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- Benjaminsson, Simon, 1982-, et al.
(författare)
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A Novel Model-Free Data Analysis Technique Based on Clustering in a Mutual Information Space : Application to Resting-State fMRI
- 2010
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Ingår i: Frontiers in Systems Neuroscience. - : Frontiers Media SA. - 1662-5137. ; 4, s. 34:1-34:8
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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- Benjaminsson, Simon, et al.
(författare)
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Adaptive sensor drift counteraction by a modular neural network
- 2010
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Ingår i: Neuroscience research. - : Elsevier BV. - 0168-0102 .- 1872-8111. ; 68, s. E212-E212
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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- Benjaminsson, Simon, 1982-, et al.
(författare)
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Nexa : A scalable neural simulator with integrated analysis
- 2012
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Ingår i: Network. - 0954-898X .- 1361-6536. ; 23:4, s. 254-271
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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- Benjaminsson, Simon, 1982-
(författare)
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On large-scale neural simulations and applications in neuroinformatics
- 2013
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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- Benjaminsson, Simon, et al.
(författare)
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Visualization of Output from Large-Scale Brain Simulations
- 2012
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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