# Bayesian spike inference from calcium imaging data

arXiv: https://arxiv.org/abs/1311.6864 · HTML: https://mlsanity.com/paper/1311.6864/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2013.

## Built on by (most cited first, 4 of 4)

- [Neuroformer: Multimodal and Multitask Generative Pretraining for Brain Data](https://mlsanity.com/paper/2311.00136/) (2023)
- [Bayesian Spike Train Inference via Non-Local Priors](https://mlsanity.com/paper/2307.10177/) (2023)
- [Overcoming the Domain Gap in Contrastive Learning of Neural Action Representations](https://mlsanity.com/paper/2111.14595/) (2021)
- [Overcoming the Domain Gap in Neural Action Representations](https://mlsanity.com/paper/2112.01176/) (2021)

## Abstract (arXiv)

We present efficient Bayesian methods for extracting neuronal spiking information from calcium imaging data. The goal of our methods is to sample from the posterior distribution of spike trains and model parameters (baseline concentration, spike amplitude etc) given noisy calcium imaging data. We present discrete time algorithms where we sample the existence of a spike at each time bin using Gibbs methods, as well as continuous time algorithms where we sample over the number of spikes and their locations at an arbitrary resolution using Metropolis-Hastings methods for point processes. We provide Rao-Blackwellized extensions that (i) marginalize over several model parameters and (ii) provide smooth estimates of the marginal spike posterior distribution in continuous time. Our methods serve as complements to standard point estimates and allow for quantification of uncertainty in estimating the underlying spike train and model parameters.