Standard Formats
Displaying: 2 Found: 5 Total: 18
NetworkML v1.8.1
NeuroML Version 1.8.1 Level 3 NetworkML
Synopsis
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
Authors
Organizations
Biological Scales
Scale | molecular | cellular | tissue | organ | organism | ecosystem |
---|---|---|---|---|---|---|
Support | unknown | unknown | intrinsic | intrinsic | unknown | unknown |
Spatial Representation
Spatial Representation Level | Compartment | Dimensions | Gradients | Spatial Structures |
---|---|---|---|---|
Support | intrinsic | intrinsic | unknown | intrinsic |
Advantage
- Multiscale Models
Modularity: yes
Components Relation
Flat Network:
no
Supported Math
Unit Support
Unit Required: yes
Support: intrinsic
Description
Annotation Support
Miriram Support: no
identifiers.org Support: no
Description
Links
Specification
Biological
Application
Format
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NetworkML v1.8.1 |
Webpage
Model repository
Software Repository
Specification
NeuroML
Description
Model Description Language for Computational Neuroscience
Derived from
Publication date
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
NeuroML 2 beta 3
NeuroML 2 beta 3
Synopsis
Description
We have developed a compact, hierarchical, XML-based language called LEMS (Low Entropy Model Specification), that can define the structure and dynamics of a wide range of biological models in a fully machine readable format. We describe how LEMS underpins the latest version of NeuroML and show that this framework can define models of ion channels, synapses, neurons and networks. Unit handling, often a source of error when reusing models, is built into the core of the language by specifying physical quantities in models in terms of the base dimensions. We show how LEMS, together with the open source Java and Python based libraries we have developed, facilitates the generation of scripts for multiple neuronal simulators and provides a route for simulator free code generation. We establish that LEMS can be used to define models from systems biology and map them to neuroscience-domain specific simulators, enabling models to be shared between these traditionally separate disciplines. LEMS and NeuroML 2 provide a new, comprehensive framework for defining computational models of neuronal and other biological systems in a machine readable format, making them more reproducible and increasing the transparency and accessibility of their underlying structure and properties.*
*(Cannon RC et al. Frontiers in Neuroinformatics. 2014;8:79. )
Publication Date
Authors
Organizations
Biological Scales
Scale | molecular | cellular | tissue | organ | organism | ecosystem |
---|---|---|---|---|---|---|
Support | intrinsic | intrinsic | intrinsic | intrinsic | unknown | unknown |
Spatial Representation
Spatial Representation Level | Compartment | Dimensions | Gradients | Spatial Structures |
---|---|---|---|---|
Support | intrinsic | intrinsic | potential | intrinsic |
Advantage
- Automated consistency checking
- Multiscale Models
- Translation from Blender
Modularity: yes
Components Relation
Flat Network:
no
Supported Math
MathML Support: no
Full MathML Support: no
Description
Uses own mathematical grammar.
Unit Support
Unit Required: yes
Support: intrinsic
Description
Annotation Support
Biological
Application
Format
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NeuroML 2 beta 3 |
NeuroML
Description
Model Description Language for Computational Neuroscience
Derived from
Publication date
Organizations
Links
Webpage
All formats for this class
not defined
Description
The licence agreement is not defined or could not be obtained.