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  • 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.
    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)
  • 3.
    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.

  • 4.
    Brouwer, A.-M.
    et al.
    TNO Human Factors.
    Hogervorst, M.A.
    TNO Human Factors.
    Herman, Pawel
    Radboud University.
    Kooi, F.
    TNO Human Factors.
    Are you really looking?: Finding the answer through fixation patterns and EEG2009In: FOUNDATIONS OF AUGMENTED COGNITION, PROCEEDINGS  , Springer, 2009, Vol. 5638 LNAI, p. 329-338Conference paper (Refereed)
    Abstract [en]

    Eye movement recordings do not tell us whether observers are 'really looking' or whether they are paying attention to something else than the visual environment. We want to determine whether an observer's main current occupation is visual or not by investigating fixation patterns and EEG. Subjects were presented with auditory and visual stimuli. In some conditions, they focused on the auditory information whereas in others they searched or judged the visual stimuli. Observers made more fixations that are less cluttered in the visual compared to the auditory tasks, and they were less variable in their average fixation location. Fixated features revealed which target the observers were looking for. Gaze was not attracted more by salient features when performing the auditory task. 8-12 Hz EEG oscillations recorded over the parieto-occipital regions were stronger during the auditory task than during visual search. Our results are directly relevant for monitoring surveillance workers.

  • 5.
    Coyle, D.
    et al.
    University of Ulster.
    Herman, Pawel Andrzej
    University of Ulster.
    Prasad, G.
    University of Ulster.
    McGinnity, T.M.
    University of Ulster.
    Multi-classifier Verification of Neural Time-Series Prediction Preprocessing for a BCI2007In: IET Irish Signals and System Conference 2007: Proc., 2007Conference paper (Refereed)
  • 6.
    Coyle, D.
    et al.
    University of Ulster.
    Prasad, G.
    University of Ulster.
    McGinnity, T.M.
    University of Ulster.
    Herman, Pawel Andrzej
    University of Ulster.
    Estimating the Predictability of EEG Recorded Over the Motor Cortex Using Information Theoretic Functionals2004In: Biomedizinische Technik (Berlin. Zeitschrift), ISSN 1862-278X, E-ISSN 0013-5585, Vol. 49, no 1, p. 43-44Article in journal (Refereed)
  • 7.
    Ekeberg, Örjan
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Fransén, Erik
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Herman, Pawel
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Kumar, Arvind
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Lindeberg, Tony
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Computational Brain Science at CST, CSC, KTH2016Other (Other academic)
    Abstract [en]

    Mission and Vision - Computational Brain Science Lab at CST, CSC, KTH

    The scientific mission of the Computational Brain Science Lab at CSC is to be at the forefront of mathematical modelling, quantitative analysis and mechanistic understanding of brain function. We perform research on (i) computational modelling of biological brain function and on (ii) developing theory, algorithms and software for building computer systems that can perform brain-like functions. Our research answers scientific questions and develops methods in these fields. We integrate results from our science-driven brain research into our work on brain-like algorithms and likewise use theoretical results about artificial brain-like functions as hypotheses for biological brain research.

    Our research on biological brain function includes sensory perception (vision, hearing, olfaction, pain), cognition (action selection, memory, learning) and motor control at different levels of biological detail (molecular, cellular, network) and mathematical/functional description. Methods development for investigating biological brain function and its dynamics as well as dysfunction comprises biomechanical simulation engines for locomotion and voice, machine learning methods for analysing functional brain images, craniofacial morphology and neuronal multi-scale simulations. Projects are conducted in close collaborations with Karolinska Institutet and Karolinska Hospital in Sweden as well as other laboratories in Europe, U.S., Japan and India.

    Our research on brain-like computing concerns methods development for perceptual systems that extract information from sensory signals (images, video and audio), analysis of functional brain images and EEG data, learning for autonomous agents as well as development of computational architectures (both software and hardware) for neural information processing. Our brain-inspired approach to computing also applies more generically to other computer science problems such as pattern recognition, data analysis and intelligent systems. Recent industrial collaborations include analysis of patient brain data with MentisCura and the startup company 13 Lab bought by Facebook.

    Our long term vision is to contribute to (i) deeper understanding of the computational mechanisms underlying biological brain function and (ii) better theories, methods and algorithms for perceptual and intelligent systems that perform artificial brain-like functions by (iii) performing interdisciplinary and cross-fertilizing research on both biological and artificial brain-like functions. 

    On one hand, biological brains provide existence proofs for guiding our research on artificial perceptual and intelligent systems. On the other hand, applying Richard Feynman’s famous statement ”What I cannot create I do not understand” to brain science implies that we can only claim to fully understand the computational mechanisms underlying biological brain function if we can build and implement corresponding computational mechanisms on a computerized system that performs similar brain-like functions.

