Real-Time Variational Method for Learning Neural Trajectory and its Dynamics
This work addresses a bottleneck for experimentalists in neuroscience by providing a real-time alternative to offline algorithms, though it appears incremental as it builds on existing variational and Kalman filter approaches.
The paper tackled the lack of real-time methods for inferring latent neural trajectories by introducing the exponential family variational Kalman filter (eVKF), an online recursive Bayesian method that simultaneously learns dynamics and achieves competitive performance on synthetic and real-world data.
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