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RatLab: an easy to use tool for place code simulations

Overview of attention for article published in Frontiers in Computational Neuroscience, January 2013
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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 (85th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

blogs
1 blog
twitter
3 X users

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
37 Mendeley
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1 CiteULike
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Title
RatLab: an easy to use tool for place code simulations
Published in
Frontiers in Computational Neuroscience, January 2013
DOI 10.3389/fncom.2013.00104
Pubmed ID
Authors

Fabian Schönfeld, Laurenz Wiskott

Abstract

In this paper we present the RatLab toolkit, a software framework designed to set up and simulate a wide range of studies targeting the encoding of space in rats. It provides open access to our modeling approach to establish place and head direction cells within unknown environments and it offers a set of parameters to allow for the easy construction of a variety of enclosures for a virtual rat as well as controlling its movement pattern over the course of experiments. Once a spatial code is formed RatLab can be used to modify aspects of the enclosure or movement pattern and plot the effect of such modifications on the spatial representation, i.e., place and head direction cell activity. The simulation is based on a hierarchical Slow Feature Analysis (SFA) network that has been shown before to establish a spatial encoding of new environments using visual input data only. RatLab encapsulates such a network, generates the visual training data, and performs all sampling automatically-with each of these stages being further configurable by the user. RatLab was written with the intention to make our SFA model more accessible to the community and to that end features a range of elements to allow for experimentation with the model without the need for specific programming skills.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 5%
France 1 3%
Colombia 1 3%
India 1 3%
Norway 1 3%
Unknown 31 84%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 24%
Researcher 8 22%
Student > Doctoral Student 6 16%
Student > Master 4 11%
Professor 4 11%
Other 5 14%
Unknown 1 3%
Readers by discipline Count As %
Neuroscience 9 24%
Agricultural and Biological Sciences 7 19%
Engineering 6 16%
Computer Science 5 14%
Psychology 3 8%
Other 5 14%
Unknown 2 5%
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 October 2020.
All research outputs
#3,718,965
of 22,715,151 outputs
Outputs from Frontiers in Computational Neuroscience
#178
of 1,336 outputs
Outputs of similar age
#39,540
of 280,748 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#15
of 131 outputs
Altmetric has tracked 22,715,151 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 1,336 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one has done well, scoring higher than 86% 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 280,748 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 85% of its contemporaries.
We're also able to compare this research output to 131 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.