Home > Research > Publications & Outputs > Fast Nonconvex Deconvolution of Calcium Imaging...

Electronic data

Links

Text available via DOI:

View graph of relations

Fast Nonconvex Deconvolution of Calcium Imaging Data

Research output: Contribution to journalJournal articlepeer-review

E-pub ahead of print
Close
<mark>Journal publication date</mark>8/02/2019
<mark>Journal</mark>Biostatistics
Issue number4
Volume21
Publication StatusE-pub ahead of print
<mark>Original language</mark>English

Abstract

Calcium imaging data promises to transform the field of neuroscience by making it possible to record from large populations of neurons simultaneously. However, determining the exact moment in time at which a neuron spikes, from a calcium imaging data set, amounts to a non-trivial deconvolution problem which is of critical importance for downstream analyses. While a number of formulations have been proposed for this task in the recent literature, in this paper we focus on a formulation recently proposed in Jewell and Witten (2018. Exact spike train inference via L0 optimization. Ann. Appl. Statist. 12(4), 2457–2482) that can accurately estimate not just the spike rate, but also the specific times at which the neuron spikes. We develop a much faster algorithm that can be used to deconvolve a fluorescence trace of 100,000 timesteps in less than a second. Furthermore, we present a modification to this algorithm that precludes the possibility of a “negative spike”. We demonstrate the performance of this algorithm for spike deconvolution on calcium imaging datasets that were recently released as part of the spikefinder challenge (http://spikefinder.codeneuro.org/). The algorithm presented in this paper was used in the Allen Institute for Brain Science’s “platform paper” to decode neural activity from the Allen Brain Observatory; this is the main scientific paper in which their data resource is presented. Our C++ implementation, along with R and python wrappers, is publicly available. R code is available on CRAN and Github, and python wrappers are available on Github; see https://github.com/jewellsean/FastLZeroSpikeInference.