Change search
Refine search result
1 - 17 of 17
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Benjaminsson, Simon
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    On large-scale neural simulations and applications in neuroinformatics2013Doctoral 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.

  • 2.
    Benjaminsson, Simon
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Fransson, Peter
    Department of Clinical Neuroscience, Karolinska Institute.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    A Novel Model-Free Data Analysis Technique Based on Clustering in a Mutual Information Space: Application to Resting-State fMRI2010In: Frontiers in Systems Neuroscience, ISSN 1662-5137, E-ISSN 1662-5137, Vol. 4, p. 34:1-34:8Article in journal (Refereed)
    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.

  • 3.
    Benjaminsson, Simon
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Herman, Pawel
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Odour discrimination and mixture segmentation in a holistic model of the mammalian olfactory systemManuscript (preprint) (Other academic)
  • 4.
    Benjaminsson, Simon
    et al.
    KTH, School of Computer Science and Communication (CSC).
    Herman, Pawel
    KTH, School of Computer Science and Communication (CSC).
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC).
    Performance of a computational model of the mammalian olfactory system2016In: Neuromorphic Olfaction, CRC Press , 2016, p. 173-211Chapter in book (Other academic)
  • 5.
    Benjaminsson, Simon
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Adaptive sensor drift counteraction by a modular neural network2011In: Chemical sensors, ISSN 0379-864X, Vol. 36, no 1, p. E41-E41Article in journal (Other academic)
  • 6.
    Benjaminsson, Simon
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Adaptive sensor drift counteraction by a modular neural network2010In: Neuroscience research, ISSN 0168-0102, E-ISSN 1872-8111, Vol. 68, p. E212-E212Article in journal (Other academic)
    Abstract [en]

    The response properties of sensors such as electronic noses vary in time due to internal or environmental factors. Recalibration is often costly or technically infeasible, which is why algorithms aimed at addressing the sensor drift problem at the data processing level have been developed. These falls in two categories: The pre-processing approaches, such as component correction [1], try to extract the direction and amount of drift in the training data and remove the drift component during operation. Adaptive algorithms, such as the self-organizing map [2], try to counteract the drift during runtime by adjusting the network to the incoming data.

    We have previously suggested a modular neural network architecture as a model of cortical layer 4 [3]. Here we show how it quite well can handle the sensor drift problem in chemosensor data. It creates a distributed and redundant code suitable for a noisy and drifting environment. A feature extraction layer governed by competitive learning allows for network adaptation during runtime. In addition, training data can be utilized to create a prediction of the underlying drift to further improve the network performance. Hence, we attempt to combine the two aforementioned methodological categories into one network model.

    The capabilities of the proposed network are demonstrated on surrogate data as well as real-world data collected from an electronic nose.

  • 7.
    Benjaminsson, Simon
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Extreme scaling of brain simulation on JUGENE2011Report (Other academic)
  • 8.
    Benjaminsson, Simon
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Nexa: A scalable neural simulator with integrated analysis2012In: Network, ISSN 0954-898X, E-ISSN 1361-6536, Vol. 23, no 4, p. 254-271Article in journal (Refereed)
    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.

  • 9.
    Benjaminsson, Simon
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Lundqvist, Mikael
    A model of categorization, learning of invariant representations and sequence prediction utilizing top-down activityManuscript (preprint) (Other academic)
  • 10.
    Benjaminsson, Simon
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Silverstein, David
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Herman, Pawel
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Melis, Paul
    Visualization Group, SARA, Amsterdam, The Netherlands.
    Slavnić, Vladimir
    Scientific Computing Laboratory, Institute of Physics Belgrade, University of Belgrade.
    Spasojević, Marko
    Scientific Computing Laboratory, Institute of Physics Belgrade, University of Belgrade.
    Alexiev, Kiril
    Department of Mathematical Methods for Sensor Information Processing, Institute of Information and Communication Technologies, Bulgaria.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Visualization of Output from Large-Scale Brain Simulations2012Report (Other academic)
    Abstract [en]

    This project concerned the development of tools for visualization of output from brain simulations performed on supercomputers. The project had two main parts: 1) creating visualizations using large-scale simulation output from existing neural simulation codes, and 2) making extensions to  some of the existing codes to allow interactive runtime (in-situ) visualization. In 1) simulation data was converted to HDF5 format and split over multiple files. Visualization pipelines were created for different types of visualizations, e.g. voltage and calcium. In 2) by using the VisIt visualization application and its libsim library, simulation code was instrumented so that VisIt could access simulation data directly. The simulation code was instrumented and tested on different clusters where control of simulation was demonstrated and in-situ visualization of neural unit’s and population data was achieved.

