Fast Online Deconvolution of Calcium Imaging Data

TL;DR

This paper introduces a fast, online algorithm for deconvolving calcium imaging data to estimate neural activity in real-time, significantly outperforming existing methods in speed and efficiency.

Contribution

The paper presents a novel online active set method for sparse non-negative deconvolution, enabling real-time neural activity estimation from calcium imaging data with linear computational complexity.

Findings

Achieves over tenfold speed increase compared to state-of-the-art solvers.

Enables real-time processing of large-scale neural imaging data.

Supports hyperparameter optimization with minimal data passes.

Abstract

Fluorescent calcium indicators are a popular means for observing the spiking activity of large neuronal populations, but extracting the activity of each neuron from raw fluorescence calcium imaging data is a nontrivial problem. We present a fast online active set method to solve this sparse non-negative deconvolution problem. Importantly, the algorithm progresses through each time series sequentially from beginning to end, thus enabling real-time online estimation of neural activity during the imaging session. Our algorithm is a generalization of the pool adjacent violators algorithm (PAVA) for isotonic regression and inherits its linear-time computational complexity. We gain remarkable increases in processing speed: more than one order of magnitude compared to currently employed state of the art convex solvers relying on interior point methods. Unlike these approaches, our method can exploit warm starts; therefore optimizing model hyperparameters only requires a handful of passes through the data. A minor modification can further improve the quality of activity inference by imposing a constraint on the minimum spike size. The algorithm enables real-time simultaneous deconvolution of $O(10^5)$ traces of whole-brain larval zebrafish imaging data on a laptop.