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  • 1.
    Brandi, Maya
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Parallelldatorcentrum, PDC.
    Brocke, E
    Talukdar, Husain Ahammad
    KTH, Skolan för datavetenskap och kommunikation (CSC).
    Hanke, Michael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Numerisk analys, NA.
    Bhalla, US
    Djurfeldt, Mikael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Parallelldatorcentrum, PDC.
    Hellgren Kotaleski, Jeanette
    INCF.
    Multiscale modeling through MUSIC multi-simulation: Modeling a dendritic spine using MOOSE and NeuroRD2011Inngår i: Front. Neuroinform. Conference Abstract: 4th INCF Congress of Neuroinformatics, 2011Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The nervous system encompasses structure and phenomena at different spatial and temporal scales from molecule to behavior. In addition, different scales are described by different physical and mathematical formalisms. The dynamics of second messenger pathways can be formulated as stochastic reaction-diffusion systems [1] while the electrical dynamics of the neuronal membrane is often described by compartment models and the Hodgkin-Huxley formalism. In neuroscience, there is an increasing need and interest to study multi-scale phenomena where multiple scales and physical formalisms are covered by a single model. While there exists simulators/frameworks, such as GENESIS and MOOSE [3], which span such scales (kinetikit/HH-models), most software applications are specialized for a given domain. Here, we report about initial steps towards a framework for multi-scale modeling which builds on the concept of multi-simulations [2]. We aim to provide a standard API and communication framework allowing parallel simulators targeted at different scales and/or different physics to communicate on-line in a cluster environment. Specifically, we show prototype work on simulating the effect on receptor induced cascades on membrane excitability. Electrical properties of a compartment model is simulated in MOOSE, while receptor induced cascades are simulated in NeuroRD  [4,7] . In a prototype system, the two simulators are connected using PyMOOSE [5] and JPype [6]. The two models with their different physical properties (membrane currents in MOOSE, molecular biophysics in NeuroRD), are joined into a single model system.  We demonstrate the interaction of the numerical solvers of two simulators (MOOSE, NeuroRD) targeted at different spatiotemporal scales and different physics while solving a multi-scale problem. We analyze some of the problems that may arise in multi-scale multi-simulations and present requirements for a generic API for parallel solvers. This work represents initial steps towards a flexible modular framework for simulation of large-scale multi-scale multi-physics problems in neuroscience. References 1. Blackwell KT: An efficient stochastic diffusion algorithm for modeling second messengers in dendrites and spines. J Neurosci Meth 2006, 157: 142-153. 2. Djurfeldt M, Hjorth J, Eppler JM, Dudani N, Helias M, Potjans TC, Bhalla US, Diesmann M, Hellgren Kotaleski J, Ekeberg Ö: Run-Time Interoperability Between Neural Network Simulators Based on the MUSIC Framework. Neurinform 2010, 8: 43-60. 3. Dudani N, Ray S, George S, Bhalla US: Multiscale modeling and interoperability in MOOSE. Neuroscience 2009, 10(Suppl 1): 54. 4. Oliveira RF, Terrin A, Di Benedetto G, Cannon RC, Koh W, Kim M, Zaccolo M, Blacwell KT: The Role of Type 4 Phosphdiesterases in Generating Microdomains of cAMP: Large Scale Stochastic Simulations.

