的跨区域相关性的情况下，先前存在的技术如何无法发现协调活动。
