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  • 1.
    Akhmetova, Dana
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Iakymchuk, Roman
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Ekeberg, Örjan
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Laure, Erwin
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Performance study of multithreaded MPI and Openmp tasking in a large scientific code2017In: Proceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 756-765, article id 7965119Conference paper (Refereed)
    Abstract [en]

    With a large variety and complexity of existing HPC machines and uncertainty regarding exact future Exascale hardware, it is not clear whether existing parallel scientific codes will perform well on future Exascale systems: they can be largely modified or even completely rewritten from scratch. Therefore, now it is important to ensure that software is ready for Exascale computing and will utilize all Exascale resources well. Many parallel programming models try to take into account all possible hardware features and nuances. However, the HPC community does not yet have a precise answer whether, for Exascale computing, there should be a natural evolution of existing models interoperable with each other or it should be a disruptive approach. Here, we focus on the first option, particularly on a practical assessment of how some parallel programming models can coexist with each other. This work describes two API combination scenarios on the example of iPIC3D [26], an implicit Particle-in-Cell code for space weather applications written in C++ and MPI plus OpenMP. The first scenario is to enable multiple OpenMP threads call MPI functions simultaneously, with no restrictions, using an MPI THREAD MULTIPLE thread safety level. The second scenario is to utilize the OpenMP tasking model on top of the first scenario. The paper reports a step-by-step methodology and experience with these API combinations in iPIC3D; provides the scaling tests for these implementations with up to 2048 physical cores; discusses occurred interoperability issues; and provides suggestions to programmers and scientists who may adopt these API combinations in their own codes.

  • 2. Bem, T.
    et al.
    Cabelguen, J. M.
    Ekeberg, Örjan
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Grillner, S.
    From swimming to walking: a single basic network for two different behaviors2003In: Biological Cybernetics, ISSN 0340-1200, E-ISSN 1432-0770, Vol. 88, no 2, p. 79-90Article in journal (Refereed)
    Abstract [en]

    In this paper we consider the hypothesis that the spinal locomotor network controlling trunk movements has remained essentially unchanged during the evolutionary transition from aquatic to terrestrial locomotion. The wider repertoire of axial motor patterns expressed by amphibians would then be explained by the influence from separate limb pattern generators, added during this evolution. This study is based on EMG data recorded in vivo from epaxial musculature in the newt Pleurodeles waltl during unrestrained swimming and walking, and on a simplified model of the lamprey spinal pattern generator for swimming. Using computer simulations, we have examined the output generated by the lamprey model network for different input drives. Two distinct inputs were identified which reproduced the main features of the swimming and walking motor patterns in the newt. The swimming pattern is generated when the network receives tonic excitation with local intensity gradients near the neck and girdle regions. To produce the walking pattern, the network must receive (in addition to a tonic excitation at the girdles) a phasic drive which is out of phase in the neck and tail regions in relation to the middle part of the body. To fit the symmetry of the walking pattern, however, the intersegmental connectivity of the network had to be modified by reversing the direction of the crossed inhibitory pathways in the rostral part of the spinal cord. This study suggests that the 'input drive required for the generation of the distinct walking pattern could, at least partly, be attributed to mechanosensory feedback received by the network directly from the intraspinal stretch-receptor system. Indeed, the input drive required resembles the pattern of activity of stretch receptors sensing the lateral bending of the trunk, as expressed during walking in urodeles. Moreover, our results indicate that a nonuniform distribution of these stretch receptors along the trunk can explain the discontinuities exhibited in the swimming pattern of the newt. Thus, original network controlling axial movements not only through a direct coupling at the central level but also via a mechanical coupling between trunk and limbs, which in turn influences the sensory signals sent back to the network. Taken together, our findings support the hypothesis of a phylogenetic conservatism of the spinal locomotor networks generating axial motor patterns from agnathans to amphibians.

  • 3.
    Bicanski, Andrej
    et al.
    School of Engineering, École Polytechnique Fédérale de Lausanne.
    Ryczko, Dimitri
    Département de Physiologie, Université de Montréa.
    Knuesel, Jérémie
    School of Engineering, École Polytechnique Fédérale de Lausanne.
    Harischandra, Nalin
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Charrier, Vanessa
    INSERM U862, Neurocentre Magendie, Université Bordeaux.
    Ekeberg, Örjan
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Cabelguen, Jean-Marie
    Neurocentre Magendie, Bordeaux University, Bordeaux Cedex, France.
    Ijspeert, Auke Jan
    School of Engineering, École Polytechnique Fédérale de Lausanne.
    Decoding the mechanisms of gait generation in salamanders by combining neurobiology, modeling and robotics2013In: Biological Cybernetics, ISSN 0340-1200, E-ISSN 1432-0770, Vol. 107, no 5, p. 545-564Article, review/survey (Refereed)
    Abstract [en]

    Vertebrate animals exhibit impressive locomotor skills. These locomotor skills are due to the complex interactions between the environment, the musculo-skeletal system and the central nervous system, in particular the spinal locomotor circuits. We are interested in decoding these interactions in the salamander, a key animal from an evolutionary point of view. It exhibits both swimming and stepping gaits and is faced with the problem of producing efficient propulsive forces using the same musculo-skeletal system in two environments with significant physical differences in density, viscosity and gravitational load. Yet its nervous system remains comparatively simple. Our approach is based on a combination of neurophysiological experiments, numerical modeling at different levels of abstraction, and robotic validation using an amphibious salamander-like robot. This article reviews the current state of our knowledge on salamander locomotion control, and presents how our approach has allowed us to obtain a first conceptual model of the salamander spinal locomotor networks. The model suggests that the salamander locomotor circuit can be seen as a lamprey-like circuit controlling axial movements of the trunk and tail, extended by specialized oscillatory centers controlling limb movements. The interplay between the two types of circuits determines the mode of locomotion under the influence of sensory feedback and descending drive, with stepping gaits at low drive, and swimming at high drive.

  • 4. Chiel, Hillel J.
    et al.
    Ting, Lena H.
    Ekeberg, Örjan
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Hartmann, Mitra J. Z.
    The Brain in Its Body: Motor Control and Sensing in a Biomechanical Context2009In: Journal of Neuroscience, ISSN 0270-6474, E-ISSN 1529-2401, Vol. 29, no 41, p. 12807-12814Article in journal (Refereed)
    Abstract [en]

    Although it is widely recognized that adaptive behavior emerges from the ongoing interactions among the nervous system, the body, and the environment, it has only become possible in recent years to experimentally study and to simulate these interacting systems. We briefly review work on molluscan feeding, maintenance of postural control in cats and humans, simulations of locomotion in lamprey, insect, cat and salamander, and active vibrissal sensing in rats to illustrate the insights that can be derived from studies of neural control and sensing within a biomechanical context. These studies illustrate that control may be shared between the nervous system and the periphery, that neural activity organizes degrees of freedom into biomechanically meaningful subsets, that mechanics alone may play crucial roles in enforcing gait patterns, and that mechanics of sensors is crucial for their function.