  • 2.
    Brandi, Maya
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Parallelldatorcentrum, PDC.
    Brocke, Ekaterina
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Talukdar, Husain A.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Hanke, Michael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Numerisk analys, NA.
    Bhalla, Upinder S.
    National Centre for Biological Sciences, Bangalore, India.
    Hällgren-Kotaleski, Jeanette
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Djurfeldt, Mikael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Parallelldatorcentrum, PDC.
    Connecting MOOSE and NeuroRD through MUSIC: towards a communication framework for multi-scale modeling2011Inngår i: Twentieth Annual Computational Neuroscience Meeting: CNS*2011, Springer Science+Business Media B.V., 2011Konferansepaper (Fagfellevurdert)
  • 3. Brette, Romain
    et al.
    Rudolph, Michelle
    Carnevale, Ted
    Hines, Michael
    Beeman, David
    Bower, James M.
    Diesmann, Markus
    Morrison, Abigail
    Goodman, Philip H.
    Harris, Frederick C., Jr.
    Zirpe, Milind
    Natschlaeger, Thomas
    Pecevski, Dejan
    Ermentrout, Bard
    Djurfeldt, Mikael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Rochel, Olivier
    Vieville, Thierry
    Muller, Eilif
    Davison, Andrew P.
    El Boustani, Sami
    Destexhe, Alain
    Simulation of networks of spiking neurons: A review of tools and strategies2007Inngår i: Journal of Computational Neuroscience, ISSN 0929-5313, E-ISSN 1573-6873, Vol. 23, nr 3, s. 349-398Artikkel, forskningsoversikt (Fagfellevurdert)
    Abstract [en]

    We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks.

  • 4.
    Brocke, Ekaterina
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Djurfeldt, Mikael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Parallelldatorcentrum, PDC.
    Efficient spike communication in the MUSIC multi-simulation framework2011Inngår i: Twentieth Annual Computational Neuroscience Meeting: CNS*2011, Springer Science+Business Media B.V., 2011Konferansepaper (Fagfellevurdert)
  • 5. Crook, S. M.
    et al.
    Bednar, J. A.
    Berger, S.
    Cannon, R.
    Davison, A. P.
    Djurfeldt, Mikael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Parallelldatorcentrum, PDC.
    Eppler, J.
    Kriener, B.
    Furber, S.
    Graham, B.
    Plesser, H. E.
    Schwabe, L.
    Smith, L.
    Steuber, V.
    Van Albada, S.
    Creating, documenting and sharing network models2012Inngår i: Network, ISSN 0954-898X, E-ISSN 1361-6536, Vol. 23, nr 4, s. 131-149Artikkel, forskningsoversikt (Fagfellevurdert)
    Abstract [en]

    As computational neuroscience matures, many simulation environments are available that are useful for neuronal network modeling. However, methods for successfully documenting models for publication and for exchanging models and model components among these projects are still under development. Here we briefly review existing software and applications for network model creation, documentation and exchange. Then we discuss a few of the larger issues facing the field of computational neuroscience regarding network modeling and suggest solutions to some of these problems, concentrating in particular on standardized network model terminology, notation, and descriptions and explicit documentation of model scaling. We hope this will enable and encourage computational neuroscientists to share their models more systematically in the future.

  • 6.
    Djurfeldt, Mikael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Large-scale simulation of neuronal systems2009Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Biologically detailed computational models of large-scale neuronal networks have now become feasible due to the development of increasingly powerful massively parallel supercomputers. We report here about the methodology involved in simulation of very large neuronal networks. Using conductance-based multicompartmental model neurons based on Hodgkin-Huxley formalism, we simulate a neuronal network model of layers II/III of the neocortex. These simulations, the largest of this type ever performed, were made on the Blue Gene/L supercomputer and comprised up to 8 million neurons and 4 billion synapses. Such model sizes correspond to the cortex of a small mammal. After a series of optimization steps, performance measurements show linear scaling behavior both on the Blue Gene/L supercomputer and on a more conventional cluster computer. Results from the simulation of a model based on more abstract formalism, and of considerably larger size, also shows linear scaling behavior on both computer architectures.