  • 8.
    Glackin, C.
    et al.
    University of Ulster.
    Maguire, L.
    University of Ulster.
    McIvor, R.
    University of Ulster.
    Humphreys, P.
    University of Ulster.
    Herman, Pawel Andrzej
    University of Ulster.
    A comparison of fuzzy strategies for corporate acquisition analysis2007In: Fuzzy sets and systems (Print), ISSN 0165-0114, E-ISSN 1872-6801, Vol. 158, no 18, p. 2039-2056Article in journal (Refereed)
    Abstract [en]

    Analysing all prospective companies for acquisition in large market sectors is an onerous task. A strategy that results in a shortlist of companies that meet certain basic criteria is required. The short-listed companies can then be further investigated in more detail later if desired. Fuzzy logic systems (FLSs) imbued with the expertise of a focal organisation's financial experts can be of great assistance in this process. In this paper an investigation into the suitability of FLSs for acquisition analysis is presented. The nuances of training and tuning are discussed. In particular, the difficulty of obtaining suitable amounts of expert data is a recurring theme throughout the paper. A strategy for circumventing this issue is presented that relies on the design of a conventional fuzzy logic rule base with the assistance of a financial expert. With the rule base created, various scenarios such as the simulation of multiple experts and the creation of expert training data are investigated. In particular, two scenarios for the creation of simulated expert data are presented. In the first the responses from the different experts are averaged, and in the second scenario the responses from all the different experts are preserved in the training data. This paper builds on previous work with scalable membership functions, however, the use of fuzzy C-means clustering and backpropagation training, are new developments. Additionally, a type-2 FLS is developed and its potential advantages are discussed for this application. The type-2 system facilitates the inclusion of the opinions of multiple experts. Both the type-1 and type-2 FLSs were trained using the backpropagation algorithm with early stopping and verified with five-fold cross-validation. Multiple runs of the five-fold method were conducted with different random orderings of the data. For this particular application, the type-1 system performed comparably with the type-2 system despite the considerable amount of variation in the expert training data. The training results have proven the methods to be capable of efficient tuning of parameters, and of reliable ranking of prospective companies.

  • 9.
    Herman, Pawel Andrzej
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Stockholm University.
    Lundqvist, Mikael
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Stockholm University.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Stockholm University.
    Nested theta to gamma oscillations and precise spatiotemporal firing during memory retrieval in a simulated attractor network2013In: Brain Research, ISSN 0006-8993, E-ISSN 1872-6240, Vol. 1536, no SI, p. 68-87Article in journal (Refereed)
    Abstract [en]

    Nested oscillations, where the phase of the underlying slow rhythm modulates the power of faster oscillations, have recently attracted considerable research attention as the increased phase-coupling of cross-frequency oscillations has been shown to relate to memory processes. Here we investigate the hypothesis that reactivations of memory patterns, induced by either external stimuli or internal dynamics, are manifested as distributed cell assemblies oscillating at gamma-like frequencies with life-times on a theta scale. For this purpose, we study the spatiotemporal oscillatory dynamics of a previously developed meso-scale attractor network model as a correlate of its memory function. The focus is on a hierarchical nested organization of neural oscillations in delta/theta (2-5Hz) and gamma frequency bands (25-35Hz), and in some conditions even in lower alpha band (8-12Hz), which emerge in the synthesized field potentials during attractor memory retrieval. We also examine spiking behavior of the network in close relation to oscillations. Despite highly irregular firing during memory retrieval and random connectivity within each cell assembly, we observe precise spatiotemporal firing patterns that repeat across memory activations at a rate higher than expected from random firing. In contrast to earlier studies aimed at modeling neural oscillations, our attractor memory network allows us to elaborate on the functional context of emerging rhythms and discuss their relevance. We provide support for the hypothesis that the dynamics of coherent delta/theta oscillations constitute an important aspect of the formation and replay of neuronal assemblies. This article is part of a Special Issue entitled Neural Coding 2012.

  • 10.
    Herman, Pawel Andrzej
    et al.
    University of Ulster.
    Prasad, Girijesh
    University of Ulster.
    McGinnity, T M
    University of Ulster.
    Investigation of the Type-2 Fuzzy Logic Approach to Classification in an EEG-based Brain-Computer Interface2005In: PROCEEDINGS OF ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, 2005, Vol. 5, p. 5354-5357Conference paper (Refereed)
    Abstract [en]

    Analysis of electroencephalogram (EEG) requires a framework that facilitates handling the uncertainties associated with the varying brain dynamics and the presence of noise. Recently, the type-2 fuzzy logic systems (T2 FLSs) have been found effective in modeling uncertain data. This paper examines the potential of the T2 FLS methodology in devising an EEG-based brain-computer interface (BCI). In particular, a T2 FLS has been designed to classify imaginary left and right hand movements based on time-frequency information extracted from the EEG with the short time Fourier transform (STFT). Robustness of the method has also been verified in the presence of additive noise. The performance of the classifier is quantified with the classification accuracy (CA). The T2 fuzzy classifier has been proven to outperform its type-1 (T1) counterpart on all data sets recorded from three subjects examined. It has also compared favorably to the well known classifier based on linear discriminant analysis (LDA).

