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Spike-Based Reinforcement Learning in Continuous State and Action Space: When Policy Gradient Methods Fail

Overview of attention for article published in PLoS Computational Biology, December 2009
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Citations

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Title
Spike-Based Reinforcement Learning in Continuous State and Action Space: When Policy Gradient Methods Fail
Published in
PLoS Computational Biology, December 2009
DOI 10.1371/journal.pcbi.1000586
Pubmed ID
Authors

Eleni Vasilaki, Nicolas Frémaux, Robert Urbanczik, Walter Senn, Wulfram Gerstner

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X Demographics

The data shown below were collected from the profile of 1 X user 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 258 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 11 4%
Switzerland 8 3%
Germany 7 3%
France 4 2%
Canada 4 2%
United States 3 1%
Netherlands 2 <1%
Japan 2 <1%
Iran, Islamic Republic of 1 <1%
Other 2 <1%
Unknown 214 83%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 77 30%
Researcher 53 21%
Student > Master 35 14%
Professor 14 5%
Professor > Associate Professor 12 5%
Other 31 12%
Unknown 36 14%
Readers by discipline Count As %
Computer Science 62 24%
Agricultural and Biological Sciences 47 18%
Engineering 33 13%
Neuroscience 28 11%
Physics and Astronomy 16 6%
Other 30 12%
Unknown 42 16%