Fig. 1: Overview of voltage imaging data and CellMincer denoising model.

a A simplified schematic diagram of a typical optical voltage imaging experiment (left). The spatially resolved fluorescence response is recorded over time to produce a voltage imaging movie. A key component of CellMincer’s preprocessing pipeline is the computation of spatial summary statistics and various auto-correlations from the entire recording, which are concatenated into a stack of global features (right). b An overview of CellMincer’s deep learning architecture. c The conditional U-Net convolutional neural network (CNN). At each step in the contracting path, the precomputed global feature stack is spatially downsampled in parallel (\({\mathscr{F}}\to {{\mathscr{F}}}^{{\prime} }\to {{\mathscr{F}}}^{{\prime}{\prime}}\to \ldots\)) and concatenated to the intermediate spatial feature maps. d The temporal post-processor neural network. The sequence of pixel embeddings are convolved with a 1D kernel along the time dimension, producing a single vector of length C. A multilayer perceptron subsequently reduces this vector to a single value. e A comparison of model performance on simulated data before and after introducing global features as a U-Net conditioner. Using global features confers an average increase of 5 dB to the denoiser, roughly corresponding to a 3-fold noise reduction. The presented data consists of several segments in which the simulated recording was performed under several neuron stimulation conditions, which are reported as separate distributions of PSNR gain. For further elaboration, see “Optimizing CellMincer network architecture and training scheduleusing Optosynth-simulated datasets” in “Methods”.