  • 7.
    Djurfeldt, Mikael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Parallelldatorcentrum, PDC.
    The Connection-set Algebra: a formalism for therepresentation of connectivity structure inneuronal network models, implementations in Python and C++, and their use in simulators2011Inngår i: Twentieth Annual Computational Neuroscience Meeting:: CNS*2011, 2011Konferansepaper (Fagfellevurdert)
  • 8.
    Djurfeldt, Mikael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Parallelldatorcentrum, PDC.
    The Connection-set Algebra-A Novel Formalism for the Representation of Connectivity Structure in Neuronal Network Models2012Inngår i: Neuroinformatics, ISSN 1539-2791, E-ISSN 1559-0089, Vol. 10, nr 3, s. 287-304Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The connection-set algebra (CSA) is a novel and general formalism for the description of connectivity in neuronal network models, from small-scale to large-scale structure. The algebra provides operators to form more complex sets of connections from simpler ones and also provides parameterization of such sets. CSA is expressive enough to describe a wide range of connection patterns, including multiple types of random and/or geometrically dependent connectivity, and can serve as a concise notation for network structure in scientific writing. CSA implementations allow for scalable and efficient representation of connectivity in parallel neuronal network simulators and could even allow for avoiding explicit representation of connections in computer memory. The expressiveness of CSA makes prototyping of network structure easy. A C+ + version of the algebra has been implemented and used in a large-scale neuronal network simulation (Djurfeldt et al., IBM J Res Dev 52(1/2):31-42, 2008b) and an implementation in Python has been publicly released.

  • 9.
    Djurfeldt, Mikael
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Parallelldatorcentrum, PDC. nternational Neuroinformatics Coordinating Facility, Stockholm, Sweden .
    Davison, Andrew P.
    Eppler, Jochen M.
    Efficient generation of connectivity in neuronal networks from simulator-independent descriptions2014Inngår i: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 8, s. 43-Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Simulator-independent descriptions of connectivity in neuronal networks promise greater ease of model sharing, improved reproducibility of simulation results, and reduced programming effort for computational neuroscientists. However, until now, enabling the use of such descriptions in a given simulator in a computationally efficient way has entailed considerable work for simulator developers, which must be repeated for each new connectivity-generating library that is developed. We have developed a generic connection generator interface that provides a standard way to connect a connectivity-generating library to a simulator, such that one library can easily be replaced by another, according to the modeler's needs. We have used the connection generator interface to connect C++ and Python implementations of the previously described connection-set algebra to the NEST simulator. We also demonstrate how the simulator-independent modeling framework PyNN can transparently take advantage of this, passing a connection description through to the simulator layer for rapid processing in C++ where a simulator supports the connection generator interface and falling-back to slower iteration in Python otherwise. A set of benchmarks demonstrates the good performance of the interface.