  • 5. De Schutter, E.
    et al.
    Ekeberg, Örjan
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Achard, P.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Biophysically detailed modelling of microcircuits and beyond2005In: TINS - Trends in Neurosciences, ISSN 0166-2236, E-ISSN 1878-108X, Vol. 28, no 10, p. 562-569Article, review/survey (Refereed)
    Abstract [en]

    Realistic bottom-up modelling has been seminal to understanding which properties of microcircuits control their dynamic behaviour, such as the locomotor rhythms generated by central pattern generators. In this article of the TINS Microcircuits Special Feature, we review recent modelling work on the leech-heartbeat and lamprey-swimming pattern generators as examples. Top-down mathematical modelling also has an important role in analyzing microcircuit properties but it has not always been easy to reconcile results from the two modelling approaches. Most realistic microcircuit models are relatively simple and need to be made more detailed to represent complex processes more accurately. We review methods to add neuromechanical feedback, biochemical pathways or full dendritic morphologies to microcircuit models. Finally, we consider the advantages and challenges of full-scale simulation of networks of microcircuits.

  • 6.
    Djurfeldt, Mikael
    et al.
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Ekeberg, Örjan
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Graybiel, Ann M.
    Brain and Cognitive Sciences, MIT, Cambridge, Boston, U.S.A:.
    Cortex-basal ganglia interaction and attractor states2001In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 38-40, p. 573-579Article in journal (Refereed)
    Abstract [en]

    We propose a set of hypotheses about how the basal ganglia contribute to information processing in cortical networks and how the cortex and basal ganglia interact during learning and behavior. We introduce a computational model on the level of system of networks. We suggest that the basal ganglia control cortical activity by pushing a local cortical network into a new attractor state, thereby selecting certain attractors over others. The ideas of temporal difference learning and convergence of corticostriatal fibers from multiple cortical areas within the striatum are combined in a modular learning system capable of acquiring behavior with sequential structure.

  • 7.
    Djurfeldt, Mikael
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Ekeberg, Örjan
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Large-scale modeling - a tool for conquering the complexity of the brain2008In: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 2, p. 1-4Article in journal (Refereed)
    Abstract [en]

    Is there any hope of achieving a thorough understanding of higher functions such as perception, memory, thought and emotion or is the stunning complexity of the brain a barrier which will limit such efforts for the foreseeable future? In this perspective we discuss methods to handle complexity, approaches to model building, and point to detailed large-scale models as a new contribution to the toolbox of the computational neuroscientist. We elucidate some aspects which distinguishes large-scale models and some of the technological challenges which they entail.

  • 8.
    Djurfeldt, Mikael
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Hjorth, Johannes
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Eppler, Jochen
    Honda Research Institute.
    Dudani, Niraj
    Helias, Moritz
    University of Freiburg, Germany.
    Potjans, Tobias
    Bhalla, Upinder
    Diesmann, Markus
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Ekeberg, Örjan
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Run-Time Interoperability Between Neuronal Network Simulators Based on the MUSIC Framework2010In: Neuroinformatics, ISSN 1539-2791, E-ISSN 1559-0089, Vol. 8, no 1, p. 43-60Article in journal (Refereed)
    Abstract [en]

    MUSIC is an API allowing large scale neuron simulators using MPI internally to exchange data during runtime. We provide experiences from the adaptation of two neuronal network simulators of different kinds, NEST and MOOSE, to this API. A multi-simulation of a cortico-striatal network model involving both simulators is performed, demonstrating how MUSIC can promote inter-operability between models written for different simulators and how these can be re-used to build a larger model system. We conclude that MUSIC fulfills the design goals of being portable and simple to adapt to existing simulators. In addition, since the MUSIC API enforces independence between the applications, the multi-simulationcould be built from pluggable component modules without adaptation of the components to each other in terms of simulation time-step or topology of connections between the modules.

  • 9.
    Djurfeldt, Mikael
    et al.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for High Performance Computing, PDC.
    Johansson, Christopher
    KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
    Ekeberg, Örjan
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Rehn, Martin
    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.
    Massively parallel simulation of brain-scale neuronal network models2005Report (Other academic)
  • 10.
    Djurfeldt, Mikael
    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.
    Johansson, Christopher
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Rehn, Martin
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Ekeberg, Örjan
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Brain-scale simulation of the neocortex on the IBM Blue Gene/L  supercomputer2008In: IBM Journal of Research and Development, ISSN 0018-8646, E-ISSN 2151-8556, Vol. 52, no 1-2, p. 31-41Article in journal (Refereed)
    Abstract [en]

    Biologically detailed large-scale models of the brain can now be simulated thanks to increasingly powerful massively parallel supercomputers. We present an overview, for the general technical reader, of a neuronal network model of layers II/III of the neocortex built with biophysical model neurons. These simulations, carried out on an IBM Blue Gene/Le supercomputer, comprise up to 22 million neurons and 11 billion synapses, which makes them the largest simulations of this type ever performed. Such model sizes correspond to the cortex of a small mammal. The SPLIT library, used for these simulations, runs on single-processor as well as massively parallel machines. Performance measurements show good scaling behavior on the Blue Gene/L supercomputer up to 8,192 processors. Several key phenomena seen in the living brain appear as emergent phenomena in the simulations. We discuss the role of this kind of model in neuroscience and note that full-scale models may be necessary to preserve natural dynamics. We also discuss the need for software tools for the specification of models as well as for analysis and visualization of output data. Combining models that range from abstract connectionist type to biophysically detailed will help us unravel the basic principles underlying neocortical function.

  • 11.
    Djurfeldt, Mikael
    et al.
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Sandberg, Anders
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Ekeberg, Örjan
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Lansner, Anders
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    See-A framework for simulation of biologically detailed and artificial neural networks and systems1999In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 26-27, p. 997-1003Article in journal (Refereed)
    Abstract [en]

    See is a software framework for simulation of biologically detailed and artficial neural networks and systems. It includes a general purpose scripting language, based on Scheme,which also can be used interactively, while the basic framework is written in C++. Models can be built on the Scheme level from `simulation objectsa, each representing a population ofneurons, a projection, etc. The simulator provides a flexible and efficient protocol for data transfer between such objects. See contains a user interface to the parallelized, platformindependent, library SPLIT intended for biologically detailed modeling of large-scale networks and is easy to extend with new user code, both on the C++ and Scheme levels.

