Standard Formats

Displaying: 3 Found: 18 Total: 18


Biophysics v1.8.1


NeuroML Version 1.8.1 Level 2 Biophysics

Synopsis

Defining biophysical properties of cells.

Description

Biologically detailed single neuron and network models are important for understanding how ion channels, synapses and anatomical connectivity underlie the complex electrical behavior of the brain. [...] We have used an Open Source software approach to develop NeuroML, a neuronal model description language based on XML (Extensible Markup Language). This enables these detailed models and their components to be defined in a standalone form, allowing them to be used across multiple simulators and archived in a standardized format. Here we describe the structure of NeuroML and demonstrate its scope by converting into NeuroML models of a number of different voltage- and ligand-gated conductances, models of electrical coupling, synaptic transmission and short-term plasticity, together with morphologically detailed models of individual neurons. We have also used these NeuroML-based components to develop an highly detailed cortical network model. NeuroML-based model descriptions were validated by demonstrating similar model behavior across five independently developed simulators. Although our results confirm that simulations run on different simulators converge, they reveal limits to model interoperability, by showing that for some models convergence only occurs at high levels of spatial and temporal discretisation, when the computational overhead is high. Our development of NeuroML as a common description language for biophysically detailed neuronal and network models enables interoperability across multiple simulation environments, thereby improving model transparency, accessibility and reuse in computational neuroscience.*

*(Gleeson P et al. PLoS Computational Biology. 2010;6(6))

Publication Date

06/2009

Authors

Gleeson, Padraig
C. Cannon, Robert
Crook, Sharon
L. Hines, Michael
O. Billings, Guy
Farinella, Matteo
M. Morse, Thomas
P. Davison, Andrew
Ray, Subhasis
S. Bhalla, Upinder
R. Barnes, Simon
D. Dimitrova, Yoana
Silver, Angus

Organizations


Biological Scales

Scale molecular cellular tissue organ organism ecosystem
Support intrinsic intrinsic unknown unknown unknown unknown

Spatial Representation

Spatial Representation Level Compartment Dimensions Gradients Spatial Structures
Support unknown unknown unknown unknown

Modeling Formalisms for this format

Software support for this format

Examples for this format


Advantage

  • Multiscale Models

Modularity: yes


Components Relation
Flat Network: no


Supported Math


Unit Support

Unit Required: yes

Support: intrinsic


Annotation Support

Miriram Support: no

identifiers.org Support: no

Description

Any kind of XML elements can be used inside the annotation element. Metadata has its own specification.

Links

Specification


There are no transformations available!

neuroml-api


Programming language

Java

Links

jNeuroML


Programming language

Java

Links

Software

NeuroML


Validation Portal

NeuroML


Description

Model Description Language for Computational Neuroscience

Derived from

XML, LEMS

Publication date

08/2001

Organizations

Links

Webpage

All formats for this class

GNU General Public License, version 2


Description

Free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation.

For detailed information see webpage.

Links

CellML 1.1


CellML 1.1

Synopsis

CellML describes models as a network of components, representing abstract concepts, that are connected through well-defined Interfaces.

Description

CellML is an XML-based exchange format developed by the University of Auckland in collaboration with Physiome Sciences, Inc. CellML 1.1 has a component-based architecture allowing a modeller to build complex systems of models that expand and reuse previously published models. CellML Metadata is a format for encoding contextual information for a model. CellML 1.1 can be used in conjunction with CellML Metadata to provide a complete description of the structure and underlying mathematics of biological models. A repository of over 200 electrophysiological, mechanical, signal transduction, and metabolic pathway models is available at www.cellml.org.*

*( Autumn A. Cuellar et al. SIMULATION December 2003 79: 740-747, doi:10.1177/0037549703040939 )

Publication Date

11/2012

Authors

Hunter, Peter
A. Cuellar, Autumn
M. Lloyd, Catherine
F. Nielsen, Poul
P. Bullivant, David
P. Nickerson, David

Organizations


Biological Scales

Scale molecular cellular tissue organ organism ecosystem
Support potential potential potential potential unknown potential

Spatial Representation

Spatial Representation Level Compartment Dimensions Gradients Spatial Structures
Support intrinsic unknown unknown unknown

Modeling Formalisms for this format

Software support for this format

Examples for this format


Advantage

  • Model components reusability
  • Multiscale Models

Modularity: yes


Components Relation
Flat Network: yes


Supported Math

MathML Support: yes

Full MathML Support: no


Unit Support

Unit Required: yes

Support: intrinsic


Annotation Support

Miriram Support: yes

identifiers.org Support: yes

Description

Metadata specification is declared modulary and is not part of the main specification. Actual version is 2.0.

