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Large-scale simulation of neuronal systems
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
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: urn:nbn:se:kth:diva-10616ISBN: 978-91-7415-323-1 (print)OAI: oai:DiVA.org:kth-10616DiVA: diva2:221070
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
List of papers
1. See-A framework for simulation of biologically detailed and artificial neural networks and systems
Open this publication in new window or tab >>See-A framework for simulation of biologically detailed and artificial neural networks and systems
1999 (English)In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 26-27, 997-1003 p.Article in journal (Refereed) Published
Abstract [en]

See is a software framework for simulation of biologically detailed and artficial neural networks and systems. It includes a general purpose scripting language, based on Scheme,which also can be used interactively, while the basic framework is written in C++. Models can be built on the Scheme level from `simulation objectsa, each representing a population ofneurons, a projection, etc. The simulator provides a flexible and efficient protocol for data transfer between such objects. See contains a user interface to the parallelized, platformindependent, library SPLIT intended for biologically detailed modeling of large-scale networks and is easy to extend with new user code, both on the C++ and Scheme levels.

Keyword
simulator, neural networks, C++, scheme, object orientation, modularity
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:kth:diva-10486 (URN)10.1016/S0925-2312(99)00096-X (DOI)000081462700127 ()
Available from: 2009-05-18 Created: 2009-05-18 Last updated: 2017-12-13Bibliographically approved
2. The Connection-set Algebra-A Novel Formalism for the Representation of Connectivity Structure in Neuronal Network Models
Open this publication in new window or tab >>The Connection-set Algebra-A Novel Formalism for the Representation of Connectivity Structure in Neuronal Network Models
2012 (English)In: Neuroinformatics, ISSN 1539-2791, E-ISSN 1559-0089, Vol. 10, no 3, 287-304 p.Article in journal (Refereed) Published
Abstract [en]

The connection-set algebra (CSA) is a novel and general formalism for the description of connectivity in neuronal network models, from small-scale to large-scale structure. The algebra provides operators to form more complex sets of connections from simpler ones and also provides parameterization of such sets. CSA is expressive enough to describe a wide range of connection patterns, including multiple types of random and/or geometrically dependent connectivity, and can serve as a concise notation for network structure in scientific writing. CSA implementations allow for scalable and efficient representation of connectivity in parallel neuronal network simulators and could even allow for avoiding explicit representation of connections in computer memory. The expressiveness of CSA makes prototyping of network structure easy. A C+ + version of the algebra has been implemented and used in a large-scale neuronal network simulation (Djurfeldt et al., IBM J Res Dev 52(1/2):31-42, 2008b) and an implementation in Python has been publicly released.

Keyword
Modeling, Connectivity, Neuronal networks, Computational neuroscience, Software, Formalism
National Category
Computer Science Neurosciences
Identifiers
urn:nbn:se:kth:diva-99500 (URN)10.1007/s12021-012-9146-1 (DOI)000305415000005 ()2-s2.0-84865527851 (Scopus ID)
Funder
Swedish e‐Science Research Center
Note

QC 20120731

Updated from manuscript to article in journal.

Available from: 2012-07-31 Created: 2012-07-31 Last updated: 2017-12-07Bibliographically approved
3. Large-scale modeling - a tool for conquering the complexity of the brain
Open this publication in new window or tab >>Large-scale modeling - a tool for conquering the complexity of the brain
2008 (English)In: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 2, 1-4 p.Article in journal (Refereed) Published
Abstract [en]

Is there any hope of achieving a thorough understanding of higher functions such as perception, memory, thought and emotion or is the stunning complexity of the brain a barrier which will limit such efforts for the foreseeable future? In this perspective we discuss methods to handle complexity, approaches to model building, and point to detailed large-scale models as a new contribution to the toolbox of the computational neuroscientist. We elucidate some aspects which distinguishes large-scale models and some of the technological challenges which they entail.

Keyword
modeling methodology, large-scale model, simulation, parallel computing, brain, cortex, computational neuroscience, subsampling
National Category
Neurosciences
Identifiers
urn:nbn:se:kth:diva-10456 (URN)10.3389/neuro.11.001.2008 (DOI)18974793 (PubMedID)2-s2.0-67650327576 (Scopus ID)
Note
QC 20100708Available from: 2009-05-15 Created: 2009-05-15 Last updated: 2017-12-13Bibliographically approved
4. Run-Time Interoperability Between Neuronal Network Simulators Based on the MUSIC Framework
Open this publication in new window or tab >>Run-Time Interoperability Between Neuronal Network Simulators Based on the MUSIC Framework
Show others...
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.

