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See-A framework for simulation of biologically detailed and artificial neural networks and systems
KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.ORCID iD: 0000-0002-2792-1622
KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.ORCID iD: 0000-0002-2358-7815
1999 (English)In: Neurocomputing, ISSN 0925-2312, 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.

Place, publisher, year, edition, pages
1999. Vol. 26-27, 997-1003 p.
Keyword [en]
simulator, neural networks, C++, scheme, object orientation, modularity
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:kth:diva-10486DOI: 10.1016/S0925-2312(99)00096-XISI: 000081462700127OAI: oai:DiVA.org:kth-10486DiVA: diva2:218054
Available from: 2009-05-18 Created: 2009-05-18 Last updated: 2011-09-21Bibliographically 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

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Ekeberg, Örjan

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