DENDRO: Recovery and denoising of whole-tree dendritic voltagefrom 2D voltage movies

The ability to image voltage at high spatiotemporal resolution across an entire dendritic tree would represent a major advance in systems and circuit neuroscience. Recent advances in genetically encoded voltage indicators (GEVIs) have brought this possibility closer to reality. However, due to fundamental tradeoffs between imaging speed, resolution, SNR, and volume, this goal has remained out of reach. Here we develop a computational method that fuses 3D anatomical information with 2D voltage video data, yielding full time-varying 3D voltage estimates. Our method, termed DENDRO, comprises two steps. In step one, we use the anatomical data to build a microscope model which maps from voltages along the tree to observed fluorescence at the imaging plane. By exploiting local spatial smoothness of the voltage signal, we parameterize the voltage signal using a set of local basis functions, which reduces the dimensionality of the problem and allows us to approximately invert the microscope model. This step leverages spatial but not temporal smoothness of the underlying signal and yields noisy 3D estimates. In step two, we train a lightweight self-supervised neural network to perform spatiotemporal denoising of the inferred voltages. On simulated data, we find that DENDRO is able to recover voltages at high accuracy across an entire dendritic tree. On real voltage movies from hippocampal slices, DENDRO recovers known dendritic phenomena at single trial resolution and millisecond time-scales, and allows visualization of backpropagating action potentials in 3D.