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On large-scale neural simulations and applications in neuroinformatics
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.ORCID iD: 0000-0001-6553-823X
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.
Series
TRITA-CSC-A, ISSN 1653-5723 ; 2013:06
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-122190ISBN: 978-91-7501-776-1 (print)OAI: oai:DiVA.org:kth-122190DiVA: diva2:621260
Public defence
2013-06-03, F3, Lindstedtsvägen 26, KTH, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

QC 20130515

Available from: 2013-05-15 Created: 2013-05-14 Last updated: 2017-08-15Bibliographically approved
List of papers
1. Nexa: A scalable neural simulator with integrated analysis
Open this publication in new window or tab >>Nexa: A scalable neural simulator with integrated analysis
2012 (English)In: Network, ISSN 0954-898X, E-ISSN 1361-6536, Vol. 23, no 4, 254-271 p.Article in journal (Refereed) Published
Abstract [en]

Large-scale neural simulations encompass challenges in simulator design, data handling and understanding of simulation output. As the computational power of supercomputers and the size of network models increase, these challenges become even more pronounced. Here we introduce the experimental scalable neural simulator Nexa, for parallel simulation of large-scale neural network models at a high level of biological abstraction and for exploration of the simulation methods involved. It includes firing-rate models and capabilities to build networks using machine learning inspired methods for e. g. self-organization of network architecture and for structural plasticity. We show scalability up to the size of the largest machines currently available for a number of model scenarios. We further demonstrate simulator integration with online analysis and real-time visualization as scalable solutions for the data handling challenges.

Keyword
Network models, simulation technology
National Category
Neurosciences Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:kth:diva-104537 (URN)000311837300009 ()23116128 (PubMedID)2-s2.0-84870666881 (Scopus ID)
Funder
Swedish Research Council, VR-621-2009-3807VINNOVASwedish Foundation for Strategic Research Swedish e‐Science Research Center
Note

QC 20121112

Available from: 2012-11-05 Created: 2012-11-05 Last updated: 2017-04-28Bibliographically approved
2. From ANN to Biomimetic Information Processing
Open this publication in new window or tab >>From ANN to Biomimetic Information Processing
2009 (English)In: BIOLOGICALLY INSPIRED SIGNAL PROCESSING FOR CHEMICAL SENSING / [ed] Gutierrez A, Marco S, 2009, Vol. 188, 33-43 p.Conference paper, Published paper (Refereed)
Abstract [en]

Artificial neural networks (ANN) are useful components in today's data analysis toolbox. They were initially inspired by the brain but are today accepted to be quite different from it. ANN typically lack scalability and mostly rely on supervised learning, both of which are biologically implausible features. Here we describe and evaluate a novel cortex-inspired hybrid algorithm. It is found to perform on par with a Support Vector Machine (SVM) in classification of activation patterns from the rat olfactory bulb. On-line unsupervised learning is shown to provide significant tolerance to sensor drift, an important property of algorithms used to analyze chemo-sensor data. Scalability of the approach is illustrated on the MNIST dataset of handwritten digits.

Series
Studies in Computational Intelligence, ISSN 1860-949X
National Category
Computer Science
Identifiers
urn:nbn:se:kth:diva-30835 (URN)10.1007/978-3-642-00176-5_2 (DOI)000266719500002 ()2-s2.0-62549146707 (Scopus ID)978-3-642-00175-8 (ISBN)
Conference
OSPEL Workshop on Bio-inspired Signal Processing Barcelona, SPAIN, 2007
Note
QC 20110310Available from: 2011-03-10 Created: 2011-03-04 Last updated: 2013-05-15Bibliographically approved
3. Odour discrimination and mixture segmentation in a holistic model of the mammalian olfactory system
Open this publication in new window or tab >>Odour discrimination and mixture segmentation in a holistic model of the mammalian olfactory system
(English)Manuscript (preprint) (Other academic)
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:kth:diva-122188 (URN)
Note

QS 2013

Available from: 2013-05-14 Created: 2013-05-14 Last updated: 2013-05-15Bibliographically approved
4. A model of categorization, learning of invariant representations and sequence prediction utilizing top-down activity
Open this publication in new window or tab >>A model of categorization, learning of invariant representations and sequence prediction utilizing top-down activity
(English)Manuscript (preprint) (Other academic)
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:kth:diva-122189 (URN)
Note