  • 11.
    Herman, Pawel Andrzej
    et al.
    University of Ulster.
    Prasad, Girijesh
    University of Ulster.
    McGinnity, Thomas Martin
    University of Ulster.
    A Fuzzy Logic Classifier Design for Enhancing BCI Performance2006Conference paper (Refereed)
    Abstract [en]

    This work is aimed at enhancing inter-session performance of Brain-Computer Interface (BCI) classification. The effective handling of uncertainties associated with changing brain dynamics is considered to be a key issue. Since fuzzy logic (FL) has been recognized as a functional and well-suited approach to capturing the effects of uncertainty, the research has been concentrated on the development of an FL classifier for a BCI system. The emphasis is placed on type-2 (T2) FL methodology that has recently emerged as an expanded version of classical type-1 (T1) FL. In this work a case study was conducted using ECoG recordings made available as part of BCI competition III. Due to high dimensionality of the signal, two-stage feature selection was devised. The overall performance of the developed BCI was assessed in off-line simulations based on the classification accuracy (CA). Comparative analysis of the designed T2FL and T1FL systems with LDA as BCI classifiers suggests that T2FL has superior capability in effective dealing with inter-session variability of the ECoG dynamics in the given subject.

  • 12.
    Herman, Pawel Andrzej
    et al.
    University of Ulster.
    Prasad, Girijesh
    University of Ulster.
    McGinnity, Thomas Martin
    University of Ulster.
    Comparative analysis of spectral approaches to feature extraction for EEG-based motor imagery classification2008In: IEEE transactions on neural systems and rehabilitation engineering, ISSN 1534-4320, E-ISSN 1558-0210, ISSN 1534-4320, Vol. 16, no 4, p. 317-326Article in journal (Refereed)
    Abstract [en]

    The quantification of the spectral content of electroencephalogram (EEG) recordings has a substantial role in clinical and scientific applications. It is of particular relevance in the analysis of event-related brain oscillatory responses. This work is focused on the identification and quantification of relevant frequency patterns in motor imagery (MI) related EEGs utilized for brain--computer interface (BCI) purposes. The main objective of the paper is to perform comparative analysis of different approaches to spectral signal representation such as power spectral density (PSD) techniques, atomic decompositions, time-frequency (t-f) energy distributions, continuous and discrete wavelet approaches, from which band power features can be extracted and used in the framework of MI classification. The emphasis is on identifying discriminative properties of the feature sets representing EEG trials recorded during imagination of either left-- or right-hand movement. Feature separability is quantified in the offline study using the classification accuracy (CA) rate obtained with linear and nonlinear classifiers. PSD approaches demonstrate the most consistent robustness and effectiveness in extracting the distinctive spectral patterns for accurately discriminating between left and right MI induced EEGs. This observation is based on an analysis of data recorded from eleven subjects over two sessions of BCI experiments. In addition, generalization capabilities of the classifiers reflected in their intersession performance are discussed in the paper..

  • 13.
    Herman, Pawel Andrzej
    et al.
    University of Ulster.
    Prasad, Girijesh
    University of Ulster.
    McGinnity, Thomas Martin
    University of Ulster.
    Critical Observations on Interval Type-2 Fuzzy Logic Approach to Uncertainty Handling in a Brain-Computer Interface Design2006In: Proc. IPMU 2006, 2006Conference paper (Refereed)
    Abstract [en]

    Effective handling of uncertainties associated with variability in brain dynamics and other factors with stochastic characteristics represents a highly challenging problem particularly for existing methods applied to the classification task within a Brain-Computer Interface (BCI). Recently, type-2 fuzzy logic (T2 FL) has been found effective in modelling uncertain data. This paper presents an enhanced Interval T2 FL methodology to the problem of inter-session classification of movement imagination-related patterns in electroencephalogram (EEG) and electrocorticogram (ECoG) recordings. The performance of the devised BCI is assessed based on the classification accuracy (CA) and is found to compare favourably to that of analogous systems employing well-known classical type-1 (T1) FLS and state-of-the-art linear discriminant analysis (LDA) as classifiers. However, the critical issues concerning learning rate selection, rule-base initialisation, selection of optimal model structure, convergence of model parameters and uncertainty bounds initialisation are observed to have a very decisive effect on the robustness of the designed BCI using T2 methodology. The paper presents some practical approaches to effectively tackle some of the issues and highlights the need for further work so that full potential of T2 FLS concept could be exploited.

  • 14.
    Herman, Pawel Andrzej
    et al.
    University of Ulster.
    Prasad, Girijesh
    University of Ulster.
    McGinnity, Thomas Martin
    University of Ulster.
    Design and on-line evaluation of type-2 fuzzy logic system-based framework for handling uncertainties in BCI classification2008In: 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008, p. 4242-4245Conference paper (Refereed)
    Abstract [en]

    The practical applicability of brain-computer interface (BCI) technology is limited due to its insufficient reliability and robustness. One of the major problems in this regard is the extensive variability and inconsistency of brain signal patterns, observed especially in electroencephalogram (EEG). This paper presents a fuzzy logic (FL) approach to the problem of handling of the resultant uncertainty effects. In particular, it outlines the design of a novel type-2 FL system (T2FLS) classifier within the framework of an EEG-based BCI, and examines its on-line applicability in the presence of shortand long-term nonstationarities of spectral EEG correlates of motor imagery (imagination of left vs. right hand movement). The developed system is shown to effectively cope with realtime constraints. In addition, a comparative post hoc analysis has revealed that the proposed T2FLS classifier outperforms conventional BCI methods, like LDA and SVM, in terms of the maximum classification accuracy (CA) rates by a relatively small, yet statistically significant, margin.

