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On large-scale neural simulations and applications in neuroinformatics
KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.ORCID-id: 0000-0001-6553-823X
2013 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Stockholm: KTH Royal Institute of Technology, 2013. , s. vi, 66
Serie
TRITA-CSC-A, ISSN 1653-5723 ; 2013:06
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:kth:diva-122190ISBN: 978-91-7501-776-1 (tryckt)OAI: oai:DiVA.org:kth-122190DiVA, id: diva2:621260
Disputation
2013-06-03, F3, Lindstedtsvägen 26, KTH, Stockholm, 13:00 (Engelska)
Opponent
Handledare
Anmärkning

QC 20130515

Tillgänglig från: 2013-05-15 Skapad: 2013-05-14 Senast uppdaterad: 2018-01-11Bibliografiskt granskad
Delarbeten
1. Nexa: A scalable neural simulator with integrated analysis
Öppna denna publikation i ny flik eller fönster >>Nexa: A scalable neural simulator with integrated analysis
2012 (Engelska)Ingår i: Network, ISSN 0954-898X, E-ISSN 1361-6536, Vol. 23, nr 4, s. 254-271Artikel i tidskrift (Refereegranskat) 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.

Nyckelord
Network models, simulation technology
Nationell ämneskategori
Neurovetenskaper Bioinformatik (beräkningsbiologi)
Identifikatorer
urn:nbn:se:kth:diva-104537 (URN)000311837300009 ()23116128 (PubMedID)2-s2.0-84870666881 (Scopus ID)
Forskningsfinansiär
Vetenskapsrådet, VR-621-2009-3807VINNOVAStiftelsen för strategisk forskning (SSF)Swedish e‐Science Research Center
Anmärkning

QC 20121112

Tillgänglig från: 2012-11-05 Skapad: 2012-11-05 Senast uppdaterad: 2018-01-12Bibliografiskt granskad
2. From ANN to Biomimetic Information Processing
Öppna denna publikation i ny flik eller fönster >>From ANN to Biomimetic Information Processing
2009 (Engelska)Ingår i: BIOLOGICALLY INSPIRED SIGNAL PROCESSING FOR CHEMICAL SENSING / [ed] Gutierrez A, Marco S, 2009, Vol. 188, s. 33-43Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Serie
Studies in Computational Intelligence, ISSN 1860-949X
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
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)
Konferens
OSPEL Workshop on Bio-inspired Signal Processing Barcelona, SPAIN, 2007
Anmärkning
QC 20110310Tillgänglig från: 2011-03-10 Skapad: 2011-03-04 Senast uppdaterad: 2018-01-12Bibliografiskt granskad
3. Odour discrimination and mixture segmentation in a holistic model of the mammalian olfactory system
Öppna denna publikation i ny flik eller fönster >>Odour discrimination and mixture segmentation in a holistic model of the mammalian olfactory system
(Engelska)Manuskript (preprint) (Övrigt vetenskapligt)
Nationell ämneskategori
Bioinformatik (beräkningsbiologi)
Identifikatorer
urn:nbn:se:kth:diva-122188 (URN)
Anmärkning

QS 2013

Tillgänglig från: 2013-05-14 Skapad: 2013-05-14 Senast uppdaterad: 2018-01-11Bibliografiskt granskad
4. A model of categorization, learning of invariant representations and sequence prediction utilizing top-down activity
Öppna denna publikation i ny flik eller fönster >>A model of categorization, learning of invariant representations and sequence prediction utilizing top-down activity
(Engelska)Manuskript (preprint) (Övrigt vetenskapligt)
Nationell ämneskategori
Bioinformatik (beräkningsbiologi)
Identifikatorer
urn:nbn:se:kth:diva-122189 (URN)
Anmärkning

QS 2013

Tillgänglig från: 2013-05-14 Skapad: 2013-05-14 Senast uppdaterad: 2018-01-11Bibliografiskt granskad
5. A Novel Model-Free Data Analysis Technique Based on Clustering in a Mutual Information Space: Application to Resting-State fMRI
Öppna denna publikation i ny flik eller fönster >>A Novel Model-Free Data Analysis Technique Based on Clustering in a Mutual Information Space: Application to Resting-State fMRI
2010 (Engelska)Ingår i: Frontiers in Systems Neuroscience, ISSN 1662-5137, E-ISSN 1662-5137, Vol. 4, s. 34:1-34:8Artikel i tidskrift (Refereegranskat) 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.

Nyckelord
Clustering, Data analysis, Functional magnetic resonance imaging, Mutual information, Parallel algorithm, Resting-state
Nationell ämneskategori
Bioinformatik (beräkningsbiologi)
Identifikatorer
urn:nbn:se:kth:diva-52478 (URN)10.3389/fnsys.2010.00034 (DOI)2-s2.0-79957813632 (Scopus ID)
Anmärkning
QC 20111221Tillgänglig från: 2011-12-21 Skapad: 2011-12-18 Senast uppdaterad: 2018-01-12Bibliografiskt granskad
6. Arterial input function derived from pairwise correlations between PET-image voxels
Öppna denna publikation i ny flik eller fönster >>Arterial input function derived from pairwise correlations between PET-image voxels
Visa övriga...
2013 (Engelska)Ingår i: Journal of Cerebral Blood Flow and Metabolism, ISSN 0271-678X, E-ISSN 1559-7016, Vol. 33, nr 7, s. 1058-1065Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
Nature Publishing Group, 2013
Nyckelord
HRRT, image-derived input function, PET, pharmacokinetic modeling, voxel correlation
Nationell ämneskategori
Neurologi
Identifikatorer
urn:nbn:se:kth:diva-122186 (URN)10.1038/jcbfm.2013.47 (DOI)000321185800011 ()2-s2.0-84880326272 (Scopus ID)
Anmärkning

QC 20130515

Tillgänglig från: 2013-05-14 Skapad: 2013-05-14 Senast uppdaterad: 2017-12-06Bibliografiskt granskad

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Doctoral thesis(2579 kB)585 nedladdningar
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