May 28, 2020 Software Open Access

Linssen, Charl; Morrison, Abigail; Eppler, Jochen Martin

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  <identifier identifierType="DOI">10.5281/zenodo.3822082</identifier>
  <creators>
    <creator>
      <creatorName>Linssen, Charl</creatorName>
      <givenName>Charl</givenName>
      <familyName>Linssen</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-8140-2866</nameIdentifier>
      <affiliation>Simulation Lab Neuroscience, Institute for Advanced Simulation, JARA, Forschungszentrum Jülich, Germany</affiliation>
    </creator>
    <creator>
      <creatorName>Morrison, Abigail</creatorName>
      <givenName>Abigail</givenName>
      <familyName>Morrison</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-6933-797X</nameIdentifier>
      <affiliation>Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), Jülich Aachen Research Alliance BRAIN Institute I, Forschungszentrum Jülich, Germany</affiliation>
    </creator>
    <creator>
      <creatorName>Eppler, Jochen Martin</creatorName>
      <givenName>Jochen Martin</givenName>
      <familyName>Eppler</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-3145-3040</nameIdentifier>
      <affiliation>Simulation Lab Neuroscience, Institute for Advanced Simulation, JARA, Forschungszentrum Jülich, Germany</affiliation>
    </creator>
  </creators>
  <titles>
    <title>ODE-toolbox: Automatic selection and generation of integration schemes for systems of ordinary differential equations</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>differential equation</subject>
    <subject>ODE</subject>
    <subject>numerical integration</subject>
    <subject>simulation</subject>
    <subject>solver</subject>
    <subject>propagator matrix</subject>
    <subject>symbolic analysis</subject>
    <subject>dynamic system</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-05-28</date>
  </dates>
  <resourceType resourceTypeGeneral="Software"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3822082</alternateIdentifier>
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  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsCitedBy" resourceTypeGeneral="Software">10.5281/zenodo.1412608</relatedIdentifier>
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  <version>2.0</version>
  <rightsList>
    <rights rightsURI="https://opensource.org/licenses/GPL-2.0">GNU General Public License v2.0 only</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Choosing the optimal solver for systems of ordinary differential equations (ODEs) is a critical step in dynamical systems simulation. ODE-toolbox is a Python package that assists in solver benchmarking, and recommends solvers on the basis of a set of user-configurable heuristics. For all dynamical equations that admit an analytic solution, ODE-toolbox generates propagator matrices that allow the solution to be calculated at machine precision. For all others, first-order update expressions are returned based on the Jacobian matrix.&lt;/p&gt;

&lt;p&gt;In addition to continuous dynamics, discrete events can be used to model instantaneous changes in system state, such as a neuronal action potential. These can be generated by the system under test, as well as applied as external stimuli, making ODE-toolbox particularly well-suited for applications in computational neuroscience.&lt;/p&gt;</description>
  </descriptions>
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