Standard Formats

Displaying: 2 Found: 5 Total: 18


NetworkML v1.8.1


NeuroML Version 1.8.1 Level 3 NetworkML

Synopsis

Describing cell placement and network connectivity.

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 unknown unknown intrinsic intrinsic unknown unknown

Spatial Representation

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

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

Description

length units requiered

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

NeuroML 2 beta 3


NeuroML 2 beta 3

Synopsis

NeuroML 2 is a LEMS (Low Entropy Model Specification) based format, that can define models of ion channels, synapses, neurons and networks.

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

09/2014

Authors

Gleeson, Padraig
C. Cannon, Robert
Crook, Sharon
Silver, Angus
Ganapathy, Gautham
Marin, Boris
Piasini, Eugenio

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

Modeling Formalisms for this format

Software support for this format

Examples for this format


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

Intrinsic unit checking

Annotation Support


Java API for NeuroML 2


Programming language

Java

Links

API

PyLEMS


Programming language

Python

Links

API

libNeuroML


Programming language

Python

Links

jNeuroML


Programming language

Java

Links

Software

jNeuroML


NeuroML


Description

Model Description Language for Computational Neuroscience

Derived from

XML, LEMS

Publication date

08/2001

Organizations

Links

Webpage

All formats for this class

not defined


Description

The licence agreement is not defined or could not be obtained.