A Component-Based Extension Framework for Large-Scale Parallel Simulations in NEURON

King, James G.; Hines, Michael L.; Hill, Sean; Goodman, Philip H.; Markram, Henry; Schürmann, Felix

doi:10.3389/neuro.11.010.2009

Cited by 19 publications

(13 citation statements)

References 23 publications

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“…The algorithms, numerics and others, are generally published, peer-reviewed and extensively tested by users, providing increased confidence in their validity. Most of these simulators also allow an intermediate approach to be used when developing novel models that do not clearly fit into the existing categories provided by that simulator; one can integrate custom code written in a general-purpose language into a simulation described in the overlaying simulator [25]. …”

Section: Neuroscience Simulatorsmentioning

confidence: 99%

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: Neuroscience Simulatorsmentioning

confidence: 99%

Reproducibility in Computational Neuroscience Models and Simulations

McDougal

Bulanova

Lytton

2016

IEEE Trans. Biomed. Eng.

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“…(2009). This framework provides three APIs, one for a compute engine , exemplified by a specially compiled version of NEURON (Carnevale and Hines 2006), a message-bus component , allowing the encapsulation of a spike communication algorithm, and, a monitoring, analysis and control component .…”

Section: Discussionmentioning

confidence: 99%

Run-Time Interoperability Between Neuronal Network Simulators Based on the MUSIC Framework

et al. 2010

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MUSIC is a standard API allowing large scale neuron simulators to exchange data within a parallel computer during runtime. A pilot implementation of this API has been released as open source. We provide experiences from the implementation of MUSIC interfaces for two neuronal network simulators of different kinds, NEST and MOOSE. A multi-simulation of a cortico-striatal network model involving both simulators is performed, demonstrating how MUSIC can promote inter-operability between models written for different simulators and how these can be re-used to build a larger model system. Benchmarks show that the MUSIC pilot implementation provides efficient data transfer in a cluster computer with good scaling. We conclude that MUSIC fulfills the design goal that it should be simple to adapt existing simulators to use MUSIC. In addition, since the MUSIC API enforces independence of the applications, the multi-simulation could be built from pluggable component modules without adaptation of the components to each other in terms of simulation time-step or topology of connections between the modules.Electronic Supplementary Material The online version of this article (doi:10.1007/s12021-010-9064-z) contains supplementary material, which is available to authorized users.

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“…A large literature deals with dynamics in physiological systems including cardiac and neural dynamics [60][61][62][63]. Although the heart and brain have incredible robustness in an individual and across species, the theoretical underpinnings of such robustness are still not well understood.…”

Section: Conclusion: Extensions and Open Questionsmentioning

confidence: 99%