  • 12.
    Ekeberg, Örjan
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    A combined neuronal and mechanical model of fish swimming1993In: Biological Cybernetics, ISSN 0340-1200, E-ISSN 1432-0770, Vol. 69, no 5-6, p. 363-374Article in journal (Refereed)
    Abstract [en]

    A simulated neural network has been connected to a simulated mechanical environment. The network is based on a model of the spinal central pattern generator producing rhythmic swimming movements in the lamprey and the model is similar to that used in earlier simulations of fictive swimming. Here, the network has been extended with a model of how motoneuron activity is transformed via the muscles to mechanical forces. Further, these forces are used in a two-dimensional mechanical model including interaction with the surrounding water, giving the movements of the different parts of the body. Finally, these movements are fed back through stretch receptors interacting with the central pattern generator. The combined model provides a platform for various simulation experiments relating the currently known neural properties and connectivity to the behavior of the animal in vivo. By varying a small set of parameters, corresponding to brainstem input to the spinal network, a variety of basic locomotor behaviors, like swimming at different speeds and turning can be produced. This paper describes the combined model and its basic properties.

  • 13.
    Ekeberg, Örjan
    et al.
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Blümel, Marcus
    University of Köln.
    Büschges, Ansgar
    University of Köln.
    Dynamic simulation of insect walking2004In: Arthropod structure & development, ISSN 1467-8039, E-ISSN 1873-5495, Vol. 33, no 3, p. 287-300Article in journal (Refereed)
    Abstract [en]

    Insect walking relies on a complex interaction between the environment, body segments, muscles and the nervous system. For the stick insect in particular, previous investigations have highlighted the role of specific sensory signals in the timing of activity of central neural networks driving the individual leg joints. The objective of the current study was to relate specific sensory and neuronal mechanisms, known from experiments on reduced preparations, to the generation of the natural sequence of events forming the step cycle in a single leg. We have done this by simulating a dynamic 3D-biomechanical model of the stick insect coupled to a reduced model of the neural control system, incorporating only the mechanisms under study. The neural system sends muscle activation levels to the biomechanical system, which in turn provides correctly timed propriosensory signals back to the neural model. The first simulations were designed to test if the currently known mechanisms would be sufficient to explain the coordinated activation of the different leg muscles in the middle leg. Two experimental situations were mimicked: restricted stepping where only the coxatrochanteral joint and the femur-tibia joint were free to move, and the unrestricted single leg movements on a friction-free surface. The first of these experimental situations is in fact similar to the preparation used in gathering much of the detailed knowledge on sensory and neuronal mechanisms. The simulations show that the mechanisms included can indeed account for the entire step cycle in both situations. The second aim was to test to what extent the same sensory and neuronal mechanisms would be adequate also for controlling the front and hind legs, despite the large differences in both leg morphology and kinematic patterns. The simulations show that front leg stepping can be generated by basically the same mechanisms while the hind leg control requires some reorganization. The simulations suggest that the influence from the femoral chordotonal organs on the network controlling levation-depression may have a reversed effect in the hind legs as compared to the middle and front legs. This, and other predictions from the model will have to be confirmed by additional experiments.

  • 14.
    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.

  • 15.
    Ekeberg, Örjan
    et al.
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Grillner, Sten
    Karolinska Institutet.
    Simulations of neuromuscular control in lamprey swimming1999In: Philosophical Transactions of the Royal Society of London. Biological Sciences, ISSN 0962-8436, E-ISSN 1471-2970, Vol. 354, no 1385, p. 895-902Article in journal (Refereed)
    Abstract [en]

    The neuronal generation of vertebrate locomotion has been extensively studied in the lamprey. Models at different levels of abstraction are being used to describe this system, from abstract nonlinear oscillators to interconnected model neurons comprising multiple compartments and a Hodgkin-Huxley representation of the most relevant ion channels. To study the role of sensory feedback by simulation, it eventually also becomes necessary to incorporate the mechanical movements in the models. By using simplifying models of muscle activation, body mechanics, counteracting water forces, and sensory feedback through stretch receptors and vestibular organs, we have been able to close the feedback loop to enable studies of the interaction between the neuronal and the mechanical systems. The neuromechanical simulations reveal that the currently known network is sufficient for generating a whole repertoire of swimming patterns. Swimming at different speeds and with different wavelengths, together with the performance of lateral turns can all be achieved by simply varying the brainstem input. The neuronal mechanisms behind pitch and roll manoeuvres are less clear. We have put forward a 'crossed-oscillators' hypothesis where partly separate dorsal and ventral circuits are postulated. Neuromechanical simulations of this system show that it is also capable of generating realistic pitch turns and rolls, and that vestibular signals can stabilize the posture during swimming.

  • 16.
    Ekeberg, Örjan
    et al.
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Grillner, Sten
    Karolinska Institutet.
    Lansner, Anders
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    The Neural Control of Fish Swimming studied through Numerical Simulations1995In: Adaptive Behavior, ISSN 1059-7123, E-ISSN 1741-2633, Vol. 3, no 4, p. 363-384Article in journal (Refereed)
    Abstract [en]

    The neuronal generation of vertebrate locomotion has been extensively studied in the lamprey. Computer simulations of this system have been carried out with different aims and with different techniques. in this article, we review some of these simulations, particularly those leading toward models that describe She interaction that occurs between the neuronal system and its mechanical environment during swimming. Here we extend these models, enabling two new experiments to be conducted. The first one addresses the role of sensory feedback by exposing the neuromechanical system to unexpected perturbations. The second one tests the validity of an earlier proposed hypothesis for the neural generation of three-dimensional (3D) steering by coupling this central pattern generator to a mechanical 3D simulation.

