Modern neural recording techniques allow neuroscientists to obtain spiking activity of multiple neurons from different brain regions over long time periods. This requires new statistical methods to be developed for understanding structure of the large-scale data, in terms of both neuron number and recording duration. In this paper, we develop a bi-clustering method to cluster the neural spiking activity spatially and temporally, according to their low-dimensional latent structures. The spatial (neuron) clusters are defined by the latent trajectories within each neural population, while the temporal (state) clusters are defined by local linear dynamics manner shared across the population. To flexibly extract the bi-clustering structure, we build the model non-parametrically, and develop an efficient Markov chain Monte Carlo (MCMC) algorithm to sample the posterior distributions of model parameters. Validating our proposed MCMC algorithm through simulations, we find the method can recover unknown parameters and true bi-clustering structures successfully. We then apply the proposed bi-clustering method to multi-regional neural recordings under different experiment settings, where we find that simultaneously considering latent trajectories and spatial-temporal clustering structures can provide us with a more accurate and interpretable result. Overall, the proposed method provides scientific insights for large-scale (counting) time series with elongated recording periods, and it can have application beyond neuroscience.

翻译：现代神经记录技术使神经科学家能够长时间获取来自不同脑区的多个神经元的脉冲活动。这需要开发新的统计方法来理解大规模数据在神经元数量和记录时长两个维度上的结构。本文提出一种双聚类方法，根据低维潜在结构对神经脉冲活动在空间和时间两个维度进行聚类。空间（神经元）聚类由每个神经群体内的潜在轨迹定义，而时间（状态）聚类则由群体间共享的局部线性动力学模式定义。为灵活提取双聚类结构，我们采用非参数方式构建模型，并开发了高效的马尔可夫链蒙特卡洛（MCMC）算法对模型参数的后验分布进行采样。通过仿真验证所提出的MCMC算法，发现该方法能成功恢复未知参数和真实双聚类结构。随后将该双聚类方法应用于不同实验条件下的多区域神经记录数据，发现同时考虑潜在轨迹与时空聚类结构能提供更准确且可解释的结果。总体而言，该方法为长记录周期的大规模（计数）时间序列提供了科学洞见，且其应用可推广至神经科学以外的领域。