Model-based decoupling of evoked and spontaneous neural activity in calcium imaging data

Marcus A. Triplett, Zac Pujic, Biao Sun, Lilach Avitan, Geoffrey J. Goodhill*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

The pattern of neural activity evoked by a stimulus can be substantially affected by ongoing spontaneous activity. Separating these two types of activity is particularly important for calcium imaging data given the slow temporal dynamics of calcium indicators. Here we present a statistical model that decouples stimulus-driven activity from low dimensional spontaneous activity in this case. The model identifies hidden factors giving rise to spontaneous activity while jointly estimating stimulus tuning properties that account for the confounding effects that these factors introduce. By applying our model to data from zebrafish optic tectum and mouse visual cortex, we obtain quantitative measurements of the extent that neurons in each case are driven by evoked activity, spontaneous activity, and their interaction. By not averaging away potentially important information encoded in spontaneous activity, this broadly applicable model brings new insight into population-level neural activity within single trials.

Original languageAmerican English
Article numbere1008330
JournalPLoS Computational Biology
Volume16
Issue number11
DOIs
StatePublished - 30 Nov 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2020 Triplett et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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