  • 17.
    Ekeberg, Örjan
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Pearson, Keir
    Univ of Alberta.
    Computer simulation of stepping in the hind legs of the cat: An examination of mechanisms regulating the stance-to-swing transition2005In: Journal of Neurophysiology, ISSN 0022-3077, E-ISSN 1522-1598, Vol. 94, no 6, p. 4256-4268Article in journal (Refereed)
    Abstract [en]

    Physiological studies in walking cats have indicated that two sensory signals are involved in terminating stance in the hind legs: one related to unloading of the leg and the other to hip extension. To study the relative importance of these two signals, we developed a three- dimensional computer simulation of the cat hind legs in which the timing of the swing- to- stance transition was controlled by signals related to the force in ankle extensor muscles, the angle at the hip joint, or a combination of both. Even in the absence of direct coupling between the controllers for each leg, stable stepping was easily obtained using either a combination of ankle force and hip position signals or the ankle force signal alone. Stable walking did not occur when the hip position signal was used alone. Coupling the two controllers by mutual inhibition restored stability, but it did not restore the correct timing of stepping of the two hind legs. Small perturbations applied during the swing phase altered the movement of the contralateral leg in a manner that tended to maintain alternating stepping when the ankle force signal was included but tended to shift coordination away from alternating when the hip position signal was used alone. We conclude that coordination of stepping of the hind legs depends critically on load- sensitive signals from each leg and that mechanical linkages between the legs, mediated by these signals, play a significant role in establishing the alternating gait.

  • 18.
    Fagergren, Anders
    et al.
    Karolinska Institutet.
    Ekeberg, Örjan
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Forssberg, Hans
    Karolinska Institutet.
    Control strategies correcting inaccurately programmed fingertip forces: Model predictions derived from human behavior2003In: Journal of Neurophysiology, ISSN 0022-3077, E-ISSN 1522-1598, Vol. 89, no 6, p. 2904-2916Article in journal (Refereed)
    Abstract [en]

    When picking up a familiar object between the index finger and the thumb, the motor commands are predetermined by the CNS to correspond to the frictional demand of the finger-object contact area. If the friction is less than expected, the object will start to slip out of the hand, giving rise to unexpected sensory information. Here we study the correction strategies of the motor system in response to an unexpected frictional demand. The motor commands to the mononeuron pool are estimated by a novel technique combining behavioral recordings and neuromuscular modelling. We first propose a mathematical model incorporating muscles, hand mechanics, and the action of lifting an object. A simple control system sends motor commands to and receives sensory signals from the model. We identify three factors influencing the efficiency of the correction: the time development of the motor command, the delay between the onset of the grip and load forces (GF-LF-delay), and how fast the lift is performed. A sensitivity analysis describes how these factors affect the ability to prevent or stop slipping and suggests an efficient control strategy that prepares and corrects for an altered frictional condition. We then analyzed fingertip grip and load forces (GF and LF) and position data from 200 lifts made by five healthy subjects. The friction was occasionally reduced, forcing an increase of the GF to prevent the object being dropped. The data were then analyzed by feeding it through the inverted model. This provided an estimate of the motor commands to the motoneuron pool. As suggested by the sensitivity analysis the GF-LF-delay was indeed used by the subjects to prevent slip. In agreement with recordings from neurons in the primary motor cortex of the monkey, a sharp burst in the estimated GF motor command (NGF) efficiently arrested any slip. The estimated motor commands indicate a control system that uses a small set of corrective commands, which together with the GF-LF-delay form efficient correction strategies. The selection of a strategy depends on the amount of tactile information reporting unexpected friction and how long it takes to arrive. We believe that this technique of estimating the motor commands behind the fingertip forces during a precision grip lift can provide a powerful tool for the investigation of the central control of the motor system.

  • 19.
    Fagergren, Anders
    et al.
    Karolinska Institutet.
    Ekeberg, Örjan
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Forssberg, Hans
    Karolinska Institutet.
    Precision grip force dynamics: A system identification approach2000In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 47, no 10, p. 1366-1375Article in journal (Refereed)
    Abstract [en]

    A linear model of the dynamics of the human precision grip is presented. The transfer function is identified as representing the peripheral motor subsystem, from the motoneuron pool to the final production of a grip force between the tip of the index finger and the thumb. The transfer function captures the limiting isometric muscle dynamics that, e.g., cortical motor areas have to act through. When identifying the transfer function we introduce a novel technique, common subsystem identification. This characterizes a specific subsystem in a complex biomechanical system. This technique requires data from two functionally different experiments that both involve the subsystem of interest. Two transfer functions, one for each experiment, are then estimated using a linear black box technique. The common mathematical factors, represented by poles and zeros, are used to form a new transfer function. It is concluded that this transfer function represents the common biological subsystem involved in both experiments. Here, we use one active and one reactive isometric grip force experiment to capture the subsystem of interest, i.e., the motoneuron pool, motor units, muscles, tendons and fingertip tissue. The characteristics of the dynamics are in agreement with previously published experiments on human neuro-muscular systems. The model, H(s) = 280/(s(2) + 22s + 280), is well suited for the representation of a force producing end-effector in simulations including a control system with sensory feedback.

  • 20.
    Fransén, Erik
    et al.
    KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
    Kozlov, Alexander
    KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
    Xie, Yuecong
    KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
    Christensen, C.
    KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
    Djurfeldt, Mikael
    KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
    Ekeberg, Örjan
    KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
    Evaluation of model scalability in parallel neural simulators2005Conference paper (Refereed)
    Abstract [en]

    A long standing belief in neuroscience has been that the brain and specifically the neocortex obtains its computational power by massive parallelism. Albeit conceptually appealing, this notion that effective processing requires large networks has not been possible to test in detailed simulations. In one project, we intend to study the generation of theta activity in the entorhinal-hippocampal system. Several simulation studies indicate that frequency and synchronization of the oscillation generated may depend on density of connectivity and/or geometry of connections. In a second project, we are studying how a model of early visual processing scales towards realistic sizes. To effectively evaluate the model, it must be scaled up to sizes where processing demands from the input given are sufficiently high, and where network size is made sufficiently large to process this information.

    We have in preliminary studies tested two parallel simulators. One is a version of pGENESIS supporting MPI from University of Sunderland, UK. The other is Split, a software produced in our own laboratory. Both have been tested on an Itanium2 cluster. Tests include variable number of processors and scaling number of neurons/compartments or number of synapses. In these simulations, average spike frequency in the network is also varied. The aim is to identify main bottle-necks. For instance, we foresee the need to parallelize the construction/layout of synapses.

  • 21.
    Hammarlund, Per
    et al.
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Ekeberg, Örjan
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Large neural network simulations on multiple hardware platforms1998In: Journal of Computational Neuroscience, ISSN 0929-5313, E-ISSN 1573-6873, Vol. 5, no 4, p. 443-459Article in journal (Refereed)
    Abstract [en]

    To efficiently simulate very large networks of interconnected neurons, particular consideration has to be given to the computer architecture being used. This article presents techniques for implementing simulators for large neural networks on a number of different computer architectures. The neuronal simulation task and the computer architectures of interest are first characterized, and the potential bottlenecks are highlighted. Then we describe the experience gained from adapting an existing simulator, sWIM, to two very different architectures-vector computers and multiprocessor workstations. This work lead to the implementation of a new simulation library, SPLIT, designed to allow efficient simulation of large networks on several architectures. Different computer architectures put different demands on the organization of both data structures and computations. Strict separation of such architecture considerations from the neuronal models and other simulation aspects makes it possible to construct both portable and extendible code.

