Modern neural recording techniques allow neuroscientists to observe the spiking activity of many neurons simultaneously. Although previous work has illustrated how activity within and between known populations of neurons can be summarized by low-dimensional latent vectors, 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. Moreover, multiple distinct populations may not be well described by a single low-dimensional, linear representation. To tackle these challenges, we develop a clustering method based on a mixture of dynamic Poisson factor analyzers (DPFA) model, with the number of clusters treated as an unknown parameter. To do the analysis of DPFA model, we propose a novel Markov chain Monte Carlo (MCMC) algorithm to efficiently sample its posterior distribution. Validating our proposed MCMC algorithm with simulations, we find that it can accurately recover the true clustering and latent states and is insensitive to the initial cluster assignments. We then apply the proposed mixture of DPFA 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:现代神经记录技术允许神经科学家同时观察许多神经神经神经神经神经神经的突飞猛进活动。 虽然先前的工作已经表明,已知神经群内部和之间活动如何可以由低维潜潜质矢量来总结,但在许多情况下,确定独特种群的因素可能并不明确。 神经元在解剖位置上有所不同,但在细胞类型和反应特性上也有差异。 此外,多个不同的人群可能无法用单一的低维线性代表来很好地描述。 为了应对这些挑战,我们开发了一个基于动态 Poisson 要素分析器(DPFA) 模型(DPFA) 的混合组合方法,其中组群的数量被作为未知的参数处理。 为了对 DPFA 模型进行分析, 我们提议了一个新型的Markov 链 Monte Carlo(MC MC) 算法, 以有效取样其外表分布。 通过模拟来验证我们提议的MC 算法的算法, 它可能无法准确恢复真实的集群和潜伏状态, 并且对最初的集群任务不敏感。 我们随后将拟议的DPFA模型混合物应用于多区域的实验记录, 在那里, 我们发现, 拟议的方法可以确定一个基于活动、 和神经分析的新的、 工具。