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The Connection-set Algebra-A Novel Formalism for the Representation of Connectivity Structure in Neuronal Network Models
KTH, School of Computer Science and Communication (CSC), Centres, Centre for High Performance Computing, PDC.
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.

Place, publisher, year, edition, pages
2012. Vol. 10, no 3, 287-304 p.
Keyword [en]
Modeling, Connectivity, Neuronal networks, Computational neuroscience, Software, Formalism
National Category
Computer Science Neurosciences
URN: urn:nbn:se:kth:diva-99500DOI: 10.1007/s12021-012-9146-1ISI: 000305415000005ScopusID: 2-s2.0-84865527851OAI: diva2:542376
Swedish e‐Science Research Center

QC 20120731

Updated from manuscript to article in journal.

Available from: 2012-07-31 Created: 2012-07-31 Last updated: 2013-04-08Bibliographically 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.
Trita-CSC-A, ISSN 1653-5723 ; 2009:06
National Category
Computer Science
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)

QC 20100722

Available from: 2009-06-03 Created: 2009-06-03 Last updated: 2013-04-08Bibliographically approved

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