Modelling the dynamics of interactions in a neuronal ensemble is an important problem in functional connectivity research. One popular framework is latent factor models (LFMs), which have achieved notable success in decoding neuronal population dynamics. However, most LFMs are specified in discrete time, where the choice of bin size significantly impacts inference results. In this work, we present what is, to the best of our knowledge, the first continuous-time multivariate spike train LFM for studying neuronal interactions and functional connectivity. We present an efficient parameter inference algorithm for our biologically justifiable model which (1) scales linearly in the number of simultaneously recorded neurons and (2) bypasses time binning and related issues. Simulation studies show that parameter estimation using the proposed model is highly accurate. Applying our LFM to experimental data from a classical conditioning study on the prefrontal cortex in rats, we found that coordinated neuronal activities are affected by (1) the onset of the cue for reward delivery, and (2) the sub-region within the frontal cortex (OFC/mPFC). These findings shed new light on our understanding of cue and outcome value encoding.

翻译：研究神经元群体中相互作用的动态建模是功能连接领域的重要问题。潜变量模型（LFM）作为常用框架，已在解码神经元群体动力学方面取得显著成功。然而，现有LFM大多指定为离散时间形式，其分箱宽度的选择会显著影响推理结果。本文首次提出用于研究神经元相互作用与功能连接的连续时间多元脉冲序列LFM。我们为该生物学可解释模型开发了高效的参数推断算法，该算法（1）计算复杂度随同步记录神经元数量线性增长，（2）规避了时间分箱及其相关问题。仿真研究表明，基于该模型的参数估计具有高度准确性。将所提LFM应用于大鼠前额叶皮层经典条件反射实验数据后，我们发现：（1）奖赏交付提示信号的出现，以及（2）前额叶皮层亚区（眶额皮层/内侧前额叶皮层）均会影响神经元协同活动。这些发现为理解提示信号与结果价值的编码机制提供了新见解。