000903876 001__ 903876
000903876 005__ 20220131120411.0
000903876 0247_ $$2doi$$a10.12751/NNCN.BC2020.0272
000903876 037__ $$aFZJ-2021-05509
000903876 041__ $$aEnglish
000903876 1001_ $$0P:(DE-Juel1)179447$$avan der Vlag, Michiel$$b0$$eCorresponding author$$ufzj
000903876 1112_ $$aBernstein Conference$$cOnline$$d2020-09-29 - 2020-10-01$$wGermany
000903876 245__ $$aRateML, a spin-off of the NeuroML and LEMS Domain Specific Languages, tailored to generate rate-based-models suited for simulators such as the Virtual Brain (TVB) featuring high performance computing and parameter sweep capabilities.
000903876 260__ $$c2020
000903876 3367_ $$033$$2EndNote$$aConference Paper
000903876 3367_ $$2BibTeX$$aINPROCEEDINGS
000903876 3367_ $$2DRIVER$$aconferenceObject
000903876 3367_ $$2ORCID$$aCONFERENCE_POSTER
000903876 3367_ $$2DataCite$$aOutput Types/Conference Poster
000903876 3367_ $$0PUB:(DE-HGF)24$$2PUB:(DE-HGF)$$aPoster$$bposter$$mposter$$s1640074464_1957$$xOther
000903876 520__ $$aWith this poster we present RateML, a spin-off of the NeuroML and LEMS Domain Specific Languages, tailored to generate rate-based-models suited for simulators such as the Virtual Brain (TVB) featuring high performance computing and parameter sweep capabilities. RateML has been developed to abstract modelling from the implementation or deployment on hardware of the to-be-simulated TVB brain model. Using RateML, code can be produced targeting different target languages and exploiting the computational capabilities of specific computing paradigms and hardware.RateML is based on the existing domain specific language 'LEMS'. Low Entropy Model Specification (LEMS) is an XML based language for specifying generic models of hybrid dynamical systems which extends a sibling language, ‘NeuroML’ (NeuroML), by providing representations for variation in cell dynamics in time; in other words, equations for cell dynamics. It enables users to generate rate-based brain models from an XML file, in which the generic features of TVB models can be addressed without needing extended knowledge regarding the optimal programming or simulation of such models. In figure 1 an example of a Kuramoto model in XML is shown.A TVB simulation often entails the exploration of many parameters to fit the simulated dynamics to empirical data e.g. EEG/MRI data. These big data exploration simulations are best aided by a high-performance compute solution. As well as regular (Python) TVB model generation, CUDA code can be generated in which certain variables can be designated with a specific range for parameter exploration. Such extensive parameter exploration can then be executed with a high degree of parallelization on a GPU. For example, for a brain model with 68 nodes, in a single kernel invocation on a V100 GPU, it is possible to simulate roughly 30,000 parallel instances using the Kuramoto model exploring the combinations of 173 coupling and 173 speed parameters in seconds. For the Epileptor, a model which is very memory demanding due to the fact that it has 6 state variables, roughly 5,000 parameter combinations can be explored in a single kernel. The models used in these experiments are generated by RateML.Thus, RateML can produce a) Python code compatible with the TVB framework, b) CUDA code which can be run directly on GPUs to perform high performance parameter fitting, and c) in the future code for Bayesian inversion.
000903876 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
000903876 536__ $$0G:(DE-Juel1)Helmholtz-SLNS$$aSLNS - SimLab Neuroscience (Helmholtz-SLNS)$$cHelmholtz-SLNS$$x1
000903876 588__ $$aDataset connected to DataCite
000903876 650_7 $$2Other$$aComputational Neuroscience
000903876 650_7 $$2Other$$aNeurons, networks, dynamical systems
000903876 7001_ $$0P:(DE-Juel1)165859$$aDiaz, Sandra$$b1$$ufzj
000903876 7001_ $$0P:(DE-HGF)0$$aWoodman, Marmaduje$$b2
000903876 7001_ $$0P:(DE-HGF)0$$aFousek, Jan$$b3
000903876 7001_ $$0P:(DE-Juel1)161525$$aPeyser, Alexander$$b4$$ufzj
000903876 7001_ $$0P:(DE-HGF)0$$aJirsa, Viktor$$b5
000903876 773__ $$a10.12751/NNCN.BC2020.0272
000903876 909CO $$ooai:juser.fz-juelich.de:903876$$pVDB
000903876 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)179447$$aForschungszentrum Jülich$$b0$$kFZJ
000903876 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)165859$$aForschungszentrum Jülich$$b1$$kFZJ
000903876 9101_ $$0I:(DE-588b)1043886400$$6P:(DE-HGF)0$$aAix-Marseille Université$$b2$$kAMU
000903876 9101_ $$0I:(DE-588b)1043886400$$6P:(DE-HGF)0$$aAix-Marseille Université$$b3$$kAMU
000903876 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)161525$$aForschungszentrum Jülich$$b4$$kFZJ
000903876 9101_ $$0I:(DE-588b)1043886400$$6P:(DE-HGF)0$$aAix-Marseille Université$$b5$$kAMU
000903876 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5111$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
000903876 9141_ $$y2021
000903876 920__ $$lyes
000903876 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
000903876 980__ $$aposter
000903876 980__ $$aVDB
000903876 980__ $$aI:(DE-Juel1)JSC-20090406
000903876 980__ $$aUNRESTRICTED