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  • 1.
    Djurfeldt, Mikael
    et al.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for High Performance Computing, PDC. nternational Neuroinformatics Coordinating Facility, Stockholm, Sweden .
    Davison, Andrew P.
    Eppler, Jochen M.
    Efficient generation of connectivity in neuronal networks from simulator-independent descriptions2014In: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 8, p. 43-Article in journal (Refereed)
    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.

  • 2.
    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.

  • 3.
    Eklund, Anders
    et al.
    Virginia Tech Carilion Research Institute, Virginia Tech, Roanoke, VA, USA.
    Dufort, Paul
    Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.
    Villani, Mattias
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    LaConte, Stephen
    Virginia Tech Carilion Research Institute, Virginia Tech, Roanoke, VA, USA/School of Biomedical Engineering and Sciences, Virginia Tech-Wake Forest University, Blacksburg, VA, USA.
    BROCCOLI: Software for fast fMRI analysis on many-core CPUs and GPUs2014In: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 8, no 24Article in journal (Refereed)
    Abstract [en]

    Analysis of functional magnetic resonance imaging (fMRI) data is becoming ever more computationally demanding as temporal and spatial resolutions improve, and large, publicly available data sets proliferate. Moreover, methodological improvements in the neuroimaging pipeline, such as non-linear spatial normalization, non-parametric permutation tests and Bayesian Markov Chain Monte Carlo approaches, can dramatically increase the computational burden. Despite these challenges, there do not yet exist any fMRI software packages which leverage inexpensive and powerful graphics processing units (GPUs) to perform these analyses. Here, we therefore present BROCCOLI, a free software package written in OpenCL (Open Computing Language) that can be used for parallel analysis of fMRI data on a large variety of hardware configurations. BROCCOLI has, for example, been tested with an Intel CPU, an Nvidia GPU, and an AMD GPU. These tests show that parallel processing of fMRI data can lead to significantly faster analysis pipelines. This speedup can be achieved on relatively standard hardware, but further, dramatic speed improvements require only a modest investment in GPU hardware. BROCCOLI (running on a GPU) can perform non-linear spatial normalization to a 1 mm3 brain template in 4–6 s, and run a second level permutation test with 10,000 permutations in about a minute. These non-parametric tests are generally more robust than their parametric counterparts, and can also enable more sophisticated analyses by estimating complicated null distributions. Additionally, BROCCOLI includes support for Bayesian first-level fMRI analysis using a Gibbs sampler. The new software is freely available under GNU GPL3 and can be downloaded from github (https://github.com/wanderine/BROCCOLI/).

  • 4.
    Gu, Xuan
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Eklund, Anders
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Evaluation of Six Phase Encoding Based Susceptibility Distortion Correction Methods for Diffusion MRI2019In: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 13, article id 76Article in journal (Refereed)
    Abstract [en]

    Purpose: Susceptibility distortions impact diffusion MRI data analysis and is typically corrected during preprocessing. Correction strategies involve three classes of methods: registration to a structural image, the use of a fieldmap, or the use of images acquired with opposing phase encoding directions. It has been demonstrated that phase encoding based methods outperform the other two classes, but unfortunately, the choice of which phase encoding based method to use is still an open question due to the absence of any systematic comparisons.

    Methods: In this paper we quantitatively evaluated six popular phase encoding based methods for correcting susceptibility distortions in diffusion MRI data. We employed a framework that allows for the simulation of realistic diffusion MRI data with susceptibility distortions. We evaluated the ability for methods to correct distortions by comparing the corrected data with the ground truth. Four diffusion tensor metrics (FA, MD, eigenvalues and eigenvectors) were calculated from the corrected data and compared with the ground truth. We also validated two popular indirect metrics using both simulated data and real data. The two indirect metrics are the difference between the corrected LR and AP data, and the FA standard deviation over the corrected LR, RL, AP, and PA data.

    Results: We found that DR-BUDDI and TOPUP offered the most accurate and robust correction compared to the other four methods using both direct and indirect evaluation metrics. EPIC and HySCO performed well in correcting b0 images but produced poor corrections for diffusion weighted volumes, and also they produced large errors for the four diffusion tensor metrics. We also demonstrate that the indirect metric (the difference between corrected LR and AP data) gives a different ordering of correction quality than the direct metric.

