↓ Skip to main content

NEURON and Python

Overview of attention for article published in Frontiers in Neuroinformatics, January 2009
Altmetric Badge

Citations

dimensions_citation
335 Dimensions

Readers on

mendeley
471 Mendeley
citeulike
2 CiteULike
connotea
1 Connotea
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
NEURON and Python
Published in
Frontiers in Neuroinformatics, January 2009
DOI 10.3389/neuro.11.001.2009
Pubmed ID
Authors

Michael L. Hines, Andrew P. Davison, Eilif Muller

Abstract

The NEURON simulation program now allows Python to be used, alone or in combination with NEURON's traditional Hoc interpreter. Adding Python to NEURON has the immediate benefit of making available a very extensive suite of analysis tools written for engineering and science. It also catalyzes NEURON software development by offering users a modern programming tool that is recognized for its flexibility and power to create and maintain complex programs. At the same time, nothing is lost because all existing models written in Hoc, including graphical user interface tools, continue to work without change and are also available within the Python context. An example of the benefits of Python availability is the use of the xml module in implementing NEURON's Import3D and CellBuild tools to read MorphML and NeuroML model specifications.

Timeline

Login to access the full chart related to this output.

If you don’t have an account, click here to discover Explorer

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 471 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 13 3%
Germany 12 3%
United Kingdom 7 1%
France 6 1%
Japan 4 <1%
Netherlands 3 <1%
Norway 3 <1%
India 3 <1%
Brazil 2 <1%
Other 13 3%
Unknown 405 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 125 27%
Researcher 98 21%
Student > Master 48 10%
Student > Bachelor 37 8%
Professor > Associate Professor 25 5%
Other 75 16%
Unknown 63 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 120 25%
Neuroscience 88 19%
Engineering 71 15%
Computer Science 41 9%
Physics and Astronomy 21 4%
Other 53 11%
Unknown 77 16%