  • 15.
    Herman, Pawel Andrzej
    et al.
    School of Computing and Intelligent Systems, University of Ulster.
    Prasad, Girijesh
    School of Computing and Intelligent Systems, University of Ulster.
    McGinnity, Thomas Martin
    School of Computing and Intelligent Systems, University of Ulster.
    Designing a robust type-2 fuzzy logic classifier for non-stationary systems with application in brain-computer interfacing2008In: IEEE SMC 2008: Proc., 2008, p. 1343-1349Conference paper (Refereed)
    Abstract [en]

    Type-2 (T2) fuzzy logic (FL) systems (T2FLSs) have shown a remarkable potential in dealing with uncertain data resulting from real-world systems with non-stationary characteristics. This paper reports on novel developments in interval T2FLS (IT2FLS) classifier design methodology so that system non-stationarities can be effectively handled. In general, the approach presented here rests on a general concept of twostage FLS design in which an initial rule base structure is first initialized and then system parameters are globally optimized. The proposed incremental enhancements of existing fuzzy techniques, adopted from the area of conventional type-1 (T1) FL, are heuristic in nature. The IT2FLS design methods have been empirically verified in this work in the realm of pattern recognition. In particular, the potential and the suitability of IT2FLS to the problem of classification of motor imagery (MI) related patterns in electroencephalogram (EEG) recordings has been investigated. The outcome of this study bears direct relevance to the development of EEG-based brain-computer interfaces (BCIs) since the problem under examination poses a major difficulty for the state-of-the-art BCI methods. The IT2FLS classifier is evaluated in this work on multi-session EEG data sets in the framework of an off-line BCI. Its performance is quantified in terms of the classification accuracy (CA) rates and has been found to be favorable to that of analogous systems employing a conventional T1 FLS, along with linear discriminant analysis (LDA) and support vector machine (SVM), commonly utilized in MI-based BCI systems.

  • 16.
    Herman, Pawel Andrzej
    et al.
    University of Ulster.
    Prasad, Girijesh
    University of Ulster.
    McGinnity, Thomas Martin
    University of Ulster.
    Feature Extraction From the EEG for a Brain-Computer Interface Using Genetic Matching Pursuit Algorithm with Gabor Dictionary2004In: IEEE SMC UK-RI 2004: Proc. IEEE SMC UK-RI 2004, 2004Conference paper (Refereed)
  • 17.
    Herman, Pawel Andrzej
    et al.
    University of Ulster.
    Prasad, Girijesh
    University of Ulster.
    McGinnity, Thomas Martin
    University of Ulster.
    Support vector-enhanced design of a T2FL approach to motor imagery-related EEG pattern recognition2007In: IEEE International Conference on Fuzzy Systems, 2007, p. 1938-1943Conference paper (Refereed)
    Abstract [en]

    The significance of the initialization procedure in the development of Type-2 fuzzy logic (T2FL) system-based classifiers should be highlighted considering their intrinsically non-linear nature. Initial structure identification has been recognized as a crucial stage in the design of an interval T2FL (IT2FL) classifier utilized in the framework of electroencephalogram (EEG)-based brain - computer interface (BCI). In conjunction with an efficient gradient-based learning algorithm it has allowed for robust exploitation of T2FL's capabilities to effectively handle uncertainties inherently associated with changing dynamics of electrical brain activity. This paper builds on the previous experiences in tackling the problem of inter-session classification of motor imagery (MI)-related EEG patterns. The major contribution of this work is an empirical investigation of the concept of support vector (SV) learning applied to structure identification of the IT2FL classifier. The SV-enhanced initialization scheme is found to compare favorably to both an arbitrary initialization and the clustering approach utilized in the preceding work in terms of the inter-session BCI classification performance of the fully trained IT2FLS evaluated on three subjects.

  • 18.
    Herman, Pawel
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Benjaminsson, S.
    Lansner, A.
    Odor recognition in an attractor network model of the mammalian olfactory cortex2017In: 2017 International Joint Conference on Neural Networks (IJCNN), Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 3561-3568, article id 7966304Conference paper (Refereed)
    Abstract [en]

    Odor recognition constitutes a key functional aspect of olfaction and in real-world scenarios it requires that odorants occurring in complex chemical mixtures are identified irrespective of their concentrations. We investigate this challenging pattern recognition problem in the framework of a three-stage computational model of the mammalian olfactory system. To this end, we first synthesize odor stimuli with the primary representations in the olfactory receptor neuron (ORN) layer and the secondary representations in the output of the olfactory bulb (OB) in the model. Next, sparse olfactory codes are extracted and fed into the recurrent network model, where as a result of Hebbian-like associative learning an attractor memory storage is produced. We demonstrate the capability of the resultant olfactory cortex (OC) model to perform robust odor recognition tasks and offer computational insights into the underlying network mechanisms.