  • 22.
    Harischandra, Nalin
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Cabelguen, Jean-Marie
    Neurocentre Magendie, Bordeaux University, Bordeaux Cedex, France.
    Ekeberg, Örjan
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    A 3D musculo-mechanical model of the salamander for the study of different gaits and modes of locomotion2010In: Frontiers in neurorobotics, ISSN 1662-5218, Vol. 4, p. 112-Article in journal (Refereed)
    Abstract [en]

    Computer simulation has been used to investigate several aspects of locomotion in salamanders. Here we introduce a three-dimensional forward dynamics mechanical model of a salamander, with physically realistic weight and size parameters. Movements of the four limbs and of the trunk and tail are generated by sets of linearly modeled skeletal muscles. In this study, activation of these muscles were driven by prescribed neural output patterns. The model was successfully used to mimic locomotion on level ground and in water. We compare the walking gait where a wave of activity in the axial muscles travels between the girdles, with the trotting gait in simulations using the musculo-mechanical model. In a separate experiment, the model is used to compare different strategies for turning while stepping; either by bending the trunk or by using side-stepping in the front legs. We found that for turning, the use of side-stepping alone or in combination with trunk bending, was more effective than the use of trunk bending alone. We conclude that the musculo-mechanical model described here together with a proper neural controller is useful for neuro-physiological experiments in silico.

  • 23.
    Harischandra, Nalin
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Ekeberg, Örjan
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    System identification of muscle-joint interactions of the cat hind limb during locomotion2008In: Biological Cybernetics, ISSN 0340-1200, E-ISSN 1432-0770, Vol. 99, no 2, p. 125-138Article in journal (Refereed)
    Abstract [en]

    Neurophysiological experiments in walking cats have shown that a number of neural control mechanisms are involved in regulating the movements of the hind legs during locomotion. It is experimentally hard to isolate individual mechanisms without disrupting the natural walking pattern and we therefore introduce a different approach where we use a model to identify what control is necessary to maintain stability in the musculo-skeletal system. We developed a computer simulation model of the cat hind legs in which the movements of each leg are produced by eight limb muscles whose activations follow a centrally generated pattern with no proprioceptive feedback. All linear transfer functions, from each muscle activation to each joint angle, were identified using the response of the joint angle to an impulse in the muscle activation at 65 postures of the leg covering the entire step cycle. We analyzed the sensitivity and stability of each muscle action on the joint angles by studying the gain and pole plots of these transfer functions. We found that the actions of most of the hindlimb muscles display inherent stability during stepping, even without the involvement of any proprioceptive feedback mechanisms, and that those musculo-skeletal systems are acting in a critically damped manner, enabling them to react quickly without unnecessary oscillations. We also found that during the late swing, the activity of the posterior biceps/semitendinosus (PB/ST) muscles causes the joints to be unstable. In addition, vastus lateralis (VL), tibialis anterior (TA) and sartorius (SAT) muscle-joint systems were found to be unstable during the late stance phase, and we conclude that those muscles require neuronal feedback to maintain stable stepping, especially during late swing and late stance phases. Moreover, we could see a clear distinction in the pole distribution (along the step cycle) for the systems related to the ankle joint from that of the other two joints, hip or knee. A similar pattern, i.e., a pattern in which the poles were scattered over the s-plane with no clear clustering according to the phase of the leg position, could be seen in the systems related to soleus (SOL) and TA muscles which would indicate that these muscles depend on neural control mechanisms, which may involve supraspinal structures, over the whole step cycle.

  • 24.
    Harischandra, Nalin
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Knuesel, Jeremei
    EPFL.
    Kozlov, Alexander
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Bicanski, Andrej
    EPFL.
    Cabelguen, Jean-Marie
    Neurocentre Magendie, Bordeaux University, Bordeaux Cedex, France.
    Ijspeert, Auke
    EPFL.
    Ekeberg, Örjan
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Sensory feedback plays a significant role in generating walking gait and in gait transition in salamanders: a simulation study2011In: Frontiers in Neurorobotics, ISSN 1662-5218, Vol. 5, p. 3:1-3:13Article in journal (Refereed)
    Abstract [en]

    Here, we investigate the role of sensory feedback in gait generation and transition by using a three-dimensional, neuro-musculo-mechanical model of a salamander with realistic physical parameters. Activation of limb and axial muscles were driven by neural output patterns obtained from a central pattern generator (CPG) which is composed of simulated spiking neurons with adaptation. The CPG consists of a body-CPG and four limb-CPGs that are interconnected via synapses both ipsilaterally and contralaterally. We use the model both with and without sensory modulation and four different combinations of ipsilateral and contralateral coupling between the limb-CPGs. We found that the proprioceptive sensory inputs are essential in obtaining a coordinated lateral sequence walking gait (walking). The sensory feedback includes the signals coming from the stretch receptor like intraspinal neurons located in the girdle regions and the limb stretch receptors residing in the hip and scapula regions of the salamander. On the other hand, walking trot gait (trotting) is more under central (CPG) influence compared to that of the peripheral or sensory feedback. We found that the gait transition from walking to trotting can be induced by increased activity of the descending drive coming from the mesencephalic locomotor region and is helped by the sensory inputs at the hip and scapula regions detecting the late stance phase. More neurophysiological experiments are required to identify the precise type of mechanoreceptors in the salamander and the neural mechanisms mediating the sensory modulation.

