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Neural system prediction and identification challenge

Overview of attention for article published in Frontiers in Neuroinformatics, January 2013
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (51st percentile)

Mentioned by

twitter
2 tweeters
googleplus
1 Google+ user

Citations

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3 Dimensions

Readers on

mendeley
28 Mendeley
citeulike
1 CiteULike
Title
Neural system prediction and identification challenge
Published in
Frontiers in Neuroinformatics, January 2013
DOI 10.3389/fninf.2013.00043
Pubmed ID
Authors

Ioannis Vlachos, Yury V. Zaytsev, Sebastian Spreizer, Ad Aertsen, Arvind Kumar

Abstract

Can we infer the function of a biological neural network (BNN) if we know the connectivity and activity of all its constituent neurons?This question is at the core of neuroscience and, accordingly, various methods have been developed to record the activity and connectivity of as many neurons as possible. Surprisingly, there is no theoretical or computational demonstration that neuronal activity and connectivity are indeed sufficient to infer the function of a BNN. Therefore, we pose the Neural Systems Identification and Prediction Challenge (nuSPIC). We provide the connectivity and activity of all neurons and invite participants (1) to infer the functions implemented (hard-wired) in spiking neural networks (SNNs) by stimulating and recording the activity of neurons and, (2) to implement predefined mathematical/biological functions using SNNs. The nuSPICs can be accessed via a web-interface to the NEST simulator and the user is not required to know any specific programming language. Furthermore, the nuSPICs can be used as a teaching tool. Finally, nuSPICs use the crowd-sourcing model to address scientific issues. With this computational approach we aim to identify which functions can be inferred by systematic recordings of neuronal activity and connectivity. In addition, nuSPICs will help the design and application of new experimental paradigms based on the structure of the SNN and the presumed function which is to be discovered.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 7%
India 1 4%
United States 1 4%
France 1 4%
Unknown 23 82%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 25%
Researcher 6 21%
Student > Doctoral Student 3 11%
Professor > Associate Professor 2 7%
Professor 2 7%
Other 4 14%
Unknown 4 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 21%
Engineering 6 21%
Neuroscience 5 18%
Computer Science 2 7%
Psychology 1 4%
Other 3 11%
Unknown 5 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 June 2016.
All research outputs
#11,580,929
of 20,942,040 outputs
Outputs from Frontiers in Neuroinformatics
#360
of 664 outputs
Outputs of similar age
#143,796
of 301,284 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#24
of 31 outputs
Altmetric has tracked 20,942,040 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 664 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.1. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
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 301,284 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 51% of its contemporaries.
We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.