  • 19.
    Herman, Pawel
    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.
    Odor recognition framework for evaluating olfactory codes2011Conference paper (Other academic)
  • 20.
    Herman, Pawel
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Lundqvist, Mikael
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Oscillations in a simulated meso-scale memory network: origin and function of theta to gamma rhythmsArticle in journal (Other academic)
  • 21.
    Herman, Pawel
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Prasad, Girijesh
    McGinnity, Thomas Martin
    Designing an Interval Type-2 Fuzzy Logic System for Handling Uncertainty Effects in Brain-Computer Interface Classification of Motor Imagery Induced EEG Patterns2017In: IEEE transactions on fuzzy systems, ISSN 1063-6706, E-ISSN 1941-0034, Vol. 25, no 1, p. 29-42Article in journal (Refereed)
    Abstract [en]

    One of the urgent challenges in the automated analysis and interpretation of electrical brain activity is the effective handling of uncertainties associated with the complexity and variability of brain dynamics, reflected in the nonstationary nature of brain signals such as electroencephalogram (EEG). This poses a severe problem for existing approaches to the classification task within brain-computer interface (BCI) systems. Recently emerged type-2 fuzzy logic (T2FL) methodology has shown a remarkable potential in dealing with uncertain information given limited insight into the nature of the data-generating mechanism. The objective of this work is, thus, to examine the applicability of the T2FL approach to the problem of EEG pattern recognition. In particular, the focus is two-fold: 1) the design methodology for the interval T2FL system (IT2FLS) that can robustly deal with inter-session as well as within-session manifestations of nonstationary spectral EEG correlates of motor imagery, and 2) the comprehensive examination of the proposed fuzzy classifier in both off-line and on-line EEG classification case studies. The on-line evaluation of the IT2FLS-controlled real-time neurofeedback over multiple recording sessions holds special importance for EEG-based BCI technology. In addition, a retrospective comparative analysis accounting for other popular BCI classifiers such as linear discriminant analysis, kernel Fisher discriminant, and support vector machines as well as a conventional type-1 FLS, simulated off-line on the recorded EEGs, has demonstrated the enhanced potential of the proposed IT2FLS approach to robustly handle uncertainty effects in BCI classification.

  • 22. Iatropoulos, G.
    et al.
    Herman, Pawel
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Lansner, Anders
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Karlgren, Jussi
    KTH, School of Electrical Engineering and Computer Science (EECS), Theoretical Computer Science, TCS. Gavagai, Slussplan 9, Stockholm, Sweden.
    Larsson, M.
    Olofsson, J. K.
    The language of smell: Connecting linguistic and psychophysical properties of odor descriptors2018In: Cognition, ISSN 0010-0277, E-ISSN 1873-7838, Vol. 178, p. 37-49Article in journal (Refereed)
    Abstract [en]

    The olfactory sense is a particularly challenging domain for cognitive science investigations of perception, memory, and language. Although many studies show that odors often are difficult to describe verbally, little is known about the associations between olfactory percepts and the words that describe them. Quantitative models of how odor experiences are described in natural language are therefore needed to understand how odors are perceived and communicated. In this study, we develop a computational method to characterize the olfaction-related semantic content of words in a large text corpus of internet sites in English. We introduce two new metrics: olfactory association index (OAI, how strongly a word is associated with olfaction) and olfactory specificity index (OSI, how specific a word is in its description of odors). We validate the OAI and OSI metrics using psychophysical datasets by showing that terms with high OAI have high ratings of perceived olfactory association and are used to describe highly familiar odors. In contrast, terms with high OSI have high inter-individual consistency in how they are applied to odors. Finally, we analyze Dravnieks's (1985) dataset of odor ratings in terms of OAI and OSI. This analysis reveals that terms that are used broadly (applied often but with moderate ratings) tend to be olfaction-unrelated and abstract (e.g., “heavy” or “light”; low OAI and low OSI) while descriptors that are used selectively (applied seldom but with high ratings) tend to be olfaction-related (e.g., “vanilla” or “licorice”; high OAI). Thus, OAI and OSI provide behaviorally meaningful information about olfactory language. These statistical tools are useful for future studies of olfactory perception and cognition, and might help integrate research on odor perception, neuroimaging, and corpus-based linguistic models of semantic organization.

  • 23.
    Karlsson, Vide
    et al.
    KTH. Gavagai, Sweden.
    Herman, Pawel
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). Gavagai, Sweden.
    Karlgren, Jussi
    KTH, School of Computer Science and Communication (CSC), Theoretical Computer Science, TCS.
    Evaluating Categorisation in Real Life: An argument against simple but impractical metrics2016In: 7th CLEF Conference and Labs of the Evaluation Forum, Springer, 2016Conference paper (Refereed)
    Abstract [en]

    Text categorisation in commercial application poses several limiting constraints on the technology solutions to be employed. This paper describes how a method with some potential improvements is eval- uated for practical purposes and argues for a richer and more expressive evaluation procedure. In this paper one such method is exemplified by a precision-recall matrix which sacrifices convenience for expressiveness. 