  • 25.
    Johansson, Christopher
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Ekeberg, Örjan
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Clustering of stored memories in an attractor network with local competition2006In: International Journal of Neural Systems, ISSN 0129-0657, E-ISSN 1793-6462, Vol. 16, no 6, p. 393-403Article in journal (Refereed)
    Abstract [en]

    In this paper we study an attractor network with units that compete locally for activation and we prove that a reduced version of it has fixpoint dynamics. An analysis, complemented by simulation experiments, of the local characteristics of the network's attractors with respect to a parameter controlling the intensity of the local competition is performed. We find that the attractors are hierarchically clustered when the parameter of the local competition is changed

  • 26.
    Kamali Sarvestani, Iman
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Ekeberg, Örjan
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Internal Connectivity of the GlobusPallidus and the Arbitration System2013Manuscript (preprint) (Other academic)
    Abstract [en]

    The rodent globus pallidus (homologue of primate external globus pallidus) has been shown to be composed of two types neuronal groups based on their location and local axon collaterals. The rostral outer layer near the striatopallidal border (GPr) has shorter but more dense local axon collaterals while the caudal inner layer (GPc) has wider and less dense axon collaterals. Moreover, the connection between the two segments is unidirectional with outer layer neurons sending inhibitory projections to the inner layer. Both segments inhibit the substantia nigra and the entopeduncular nucleus (homologue of primate internal globus pallidus). We have created a model of the basal ganglia arbitration subsystem composed of the subthalamic nucleus, the two segments of the pallidus as well as the entopeduncular nucleus and the substantia nigra in order to assess functional roles of the two pallidal segments. The simulations reveal that both segments of the pallidum are involved in winner-take-all structure of the arbitration system but the type of information competing is different in the two subsystems. In the STN-GPr network, strong lateral inhibition between pallidal neurons representing muscles leads to selection of a muscle which has been (due to noise or other reasons) randomly overactivated. In contrast, in STN-GPc network actions (each utilizing many muscles) compete. Our simulations suggest that both networks are active during selection and execution of movements. If overactivation of a muscle is accompanied with dopamine flow, the GPr-GPc connection together with local axonal network of GPc suppress other muscles and reinforce the muscle whose overactivity has caused the dopaminergic flow. Simulated lesions of these neuronal groups also show different results. Lesioning GPr results in synchronous activity in GPc and SNr but the mean firing rate of these nuclei remains untouched. Lesioning GPc on the other hand lifts the activity in the SNr drastically but does not create synchrony in any of the nuclei. The results suggest that STN-GPc and STN-GPr can be considered as two different subsystems working both in synergy and in competition.

  • 27.
    Kamali Sarvestani, Iman
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Kozlov, Alexander
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Harischandra, Nalin
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Grillner, Sten
    Karolinska Institutet.
    Ekeberg, Örjan
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    A computational model of visually guided locomotion in lamprey2013In: Biological Cybernetics, ISSN 0340-1200, E-ISSN 1432-0770, Vol. 107, no 5, p. 497-512Article in journal (Refereed)
    Abstract [en]

    This study addresses mechanisms for the generation and selection of visual behaviors in anamniotes. To demonstrate the function of these mechanisms, we have constructed an experimental platform where a simulated animal swims around in a virtual environment containing visually detectable objects. The simulated animal moves as a result of simulated mechanical forces between the water and its body. The undulations of the body are generated by contraction of simulated muscles attached to realistic body components. Muscles are driven by simulated motoneurons within networks of central pattern generators. Reticulospinal neurons, which drive the spinal pattern generators, are in turn driven directly and indirectly by visuomotor centers in the brainstem. The neural networks representing visuomotor centers receive sensory input from a simplified retina. The model also includes major components of the basal ganglia, as these are hypothesized to be key components in behavior selection. We have hypothesized that sensorimotor transformation in tectum and pretectum transforms the place-coded retinal information into rate-coded turning commands in the reticulospinal neurons via a recruitment network mimicking the layered structure of tectal areas. Via engagement of the basal ganglia, the system proves to be capable of selecting among several possible responses, even if exposed to conflicting stimuli. The anatomically based structure of the control system makes it possible to disconnect different neural components, yielding concrete predictions of how animals with corresponding lesions would behave. The model confirms that the neural networks identified in the lamprey are capable of responding appropriately to simple, multiple, and conflicting stimuli.

  • 28.
    Kamali Sarvestani, Iman
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Lindahl, Mikael
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Hellgren Kotaleski, Jeanette
    Ekeberg, Örjan
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    The arbitration-extension hypothesis: A hierarchical interpretation of the functional organization of the basal ganglia2011In: Frontiers in Systems Neuroscience, ISSN 1662-5137, E-ISSN 1662-5137, Vol. 5, p. 13-Article in journal (Refereed)
    Abstract [en]

    Based on known anatomy and physiology, we present a hypothesis where the basal ganglia motor loop is hierarchically organized in two main subsystems: the arbitration system and the extension system. The arbitration system, comprised of the subthalamic nucleus, globus pallidus, and pedunculopontine nucleus, serves the role of selecting one out of several candidate actions as they are ascending from various brain stem motor regions and aggregated in the centromedian thalamus or descending from the extension system or from the cerebral cortex. This system is an action-input/action-output system whose winner-take-all mechanism finds the strongest response among several candidates to execute. This decision is communicated back to the brain stem by facilitating the desired action via cholinergic/glutamatergic projections and suppressing conflicting alternatives via GABAergic connections. The extension system, comprised of the striatum and, again, globus pallidus, can extend the repertoire of responses by learning to associate novel complex states to certain actions. This system is a state-input/action-output system, whose organization enables it to encode arbitrarily complex Boolean logic rules using striatal neurons that only fire given specific constellations of inputs (Boolean AND) and pallidal neurons that are silenced by any striatal input (Boolean OR). We demonstrate the capabilities of this hierarchical system by a computational model where a simulated generic "animal" interacts with an environment by selecting direction of movement based on combinations of sensory stimuli, some being appetitive, others aversive or neutral. While the arbitration system can autonomously handle conflicting actions proposed by brain stem motor nuclei, the extension system is required to execute learned actions not suggested by external motor centers. Being precise in the functional role of each component of the system, this hypothesis generates several readily testable predictions.

  • 29.
    Kann, Viggo
    et al.
    KTH.
    Ekeberg, Örjan
    KTH.
    Student based program development2018In: ITiCSE 2018 Proceedings of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education, Association for Computing Machinery (ACM), 2018, p. 379-379Conference paper (Refereed)
    Abstract [en]

    The aim of this work is to investigate a new method of involving all students in the continued development of an educational program. Using this method, we have obtained a list of well-scrutinized suggestions for improvement that have support among the students, and that we can start to implement. We have also saved a large pool of suggestions that could be used in the future.