Links

Specification


CellML API


Programming language

C++

Links

API

CellML API - Java


Programming language

Java

Links

API

CellML API - Python


Programming language

Python

Links

API

OpenCell


CellML


Description

CellML describes mathematical models as a network of reusable components. The components harbor variables that change due to underling mathematical equations. The elements of CellML describe low level concepts, therefore CellML can be used in variety of not only biological topics.

Derived from

XML

Publication date

08/2001

Organizations

Links

Webpage

All formats for this class

CellML free to use license


Description

The following are the official terms of use for the CellML language:

Individuals may:

  • (a) freely use, publish, and redistribute the CellML Format and documentation
  • (b) write and sell applications which create, load, or write CellML-valid XML files
  • (c) distribute or sell their own CellML-valid XML files
  • (d) transmit verbatim copies of the CellML Format and documentation to any person, without restriction.

 

Links

License

ChannelML v1.8.1


NeuroML Version 1.8.1 Level 2 ChannelML

Synopsis

Description of the properties of channels.

Description

Biologically detailed single neuron and network models are important for understanding how ion channels, synapses and anatomical connectivity underlie the complex electrical behavior of the brain. [...] We have used an Open Source software approach to develop NeuroML, a neuronal model description language based on XML (Extensible Markup Language). This enables these detailed models and their components to be defined in a standalone form, allowing them to be used across multiple simulators and archived in a standardized format. Here we describe the structure of NeuroML and demonstrate its scope by converting into NeuroML models of a number of different voltage- and ligand-gated conductances, models of electrical coupling, synaptic transmission and short-term plasticity, together with morphologically detailed models of individual neurons. We have also used these NeuroML-based components to develop an highly detailed cortical network model. NeuroML-based model descriptions were validated by demonstrating similar model behavior across five independently developed simulators. Although our results confirm that simulations run on different simulators converge, they reveal limits to model interoperability, by showing that for some models convergence only occurs at high levels of spatial and temporal discretisation, when the computational overhead is high. Our development of NeuroML as a common description language for biophysically detailed neuronal and network models enables interoperability across multiple simulation environments, thereby improving model transparency, accessibility and reuse in computational neuroscience.*

*(Gleeson P et al. PLoS Computational Biology. 2010;6(6))

Publication Date

06/2001

Authors

Gleeson, Padraig
C. Cannon, Robert
Crook, Sharon
L. Hines, Michael
O. Billings, Guy
Farinella, Matteo
M. Morse, Thomas
P. Davison, Andrew
Ray, Subhasis
S. Bhalla, Upinder
R. Barnes, Simon
D. Dimitrova, Yoana
Silver, Angus

Organizations


Biological Scales

Scale molecular cellular tissue organ organism ecosystem
Support intrinsic intrinsic unknown unknown unknown unknown

Spatial Representation

Spatial Representation Level Compartment Dimensions Gradients Spatial Structures
Support unknown unknown unknown unknown

Modeling Formalisms for this format

Software support for this format

Examples for this format


Advantage

  • Multiscale Models

Modularity: yes


Components Relation
Flat Network: no


Supported Math

MathML Support: no

Full MathML Support: no

Description

Own mathematical definitions in form of higher level abstractions. Or a freeform expression


Unit Support

Unit Required: yes

Support: intrinsic


Annotation Support

Miriram Support: no

identifiers.org Support: no

Description

Any kind of XML elements can be used inside the annotation element. Metadata has its own specification.

Links

Specification


There are no transformations available!

neuroml-api


Programming language

Java

Links

jNeuroML


Programming language

Java

Links

Software

NeuroML


Validation Portal

NeuroML


Description

Model Description Language for Computational Neuroscience

Derived from

XML, LEMS

Publication date

08/2001

Organizations

Links

Webpage

All formats for this class

GNU General Public License, version 2


Description

Free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation.

For detailed information see webpage.

Links