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A Simple Method to Simultaneously Detect and Identify Spikes from Raw Extracellular Recordings

Overview of attention for article published in Frontiers in Neuroscience, December 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

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17 X users
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74 Mendeley
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Title
A Simple Method to Simultaneously Detect and Identify Spikes from Raw Extracellular Recordings
Published in
Frontiers in Neuroscience, December 2015
DOI 10.3389/fnins.2015.00452
Pubmed ID
Authors

Panagiotis C. Petrantonakis, Panayiota Poirazi

Abstract

The ability to track when and which neurons fire in the vicinity of an electrode, in an efficient and reliable manner can revolutionize the neuroscience field. The current bottleneck lies in spike sorting algorithms; existing methods for detecting and discriminating the activity of multiple neurons rely on inefficient, multi-step processing of extracellular recordings. In this work, we show that a single-step processing of raw (unfiltered) extracellular signals is sufficient for both the detection and identification of active neurons, thus greatly simplifying and optimizing the spike sorting approach. The efficiency and reliability of our method is demonstrated in both real and simulated data.

X Demographics

X Demographics

The data shown below were collected from the profiles of 17 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 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
France 2 3%
Chile 1 1%
Singapore 1 1%
United Kingdom 1 1%
Unknown 67 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 28%
Researcher 21 28%
Student > Master 8 11%
Student > Doctoral Student 3 4%
Student > Bachelor 3 4%
Other 9 12%
Unknown 9 12%
Readers by discipline Count As %
Neuroscience 18 24%
Agricultural and Biological Sciences 16 22%
Engineering 16 22%
Computer Science 5 7%
Psychology 3 4%
Other 6 8%
Unknown 10 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 21 July 2016.
All research outputs
#4,166,588
of 25,371,288 outputs
Outputs from Frontiers in Neuroscience
#3,431
of 11,538 outputs
Outputs of similar age
#63,912
of 395,281 outputs
Outputs of similar age from Frontiers in Neuroscience
#32
of 136 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,538 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.9. This one has gotten more attention than average, scoring higher than 70% 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 395,281 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 136 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.