Bayesian spike inference from calcium imaging data

Eftychios A. Pnevmatikakis, Josh Merel, Ari Pakman, Liam Paninski

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

37 Scopus citations

Abstract

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 that the existence of a spike at each time bin using Gibbs methods, as well as continuous time algorithms that 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.

Original languageEnglish
Title of host publicationConference Record of the 47th Asilomar Conference on Signals, Systems and Computers
PublisherInstitute of Electrical and Electronics Engineers
Pages349-353
Number of pages5
ISBN (Print)9781479923908
DOIs
StatePublished - 1 Jan 2013
Externally publishedYes
Event2013 47th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States
Duration: 3 Nov 20136 Nov 2013

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Conference

Conference2013 47th Asilomar Conference on Signals, Systems and Computers
Country/TerritoryUnited States
CityPacific Grove, CA
Period3/11/136/11/13

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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