Blind deconvolution for spike inference from fluorescence recordings

Abstract

The parallel developments of genetically-encoded calcium indicators and fast fluorescence imaging techniques allows one to simultaneously record neural activity of extended neuronal populations in vivo. To fully harness the potential of functional imaging, one needs to infer the sequence of action potentials from fluorescence traces. Here we build on recently proposed computational approaches to develop a blind sparse deconvolution (BSD) algorithm based on a generative model for inferring spike trains from fluorescence traces. BSD features, (1) automatic (fully unsupervised) estimation of the hyperparameters, such as spike amplitude, noise level and rise and decay time constants, (2) a novel analytical estimate of the sparsity prior, which yields enhanced robustness and computational speed with respect to existing methods, (3) automatic thresholding for binarizing spikes that maximizes the precision-recall performance, (4) super-resolution capabilities increasing the temporal resolution beyond the fluorescence signal acquisition rate. BSD also uniquely provides theoretically-grounded estimates of the expected performance of the spike reconstruction in terms of precision-recall and temporal accuracy for each recording. The performance of the algorithm is established using synthetic data and through the SpikeFinder challenge, a community-based initiative for spike-rate inference benchmarking based on a collection of joint electrophysiological and fluorescence recordings. Our method outperforms classical sparse deconvolution algorithms in terms of robustness, speed and/or accuracy and performs competitively in the SpikeFinder challenge. This algorithm is modular, easy-to-use and made freely available. Its novel features can thus be incorporated in a straightforward way into existing calcium imaging packages.