QS 2013

Available from: 2013-05-14 Created: 2013-05-14 Last updated: 2013-05-15Bibliographically approved
5. A Novel Model-Free Data Analysis Technique Based on Clustering in a Mutual Information Space: Application to Resting-State fMRI
Open this publication in new window or tab >>A Novel Model-Free Data Analysis Technique Based on Clustering in a Mutual Information Space: Application to Resting-State fMRI
2010 (English)In: Frontiers in Systems Neuroscience, ISSN 1662-5137, E-ISSN 1662-5137, Vol. 4, 34:1-34:8 p.Article in journal (Refereed) Published
Abstract [en]

Non-parametric data-driven analysis techniques can be used to study datasets with few assumptions about the data and underlying experiment. Variations of independent component analysis (ICA) have been the methods mostly used on fMRI data, e.g., in finding resting-state networks thought to reflect the connectivity of the brain. Here we present a novel data analysis technique and demonstrate it on resting-state fMRI data. It is a generic method with few underlying assumptions about the data. The results are built from the statistical relations between all input voxels, resulting in a whole-brain analysis on a voxel level. It has good scalability properties and the parallel implementation is capable of handling large datasets and databases. From the mutual information between the activities of the voxels over time, a distance matrix is created for all voxels in the input space. Multidimensional scaling is used to put the voxels in a lower-dimensional space reflecting the dependency relations based on the distance matrix. By performing clustering in this space we can find the strong statistical regularities in the data, which for the resting-state data turns out to be the resting-state networks. The decomposition is performed in the last step of the algorithm and is computationally simple. This opens up for rapid analysis and visualization of the data on different spatial levels, as well as automatically finding a suitable number of decomposition components.

Keyword
Clustering, Data analysis, Functional magnetic resonance imaging, Mutual information, Parallel algorithm, Resting-state
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:kth:diva-52478 (URN)10.3389/fnsys.2010.00034 (DOI)2-s2.0-79957813632 (Scopus ID)
Note
QC 20111221Available from: 2011-12-21 Created: 2011-12-18 Last updated: 2017-12-08Bibliographically approved
6. Arterial input function derived from pairwise correlations between PET-image voxels
Open this publication in new window or tab >>Arterial input function derived from pairwise correlations between PET-image voxels
Show others...
2013 (English)In: Journal of Cerebral Blood Flow and Metabolism, ISSN 0271-678X, E-ISSN 1559-7016, Vol. 33, no 7, 1058-1065 p.Article in journal (Refereed) Published
Abstract [en]

A metabolite corrected arterial input function is a prerequisite for quantification of positron emission tomography (PET) data by compartmental analysis. This quantitative approach is also necessary for radioligands without suitable reference regions in brain. The measurement is laborious and requires cannulation of a peripheral artery, a procedure that can be associated with patient discomfort and potential adverse events. A non invasive procedure for obtaining the arterial input function is thus preferable. In this study, we present a novel method to obtain image-derived input functions (IDIFs). The method is based on calculation of the Pearson correlation coefficient between the time-activity curves of voxel pairs in the PET image to localize voxels displaying blood-like behavior. The method was evaluated using data obtained in human studies with the radioligands [11C]flumazenil and [11C]AZ10419369, and its performance was compared with three previously published methods. The distribution volumes (VT) obtained using IDIFs were compared with those obtained using traditional arterial measurements. Overall, the agreement in VT was good (~3% difference) for input functions obtained using the pairwise correlation approach. This approach performed similarly or even better than the other methods, and could be considered in applied clinical studies. Applications to other radioligands are needed for further verification.

Place, publisher, year, edition, pages
Nature Publishing Group, 2013
Keyword
HRRT, image-derived input function, PET, pharmacokinetic modeling, voxel correlation
National Category
Neurology
Identifiers
urn:nbn:se:kth:diva-122186 (URN)10.1038/jcbfm.2013.47 (DOI)000321185800011 ()2-s2.0-84880326272 (Scopus ID)
Note

QC 20130515

Available from: 2013-05-14 Created: 2013-05-14 Last updated: 2017-12-06Bibliographically approved

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