    Conclusion: We suggest researchers to use DR-BUDDI or TOPUP for susceptibility distortion correction. The two indirect metrics (the difference between corrected LR and AP data, and the FA standard deviation) should be interpreted together as a measure of distortion correction quality. The performance ranking of the various tools inferred from direct and indirect metrics differs slightly. However, across all tools, the results of direct and indirect metrics are highly correlated indicating that the analysis of indirect metrics may provide a good proxy of the performance of a correction tool if assessment using direct metrics is not feasible.

  • 5.
    Gu, Xuan
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Eklund, Anders
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Özarslan, Evren
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Using the wild bootstrap to quantify uncertainty in mean apparent propagator MRI2019In: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 13, article id 43Article in journal (Refereed)
    Abstract [en]

    Purpose: Estimation of uncertainty of MAP-MRI metricsis an important topic, for several reasons. Bootstrap deriveduncertainty, such as the standard deviation, providesvaluable information, and can be incorporated in MAP-MRIstudies to provide more extensive insight.

    Methods: In this paper, the uncertainty of different MAPMRImetrics was quantified by estimating the empirical distributionsusing the wild bootstrap. We applied the wildbootstrap to both phantom data and human brain data, andobtain empirical distributions for theMAP-MRImetrics returnto-origin probability (RTOP), non-Gaussianity (NG) and propagatoranisotropy (PA).

    Results: We demonstrated the impact of diffusion acquisitionscheme (number of shells and number of measurementsper shell) on the uncertainty of MAP-MRI metrics.We demonstrated how the uncertainty of these metrics canbe used to improve group analyses, and to compare differentpreprocessing pipelines. We demonstrated that withuncertainty considered, the results for a group analysis canbe different.

    Conclusion: Bootstrap derived uncertain measures provideadditional information to the MAP-MRI derived metrics, andshould be incorporated in ongoing and future MAP-MRIstudies to provide more extensive insight.

  • 6. Hansson, Kristin
    et al.
    Jafari-Mamaghani, Mehrdad
    Stockholm University, Faculty of Science, Department of Mathematics. Karolinska Institutet, Sweden.
    Krieger, Patrik
    RipleyGUI: software for analyzing spatial patterns in 3D cell distributions2013In: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 7, article id 5Article in journal (Refereed)
    Abstract [en]

    The true revolution in the age of digital neuroanatomy is the ability to extensively quantify anatomical structures and thus investigate structure-function relationships in great detail. To facilitate the quantification of neuronal cell patterns we have developed RipleyGUI, a MATLAB-based software that can be used to detect patterns in the 3D distribution of cells. RipleyGUI uses Ripley's K-function to analyze spatial distributions. In addition the software contains statistical tools to determine quantitative statistical differences, and tools for spatial transformations that are useful for analyzing non-stationary point patterns. The software has a graphical user interface making it easy to use without programming experience, and an extensive user manual explaining the basic concepts underlying the different statistical tools used to analyze spatial point patterns. The described analysis tool can be used for determining the spatial organization of neurons that is important for a detailed study of structure function relationships. For example, neocortex that can be subdivided into six layers based on cell density and cell types can also be analyzed in terms of organizational principles distinguishing the layers.

  • 7. Jordan, Jakob
    et al.
    Ippen, Tammo
    Helias, Moritz
    Kitayama, Itaru
    Sato, Mitsuhisa
    Igarashi, Jun
    Diesmann, Markus
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). Julich Res Ctr, Inst Neurosci & Med INM 6, Julich, Germany; Julich Res Ctr, Inst Adv Simulat IAS 6, Julich, Germany; ulich Res Ctr, JARA Inst Brain Struct Funct Relationships INM 10, Julich, Germany; Rhein Westfal TH Aachen, Fac 1, Dept Phys, Aachen, Germany; Julich Res Ctr, Simulat Lab Neurosci, Bernstein Facil Simulat & Da, Julich, Germany.
    Kunkel, Susanne
    Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers2018In: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 12, article id 2Article in journal (Refereed)
    Abstract [en]

    State-of-the-art software tools for neuronal network simulations scale to the largest computing systems available today and enable investigations of large-scale networks of up to 10 % of the human cortex at a resolution of individual neurons and synapses. Due to an upper limit on the number of incoming connections of a single neuron, network connectivity becomes extremely sparse at this scale. To manage computational costs, simulation software ultimately targeting the brain scale needs to fully exploit this sparsity. Here we present a two-tier connection infrastructure and a framework for directed communication among compute nodes accounting for the sparsity of brain-scale networks. We demonstrate the feasibility of this approach by implementing the technology in the NEST simulation code and we investigate its performance in different scaling scenarios of typical network simulations. Our results show that the new data structures and communication scheme prepare the simulation kernel for post-petascale high-performance computing facilities without sacrificing performance in smaller systems.

