NEURON and Python

By Michael L. Hines, Andrew P. Davison, Eilif B. Muller. Front. Neuroinform., 2008

388 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Hardware and Architecture

View Paper: https://www.semanticscholar.org/paper/f8821767a2cd223cbdacf174eedd02578bcfc4a3

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Citations in the corpus

Stimfit: quantifying electrophysiological data with Python (2013, methodology): “Python is widely used in neuro-science (Davison et al., 2009) and has been adopted by most popular neural simulation environments, such as NEURON (Hines et al., 2009), NEST (Eppler et al., 2009), and more recently Genesis (Cornelis et al., 2012).”
Nengo: a Python tool for building large-scale functional brain models (2014, methodology): “While NEST and NEURON were not run on multiple cores using message passing, the reference simulator of Nengo also only uses one CPU core.”
Python in neuroscience (2015, methodology): “The range of modeling domains of these simulators is wide, from stochastic simulation of coupled reaction-diﬀusion systems (STEPS), through simulation of morphologically detailed neurons and networks (NEURON, MOOSE), highly-eﬃcient large-scale networks of spiking point neurons (NEST, PCSIM, NCS, Bri”
NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail (2010, methodology): “NEURON allows native import and export of cells in both Level 1 and 2 NeuroML formats”