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  • 1.
    Benjaminsson, Simon
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lundqvist, Mikael
    A model of categorization, learning of invariant representations and sequence prediction utilizing top-down activityManuskript (preprint) (Annet vitenskapelig)
  • 2. Bruederle, Daniel
    et al.
    Petrovici, Mihai A.
    Vogginger, Bernhard
    Ehrlich, Matthias
    Pfeil, Thomas
    Millner, Sebastian
    Gruebl, Andreas
    Wendt, Karsten
    Mueller, Eric
    Schwartz, Marc-Olivier
    de Oliveira, Dan Husmann
    Jeltsch, Sebastian
    Fieres, Johannes
    Schilling, Moritz
    Mueller, Paul
    Breitwieser, Oliver
    Petkov, Venelin
    Muller, Lyle
    Davison, Andrew P.
    Krishnamurthy, Pradeep
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Kremkow, Jens
    Lundqvist, Mikael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Muller, Eilif
    Partzsch, Johannes
    Scholze, Stefan
    Zuehl, Lukas
    Mayr, Christian
    Destexhe, Alain
    Diesmann, Markus
    Potjans, Tobias C.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Schueffny, Rene
    Schemmel, Johannes
    Meier, Karlheinz
    A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems2011Inngår i: Biological Cybernetics, ISSN 0340-1200, E-ISSN 1432-0770, Vol. 104, nr 4-5, s. 263-296Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In this article, we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results.

  • 3.
    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)
  • 4.
    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.

  • 5.
    Herman, Pawel Andrzej
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. Stockholm University.
    Lundqvist, Mikael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. Stockholm University.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. Stockholm University.
    Nested theta to gamma oscillations and precise spatiotemporal firing during memory retrieval in a simulated attractor network2013Inngår i: Brain Research, ISSN 0006-8993, E-ISSN 1872-6240, Vol. 1536, nr SI, s. 68-87Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

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

  • 6.
    Herman, Pawel
    et al.
    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.
    Oscillations in a simulated meso-scale memory network: origin and function of theta to gamma rhythmsArtikkel i tidsskrift (Annet vitenskapelig)
  • 7.
    Lansner, Anders
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lundqvist, Mikael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Modeling Coordination in the Neocortex at the Microcircuit and Global Network Level2010Inngår i: Dynamic Coordination in the Brain: From Neurons to Mind / [ed] von der Malsburg, C., Phillips W. A., Singer W., MIT Press, 2010, s. 83-99Kapittel i bok, del av antologi (Annet vitenskapelig)
  • 8.
    Lundqvist, Mikael
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Compte, Albert
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Bistable, Irregular Firing and Population Oscillations in a Modular Attractor Memory Network2010Inngår i: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 6, nr 6, s. e1000803-Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Attractor neural networks are thought to underlie working memory functions in the cerebral cortex. Several such models have been proposed that successfully reproduce firing properties of neurons recorded from monkeys performing working memory tasks. However, the regular temporal structure of spike trains in these models is often incompatible with experimental data. Here, we show that the in vivo observations of bistable activity with irregular firing at the single cell level can be achieved in a large-scale network model with a modular structure in terms of several connected hypercolumns. Despite high irregularity of individual spike trains, the model shows population oscillations in the beta and gamma band in ground and active states, respectively. Irregular firing typically emerges in a high-conductance regime of balanced excitation and inhibition. Population oscillations can produce such a regime, but in previous models only a non-coding ground state was oscillatory. Due to the modular structure of our network, the oscillatory and irregular firing was maintained also in the active state without fine-tuning. Our model provides a novel mechanistic view of how irregular firing emerges in cortical populations as they go from beta to gamma oscillations during memory retrieval.

