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  • 1. 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, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Kremkow, Jens
    Lundqvist, Mikael
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Muller, Eilif
    Partzsch, Johannes
    Scholze, Stefan
    Zuehl, Lukas
    Mayr, Christian
    Destexhe, Alain
    Diesmann, Markus
    Potjans, Tobias C.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Schueffny, Rene
    Schemmel, Johannes
    Meier, Karlheinz
    A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems2011In: Biological Cybernetics, ISSN 0340-1200, E-ISSN 1432-0770, Vol. 104, no 4-5, p. 263-296Article in journal (Refereed)
    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.

  • 2.
    Krishnamurthy, Pradeep
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Silberberg, G.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    A Cortical Attractor Network with Martinotti Cells Driven by Facilitating Synapses2012In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 7, no 4, p. e30752-Article in journal (Refereed)
    Abstract [en]

    The population of pyramidal cells significantly outnumbers the inhibitory interneurons in the neocortex, while at the same time the diversity of interneuron types is much more pronounced. One acknowledged key role of inhibition is to control the rate and patterning of pyramidal cell firing via negative feedback, but most likely the diversity of inhibitory pathways is matched by a corresponding diversity of functional roles. An important distinguishing feature of cortical interneurons is the variability of the short-term plasticity properties of synapses received from pyramidal cells. The Martinotti cell type has recently come under scrutiny due to the distinctly facilitating nature of the synapses they receive from pyramidal cells. This distinguishes these neurons from basket cells and other inhibitory interneurons typically targeted by depressing synapses. A key aspect of the work reported here has been to pinpoint the role of this variability. We first set out to reproduce quantitatively based on in vitro data the di-synaptic inhibitory microcircuit connecting two pyramidal cells via one or a few Martinotti cells. In a second step, we embedded this microcircuit in a previously developed attractor memory network model of neocortical layers 2/3. This model network demonstrated that basket cells with their characteristic depressing synapses are the first to discharge when the network enters an attractor state and that Martinotti cells respond with a delay, thereby shifting the excitation-inhibition balance and acting to terminate the attractor state. A parameter sensitivity analysis suggested that Martinotti cells might, in fact, play a dominant role in setting the attractor dwell time and thus cortical speed of processing, with cellular adaptation and synaptic depression having a less prominent role than previously thought.

  • 3.
    Krishnamurthy, Pradeep
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Stockholm University, Sweden.
    Silberberg, Gilad
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Stockholm University, Sweden.
    Long-range recruitment of Martinotti cells causes surround suppression and promotes saliency in an attractor network model2015In: Frontiers in Neural Circuits, ISSN 1662-5110, E-ISSN 1662-5110, Vol. 9, article id 60Article in journal (Refereed)
    Abstract [en]

    Although the importance of long-range connections for cortical information processing has been acknowledged for a long time, most studies focused on the long-range interactions between excitatory cortical neurons. Inhibitory interneurons play an important role in cortical computation and have thus far been studied mainly with respect to their local synaptic interactions within the cortical microcircuitry. A recent study showed that long-range excitatory connections onto Martinotti cells (MC) mediate surround suppression. Here we have extended our previously reported attractor network of pyramidal cells (PC) and MC by introducing long-range connections targeting MC. We have demonstrated how the network with Martinotti cell-mediated long-range inhibition gives rise to surround suppression and also promotes saliency of locations at which simple non-uniformities in the stimulus field are introduced. Furthermore, our analysis suggests that the presynaptic dynamics of MC is only ancillary to its orientation tuning property in enabling the network with saliency detection. Lastly, we have also implemented a disinhibitory pathway mediated by another interneuron type (VIP interneurons), which inhibits MC and abolishes surround suppression.

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