Modern neural recording techniques allow neuroscientists to obtain spiking activity of multiple neurons from different brain regions over long time periods, which requires new statistical methods to be developed for understanding structure of the large-scale data. 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 (populationally) synchronous local linear dynamics shared with different periods. 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 potentially have application beyond neuroscience.

翻译：现代神经记录技术使神经科学家能够获取长时段内跨脑区多个神经元的脉冲活动，这要求开发新的统计方法来理解大规模数据的结构。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进行空间和时间维度的聚类。空间（神经元）聚类由每个神经群体内部的潜在轨迹定义，而时间（状态）聚类则由不同时期共享的群体同步局部线性动力学定义。为灵活提取双聚类结构，我们采用非参数方式构建模型，并开发了一种高效的马尔可夫链蒙特卡洛算法来采样模型参数的后验分布。通过仿真验证所提出的MCMC算法，发现该方法可成功恢复未知参数和真实双聚类结构。随后我们将该双聚类方法应用于不同实验设置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，所提方法为长记录时段的大规模（计数）时间序列提供了科学洞见，并可能拓展应用于神经科学以外的领域。