  • 9.
    Lundqvist, Mikael
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Herman, Pawel
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Effect of Prestimulus Alpha Power, Phase, and Synchronization on Stimulus Detection Rates in a Biophysical Attractor Network Model2013Inngår i: Journal of Neuroscience, ISSN 0270-6474, E-ISSN 1529-2401, Vol. 33, nr 29, s. 11817-11824Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

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

  • 10.
    Lundqvist, Mikael
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Herman, Pawel
    Lansner, Anders
    Theta and Gamma Power Increases and Alpha/Beta Power Decreases with Memory Load in an Attractor Network Model2010Inngår i: Journal of cognitive neuroscience, ISSN 0898-929X, E-ISSN 1530-8898, Vol. 23, nr 10, s. 3008-3020Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

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

  • 11.
    Lundqvist, Mikael
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Herman, Pawel
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Variability of spike firing during theta-coupled replay of memories in a simulated attractor network2012Inngår i: Brain Research, ISSN 0006-8993, E-ISSN 1872-6240, Vol. 1434, s. 152-161Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

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

  • 12.
    Lundqvist, Mikael
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. Stockholm University, Sweden.
    Herman, Pawel
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. Stockholm University, Sweden.
    Palva, M.
    Palva, S.
    Silverstein, David
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. Stockholm University, Sweden.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. Stockholm University, Sweden.
    Stimulus detection rate and latency, firing rates and 1-40Hz oscillatory power are modulated by infra-slow fluctuations in a bistable attractor network model2013Inngår i: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 83, s. 458-471Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

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

  • 13.
    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.

  • 14.
    Lundqvist, Mikael
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Rehn, Martin
    KTH, Skolan för datavetenskap och kommunikation (CSC), Numerisk Analys och Datalogi, NADA.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Attractor dynamics in a modular network model of the cerebral cortex2006Inngår i: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 69, nr 10-12, s. 1155-1159Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Computational models of cortical associative memory often take a top-down approach. We have previously described such an abstract model with a hypercolumnar structure. Here we explore a similar, biophysically detailed but subsampled network model of neocortex. We study how the neurodynamics and associative memory properties of this biophysical model relate to the abstract model as well as to experimental data. The resulting network exhibits attractor dynamics; pattern completion and pattern rivalry. It reproduces several features of experimentally observed local UP states, as well as oscillatory behavior on the gamma and theta time scales observed in the cerebral cortex.

  • 15. Petrovici, Mihai A.
    et al.
    Vogginger, Bernhard
    Mueller, Paul
    Breitwieser, Oliver
    Lundqvist, Mikael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Muller, Lyle
    Ehrlich, Matthias
    Destexhe, Alain
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Schueffny, Rene
    Schemmel, Johannes
    Meier, Karlheinz
    Characterization and Compensation of Network-Level Anomalies in Mixed-Signal Neuromorphic Modeling Platforms2014Inngår i: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 9, nr 10, s. e108590-Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Advancing the size and complexity of neural network models leads to an ever increasing demand for computational resources for their simulation. Neuromorphic devices offer a number of advantages over conventional computing architectures, such as high emulation speed or low power consumption, but this usually comes at the price of reduced configurability and precision. In this article, we investigate the consequences of several such factors that are common to neuromorphic devices, more specifically limited hardware resources, limited parameter configurability and parameter variations due to fixed-pattern noise and trial-to-trial variability. Our final aim is to provide an array of methods for coping with such inevitable distortion mechanisms. As a platform for testing our proposed strategies, we use an executable system specification (ESS) of the BrainScaleS neuromorphic system, which has been designed as a universal emulation back-end for neuroscientific modeling. We address the most essential limitations of this device in detail and study their effects on three prototypical benchmark network models within a well-defined, systematic workflow. For each network model, we start by defining quantifiable functionality measures by which we then assess the effects of typical hardware-specific distortion mechanisms, both in idealized software simulations and on the ESS. For those effects that cause unacceptable deviations from the original network dynamics, we suggest generic compensation mechanisms and demonstrate their effectiveness. Both the suggested workflow and the investigated compensation mechanisms are largely back-end independent and do not require additional hardware configurability beyond the one required to emulate the benchmark networks in the first place. We hereby provide a generic methodological environment for configurable neuromorphic devices that are targeted at emulating large-scale, functional neural networks.

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