Fast online deconvolution of calcium imaging data

TLDR

Fluorescent calcium indicators are widely used to observe neuronal spiking, but extracting individual neuron activity from raw fluorescence imaging data remains a challenging problem. This study introduces a fast online active‑set method to solve the sparse non‑negative deconvolution problem in calcium imaging. The algorithm processes each time series sequentially, generalizes the pool adjacent violators algorithm for isotonic regression, runs in linear time, exploits warm starts to reduce hyperparameter tuning, and can be refined by enforcing a minimum spike size constraint. It delivers more than a tenfold speedup over state‑of‑the‑art convex solvers and can deconvolve on the order of 10⁵ whole‑brain larval zebrafish traces in real time on a laptop.

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.