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Odour discrimination and mixture segmentation in a holistic model of the mammalian olfactory system
KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.ORCID-id: 0000-0001-6553-823X
KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.ORCID-id: 0000-0002-2358-7815
(engelsk)Manuskript (preprint) (Annet vitenskapelig)
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-122188OAI: oai:DiVA.org:kth-122188DiVA, id: diva2:621254
Merknad

QS 2013

Tilgjengelig fra: 2013-05-14 Laget: 2013-05-14 Sist oppdatert: 2018-01-11bibliografisk kontrollert
Inngår i avhandling
1. On large-scale neural simulations and applications in neuroinformatics
Åpne denne publikasjonen i ny fane eller vindu >>On large-scale neural simulations and applications in neuroinformatics
2013 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

This thesis consists of three parts related to the in silico study of the brain: technologies for large-scale neural simulations, neural algorithms and models and applications in large-scale data analysis in neuroinformatics. All parts rely on the use of supercomputers.

A large-scale neural simulator is developed where techniques are explored for the simulation, analysis and visualization of neural systems on a high biological abstraction level. The performance of the simulator is investigated on some of the largest supercomputers available.

Neural algorithms and models on a high biological abstraction level are presented and simulated. Firstly, an algorithm for structural plasticity is suggested which can set up connectivity and response properties of neural units from the statistics of the incoming sensory data. This can be used to construct biologically inspired hierarchical sensory pathways. Secondly, a model of the mammalian olfactory system is presented where we suggest a mechanism for mixture segmentation based on adaptation in the olfactory cortex. Thirdly, a hierarchical model is presented which uses top-down activity to shape sensory representations and which can encode temporal history in the spatial representations of populations.

Brain-inspired algorithms and methods are applied to two neuroinformatics applications involving large-scale data analysis. In the first application, we present a way to extract resting-state networks from functional magnetic resonance imaging (fMRI) resting-state data where the final extraction step is computationally inexpensive, allowing for rapid exploration of the statistics in large datasets and their visualization on different spatial scales. In the second application, a method to estimate the radioactivity level in arterial plasma from segmented blood vessels from positron emission tomography (PET) images is presented. The method outperforms previously reported methods to a degree where it can partly remove the need for invasive arterial cannulation and continuous sampling of arterial blood during PET imaging.

In conclusion, this thesis provides insights into technologies for the simulation of large-scale neural models on supercomputers, their use to study mechanisms for the formation of neural representations and functions in hierarchical sensory pathways using models on a high biological abstraction level and the use of large-scale, fine-grained data analysis in neuroinformatics applications.

sted, utgiver, år, opplag, sider
Stockholm: KTH Royal Institute of Technology, 2013. s. vi, 66
Serie
TRITA-CSC-A, ISSN 1653-5723 ; 2013:06
HSV kategori
Identifikatorer
urn:nbn:se:kth:diva-122190 (URN)978-91-7501-776-1 (ISBN)
Disputas
2013-06-03, F3, Lindstedtsvägen 26, KTH, Stockholm, 13:00 (engelsk)
Opponent
Veileder
Merknad

QC 20130515

Tilgjengelig fra: 2013-05-15 Laget: 2013-05-14 Sist oppdatert: 2018-01-11bibliografisk kontrollert

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Herman, Pawel

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