Rxivist logo

A zero-inflated gamma model for post-deconvolved calcium imaging traces

By Xue-Xin Wei, Ding Zhou, Andres Grosmark, Zaki Ajabi, Fraser Sparks, Pengcheng Zhou, Mark Brandon, Attila Losonczy, Liam Paninski

Posted 14 May 2019
bioRxiv DOI: 10.1101/637652

Calcium imaging is a critical tool for measuring the activity of large neural populations. Much effort has been devoted to developing ``pre-processing" tools applied to calcium video data, addressing the important issues of e.g., motion correction, denoising, compression, demixing, and deconvolution. However, computational modeling of deconvolved calcium signals (i.e., the estimated activity extracted by a pre-processing pipeline) is just as critical for interpreting calcium measurements. Surprisingly, these issues have to date received significantly less attention. To fill this gap, we examine the statistical properties of the deconvolved activity estimates, and propose several density models for these random signals. These models include a zero-inflated gamma (ZIG) model, which characterizes the calcium responses as a mixture of a gamma distribution and a point mass which serves to model zero responses. We apply the resulting models to neural encoding and decoding problems. We find that the ZIG model outperforms simpler models (e.g., Poisson or Bernoulli models) in the context of both simulated and real neural data, and can therefore play a useful role in bridging calcium imaging analysis methods with tools for analyzing activity in large neural populations.

Download data

  • Downloaded 715 times
  • Download rankings, all-time:
    • Site-wide: 20,602 out of 94,912
    • In neuroscience: 3,388 out of 16,862
  • Year to date:
    • Site-wide: 14,830 out of 94,912
  • Since beginning of last month:
    • Site-wide: 12,554 out of 94,912

Altmetric data


Downloads over time

Distribution of downloads per paper, site-wide


PanLingua

Sign up for the Rxivist weekly newsletter! (Click here for more details.)


News