  • 11.
    Hrastinski, Stefan
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Learning.
    Stenbom, Stefan
    KTH, School of Industrial Engineering and Management (ITM), Learning.
    Benjaminsson, Simon
    KTH, School of Industrial Engineering and Management (ITM), Learning. Smartera AB.
    Jansson, Malin
    KTH, School of Industrial Engineering and Management (ITM), Learning.
    Identifying and exploring the effects of different types of tutor questions in individual online synchronous tutoring in mathematics2019In: Interactive Learning Environments, ISSN 1049-4820, E-ISSN 1744-5191Article in journal (Refereed)
    Abstract [en]

    Although we know that asking questions is an essential aspect of onlinetutoring, there is limited research on this topic. The aim of this paperwas to identify commonly used direct question types and explore theeffects of using these question types on conversation intensity, approachto tutoring, perceived satisfaction and perceived learning. The researchsetting was individual online synchronous tutoring in mathematics. Theempirical data was based on 13,317 logged conversations and aquestionnaire. The tutors used a mix of open, more student-centredquestions, and closed, more teacher-centred questions. In contrast toprevious research, this study provides a more positive account indicatingthat it is indeed possible to train tutors to focus on asking questions,rather than delivering content. Frequent use of many of the questiontypes contributed to increased conversation intensity. However, therewere few question types that were associated with statisticallysignificant effects on perceived satisfaction or learning. There are nosilver bullet question types that by themselves led to positive effects onperceived satisfaction and learning. The question types could be used byteachers and teacher students when reflecting on what types ofquestions they are asking, and what kind of questions they could be asking.

  • 12.
    Lansner, Anders
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Benjaminsson, Simon
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Johansson, Christopher
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    From ANN to Biomimetic Information Processing2009In: BIOLOGICALLY INSPIRED SIGNAL PROCESSING FOR CHEMICAL SENSING / [ed] Gutierrez A, Marco S, 2009, Vol. 188, p. 33-43Conference 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.

  • 13.
    Persaud, Krishna
    et al.
    The University of Manchester.
    Bernabei, Mara
    The University of Manchester.
    Benjaminsson, Simon
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Herman, Pawel
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Reverse Engineering of Nature in the field of Chemical Sensors2012Conference paper (Refereed)
    Abstract [en]

    A large scale chemical sensor array consisting of 16384 conducting polymer elements was developed emulating characteristics of the biological olfactory receptor system. A biologically realistic computational model of the olfactory cortex was developed and the data from the large array was used to test the performance of the system. Classification of odorants and segmentation of mixtures of were investigated and the results were compared to that from support vector machine algorithms.

  • 14.
    Rehn, Erik M
    et al.
    Bernstein Center for Computational Neuroscience.
    Benjaminsson, Simon
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Event-based Sensor Interface for Supercomputer scale Neural Networks2012Report (Other academic)
  • 15. Schain, Martin
    et al.
    Benjaminsson, Simon
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Varnas, Katarina
    Forsberg, Anton
    Halldin, Christer
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Farde, Lars
    Varrone, Andrea
    Image derived input function using a multivariate analysis method based on pair-wise correlation between PET-image voxels2012In: Journal of Cerebral Blood Flow and Metabolism, ISSN 0271-678X, E-ISSN 1559-7016, Vol. 32, p. S149-S151Article in journal (Other academic)
  • 16.
    Schain, Martin
    et al.
    Karolinska Institutet.
    Benjaminsson, Simon
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Varnäs, Katarina
    Karolinska Institutet.
    Forsberg, Anton
    Karolinska Institutet.
    Halldin, Christer
    Karolinska Institutet.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Farde, Lars
    Karolinska Institutet.
    Varrone, Andrea
    Arterial input function derived from pairwise correlations between PET-image voxels2013In: Journal of Cerebral Blood Flow and Metabolism, ISSN 0271-678X, E-ISSN 1559-7016, Vol. 33, no 7, p. 1058-1065Article in journal (Refereed)
    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.

  • 17.
    Stenbom, Stefan
    et al.
    KTH, School of Education and Communication in Engineering Science (ECE), Learning.
    Benjaminsson, Simon
    KTH, School of Education and Communication in Engineering Science (ECE), Learning.
    Hrastinski, Stefan
    KTH, School of Education and Communication in Engineering Science (ECE), Learning.
    Cleveland-Innes, Martha
    KTH, School of Education and Communication in Engineering Science (ECE), Learning.
    Digital badges for in-service training of online tutors2016Conference paper (Refereed)
    Abstract [en]

    In this presentation, an application where digital badges are used for continuing training of online tutors is reviewed. First, we present how digital badges are used in a math tutoring service for K–12 students. Then, we discuss benefits and challenges of digital badges for development of in-service online tutors.

1 - 17 of 17
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf