Linking computational neuroscience simulation tools—a pragmatic approach to component-based development

Howell, F.; Cannon, Robert C.; Goddard, Nigel; Bringmann, Holger C.; Rogister, P.; Cornelis, Hugo

doi:10.1016/s0925-2312(02)00781-6

Cited by 5 publications

(3 citation statements)

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“…The presented component-based Extension Framework represents a publically available version of a simulator environment Neurodamus developed within the Blue Brain Project (Frye et al, 2006 ). The design of the Extension Framework resembles some of the component-based modularity of the NeoSim project (Goddard et al, 2001 ; Howell et al, 2003 ), but the distinguishing feature is that it does not address the problem of a common model specification, which possibly impedes using state-of-the art functionality of neurosimulators. By allowing models to be specified in the simulator's specification language it allows utilizing a particular simulator's cutting edge feature set while gaining extensibility and tool chain stability.…”

Section: Discussionmentioning

confidence: 99%

“…Previous efforts to provide a standardized component framework for neuronal simulations, namely the NeoSim project (Goddard et al, 2001 ; Howell et al, 2003 ), have proposed to use component-based concepts in neuronal simulations to provide an abstraction of the specific compute engine and allow reusability of components. At the same time the NeoSim project tried to address the common model specification problem through NeuroML.…”

Section: Introductionmentioning

confidence: 99%

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

King

Hines

Hill

et al. 2009

Front. Neuroinform.

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As neuronal simulations approach larger scales with increasing levels of detail, the neurosimulator software represents only a part of a chain of tools ranging from setup, simulation, interaction with virtual environments to analysis and visualizations. Previously published approaches to abstracting simulator engines have not received wide-spread acceptance, which in part may be to the fact that they tried to address the challenge of solving the model specifi cation problem. Here, we present an approach that uses a neurosimulator, in this case NEURON, to describe and instantiate the network model in the simulator's native model language but then replaces the main integration loop with its own. Existing parallel network models are easily adopted to run in the presented framework. The presented approach is thus an extension to NEURON but uses a component-based architecture to allow for replaceable spike exchange components and pluggable components for monitoring, analysis, or control that can run in this framework alongside with the simulation.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

King

Hines

Hill

et al. 2009

Front. Neuroinform.

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“…Many integration efforts have already been made (or are underway) that meet some of the above challenges in more constrained domains-just in Computational Neuroscience, for instance, NSL [26], CATACOMB [27], MOOSE [31], and NEST [32], to name but a few. Our concern, here, is to solve these challenges in such a way that integration can take place more generally; therefore, we do not discuss these more targeted solutions.…”

Section: Related Projectsmentioning

confidence: 99%

BRAHMS: Novel middleware for integrated systems computation

Mitchinson¹,

Chan²,

Chambers³

et al. 2010

Advanced Engineering Informatics

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Biological computational modellers are becoming increasingly interested in building large, eclectic models, including components on many different computational substrates, both biological and non-biological. At the same time, the rise of the philosophy of embodied modelling is generating a need to deploy biological models as controllers for robots in real-world environments. Finally, robotics engineers are beginning to find value in seconding biomimetic control strategies for use on practical robots. Together with the ubiquitous desire to make good on past software development effort, these trends are throwing up new challenges of intellectual and technological integration (for example across scales, across disciplines, and even across time)-challenges that are unmet by existing software frameworks. Here, we outline these challenges in detail, and go on to describe a newly developed software framework, BRAHMS, that meets them. BRAHMS is a tool for integrating computational process modules into a viable, computable system; its generality and flexibility facilitate integration across barriers, such as those described above, in a coherent and effective way. We go on to describe several cases where BRAHMS has been successfully deployed in practical situations. We also show excellent performance in comparison with a monolithic development approach. Additional benefits of developing in the framework include source code self-documentation, automatic coarse-grained parallelisation, cross-language integration, data logging, performance monitoring, and will include dynamic load-balancing and 'pause & continue' execution. BRAHMS is built on the nascent, and similarly general purpose, model markup language, SystemML. This will, in future, also facilitate repeatability and accountability (same answers ten years from now), transparent automatic software distribution, and interfacing with other SystemML tools.

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