  • 30.
    Lindahl, Mikael
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Sarvestani, Iman Kamali
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Ekeberg, Örjan
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Hällgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Signal enhancement in the output stage of the basal ganglia by synaptic short-term plasticity in the direct, indirect, and hyperdirect pathways2013In: Frontiers in Computational Neuroscience, ISSN 1662-5188, E-ISSN 1662-5188, Vol. 7, p. UNSP 76-Article in journal (Refereed)
    Abstract [en]

    Many of the synapses in the basal ganglia display short-term plasticity. Still, computational models have not yet been used to investigate how this affects signaling. Here we use a model of the basal ganglia network, constrained by available data, to quantitatively investigate how synaptic short-term plasticity affects the substantia nigra reticulata (SNr), the basal ganglia output nucleus. We find that SNr becomes particularly responsive to the characteristic burst-like activity seen in both direct and indirect pathway striatal medium spiny neurons (MSN). As expected by the standard model, direct pathway MSNs are responsible for decreasing the activity in SNr. In particular, our simulations indicate that bursting in only a few percent of the direct pathway MSNs is sufficient for completely inhibiting SNr neuron activity. The standard model also suggests that SNr activity in the indirect pathway is controlled by MSNs disinhibiting the subthalamic nucleus (STN) via the globus pallidus externa (GPe). Our model rather indicates that SNr activity is controlled by the direct GPe-SNr projections. This is partly because GPe strongly inhibits SNr but also due to depressing STN-SNr synapses. Furthermore, depressing GPe-SNr synapses allow the system to become sensitive to irregularly firing GPe subpopulations, as seen in dopamine depleted conditions, even when the GPe mean firing rate does not change. Similar to the direct pathway, simulations indicate that only a few percent of bursting indirect pathway MSNs can significantly increase the activity in SNr. Finally, the model predicts depressing STN-SNr synapses, since such an assumption explains experiments showing that a brief transient activation of the hyperdirect pathway generates a tri-phasic response in SNr, while a sustained STN activation has minor effects. This can be explained if STN-SNr synapses are depressing such that their effects are counteracted by the (known) depressing GPe-SNr inputs.

  • 31.
    Lindkvist, Emelie
    et al.
    Stockholm Resilience Centre, Stockholm University.
    Ekeberg, Örjan
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Norberg, Jon
    Stockholm Resilience Centre, Stockholm University.
    Strategies for sustainable management of renewable resources during environmental change2017In: Proceedings of the Royal Society of London. Biological Sciences, ISSN 0962-8452, E-ISSN 1471-2954, Vol. 284, article id 20162762Article in journal (Refereed)
    Abstract [en]

    As a consequence of global environmental change, management strategies that can deal with unexpected change in resource dynamics are becoming increasingly important. In this paper we undertake a novel approach to studying resource growth problems using a computational form of adaptive management to find optimal strategies for prevalent natural resource management dilemmas. We scrutinize adaptive management, or learning-by-doing, to better understand how to simultaneously manage and learn about a system when its dynamics are unknown. We study important trade-offs in decision-making with respect to choosing optimal actions (harvest efforts) for sustainable management during change. This is operationalized through an artificially intelligent model where we analyze how different trends and fluctuations in growth rates of a renewable resource affect the performance of different management strategies. Our results show that the optimal strategy for managing resources with declining growth is capable of managing resources with fluctuating or increasing growth at a negligible cost, creating in a management strategy that is both efficient and robust towards future unknown changes. To obtain this strategy, adaptive management should strive for: high learning rates to new knowledge, high valuation of future outcomes and modest exploration around what is perceived as the optimal action.

  • 32.
    Manfredi, L
    et al.
    Institute for Medical Science and Technology (IMSaT), University of Dundee, Wilson House, 1 Wurzburg Loan, Dundee Medipark, Dundee DD2 1FD, UK.
    Assaf, T.
    Bristol Robotics Laboratory, Frenchay Campus, Bristol BS16 1QY, UK.
    Mintchev, S.
    The BioRobotics Institute, Scuola Superiore Sant’Anna (SSSA), Viale Rinaldo Piaggio 34, 56025 Pontedera (Pisa), Italy.
    Marrazza, S.
    The BioRobotics Institute, Scuola Superiore Sant’Anna (SSSA), Viale Rinaldo Piaggio 34, 56025 Pontedera (Pisa), Italy.
    Capantini, L.
    Department of Neuroscience, Nobel Institute for Neurophysiology, Karolinska Institutet.
    Orofino, S.
    The BioRobotics Institute, Scuola Superiore Sant’Anna (SSSA), Viale Rinaldo Piaggio 34, 56025 Pontedera (Pisa), Italy.
    Ascari, L.
    HENESIS srl, Viale dei Mille 108, 43125 Parma, Italy.
    Grillner, Sten
    Department of Neuroscience, Nobel Institute for Neurophysiology, Karolinska Institutet.
    Wallén, Peter
    Department of Neuroscience, Nobel Institute for Neurophysiology, Karolinska Institutet.
    Ekeberg, Örjan
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Stefanini, C.
    The BioRobotics Institute, Scuola Superiore Sant’Anna (SSSA), Viale Rinaldo Piaggio 34, 56025 Pontedera (Pisa), Italy.
    Dario, Paulo
    The BioRobotics Institute, Scuola Superiore Sant’Anna (SSSA), Viale Rinaldo Piaggio 34, 56025 Pontedera (Pisa), Italy.
    A bioinspired autonomous swimming robot as a tool for studying goal-directed locomotion2013In: Biological Cybernetics, ISSN 0340-1200, E-ISSN 1432-0770, Vol. 107, no 5, p. 513-527Article in journal (Refereed)
    Abstract [en]

    The bioinspired approach has been key in combining the disciplines of robotics with neuroscience in an effective and promising fashion. Indeed, certain aspects in the field of neuroscience, such as goal-directed locomotion and behaviour selection, can be validated through robotic artefacts. In particular, swimming is a functionally important behaviour where neuromuscular structures, neural control architecture and operation can be replicated artificially following models from biology and neuroscience. In this article, we present a biomimetic system inspired by the lamprey, an early vertebrate that locomotes using anguilliform swimming. The artefact possesses extra- and proprioceptive sensory receptors, muscle-like actuation, distributed embedded control and a vision system. Experiments on optimised swimming and on goal-directed locomotion are reported, as well as the assessment of the performance of the system,which shows high energy efficiency and adaptive behaviour. While the focus is on providing a robotic platform for testing biological models, the reported system can also be of major relevance for the development of engineering system applications.

