A key question in brain sciences is how to identify time-evolving functional connectivity, such as that obtained from recordings of neuronal activity over time. We wish to explain the observed phenomena in terms of latent states which, in the case of neuronal activity, might correspond to subnetworks of neurons within a brain or organoid. Many existing approaches assume that only one latent state can be active at a time, in contrast to our domain knowledge. We propose a switching dynamical system based on the factorial hidden Markov model. Unlike existing approaches, our model acknowledges that neuronal activity can be caused by multiple subnetworks, which may be activated either jointly or independently. A change in one part of the network does not mean that the entire connectivity pattern will change. We pair our model with scalable variational inference algorithm, using a concrete relaxation of the underlying factorial hidden Markov model, to effectively infer the latent states and model parameters. We show that our algorithm can recover ground-truth structure and yield insights about the maturation of neuronal activity in microelectrode array recordings from in vitro neuronal cultures.

翻译：脑科学中的一个关键问题是如何识别随时间演化的功能性连接，例如从神经元活动随时间变化的记录中获得的连接。我们希望用潜在状态来解释观察到的现象，在神经元活动的情况下，这些潜在状态可能对应于大脑或类器官内的神经元子网络。与我们的领域知识相反，许多现有方法假设一次只能有一个潜在状态处于活跃状态。我们提出了一种基于因子隐马尔可夫模型的切换动态系统。与现有方法不同，我们的模型承认神经元活动可能由多个子网络引起，这些子网络可以联合或独立激活。网络中某一部分的变化并不意味着整个连接模式会改变。我们将模型与可扩展的变分推理算法相结合，通过对底层因子隐马尔可夫模型进行具体松弛，以有效推断潜在状态和模型参数。我们证明，我们的算法能够恢复真实结构，并从体外神经元培养的微电极阵列记录中揭示神经元活动成熟过程的洞见。