Inferring Neural Population Spiking Rate from Wide-Field Calcium Imaging

Abstract

Wide-field calcium imaging techniques allow recordings of high-resolution neuronal activity across one or more brain regions. However, since the recordings capture light emission generated by the fluorescence of the calcium indicator, the neural activity that drives the calcium changes is masked by the calcium indicator dynamics. Here we develop and evaluate new methods to deconvolve the calcium traces and estimate the underlying neural spiking rate. Our methods take into account both the noise in the recordings and the temporal dynamics of the calcium indicator response. Our first proposal estimates firing rates that are constant over discrete time bins. The size of each time bin depends on the data and is determined dynamically. Our second proposal estimates the rate as a continuous function and is meant for studies that look for slow rate fluctuations rather than abrupt changes. We compare our results with those of two alternative approaches: direct deconvolution using a first differences approach, and the Lucy-Richardson image recovery method, adapted to recover temporal dynamics. We show that our methods outperform competitors on synthetic data as well as on wide-field calcium recordings in which the spikes were recorded in parallel using multi-channel silicon probe.