With advances in neural recording techniques, neuroscientists are now able to record the spiking activity of many hundreds of neurons simultaneously, and new statistical methods are needed to understand the structure of this large-scale neural population activity. Although previous work has tried to summarize neural activity within and between known populations by extracting low-dimensional latent factors, in many cases what determines a unique population may be unclear. Neurons differ in their anatomical location, but also, in their cell types and response properties. To identify populations directly related to neural activity, we develop a clustering method based on a mixture of dynamic Poisson factor analyzers (mixDPFA) model, with the number of clusters and dimension of latent factors for each cluster treated as unknown parameters. To analyze the proposed mixDPFA model, we propose a Markov chain Monte Carlo (MCMC) algorithm to efficiently sample its posterior distribution. Validating our proposed MCMC algorithm through simulations, we find that it can accurately recover the unknown parameters and the true clustering in the model, and is insensitive to the initial cluster assignments. We then apply the proposed mixDPFA model to multi-region experimental recordings, where we find that the proposed method can identify novel, reliable clusters of neurons based on their activity, and may, thus, be a useful tool for neural data analysis.

翻译：随着神经记录技术的进步，神经科学家现在能够同时记录数百个神经元的尖峰活动，因此需要新的统计方法来理解这种大规模神经群体活动的结构。尽管已有研究通过提取低维潜在因子试图总结已知群体内及群体间的神经活动，但在许多情况下，决定独特群体的因素尚不明确。神经元在解剖位置、细胞类型及反应特性上均有差异。为识别与神经活动直接相关的群体，我们提出了一种基于动态泊松因子分析混合模型（mixDPFA）的聚类方法，并将聚类数量及每个聚类潜在因子维度视为未知参数。针对所提出的mixDPFA模型，我们设计了一种马尔可夫链蒙特卡洛（MCMC）算法，以高效地采样其后验分布。通过仿真验证，该MCMC算法能够准确恢复模型中的未知参数和真实聚类，且对初始聚类分配不敏感。随后我们将mixDPFA模型应用于多区域实验记录数据，发现该方法能够基于神经元活动识别出新颖且可靠的聚类，因此有望成为神经数据分析的有效工具。