  • 10.
    Djurfeldt, Mikael
    et al.
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Ekeberg, Örjan
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Graybiel, Ann M.
    Brain and Cognitive Sciences, MIT, Cambridge, Boston, U.S.A:.
    Cortex-basal ganglia interaction and attractor states2001Inngår i: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 38-40, s. 573-579Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 11.
    Djurfeldt, Mikael
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Ekeberg, Örjan
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Large-scale modeling - a tool for conquering the complexity of the brain2008Inngår i: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 2, s. 1-4Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 12.
    Djurfeldt, Mikael
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Hjorth, Johannes
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Eppler, Jochen
    Honda Research Institute.
    Dudani, Niraj
    Helias, Moritz
    University of Freiburg, Germany.
    Potjans, Tobias
    Bhalla, Upinder
    Diesmann, Markus
    Hellgren Kotaleski, Jeanette
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Ekeberg, Örjan
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Run-Time Interoperability Between Neuronal Network Simulators Based on the MUSIC Framework2010Inngår i: Neuroinformatics, ISSN 1539-2791, E-ISSN 1559-0089, Vol. 8, nr 1, s. 43-60Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 13.
    Djurfeldt, Mikael
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Parallelldatorcentrum, PDC.
    Johansson, Christopher
    KTH, Skolan för datavetenskap och kommunikation (CSC), Numerisk Analys och Datalogi, NADA.
    Ekeberg, Örjan
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Rehn, Martin
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lundqvist, Mikael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Massively parallel simulation of brain-scale neuronal network models2005Rapport (Annet vitenskapelig)
  • 14.
    Djurfeldt, Mikael
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    1st INCF Workshop on Large-scale Modeling of the Nervous System2007Rapport (Annet vitenskapelig)
  • 15.
    Djurfeldt, Mikael
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Memory capacity in a model of cortical layers II/III2008Konferansepaper (Fagfellevurdert)
  • 16.
    Djurfeldt, Mikael
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lundqvist, Mikael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Johansson, Christopher
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Rehn, Martin
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Ekeberg, Örjan
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Brain-scale simulation of the neocortex on the IBM Blue Gene/L  supercomputer2008Inngår i: IBM Journal of Research and Development, ISSN 0018-8646, E-ISSN 2151-8556, Vol. 52, nr 1-2, s. 31-41Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 17.
    Djurfeldt, Mikael
    et al.
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Sandberg, Anders
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Ekeberg, Örjan
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Lansner, Anders
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    See-A framework for simulation of biologically detailed and artificial neural networks and systems1999Inngår i: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 26-27, s. 997-1003Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 18.
    Fransén, Erik
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Numerisk Analys och Datalogi, NADA.
    Kozlov, Alexander
    KTH, Skolan för datavetenskap och kommunikation (CSC), Numerisk Analys och Datalogi, NADA.
    Xie, Yuecong
    KTH, Skolan för datavetenskap och kommunikation (CSC), Numerisk Analys och Datalogi, NADA.
    Christensen, C.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Numerisk Analys och Datalogi, NADA.
    Djurfeldt, Mikael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Numerisk Analys och Datalogi, NADA.
    Ekeberg, Örjan
    KTH, Skolan för datavetenskap och kommunikation (CSC), Numerisk Analys och Datalogi, NADA.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Numerisk Analys och Datalogi, NADA.
    Evaluation of model scalability in parallel neural simulators2005Konferansepaper (Fagfellevurdert)
    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.

  • 19.
    Kootstra, Geert
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Wilming, N.
    Schmidt, N. M.
    Djurfeldt, Mikael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Parallelldatorcentrum, PDC.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    König, P.
    Learning and adaptation of sensorimotor contingencies: Prism-adaptation, a case study2012Inngår i: From Animals to Animats 12, Springer Berlin/Heidelberg, 2012, Vol. 7426 LNAI, s. 341-350Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper focuses on learning and adaptation of sensorimotor contingencies. As a specific case, we investigate the application of prism glasses, which change visual-motor contingencies. After an initial disruption of sensorimotor coordination, humans quickly adapt. However, scope and generalization of that adaptation is highly dependent on the type of feedback and exhibits markedly different degrees of generalization. We apply a model with a specific interaction of forward and inverse models to a robotic setup and subject it to the identical experiments that have been used on previous human psychophysical studies. Our model demonstrates both locally specific adaptation and global generalization in accordance with the psychophysical experiments. These results emphasize the role of the motor system for sensory processes and open an avenue to improve on sensorimotor processing.

  • 20.
    Lundqvist, Mikael
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Rehn, Martin
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Djurfeldt, Mikael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Attractor dynamics in a modular network model of neocortex2006Inngår i: Network, ISSN 0954-898X, E-ISSN 1361-6536, Network: Computation in Neural Systems, Vol. 17, nr 3, s. 253-276Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Starting from the hypothesis that the mammalian neocortex to a first approximation functions as an associative memory of the attractor network type, we formulate a quantitative computational model of neocortical layers 2/3. The model employs biophysically detailed multi-compartmental model neurons with conductance based synapses and includes pyramidal cells and two types of inhibitory interneurons, i.e., regular spiking non-pyramidal cells and basket cells. The simulated network has a minicolumnar as well as a hypercolumnar modular structure and we propose that minicolumns rather than single cells are the basic computational units in neocortex. The minicolumns are represented in full scale and synaptic input to the different types of model neurons is carefully matched to reproduce experimentally measured values and to allow a quantitative reproduction of single cell recordings. Several key phenomena seen experimentally in vitro and in vivo appear as emergent features of this model. It exhibits a robust and fast attractor dynamics with pattern completion and pattern rivalry and it suggests an explanation for the so-called attentional blink phenomenon. During assembly dynamics, the model faithfully reproduces several features of local UP states, as they have been experimentally observed in vitro, as well as oscillatory behavior similar to that observed in the neocortex.

