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A model of categorization, learning of invariant representations and sequence prediction utilizing top-down activity
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
(English)Manuscript (preprint) (Other academic)
National Category
Bioinformatics (Computational Biology)
URN: urn:nbn:se:kth:diva-122189OAI: diva2:621256

QS 2013

Available from: 2013-05-14 Created: 2013-05-14 Last updated: 2013-05-15Bibliographically approved
In thesis
1. On large-scale neural simulations and applications in neuroinformatics
Open this publication in new window or tab >>On large-scale neural simulations and applications in neuroinformatics
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
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.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2013. vi, 66 p.
Trita-CSC-A, ISSN 1653-5723 ; 2013:06
National Category
Computer Science
urn:nbn:se:kth:diva-122190 (URN)978-91-7501-776-1 (ISBN)
Public defence
2013-06-03, F3, Lindstedtsvägen 26, KTH, Stockholm, 13:00 (English)

QC 20130515

Available from: 2013-05-15 Created: 2013-05-14 Last updated: 2013-05-15Bibliographically approved

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Benjaminsson, SimonLundqvist, Mikael
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