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Run-Time Interoperability Between Neuronal Network Simulators Based on the MUSIC Framework
KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
Honda Research Institute.
Visa övriga samt affilieringar
2010 (Engelska)Ingår i: Neuroinformatics, ISSN 1539-2791, E-ISSN 1559-0089, Vol. 8, nr 1, s. 43-60Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
2010. Vol. 8, nr 1, s. 43-60
Nyckelord [en]
MUSIC, Large-scale simulation, Computer simulation, Computational neuroscience, Neuronal network models, Inter-operability, MPI, Parallel processing
Nationell ämneskategori
Bioinformatik (beräkningsbiologi)
Identifikatorer
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, id: diva2:218055
Forskningsfinansiär
Swedish e‐Science Research Center
Anmärkning
Uppdaterad till artikel 20100709 QC 20100709Tillgänglig från: 2009-05-18 Skapad: 2009-05-18 Senast uppdaterad: 2018-01-13Bibliografiskt granskad
Ingår i avhandling
1. Large-scale simulation of neuronal systems
Öppna denna publikation i ny flik eller fönster >>Large-scale simulation of neuronal systems
2009 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Stockholm: KTH, 2009. s. xii, 65
Serie
Trita-CSC-A, ISSN 1653-5723 ; 2009:06
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:kth:diva-10616 (URN)978-91-7415-323-1 (ISBN)
Disputation
2009-06-09, Sal F2, KTH, Lindstedtsvägen 26, Stockholm, 10:00 (Engelska)
Opponent
Handledare
Anmärkning

QC 20100722

Tillgänglig från: 2009-06-03 Skapad: 2009-06-03 Senast uppdaterad: 2018-01-13Bibliografiskt granskad
2. Computer Modelling of Neuronal Interactions in the Striatum
Öppna denna publikation i ny flik eller fönster >>Computer Modelling of Neuronal Interactions in the Striatum
2009 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Stockholm: KTH, 2009. s. 79
Serie
Trita-CSC-A, ISSN 1653-5723 ; 2009:08
Nyckelord
striatum, fast spiking interneurons, medium spiny projection neurons, gap junctions, interoperability, MUSIC
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:kth:diva-10523 (URN)978-91-7415-331-6 (ISBN)
Disputation
2009-06-11, Svedbergssalen (FD5), Roslagstullsbacken 21, Alba Nova, 09:00 (Engelska)
Opponent
Handledare
Anmärkning
QC 20100720Tillgänglig från: 2009-06-03 Skapad: 2009-05-20 Senast uppdaterad: 2018-01-13Bibliografiskt granskad

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Djurfeldt, MikaelHjorth, JohannesHellgren Kotaleski, JeanetteEkeberg, Örjan
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