Keyword
MUSIC, Large-scale simulation, Computer simulation, Computational neuroscience, Neuronal network models, Inter-operability, MPI, Parallel processing
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:kth:diva-10487 (URN)10.1007/s12021-010-9064-z (DOI)000276344300006 ()20195795 (PubMedID)2-s2.0-77953106373 (Scopus ID)
Funder
Swedish e‐Science Research Center
Note
Uppdaterad till artikel 20100709 QC 20100709Available from: 2009-05-18 Created: 2009-05-18 Last updated: 2017-12-13Bibliographically approved
5. Massively parallel simulation of brain-scale neuronal network models
Open this publication in new window or tab >>Massively parallel simulation of brain-scale neuronal network models
Show others...
2005 (English)Report (Other academic)
Publisher
26 p.
Series
Trita-NA-P, 0513
National Category
Computer Science
Identifiers
urn:nbn:se:kth:diva-10606 (URN)
Note
QC 20100709Available from: 2009-06-02 Created: 2009-06-02 Last updated: 2012-03-21Bibliographically approved
6. Brain-scale simulation of the neocortex on the IBM Blue Gene/L  supercomputer
Open this publication in new window or tab >>Brain-scale simulation of the neocortex on the IBM Blue Gene/L  supercomputer
Show others...
2008 (English)In: IBM Journal of Research and Development, ISSN 0018-8646, E-ISSN 2151-8556, Vol. 52, no 1-2, 31-41 p.Article in journal (Refereed) Published
Abstract [en]

Biologically detailed large-scale models of the brain can now be simulated thanks to increasingly powerful massively parallel supercomputers. We present an overview, for the general technical reader, of a neuronal network model of layers II/III of the neocortex built with biophysical model neurons. These simulations, carried out on an IBM Blue Gene/Le supercomputer, comprise up to 22 million neurons and 11 billion synapses, which makes them the largest simulations of this type ever performed. Such model sizes correspond to the cortex of a small mammal. The SPLIT library, used for these simulations, runs on single-processor as well as massively parallel machines. Performance measurements show good scaling behavior on the Blue Gene/L supercomputer up to 8,192 processors. Several key phenomena seen in the living brain appear as emergent phenomena in the simulations. We discuss the role of this kind of model in neuroscience and note that full-scale models may be necessary to preserve natural dynamics. We also discuss the need for software tools for the specification of models as well as for analysis and visualization of output data. Combining models that range from abstract connectionist type to biophysically detailed will help us unravel the basic principles underlying neocortical function.

National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:kth:diva-10492 (URN)10.1147/rd.521.0031 (DOI)000253014700005 ()2-s2.0-40849124518 (Scopus ID)
Note
QC 20100709Available from: 2009-05-19 Created: 2009-05-19 Last updated: 2017-12-13Bibliographically approved
7. Attractor dynamics in a modular network model of neocortex
Open this publication in new window or tab >>Attractor dynamics in a modular network model of neocortex
2006 (English)In: Network, ISSN 0954-898X, E-ISSN 1361-6536, Network: Computation in Neural Systems, Vol. 17, no 3, 253-276 p.Article in journal (Refereed) Published
Abstract [en]

Starting from the hypothesis that the mammalian neocortex to a first approximation functions as an associative memory of the attractor network type, we formulate a quantitative computational model of neocortical layers 2/3. The model employs biophysically detailed multi-compartmental model neurons with conductance based synapses and includes pyramidal cells and two types of inhibitory interneurons, i.e., regular spiking non-pyramidal cells and basket cells. The simulated network has a minicolumnar as well as a hypercolumnar modular structure and we propose that minicolumns rather than single cells are the basic computational units in neocortex. The minicolumns are represented in full scale and synaptic input to the different types of model neurons is carefully matched to reproduce experimentally measured values and to allow a quantitative reproduction of single cell recordings. Several key phenomena seen experimentally in vitro and in vivo appear as emergent features of this model. It exhibits a robust and fast attractor dynamics with pattern completion and pattern rivalry and it suggests an explanation for the so-called attentional blink phenomenon. During assembly dynamics, the model faithfully reproduces several features of local UP states, as they have been experimentally observed in vitro, as well as oscillatory behavior similar to that observed in the neocortex.

Keyword
cortex, UP State, attentional blink, attractor dynamics, synchronization
National Category
Neurosciences
Identifiers
urn:nbn:se:kth:diva-6310 (URN)10.1080/09548980600774619 (DOI)000244140900003 ()2-s2.0-33845421947 (Scopus ID)
Note

QC 20150729

Available from: 2006-11-01 Created: 2006-11-01 Last updated: 2017-12-14Bibliographically approved

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Citation style
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  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
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  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
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  • asciidoc
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