  • 33. Natesan, D.
    et al.
    Saxena, N.
    Ekeberg, Örjan
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Sane, S. P.
    Airflow mediated antennal positioning in flying hawkmoths2016In: Integrative and Comparative Biology, ISSN 1540-7063, E-ISSN 1557-7023, Vol. 56, p. E159-E159Article in journal (Other academic)
  • 34.
    Pearson, Keir
    et al.
    Univ of Alberta.
    Ekeberg, Örjan
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Bueschges, Ansgar
    Univ of Köln.
    Assessing sensory function in locomotor systems using neuro-mechanical simulations2006In: TINS - Trends in Neurosciences, ISSN 0166-2236, E-ISSN 1878-108X, Vol. 29, no 11, p. 625-631Article, review/survey (Refereed)
    Abstract [en]

    Computer simulations are being used increasingly to gain an understanding of the complex interactions between the neuronal, sensory, muscular and mechanical components of locomotor systems. Recent neuromechanical simulations of walking in humans, cats and insects, and of swimming in lampreys, have provided new information on the functional role of specific groups of sensory receptors in regulating locomotion. As we discuss in this review, these studies also make it clear that a full understanding of the neural and mechanical mechanisms that underlie locomotion can be achieved only by using simulations in parallel with physiological investigations. The widespread implementation of this approach would be enhanced by the development of freely available and easy-to-use software tools.

  • 35.
    Roos, Mikael
    et al.
    KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
    Yamamoto, Shin-Ichiro
    Fransen, Erik
    KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
    Ekeberg, Örjan
    KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
    Tasuku, Miyoshi
    Modeling and controlling a gait training system utilizing a biarticular muscle model2006In: Japan Society of Mechanical Engineers Symposium on Welfare Engineering, Vol. 2006, p. 234-235Article in journal (Refereed)
    Abstract [en]

    In the purpose of rehabilitation for people unable to perform a normal gait pattern a pneumatically operated gait training system with a biarticular muscle model utilizing rubbertuators have been developed. The pneumatics and the biarticular characteristics make the system difficult to control. In this research paper machine learning techniques have been used in an attempt to design a control system for the pneumatic gait. Preliminary results indicate that inverse plant modeling using Artificial Neural Networks (ANN) might be a successful approach.

  • 36.
    Sandberg, Anders
    et al.
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Lansner, Anders
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Petersson, K. M.
    Ekeberg, Örjan
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    A Bayesian attractor network with incremental learning2002In: Network, ISSN 0954-898X, E-ISSN 1361-6536, Vol. 13, no 2, p. 179-194Article in journal (Refereed)
    Abstract [en]

    A realtime online learning system with capacity limits needs to gradually forget old information in order to avoid catastrophic forgetting. This can be achieved by allowing new information to overwrite old, as in a so-called palimpsest memory. This paper describes an incremental learning rule based on the Bayesian confidence propagation neural network that has palimpsest properties when employed in an attractor neural network. The network does not suffer from catastrophic forgetting, has a capacity dependent on the learning time constant and exhibits faster convergence for newer patterns.

  • 37.
    Sandberg, Anders
    et al.
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Lansner, Anders
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Petersson, K. M.
    Ekeberg, Örjan
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    A palimpsest memory based on an incremental Bayesian learning rule2000In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 32, p. 987-994Article in journal (Refereed)
    Abstract [en]

    Capacity limited memory systems need to gradually forget old information in order to avoid catastrophic forgetting where all stored information is lost. This can be achieved by allowing new information to overwrite old, as in the so-called palimpsest memory. This paper describes a new such learning rule employed in an attractor neural network. The network does not exhibit catastrophic forgetting, has a capacity dependent on the learning time constant and exhibits recency effects in retrieval.

  • 38.
    Wang, Ruoli
    et al.
    KTH, School of Engineering Sciences (SCI), Mechanics, Biomechanics. KTH, School of Engineering Sciences (SCI), Centres, BioMEx. Karolinska Institutet, Sweden.
    Herman, Pawel
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Ekeberg, Örjan
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Gäverth, Johan
    Dept Women's and Children's Health, Karolinska Institutet.
    Fagergren, Anders
    AggeroMedTech AB, Stockholm.
    Forssberg, Hans
    Dept Women's and Children's Health, Karolinska Institutet.
    Neural and non-neural related properties in the spastic wrist flexors: An optimization study2017In: Medical Engineering and Physics, ISSN 1350-4533, E-ISSN 1873-4030, Vol. 47, p. 198-209Article in journal (Refereed)
    Abstract [en]

    Quantifying neural and non-neural contributions to increased joint resistance in spasticity is essential for a better understanding of its pathophysiological mechanisms and evaluating different intervention strategies. However, direct measurement of spasticity-related manifestations, e.g., motoneuron and biophysical properties in humans, is extremely challenging. In this vein, we developed a forward neuromusculoskeletal model that accounts for dynamics of muscle spindles, motoneuron pools, muscle activation and musculotendon of wrist flexors and relies on the joint angle and resistant torque as the only input measurement variables. By modeling the stretch reflex pathway, neural and non-neural related properties of the spastic wrist flexors were estimated during the wrist extension test. Joint angle and resistant torque were collected from 17 persons with chronic stroke and healthy controls using NeuroFlexor, a motorized force measurement device during the passive wrist extension test. The model was optimized by tuning the passive and stretch reflex-related parameters to fit the measured torque in each participant. We found that persons with moderate and severe spasticity had significantly higher stiffness than controls. Among subgroups of stroke survivors, the increased neural component was mainly due to a lower muscle spindle rate at 50% of the motoneuron recruitment. The motoneuron pool threshold was highly correlated to the motoneuron pool gain in all subgroups. The model can describe the overall resistant behavior of the wrist joint during the test. Compared to controls, increased resistance was predominantly due to higher elasticity and neural components. We concluded that in combination with the NeuroFlexor measurement, the proposed neuromusculoskeletal model and optimization scheme served as suitable tools for investigating potential parameter changes along the stretch-reflex pathway in persons with spasticity.

  • 39.
    Widing, Erik
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Ekeberg, Örjan
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Tailoring biomechanical model meshes for aero-acoustic simulations2015In: Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, ISSN 2168-1171Article in journal (Refereed)
    Abstract [en]

    To simulate the airflow and acoustic wave propagation associated with voice production, a closed surface mesh representing the vocal tract is needed. Biomechanically, the vocal tract is composed of surfaces from several different anatomical structures. We present a method for assembling a dynamic vocal tract mesh by trimming and stitching surface meshes tracking biomechanical models of relevant structures. Two algorithms, one for trimming and one for stitching, are used to first isolate surface mesh patches that are in contact with the airway and then merge them into a closed surface. The algorithms rely on manually selected boundaries and are able to cover gaps between mesh patches. Test cases are used to illustrate how the algorithms behave in various situations. The algorithms are implemented in the toolkit ArtiSynth where many relevant biomechanical models are already available.

1 - 39 of 39
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