  • 8.
    Jordan, Jakob
    et al.
    Julich Res Ctr, Inst Neurosci & Med INM 6, Julich, Germany.;Julich Res Ctr, Inst Adv Simulat IAS 6, Julich, Germany.;Julich Res Ctr, JARA Inst Brain Struct Funct Relationships INM 10, Julich, Germany..
    Ippen, Tammo
    Julich Res Ctr, Inst Neurosci & Med INM 6, Julich, Germany.;Julich Res Ctr, Inst Adv Simulat IAS 6, Julich, Germany.;Julich Res Ctr, JARA Inst Brain Struct Funct Relationships INM 10, Julich, Germany.;Norwegian Univ Life Sci, Fac Sci & Technol, As, Norway..
    Helias, Moritz
    Julich Res Ctr, Inst Neurosci & Med INM 6, Julich, Germany.;Julich Res Ctr, Inst Adv Simulat IAS 6, Julich, Germany.;Julich Res Ctr, JARA Inst Brain Struct Funct Relationships INM 10, Julich, Germany.;Rhein Westfal TH Aachen, Dept Phys, Fac 1, Aachen, Germany..
    Kitayama, Itaru
    RIKEN, Adv Inst Computat Sci, Kobe, Hyogo, Japan..
    Sato, Mitsuhisa
    RIKEN, Adv Inst Computat Sci, Kobe, Hyogo, Japan..
    Igarashi, Jun
    RIKEN, Computat Engn Applicat Unit, Wako, Saitama, Japan..
    Diesmann, Markus
    Julich Res Ctr, Inst Neurosci & Med INM 6, Julich, Germany.;Julich Res Ctr, Inst Adv Simulat IAS 6, Julich, Germany.;Julich Res Ctr, JARA Inst Brain Struct Funct Relationships INM 10, Julich, Germany.;Rhein Westfal TH Aachen, Dept Phys, Fac 1, Aachen, Germany.;Rhein Westfal TH Aachen, Med Fac, Dept Psychiat Psychotherapy & Psychosomat, Aachen, Germany..
    Kunkel, Susanne
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). Julich Res Ctr, Simulat Lab Neurosci Bernstein Facil Simulat & Da, Julich, Germany..
    Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers (vol 12, 2, 2018)2018In: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 12, article id 34Article in journal (Refereed)
  • 9. Klaus, A.
    et al.
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Synchronization effects between striatal fast-spiking interneurons forming networks with different topologies2008In: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196Article in journal (Other academic)
    Abstract [en]

    The basal ganglia are involved in executive functions of the forebrain, such as the planning and selection of motor behavior. In the striatum, which is the input stage of the basal ganglia system, fast-spiking interneurons provide an effective feedforward inhibition to the medium-sized spiny projection neurons. Thus, these fast-spiking neurons are able to control the striatal output to later stages in the basal ganglia. Recently, in modeling studies it has been shown that pairs of cells as well as randomly connected networks of electrically coupled fast-spiking cells are able to synchronize their activity. Here we want to investigate the influence of network topology and network size on the synchronization in a simulated network of striatal fast-spiking interneurons. We use a biophysically detailed single-cell model of the fast-spiking interneuron with 127 compartments (Hellgren Kotaleski et al., J Neurophysiology, 95: 331-41, 2006; Hjorth et al., Neurocomputing 70: 1887–1891, 2007), and parallelize the network model of electrically coupled fast-spiking cells using PGENESIS running on a Blue Gene/L supercomputer. General network statistics and synaptic input is constrained by published data from the striatum. Network topology is varied from ’regular’ over ’small-world’ to ’random’ (Watts & Strogatz, Nature 393: 440–442, 1998). Using common statistical measures, we will determine the extent of local and global synchronization for each network topology. Furthermore, we investigate the interactions in the network by means of Ising models (Schneidman et al., Nature 440: 1007–1012, 2006). We are particularly interested in the relation between the ’interaction’ – as obtained by the Ising model – and the underlying network topology; e. g., do directly coupled fast-spiking interneuron pairs synchronize most?So far, the small amount of fast-spiking cells in the striatum (less than 2 %) makes experimental studies on the network level difficult or even impossible. With our study we hope to gain a better understanding of interaction effects in the feedforward inhibitory network of the striatum.

