Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models

Recent advances in optical voltage sensors have brought us closer to a critical goal in cellular neuroscience: imaging the full spatiotemporal voltage on a dendritic tree. However, current sensors and imaging approaches still face significant limitations in SNR and sampling frequency; therefore statistical denoising methods remain critical for understanding single-trial spatiotemporal dendritic voltage dynamics. Previous denoising approaches were either based on an inadequate linear voltage model or scaled poorly to large trees. Here we introduce a scalable fully Bayesian approach. We develop a generative nonlinear model that requires few parameters per dendritic compartment but is nonetheless flexible enough to sample realistic spatiotemporal data. The model captures potentially different dynamics in each compartment and leverages biophysical knowledge to constrain intra- and inter-compartmental dynamics. We obtain a full posterior distribution over spatiotemporal voltage via an efficient augmented block-Gibbs sampling algorithm. The nonlinear smoother model outperforms previously developed linear methods, and scales to much larger systems than previous methods based on sequential Monte Carlo approaches.