Latent variable models have become instrumental in computational neuroscience for reasoning about neural computation. This has fostered the development of powerful offline algorithms for extracting latent neural trajectories from neural recordings. However, despite the potential of real time alternatives to give immediate feedback to experimentalists, and enhance experimental design, they have received markedly less attention. In this work, we introduce the exponential family variational Kalman filter (eVKF), an online recursive Bayesian method aimed at inferring latent trajectories while simultaneously learning the dynamical system generating them. eVKF works for arbitrary likelihoods and utilizes the constant base measure exponential family to model the latent state stochasticity. We derive a closed-form variational analogue to the predict step of the Kalman filter which leads to a provably tighter bound on the ELBO compared to another online variational method. We validate our method on synthetic and real-world data, and, notably, show that it achieves competitive performance

翻译：潜在变量模型已成为计算神经科学中推理神经计算的重要工具。这促进了从神经记录中提取潜在神经轨迹的强大离线算法的发展。然而，尽管实时替代方法具有为实验者提供即时反馈并增强实验设计的潜力，它们却未受到足够关注。本文提出指数族变分卡尔曼滤波器（eVKF），这是一种在线递归贝叶斯方法，旨在推断潜在轨迹的同时学习生成这些轨迹的动态系统。eVKF适用于任意似然函数，并利用常基测度指数族对潜在状态的随机性进行建模。我们推导出卡尔曼滤波器预测步骤的闭式变分对应形式，相较于另一种在线变分方法，该方法可证明获得更紧的ELBO边界。我们在合成数据和真实数据上验证了该方法，并特别展示了其具有竞争性的性能。