  • 21.
    Nazem, Ali
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Kootstra, Geert
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Djurfeldt, Mikael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Parallelldatorcentrum, PDC. KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Interfacing a parallel simulation of a neuronal network to robotic hardware using MUSIC, with application to real-time figure-ground segregation.2011Inngår i: BMC neuroscience (Online), ISSN 1471-2202, E-ISSN 1471-2202, Vol. 12, nr Suppl 1, s. 78-78Artikkel i tidsskrift (Fagfellevurdert)
  • 22.
    Nazem, Ali
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Kootstra, Geert
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Djurfeldt, Mikael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Parallelldatorcentrum, PDC. KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Parallel implementation of a biologically inspired model of figure-ground segregation: Application to real-time data using MUSIC2011Inngår i: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    MUSIC, the multi-simulation coordinator, supports communication between neuronal-network simulators, or other (parallel) applications, running in a cluster super-computer. Here, we have developed a class library that interfaces between MUSIC-enabled software and applications running on computers outside of the cluster. Specifically, we have used this component to interface the cameras of a robotic head to a neuronal-network simulation running on a Blue Gene/L supercomputer. Additionally, we have developed a parallel implementation of a model for figure ground segregation based on neuronal activity in the Macaque visual cortex. The interface enables the figure ground segregation application to receive real-world images in real-time from the robot. Moreover, it enables the robot to be controlled by the neuronal network.

  • 23. Raikov, Ivan
    et al.
    Cannon, Robert
    Clewley, Robert
    Cornelis, Hugo
    Davison, Andrew
    De Schutter, Erik
    Djurfeldt, Mikael
    Computational Neuroscience Unit, Okinawa Institute of Science and Technology, Okinawa, Japan.
    Gleeson, Padraig
    Gorchetnikov, Anatoli
    Plesser, Hans Ekkehard
    Hill, Sean
    Hines, Michael
    Kriener, Birgit
    Le Franc, Yann
    Lo, Chung-Chan
    Morrison, Abigail
    Muller, Eilif
    Ray, Subhasis
    Schwabe, Lars
    Szatmary, Botond
    NineML: the network interchange for neuroscience modeling language2011Inngår i: Twentieth Annual Computational Neuroscience Meeting: CNS*2011 / [ed] Jean-Marc Fellous and Astrid Prinz, Springer Science+Business Media B.V., 2011Konferansepaper (Fagfellevurdert)
  • 24. Weidel, Philipp
    et al.
    Djurfeldt, Mikael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Parallelldatorcentrum, PDC.
    Duarte, Renato C.
    Morrison, Abigail
    Closed Loop Interactions between Spiking Neural Network and Robotic Simulators Based on MUSIC and ROS2016Inngår i: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 10, artikkel-id 31Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In order to properly assess the function and computational properties of simulated neural systems, it is necessary to account for the nature of the stimuli that drive the system. However, providing stimuli that are rich and yet both reproducible and amenable to experimental manipulations is technically challenging, and even more so if a closed-loop scenario is required. In this work, we present a novel approach to solve this problem, connecting robotics and neural network simulators. We implement a middleware solution that bridges the Robotic Operating System (ROS) to the Multi-Simulator Coordinator (MUSIC). This enables any robotic and neural simulators that implement the corresponding interfaces to be efficiently coupled, allowing real-time performance for a wide range of configurations. This work extends the toolset available for researchers in both neurorobotics and computational neuroscience, and creates the opportunity to perform closed-loop experiments of arbitrary complexity to address questions in multiple areas, including embodiment, agency, and reinforcement learning.

1 - 24 of 24
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