# Fast Online Deconvolution of Calcium Imaging Data

arXiv: https://arxiv.org/abs/1609.00639 · HTML: https://mlsanity.com/paper/1609.00639/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2016.

## Built on by (most cited first, 9 of 9)

- [Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time](https://mlsanity.com/paper/2111.00070/) (2021)
- [Asymptotics of Ridge Regression in Convolutional Models](https://mlsanity.com/paper/2103.04557/) (2021)
- [Covariance-Free Sparse Bayesian Learning](https://mlsanity.com/paper/2105.10439/) (2021)
- [Bayesian Spike Train Inference via Non-Local Priors](https://mlsanity.com/paper/2307.10177/) (2023)
- [QuantFormer: Learning to Quantize for Neural Activity Forecasting in Mouse Visual Cortex](https://mlsanity.com/paper/2412.07264/) (2024)
- [Rapid detection and recognition of whole brain activity in a freely behaving Caenorhabditis elegans](https://mlsanity.com/paper/2109.10474/) (2021)
- [Integration of Calcium Imaging Traces via Deep Generative Modeling](https://mlsanity.com/paper/2501.14615/) (2025)
- [Automatic Neuronal Activity Segmentation in Fast Four Dimensional Spatio-Temporal Fluorescence Imaging using Bayesian Approach](https://mlsanity.com/paper/2512.19032/) (2025)
- [Retrieval-Augmented Visual Prompting: Guiding Foundation Models in Two-Photon Imaging](https://mlsanity.com/paper/2608.21970/) (2026)

## Abstract (arXiv)

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(105) traces of whole-brain larval zebrafish imaging data on a laptop.