Fast non-negative deconvolution for spike train inference from population calcium imaging

TL;DR

This paper introduces a rapid non-negative deconvolution algorithm for inferring neuronal spike trains from calcium imaging data, outperforming traditional methods and enabling real-time analysis of large neural populations.

Contribution

The work presents a novel, fast non-negative deconvolution method that improves spike train inference accuracy and efficiency from calcium imaging data, without requiring calibration experiments.

Findings

Outperforms Wiener filtering in spike inference accuracy.

Enables real-time inference for over 100 neurons.

Parameters can be estimated solely from fluorescence data.

Abstract

Calcium imaging for observing spiking activity from large populations of neurons are quickly gaining popularity. While the raw data are fluorescence movies, the underlying spike trains are of interest. This work presents a fast non-negative deconvolution filter to infer the approximately most likely spike train for each neuron, given the fluorescence observations. This algorithm outperforms optimal linear deconvolution (Wiener filtering) on both simulated and biological data. The performance gains come from restricting the inferred spike trains to be positive (using an interior-point method), unlike the Wiener filter. The algorithm is fast enough that even when imaging over 100 neurons, inference can be performed on the set of all observed traces faster than real-time. Performing optimal spatial filtering on the images further refines the estimates. Importantly, all the parameters required to perform the inference can be estimated using only the fluorescence data, obviating the need to perform joint electrophysiological and imaging calibration experiments.