  • 24. Lundqvist, M.
    et al.
    Herman, Pawel
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Warden, M. R.
    Brincat, S. L.
    Miller, E. K.
    Gamma and beta bursts during working memory readout suggest roles in its volitional control2018In: Nature Communications, ISSN 2041-1723, E-ISSN 2041-1723, Vol. 9, no 1, article id 394Article in journal (Refereed)
    Abstract [en]

    Working memory (WM) activity is not as stationary or sustained as previously thought. There are brief bursts of gamma (~50-120 Hz) and beta (~20-35 Hz) oscillations, the former linked to stimulus information in spiking. We examined these dynamics in relation to readout and control mechanisms of WM. Monkeys held sequences of two objects in WM to match to subsequent sequences. Changes in beta and gamma bursting suggested their distinct roles. In anticipation of having to use an object for the match decision, there was an increase in gamma and spiking information about that object and reduced beta bursting. This readout signal was only seen before relevant test objects, and was related to premotor activity. When the objects were no longer needed, beta increased and gamma decreased together with object spiking information. Deviations from these dynamics predicted behavioral errors. Thus, beta could regulate gamma and the information in WM.

  • 25.
    Lundqvist, Mikael
    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.
    Effect of Prestimulus Alpha Power, Phase, and Synchronization on Stimulus Detection Rates in a Biophysical Attractor Network Model2013In: Journal of Neuroscience, ISSN 0270-6474, E-ISSN 1529-2401, Vol. 33, no 29, p. 11817-11824Article in journal (Refereed)
    Abstract [en]

    Spontaneous oscillations measured by local field potentials, electroencephalograms and magnetoencephalograms exhibit a pronounced peak in the alpha band (8-12 Hz) in humans and primates. Both instantaneous power and phase of these ongoing oscillations have commonly been observed to correlate with psychophysical performance in stimulus detection tasks. We use a novel model-based approach to study the effect of prestimulus oscillations on detection rate. A previously developed biophysically detailed attractor network exhibits spontaneous oscillations in the alpha range before a stimulus is presented and transiently switches to gamma-like oscillations on successful detection. We demonstrate that both phase and power of the ongoing alpha oscillations modulate the probability of such state transitions. The power can either positively or negatively correlate with the detection rate, in agreement with experimental findings, depending on the underlying neural mechanism modulating the oscillatory power. Furthermore, the spatially distributed alpha oscillators of the network can be synchronized by global nonspecific weak excitatory signals. These synchronization events lead to transient increases in alpha-band power and render the network sensitive to the exact timing of target stimuli, making the alpha cycle function as a temporal mask in line with recent experimental observations. Our results are relevant to several studies that attribute a modulatory role to prestimulus alpha dynamics.

  • 26.
    Lundqvist, Mikael
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Herman, Pawel
    Lansner, Anders
    Theta and Gamma Power Increases and Alpha/Beta Power Decreases with Memory Load in an Attractor Network Model2010In: Journal of cognitive neuroscience, ISSN 0898-929X, E-ISSN 1530-8898, Vol. 23, no 10, p. 3008-3020Article in journal (Refereed)
    Abstract [en]

    Changes in oscillatory brain activity are strongly correlated with performance in cognitive tasks and modulations in specific frequency bands are associated with working memory tasks. Mesoscale network models allow the study of oscillations as an emergent feature of neuronal activity. Here we extend a previously developed attractor network model, shown to faithfully reproduce single-cell activity during retention and memory recall, with synaptic augmentation. This enables the network to function as a multi-item working memory by cyclic reactivation of up to six items. The reactivation happens at theta frequency, consistently with recent experimental findings, with increasing theta power for each additional item loaded in the network's memory. Furthermore, each memory reactivation is associated with gamma oscillations. Thus, single-cell spike trains as well as gamma oscillations in local groups are nested in the theta cycle. The network also exhibits an idling rhythm in the alpha/beta band associated with a noncoding global attractor. Put together, the resulting effect is increasing theta and gamma power and decreasing alpha/beta power with growing working memory load, rendering the network mechanisms involved a plausible explanation for this often reported behavior.

  • 27.
    Lundqvist, Mikael
    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.
    Variability of spike firing during theta-coupled replay of memories in a simulated attractor network2012In: Brain Research, ISSN 0006-8993, E-ISSN 1872-6240, Vol. 1434, p. 152-161Article in journal (Refereed)
    Abstract [en]

    Simulation work has recently shown that attractor networks can reproduce Poisson-like variability of single cell spiking, with coefficient of variation (Cv(2)) around unity, consistent with cortical data. However, the use of local variability (Lv) measures has revealed area- and layer-specific deviations from Poisson-like firing. In order to test these findings in silico we used a biophysically detailed attractor network model. We show that Lv well above 1, specifically found in superficial cortical layers and prefrontal areas, can indeed be reproduced in such networks and is consistent with periodic replay rather than persistent firing. The memory replay at the theta time scale provides a framework for a multi-item memory storage in the model. This article is part of a Special Issue entitled Neural Coding.

  • 28.
    Lundqvist, Mikael
    et al.
    MIT, Picower Inst Learning & Memory, 43 Vassar St, Cambridge, MA 02139 USA.;MIT, Dept Brain & Cognit Sci, 43 Vassar St, Cambridge, MA 02139 USA..
    Herman, Pawel
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH Royal Inst.
    Miller, Earl K.
    MIT, Picower Inst Learning & Memory, 43 Vassar St, Cambridge, MA 02139 USA.;MIT, Dept Brain & Cognit Sci, 43 Vassar St, Cambridge, MA 02139 USA..
    Working Memory: Delay Activity, Yes! Persistent Activity? Maybe Not2018In: Journal of Neuroscience, ISSN 0270-6474, E-ISSN 1529-2401, Vol. 38, no 32, p. 7013-7019Article in journal (Refereed)
    Abstract [en]

    Persistent spiking has been thought to underlie working memory (WM). However, virtually all of the evidence for this comes from studies that averaged spiking across time and across trials, which masks the details. On single trials, activity often occurs in sparse transient bursts. This has important computational and functional advantages. In addition, examination of more complex tasks reveals neural coding in WM is dynamic over the course of a trial. All this suggests that spiking is important for WM, but that its role is more complex than simply persistent spiking.

