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libNeuroML and PyLEMS: using Python to combine procedural and declarative modeling approaches in computational neuroscience
NeuroML is an XML-based model description language, which provides a powerful common data format for defining and exchanging models of neurons and neuronal networks. In the latest version of NeuroML, the structure and behavior of ion channel, synapse, cell, and network model descriptions are based o...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4005938/ https://www.ncbi.nlm.nih.gov/pubmed/24795618 http://dx.doi.org/10.3389/fninf.2014.00038 |
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author | Vella, Michael Cannon, Robert C. Crook, Sharon Davison, Andrew P. Ganapathy, Gautham Robinson, Hugh P. C. Silver, R. Angus Gleeson, Padraig |
author_facet | Vella, Michael Cannon, Robert C. Crook, Sharon Davison, Andrew P. Ganapathy, Gautham Robinson, Hugh P. C. Silver, R. Angus Gleeson, Padraig |
author_sort | Vella, Michael |
collection | PubMed |
description | NeuroML is an XML-based model description language, which provides a powerful common data format for defining and exchanging models of neurons and neuronal networks. In the latest version of NeuroML, the structure and behavior of ion channel, synapse, cell, and network model descriptions are based on underlying definitions provided in LEMS, a domain-independent language for expressing hierarchical mathematical models of physical entities. While declarative approaches for describing models have led to greater exchange of model elements among software tools in computational neuroscience, a frequent criticism of XML-based languages is that they are difficult to work with directly. Here we describe two Application Programming Interfaces (APIs) written in Python (http://www.python.org), which simplify the process of developing and modifying models expressed in NeuroML and LEMS. The libNeuroML API provides a Python object model with a direct mapping to all NeuroML concepts defined by the NeuroML Schema, which facilitates reading and writing the XML equivalents. In addition, it offers a memory-efficient, array-based internal representation, which is useful for handling large-scale connectomics data. The libNeuroML API also includes support for performing common operations that are required when working with NeuroML documents. Access to the LEMS data model is provided by the PyLEMS API, which provides a Python implementation of the LEMS language, including the ability to simulate most models expressed in LEMS. Together, libNeuroML and PyLEMS provide a comprehensive solution for interacting with NeuroML models in a Python environment. |
format | Online Article Text |
id | pubmed-4005938 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-40059382014-05-02 libNeuroML and PyLEMS: using Python to combine procedural and declarative modeling approaches in computational neuroscience Vella, Michael Cannon, Robert C. Crook, Sharon Davison, Andrew P. Ganapathy, Gautham Robinson, Hugh P. C. Silver, R. Angus Gleeson, Padraig Front Neuroinform Neuroscience NeuroML is an XML-based model description language, which provides a powerful common data format for defining and exchanging models of neurons and neuronal networks. In the latest version of NeuroML, the structure and behavior of ion channel, synapse, cell, and network model descriptions are based on underlying definitions provided in LEMS, a domain-independent language for expressing hierarchical mathematical models of physical entities. While declarative approaches for describing models have led to greater exchange of model elements among software tools in computational neuroscience, a frequent criticism of XML-based languages is that they are difficult to work with directly. Here we describe two Application Programming Interfaces (APIs) written in Python (http://www.python.org), which simplify the process of developing and modifying models expressed in NeuroML and LEMS. The libNeuroML API provides a Python object model with a direct mapping to all NeuroML concepts defined by the NeuroML Schema, which facilitates reading and writing the XML equivalents. In addition, it offers a memory-efficient, array-based internal representation, which is useful for handling large-scale connectomics data. The libNeuroML API also includes support for performing common operations that are required when working with NeuroML documents. Access to the LEMS data model is provided by the PyLEMS API, which provides a Python implementation of the LEMS language, including the ability to simulate most models expressed in LEMS. Together, libNeuroML and PyLEMS provide a comprehensive solution for interacting with NeuroML models in a Python environment. Frontiers Media S.A. 2014-04-23 /pmc/articles/PMC4005938/ /pubmed/24795618 http://dx.doi.org/10.3389/fninf.2014.00038 Text en Copyright © 2014 Vella, Cannon, Crook, Davison, Ganapathy, Robinson, Silver and Gleeson. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Vella, Michael Cannon, Robert C. Crook, Sharon Davison, Andrew P. Ganapathy, Gautham Robinson, Hugh P. C. Silver, R. Angus Gleeson, Padraig libNeuroML and PyLEMS: using Python to combine procedural and declarative modeling approaches in computational neuroscience |
title | libNeuroML and PyLEMS: using Python to combine procedural and declarative modeling approaches in computational neuroscience |
title_full | libNeuroML and PyLEMS: using Python to combine procedural and declarative modeling approaches in computational neuroscience |
title_fullStr | libNeuroML and PyLEMS: using Python to combine procedural and declarative modeling approaches in computational neuroscience |
title_full_unstemmed | libNeuroML and PyLEMS: using Python to combine procedural and declarative modeling approaches in computational neuroscience |
title_short | libNeuroML and PyLEMS: using Python to combine procedural and declarative modeling approaches in computational neuroscience |
title_sort | libneuroml and pylems: using python to combine procedural and declarative modeling approaches in computational neuroscience |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4005938/ https://www.ncbi.nlm.nih.gov/pubmed/24795618 http://dx.doi.org/10.3389/fninf.2014.00038 |
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