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A Novel Multiple Objective Optimization Framework for Constraining Conductance-Based Neuron Models by Experimental Data

Overview of attention for article published in Frontiers in Neuroscience, October 2007
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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1 patent
wikipedia
1 Wikipedia page

Citations

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275 Dimensions

Readers on

mendeley
303 Mendeley
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4 CiteULike
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Title
A Novel Multiple Objective Optimization Framework for Constraining Conductance-Based Neuron Models by Experimental Data
Published in
Frontiers in Neuroscience, October 2007
DOI 10.3389/neuro.01.1.1.001.2007
Pubmed ID
Authors

Shaul Druckmann, Yoav Banitt, Albert Gidon, Felix Schürmann, Henry Markram, Idan Segev

Abstract

We present a novel framework for automatically constraining parameters of compartmental models of neurons, given a large set of experimentally measured responses of these neurons. In experiments, intrinsic noise gives rise to a large variability (e.g., in firing pattern) in the voltage responses to repetitions of the exact same input. Thus, the common approach of fitting models by attempting to perfectly replicate, point by point, a single chosen trace out of the spectrum of variable responses does not seem to do justice to the data. In addition, finding a single error function that faithfully characterizes the distance between two spiking traces is not a trivial pursuit. To address these issues, one can adopt a multiple objective optimization approach that allows the use of several error functions jointly. When more than one error function is available, the comparison between experimental voltage traces and model response can be performed on the basis of individual features of interest (e.g., spike rate, spike width). Each feature can be compared between model and experimental mean, in units of its experimental variability, thereby incorporating into the fitting this variability. We demonstrate the success of this approach, when used in conjunction with genetic algorithm optimization, in generating an excellent fit between model behavior and the firing pattern of two distinct electrical classes of cortical interneurons, accommodating and fast-spiking. We argue that the multiple, diverse models generated by this method could serve as the building blocks for the realistic simulation of large neuronal networks.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 303 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 9 3%
United Kingdom 6 2%
France 5 2%
Germany 4 1%
Switzerland 3 <1%
Israel 3 <1%
Sweden 2 <1%
Japan 2 <1%
Brazil 1 <1%
Other 10 3%
Unknown 258 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 86 28%
Researcher 64 21%
Student > Master 40 13%
Professor > Associate Professor 19 6%
Student > Bachelor 17 6%
Other 50 17%
Unknown 27 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 92 30%
Neuroscience 63 21%
Engineering 36 12%
Computer Science 35 12%
Physics and Astronomy 12 4%
Other 27 9%
Unknown 38 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 14 November 2023.
All research outputs
#5,447,195
of 25,374,917 outputs
Outputs from Frontiers in Neuroscience
#4,099
of 11,541 outputs
Outputs of similar age
#15,936
of 84,133 outputs
Outputs of similar age from Frontiers in Neuroscience
#5
of 9 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,541 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 64% 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 84,133 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.