  • 29.
    Lundqvist, Mikael
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Stockholm University, Sweden.
    Herman, Pawel
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Stockholm University, Sweden.
    Palva, M.
    Palva, S.
    Silverstein, David
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Stockholm University, Sweden.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Stockholm University, Sweden.
    Stimulus detection rate and latency, firing rates and 1-40Hz oscillatory power are modulated by infra-slow fluctuations in a bistable attractor network model2013In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 83, p. 458-471Article in journal (Refereed)
    Abstract [en]

    Recordings of membrane and field potentials, firing rates, and oscillation amplitude dynamics show that neuronal activity levels in cortical and subcortical structures exhibit infra-slow fluctuations (ISFs) on time scales from seconds to hundreds of seconds. Similar ISFs are salient also in blood-oxygenation-level dependent (BOLD) signals as well as in psychophysical time series. Functional consequences of ISFs are not fully understood. Here, they were investigated along with dynamical implications of ISFs in large-scale simulations of cortical network activity. For this purpose, a biophysically detailed hierarchical attractor network model displaying bistability and operating in an oscillatory regime was used. ISFs were imposed as slow fluctuations in either the amplitude or frequency of fast synaptic noise. We found that both mechanisms produced an ISF component in the synthetic local field potentials (LFPs) and modulated the power of 1-40. Hz oscillations. Crucially, in a simulated threshold-stimulus detection task (TSDT), these ISFs were strongly correlated with stimulus detection probabilities and latencies. The results thus show that several phenomena observed in many empirical studies emerge concurrently in the model dynamics, which yields mechanistic insight into how infra-slow excitability fluctuations in large-scale neuronal networks may modulate fast oscillations and perceptual processing. The model also makes several novel predictions that can be experimentally tested in future studies.

  • 30. Lundqvist, Mikael
    et al.
    Herman, Pawel
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Warden, Melissa R.
    Brincat, Scott L.
    Miller, Earl K.
    Gamma and beta bursts during working memory readout suggest roles in its volitional control2018In: Nature Communications, ISSN 2041-1723, E-ISSN 2041-1723, Vol. 9, article id 394Article in journal (Refereed)
    Abstract [en]

    Working memory (WM) activity is not as stationary or sustained as previously thought. There are brief bursts of gamma (similar to 50-120 Hz) and beta (similar to 20-35 Hz) oscillations, the former linked to stimulus information in spiking. We examined these dynamics in relation to readout and control mechanisms of WM. Monkeys held sequences of two objects in WM to match to subsequent sequences. Changes in beta and gamma bursting suggested their distinct roles. In anticipation of having to use an object for the match decision, there was an increase in gamma and spiking information about that object and reduced beta bursting. This readout signal was only seen before relevant test objects, and was related to premotor activity. When the objects were no longer needed, beta increased and gamma decreased together with object spiking information. Deviations from these dynamics predicted behavioral errors. Thus, beta could regulate gamma and the information in WM.

  • 31. Lundqvist, Mikael
    et al.
    Rose, Jonas
    Herman, Pawel
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Brincat, Scott L.
    Buschman, Timothy J.
    Miller, Earl K.
    Gamma and Beta Bursts Underlie Working Memory2016In: Neuron, ISSN 0896-6273, E-ISSN 1097-4199, Vol. 90, no 1, p. 152-164Article in journal (Refereed)
    Abstract [en]

    Working memory is thought to result from sustained neuron spiking. However, computational models suggest complex dynamics with discrete oscillatory bursts. We analyzed local field potential (LFP) and spiking from the prefrontal cortex (PFC) of monkeys performing a working memory task. There were brief bursts of narrow-band gamma oscillations (45-100 Hz), varied in time and frequency, accompanying encoding and re-activation of sensory information. They appeared at a minority of recording sites associated with spiking reflecting the to-be-remembered items. Beta oscillations (20-35 Hz) also occurred in brief, variable bursts but reflected a default state interrupted by encoding and decoding. Only activity of neurons reflecting encoding/decoding correlated with changes in gamma burst rate. Thus, gamma bursts could gate access to, and prevent sensory interference with, working memory. This supports the hypothesis that working memory is manifested by discrete oscillatory dynamics and spiking, not sustained activity.

