用于动态神经潜在嵌入的潜在空间网络模型
A latent space network model for dynamic neural latent embedding
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中文总结 AI 辅助
该研究提出一种潜在空间网络模型，用于分析小鼠视觉辨别任务下的神经元锋电位时间序列，结合嵌套隐藏马尔可夫结构捕捉时间依赖，可表征脑活动动态及溢出效应。
中文摘要 AI 辅助
我们提出了一种新型潜在空间网络模型，用于分析神经元锋电位序列数据的多变量时间序列。该方法的灵感来源于一项小鼠实验研究，在该研究中，研究人员在一系列视觉辨别任务下收集了神经元反应。我们采用潜在变量框架来建模聚合脑区的放电率，同时通过隐藏网络结构推断脑区之间的相互作用。该相互作用网络被嵌入到几何潜在空间中，支持可解释的可视化和基于模型的新型摘要。所提出的框架对潜在变量提供了直观解释，这些变量与节点特征中心性度量存在概念上的关联。为了捕捉时间依赖性，我们纳入了嵌套隐藏马尔可夫结构，该结构可灵活表示由实验条件变化引发的非线性偏移。我们还通过推导防止退化的充分条件，建立了该模型的理论性质，从而为模型假设提供指导。总体而言，所提出的方法提供了一个统一框架，可通过隐藏潜在空间网络模型表征脑活动、其时间动态以及溢出效应。
英文摘要
We introduce a novel latent space network model for analyzing multivariate time series of neural spike-train data. The methodology is motivated by an experimental study in mice, where neuronal responses were collected under a sequence of visual discrimination tasks. We adopt a latent variable framework to model the firing rates of aggregated brain areas, while simultaneously inferring the interactions between regions via a hidden network structure. This interaction network is embedded in a geometric latent space, enabling interpretable visualizations and novel model-based summaries. The proposed framework provides an intuitive interpretation of the latent variables, which bear a conceptual connection to node eigen-centrality measures. To capture temporal dependence, we incorporate a nested hidden Markov structure that can flexibly represent non-linear shifts that are induced by the changing of experimental conditions. We further establish theoretical properties of the model by deriving sufficient conditions that prevent degeneracy, thereby guiding our model assumptions. Overall, the proposed methodology provides a unified framework to characterize brain activity, its temporal dynamics, and spillover effects through a hidden latent space network model.
