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Run-Time Interoperability Between Neuronal Network Simulators Based on the MUSIC Framework
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
Honda Research Institute.
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2010 (English)In: Neuroinformatics, ISSN 1539-2791, E-ISSN 1559-0089, Vol. 8, no 1, 43-60 p.Article in journal (Refereed) Published
Abstract [en]

MUSIC is an API allowing large scale neuron simulators using MPI internally to exchange data during runtime. We provide experiences from the adaptation of two neuronal network simulators of different kinds, NEST and MOOSE, to this API. 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. We conclude that MUSIC fulfills the design goals of being portable and simple to adapt to existing simulators. In addition, since the MUSIC API enforces independence between the applications, the multi-simulationcould 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.

Place, publisher, year, edition, pages
2010. Vol. 8, no 1, 43-60 p.
Keyword [en]
MUSIC, Large-scale simulation, Computer simulation, Computational neuroscience, Neuronal network models, Inter-operability, MPI, Parallel processing
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:kth:diva-10487DOI: 10.1007/s12021-010-9064-zISI: 000276344300006PubMedID: 20195795Scopus ID: 2-s2.0-77953106373OAI: oai:DiVA.org:kth-10487DiVA: diva2:218055
Funder
Swedish e‐Science Research Center
Note
Uppdaterad till artikel 20100709 QC 20100709Available from: 2009-05-18 Created: 2009-05-18 Last updated: 2012-05-22Bibliographically approved
In thesis
1. Large-scale simulation of neuronal systems
Open this publication in new window or tab >>Large-scale simulation of neuronal systems
2009 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Biologically detailed computational models of large-scale neuronal networks have now become feasible due to the development of increasingly powerful massively parallel supercomputers. We report here about the methodology involved in simulation of very large neuronal networks. Using conductance-based multicompartmental model neurons based on Hodgkin-Huxley formalism, we simulate a neuronal network model of layers II/III of the neocortex. These simulations, the largest of this type ever performed, were made on the Blue Gene/L supercomputer and comprised up to 8 million neurons and 4 billion synapses. Such model sizes correspond to the cortex of a small mammal. After a series of optimization steps, performance measurements show linear scaling behavior both on the Blue Gene/L supercomputer and on a more conventional cluster computer. Results from the simulation of a model based on more abstract formalism, and of considerably larger size, also shows linear scaling behavior on both computer architectures.

Place, publisher, year, edition, pages
Stockholm: KTH, 2009. xii, 65 p.
Series
Trita-CSC-A, ISSN 1653-5723 ; 2009:06
National Category
Computer Science
Identifiers
urn:nbn:se:kth:diva-10616 (URN)978-91-7415-323-1 (ISBN)
Public defence
2009-06-09, Sal F2, KTH, Lindstedtsvägen 26, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 20100722

Available from: 2009-06-03 Created: 2009-06-03 Last updated: 2013-04-08Bibliographically approved
2. Computer Modelling of Neuronal Interactions in the Striatum
Open this publication in new window or tab >>Computer Modelling of Neuronal Interactions in the Striatum
2009 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Large parts of the cortex and the thalamus project into the striatum,which serves as the input stage of the basal ganglia. Information isintegrated in the striatal neural network and then passed on, via themedium spiny (MS) projection neurons, to the output stages of thebasal ganglia. In addition to the MS neurons there are also severaltypes of interneurons in the striatum, such as the fast spiking (FS)interneurons. I focused my research on the FS neurons, which formstrong inhibitory synapses onto the MS neurons. These striatal FSneurons are sparsely connected by electrical synapses (gap junctions),which are commonly presumed to synchronise their activity.Computational modelling with the GENESIS simulator was used toinvestigate the effect of gap junctions on a network of synapticallydriven striatal FS neurons. The simulations predicted a reduction infiring frequency dependent on the correlation between synaptic inputsto the neighbouring neurons, but only a slight synchronisation. Thegap junction effects on modelled FS neurons showing sub-thresholdoscillations and stuttering behaviour confirm these results andfurther indicate that hyperpolarising inputs might regulate the onsetof stuttering.The interactions between MS and FS neurons were investigated byincluding a computer model of the MS neuron. The hypothesis was thatdistal GABAergic input would lower the amplitude of back propagatingaction potentials, thereby reducing the calcium influx in thedendrites. The model verified this and further predicted that proximalGABAergic input controls spike timing, but not the amplitude ofdendritic calcium influx after initiation.Connecting models of neurons written in different simulators intonetworks raised technical problems which were resolved by integratingthe simulators within the MUSIC framework. This thesis discusses theissues encountered by using this implementation and gives instructionsfor modifying MOOSE scripts to use MUSIC and provides guidelines forachieving compatibility between MUSIC and other simulators.This work sheds light on the interactions between striatal FS and MSneurons. The quantitative results presented could be used to developa large scale striatal network model in the future, which would beapplicable to both the healthy and pathological striatum.

Place, publisher, year, edition, pages
Stockholm: KTH, 2009. 79 p.
Series
Trita-CSC-A, ISSN 1653-5723 ; 2009:08
Keyword
striatum, fast spiking interneurons, medium spiny projection neurons, gap junctions, interoperability, MUSIC
National Category
Computer Science
Identifiers
urn:nbn:se:kth:diva-10523 (URN)978-91-7415-331-6 (ISBN)
Public defence
2009-06-11, Svedbergssalen (FD5), Roslagstullsbacken 21, Alba Nova, 09:00 (English)
Opponent
Supervisors
Note
QC 20100720Available from: 2009-06-03 Created: 2009-05-20 Last updated: 2010-07-20Bibliographically approved

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Hellgren Kotaleski, JeanetteEkeberg, Örjan

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