  • 32. Mbuvha, R.
    et al.
    Jonsson, M.
    Ehn, N.
    Herman, Pawel
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Bayesian neural networks for one-hour ahead wind power forecasting2017In: 2017 6th International Conference on Renewable Energy Research and Applications, ICRERA 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017, Vol. 2017, p. 591-596Conference paper (Refereed)
    Abstract [en]

    The greatest concern facing renewable energy sources like wind is the uncertainty in production volumes as their generation ability is inherently dependent on weather conditions. When providing forecasts for newly commissioned wind farms there is a limited amount of historical power production data, while the number of potential features from different weather forecast providers is vast. Bayesian regularization is therefore seen as a possible technique for reducing model overfitting problems that may arise. This work investigates Bayesian Neural Networks for one-hour ahead forecasting of wind power generation. Initial results show that Bayesian Neural Networks display equivalent predictive performance to Neural Networks trained by Maximum Likelihood. Further results show that Bayesian Neural Networks become superior after removing irrelevant features using Automatic Relevance Determination(ARD).

  • 33.
    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.

  • 34.
    Prasad, G.
    et al.
    University of Ulster.
    Herman, Pawel Andrzej
    University of Ulster.
    Coyle, D.
    University of Ulster.
    McDonough, S.
    University of Ulster.
    Crosbie, J.
    University of Ulster.
    Applying a brain-computer interface to support motor imagery practice in people with stroke for upper limb recovery: A feasibility study2010In: Journal of NeuroEngineering and Rehabilitation, ISSN 1743-0003, E-ISSN 1743-0003, Vol. 7, no 1, p. 60-Article in journal (Refereed)
    Abstract [en]

    There is now sufficient evidence that using a rehabilitation protocol involving motor imagery (MI) practice in conjunction with physical practice (PP) of goal-directed rehabilitation tasks leads to enhanced functional recovery of paralyzed limbs among stroke sufferers. It is however difficult to confirm patient engagement during an MI in the absence of any on-line measure. Fortunately an EEG-based brain-computer interface (BCI) can provide an on-line measure of MI activity as a neurofeedback for the BCI user to help him/her focus better on the MI task. However initial performance of novice BCI users may be quite moderate and may cause frustration. This paper reports a pilot study in which a BCI system is used to provide a computer game-based neurofeedback to stroke participants during the MI part of a protocol. Methods. The participants included five chronic hemiplegic stroke sufferers. Participants received up to twelve 30-minute MI practice sessions (in conjunction with PP sessions of the same duration) on 2 days a week for 6 weeks. The BCI neurofeedback performance was evaluated based on the MI task classification accuracy (CA) rate. A set of outcome measures including action research arm test (ARAT) and grip strength (GS), was made use of in assessing the upper limb functional recovery. In addition, since stroke sufferers often experience physical tiredness, which may influence the protocol effectiveness, their fatigue and mood levels were assessed regularly. Results. Positive improvement in at least one of the outcome measures was observed in all the participants, while improvements approached a minimal clinically important difference (MCID) for the ARAT. The on-line CA of MI induced sensorimotor rhythm (SMR) modulation patterns in the form of lateralized event-related desynchronization (ERD) and event-related synchronization (ERS) effects, for novice participants was in a moderate range of 60-75% within the limited 12 training sessions. The ERD/ERS change from the first to the last session was statistically significant for only two participants. Conclusions. Overall the crucial observation is that the moderate BCI classification performance did not impede the positive rehabilitation trends as quantified with the rehabilitation outcome measures adopted in this study. Therefore it can be concluded that the BCI supported MI is a feasible intervention as part of a post-stroke rehabilitation protocol combining both PP and MI practice of rehabilitation tasks. Although these findings are promising, the scope of the final conclusions is limited by the small sample size and the lack of a control group.

  • 35.
    Prasad, G.
    et al.
    University of Ulster.
    Herman, Pawel Andrzej
    University of Ulster.
    Coyle, D.
    University of Ulster.
    McDonough, S.
    University of Ulster.
    Crosbie, J.
    University of Ulster.
    Using motor imagery based brain-computer interface for post-stroke rehabilitation2009In: 2009 4TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING, 2009, p. 251-255Conference paper (Refereed)
    Abstract [en]

    There is now sufficient evidence that using a rehabilitation protocol involving motor imagery (MI) practice (or mental practice (MP)) in conjunction with physical practice (PP) of goal-directed rehabilitation tasks leads to enhanced functional recovery of paralyzed limbs among stroke sufferers. It is however difficult to ensure patient engagement during MP in the absence of any on-line measure of the MP. Fortunately in an EEG-based brain-computer interface (BCI), an on-line measure of MI activity is used to devise neurofeedback for the BCI user to help him/her focus better on the task. This paper reports a pilot study in which an EEG-based BCI system is used to provide neurofeedback to stroke participants during the MP part of the rehabilitation protocol. This helps patients to undertake the MP with stronger focus. The participants included five chronic stroke sufferers. The trial was undertaken for 12 sessions over a period of 6 weeks. A set of rehabilitation outcome measures including action research arm test (ARAT) and motricity index was made use of in assessing functional recovery. Moderate improvements approaching a minimal clinically important difference (MCID) were observed for the ARAT. Small positive improvements were also observed in other outcome measures. Participants appeared highly enthusiastic about participating in the study and regularly attended all the sessions. Although without a randomized control trial, it is difficult to ascertain whether the enhanced rehabilitation gain is primarily because of BCI neurofeedack, the positive gains in outcome measures demonstrate the potential and feasibility of using BCI for post-stroke rehabilitation.

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