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The Connection-set Algebra-A Novel Formalism for the Representation of Connectivity Structure in Neuronal Network Models
KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Parallelldatorcentrum, PDC.
2012 (engelsk)Inngår i: Neuroinformatics, ISSN 1539-2791, E-ISSN 1559-0089, Vol. 10, nr 3, s. 287-304Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
2012. Vol. 10, nr 3, s. 287-304
Emneord [en]
Modeling, Connectivity, Neuronal networks, Computational neuroscience, Software, Formalism
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-99500DOI: 10.1007/s12021-012-9146-1ISI: 000305415000005PubMedID: 22437992Scopus ID: 2-s2.0-84865527851OAI: oai:DiVA.org:kth-99500DiVA, id: diva2:542376
Forskningsfinansiär
Swedish e‐Science Research Center
Merknad

QC 20120731

Updated from manuscript to article in journal.

Tilgjengelig fra: 2012-07-31 Laget: 2012-07-31 Sist oppdatert: 2020-03-09bibliografisk kontrollert
Inngår i avhandling
1. Large-scale simulation of neuronal systems
Åpne denne publikasjonen i ny fane eller vindu >>Large-scale simulation of neuronal systems
2009 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Stockholm: KTH, 2009. s. xii, 65
Serie
Trita-CSC-A, ISSN 1653-5723 ; 2009:06
HSV kategori
Identifikatorer
urn:nbn:se:kth:diva-10616 (URN)978-91-7415-323-1 (ISBN)
Disputas
2009-06-09, Sal F2, KTH, Lindstedtsvägen 26, Stockholm, 10:00 (engelsk)
Opponent
Veileder
Merknad

QC 20100722

Tilgjengelig fra: 2009-06-03 Laget: 2009-06-03 Sist oppdatert: 2018-01-13bibliografisk kontrollert

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