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Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data

Overview of attention for article published in Neuron, January 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (59th percentile)

Mentioned by

news
1 news outlet
blogs
2 blogs
twitter
27 X users
patent
2 patents

Citations

dimensions_citation
863 Dimensions

Readers on

mendeley
1543 Mendeley
citeulike
4 CiteULike
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Title
Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data
Published in
Neuron, January 2016
DOI 10.1016/j.neuron.2015.11.037
Pubmed ID
Authors

Eftychios A. Pnevmatikakis, Daniel Soudry, Yuanjun Gao, Timothy A. Machado, Josh Merel, David Pfau, Thomas Reardon, Yu Mu, Clay Lacefield, Weijian Yang, Misha Ahrens, Randy Bruno, Thomas M. Jessell, Darcy S. Peterka, Rafael Yuste, Liam Paninski

Abstract

We present a modular approach for analyzing calcium imaging recordings of large neuronal ensembles. Our goal is to simultaneously identify the locations of the neurons, demix spatially overlapping components, and denoise and deconvolve the spiking activity from the slow dynamics of the calcium indicator. Our approach relies on a constrained nonnegative matrix factorization that expresses the spatiotemporal fluorescence activity as the product of a spatial matrix that encodes the spatial footprint of each neuron in the optical field and a temporal matrix that characterizes the calcium concentration of each neuron over time. This framework is combined with a novel constrained deconvolution approach that extracts estimates of neural activity from fluorescence traces, to create a spatiotemporal processing algorithm that requires minimal parameter tuning. We demonstrate the general applicability of our method by applying it to in vitro and in vivo multi-neuronal imaging data, whole-brain light-sheet imaging data, and dendritic imaging data.

X Demographics

X Demographics

The data shown below were collected from the profiles of 27 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1,543 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 20 1%
France 4 <1%
Japan 4 <1%
Germany 3 <1%
Spain 3 <1%
United Kingdom 3 <1%
China 3 <1%
Netherlands 2 <1%
Switzerland 2 <1%
Other 7 <1%
Unknown 1492 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 474 31%
Researcher 315 20%
Student > Master 136 9%
Student > Bachelor 113 7%
Student > Doctoral Student 91 6%
Other 196 13%
Unknown 218 14%
Readers by discipline Count As %
Neuroscience 580 38%
Agricultural and Biological Sciences 313 20%
Engineering 132 9%
Physics and Astronomy 69 4%
Computer Science 47 3%
Other 147 10%
Unknown 255 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 38. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 September 2023.
All research outputs
#1,086,965
of 25,503,365 outputs
Outputs from Neuron
#1,971
of 9,557 outputs
Outputs of similar age
#18,727
of 400,797 outputs
Outputs of similar age from Neuron
#49
of 119 outputs
Altmetric has tracked 25,503,365 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,557 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 33.3. This one has done well, scoring higher than 79% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 400,797 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 119 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 59% of its contemporaries.