libNeuroML and PyLEMS: using Python to combine procedural and declarative modeling approaches in computational neuroscience

Vella, M.C.; Cannon, Robert C.; Crook, Sharon; Davison, Andrew P.; Ganapathy, Gautham; Robinson, Hugh P. C.; Silver, R. Angus; Gleeson, Padraig

doi:10.3389/fninf.2014.00038

Cited by 29 publications

(27 citation statements)

References 27 publications

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“…NeuroML is serializable as either XML or JSON [106]. In NeuroML version 1, channel kinetics was limited to a set of predefined forms; version 2, currently in beta, introduces Low Entropy Model Specification (LEMS) [90], which allows modelers to define their own kinetic forms.…”

Section: Declarative Model Descriptionsmentioning

confidence: 99%

“…In NeuroML version 1, channel kinetics was limited to a set of predefined forms; version 2, currently in beta, introduces Low Entropy Model Specification (LEMS) [90], which allows modelers to define their own kinetic forms. It continues to provide a reference set of channel types [106]. To reduce the risk of misinterpreting models, the NeuroML standard requires all units to be explicitly specified [106].…”

Section: Declarative Model Descriptionsmentioning

confidence: 99%

“…It continues to provide a reference set of channel types [106]. To reduce the risk of misinterpreting models, the NeuroML standard requires all units to be explicitly specified [106]. The NeuroML group provides a number of tools to simplify working with NeuroML, including libNeuroML and PyLEMS [106].…”

Section: Declarative Model Descriptionsmentioning

confidence: 99%

“…To reduce the risk of misinterpreting models, the NeuroML standard requires all units to be explicitly specified [106]. The NeuroML group provides a number of tools to simplify working with NeuroML, including libNeuroML and PyLEMS [106]. Many simulators and other analysis tools provide at least partial support for NeuroML (a list is provided at www.neuroml.org/tool support).…”

Section: Declarative Model Descriptionsmentioning

confidence: 99%

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Reproducibility in Computational Neuroscience Models and Simulations

McDougal

Bulanova

Lytton

2016

IEEE Trans. Biomed. Eng.

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Objective Like all scientific research, computational neuroscience research must be reproducible. Big data science, including simulation research, cannot depend exclusively on journal articles as the method to provide the sharing and transparency required for reproducibility. Methods Ensuring model reproducibility requires the use of multiple standard software practices and tools, including version control, strong commenting and documentation, and code modularity. Results Building on these standard practices, model sharing sites and tools have been developed that fit into several categories: 1. standardized neural simulators, 2. shared computational resources, 3. declarative model descriptors, ontologies and standardized annotations; 4. model sharing repositories and sharing standards. Conclusion A number of complementary innovations have been proposed to enhance sharing, transparency and reproducibility. The individual user can be encouraged to make use of version control, commenting, documentation and modularity in development of models. The community can help by requiring model sharing as a condition of publication and funding. Significance Model management will become increasingly important as multiscale models become larger, more detailed and correspondingly more difficult to manage by any single investigator or single laboratory. Additional big data management complexity will come as the models become more useful in interpreting experiments, thus increasing the need to ensure clear alignment between modeling data, both parameters and results, and experiment.

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Section: Declarative Model Descriptionsmentioning

confidence: 99%

Section: Declarative Model Descriptionsmentioning

confidence: 99%

Section: Declarative Model Descriptionsmentioning

confidence: 99%

Section: Declarative Model Descriptionsmentioning

confidence: 99%

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Reproducibility in Computational Neuroscience Models and Simulations

McDougal

Bulanova

Lytton

2016

IEEE Trans. Biomed. Eng.

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“…These include: natively parsing and simulating models specified in LEMS (including point neuron cell models/networks in NeuroML); converting NeuroML models to simulator-specific code (for currently supported simulators see Suppl. 53 . pyNeuroML ( https://github.com/NeuroML/pyNeuroML ) is a Python package which builds on libNeuroML and bundles a copy of jNeuroML, allowing access to all of its functionality from Python scripts (most importantly converting NeuroML models to simulator code, running them and reloading the results).…”

Section: Neuroml 2 and Lems Librariesmentioning

confidence: 99%

Open Source Brain: a collaborative resource for visualizing, analyzing, simulating and developing standardized models of neurons and circuits

Gleeson

Carandini

Marin

et al. 2018

Preprint

Self Cite

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Computational models are powerful tools for investigating brain function in health and disease. However, biologically detailed neuronal and circuit models are complex and implemented in a range of specialized languages, making them inaccessible and opaque to many neuroscientists. This has limited critical evaluation of models by the scientific community and impeded their refinement and widespread adoption. To address this, we have combined advances in standardizing models, open source software development and web technologies to develop Open Source Brain, a platform for visualizing, simulating, disseminating and collaboratively developing standardized models of neurons and circuits from a range of brain regions. Model structure and parameters can be visualized and their dynamical properties explored through browser-controlled simulations, without writing code. Open Source Brain makes neural models transparent and accessible and facilitates testing, critical evaluation and refinement, thereby helping to improve the accuracy and reproducibility of models, and their dissemination to the wider community.

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