CosMIC: a consistent metric for spike inference from calcium imaging

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Title: CosMIC: a consistent metric for spike inference from calcium imaging
Authors: Reynolds, SC
Abrahamsson, T
Sjostrom, PJ
Schultz, S
Dragotti, PL
Item Type: Journal Article
Abstract: In recent years, the development of algorithms to detect neuronal spiking activity from two-photon calcium imaging data has received much attention. Meanwhile, few researchers have examined the metrics used to assess the similarity of detected spike trains with the ground truth. We highlight the limitations of the two most commonly used metrics, the spike train correlation and success rate, and propose an alternative, which we refer to as CosMIC. Rather than operating on the true and estimated spike trains directly, the proposed metric assesses the similarity of the pulse trains obtained from convolution of the spike trains with a smoothing pulse. The pulse width, which is derived from the statistics of the imaging data, reflects the temporal tolerance of the metric. The final metric score is the size of the commonalities of the pulse trains as a fraction of their average size. Viewed through the lens of set theory, CosMIC resembles a continuous Sørensen-Dice coefficient — an index commonly used to assess the similarity of discrete, presence/absence data. We demonstrate the ability of the proposed metric to discriminate the precision and recall of spike train estimates. Unlike the spike train correlation, which appears to reward overestimation, the proposed metric score is maximised when the correct number of spikes have been detected. Furthermore, we show that CosMIC is more sensitive to the temporal precision of estimates than the success rate.
Issue Date: 1-Oct-2018
Date of Acceptance: 26-Apr-2018
URI: http://hdl.handle.net/10044/1/60305
DOI: https://dx.doi.org/10.1162/neco_a_01114
ISSN: 0899-7667
Publisher: Massachusetts Institute of Technology Press (MIT Press)
Start Page: 2726
End Page: 2756
Journal / Book Title: Neural Computation
Volume: 30
Issue: 10
Copyright Statement: © 2018 by the Massachusetts Institute of Technology. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
Sponsor/Funder: Commission of the European Communities
Biotechnology and Biological Sciences Research Council (BBSRC)
Commission of the European Communities
The Royal Society
The Michael Uren Foundation
National Institutes of Health
Funder's Grant Number: 277800
BB/K001817/1
289146
IF110059
N/A
UPMC: C15/0244
Keywords: Science & Technology
Technology
Life Sciences & Biomedicine
Computer Science, Artificial Intelligence
Neurosciences
Computer Science
Neurosciences & Neurology
CELLULAR RESOLUTION
IN-VIVO
DECONVOLUTION
CORTEX
Artificial Intelligence & Image Processing
MD Multidisciplinary
Publication Status: Published
Online Publication Date: 2018-09-24
Appears in Collections:Faculty of Engineering
Bioengineering
Electrical and Electronic Engineering



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