  • 10. Kunkel, Susanne
    et al.
    Schenck, Wolfram
    The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code2017In: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 11, article id 40Article in journal (Refereed)
    Abstract [en]

    NEST is a simulator for spiking neuronal networks that commits to a general purpose approach: It allows for high flexibility in the design of network models, and its applications range from small-scale simulations on laptops to brain-scale simulations on supercomputers. Hence, developers need to test their code for various use cases and ensure that changes to code do not impair scalability. However, running a full set of benchmarks on a supercomputer takes up precious compute-time resources and can entail long queuing times. Here, we present the NEST dry-run mode, which enables comprehensive dynamic code analysis without requiring access to high-performance computing facilities. A dry-run simulation is carried out by a single process, which performs all simulation steps except communication as if it was part of a parallel environment with many processes. We show that measurements of memory usage and runtime of neuronal network simulations closely match the corresponding dry-run data. Furthermore, we demonstrate the successful application of the dry-run mode in the areas of profiling and performance modeling.

  • 11.
    Lindén, Henrik
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Hagen, E.
    Łeski, S.
    Norheim, E. S.
    Pettersen, K. H.
    Einevoll, G. T.
    LFPy: A tool for biophysical simulation of extracellular potentials generated by detailed model neurons2014In: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 7, no Jan, p. 41-Article in journal (Refereed)
    Abstract [en]

    Electrical extracellular recordings, i.e., recordings of the electrical potentials in the extracellular medium between cells, have been a main work-horse in electrophysiology for almost a century. The high-frequency part of the signal (>500 Hz), i.e., the multi-unit activity (MUA), contains information about the firing of action potentials in surrounding neurons, while the low-frequency part, the local field potential (LFP), contains information about how these neurons integrate synaptic inputs. As the recorded extracellular signals arise from multiple neural processes, their interpretation is typically ambiguous and difficult. Fortunately, a precise biophysical modeling scheme linking activity at the cellular level and the recorded signal has been established: the extracellular potential can be calculated as a weighted sum of all transmembrane currents in all cells located in the vicinity of the electrode. This computational scheme can considerably aid the modeling and analysis of MUA and LFP signals. Here, we describe LFPy, an open source Python package for numerical simulations of extracellular potentials. LFPy consists of a set of easy-to-use classes for defining cells, synapses and recording electrodes as Python objects, implementing this biophysical modeling scheme. It runs on top of the widely used NEURON simulation environment, which allows for flexible usage of both new and existing cell models. Further, calculation of extracellular potentials using the line-source-method is efficiently implemented. We describe the theoretical framework underlying the extracellular potential calculations and illustrate by examples how LFPy can be used both for simulating LFPs, i.e., synaptic contributions from single cells as well a populations of cells, and MUAs, i.e., extracellular signatures of action potentials.

  • 12.
    Nazem, Ali
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Kootstra, Geert
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Kragic, Danica
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Djurfeldt, Mikael
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for High Performance Computing, PDC. KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Parallel implementation of a biologically inspired model of figure-ground segregation: Application to real-time data using MUSIC2011In: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196Article in journal (Refereed)
    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.

  • 13.
    Pauli, Robin
    et al.
    Julich Res Ctr, Inst Neurosci & Med INM 6, Julich, Germany.;Julich Res Ctr, Inst Adv Simulat IAS 6, Julich, Germany.;Julich Res Ctr, JARA BRAIN Inst 1, Julich, Germany..
    Weidel, Philipp
    Julich Res Ctr, Inst Neurosci & Med INM 6, Julich, Germany.;Julich Res Ctr, Inst Adv Simulat IAS 6, Julich, Germany.;Julich Res Ctr, JARA BRAIN Inst 1, Julich, Germany..
    Kunkel, Susanne
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). Norwegian Univ Life Sci, Fac Sci & Technol, As, Norway.
    Morrison, Abigail
    Julich Res Ctr, Inst Neurosci & Med INM 6, Julich, Germany.;Julich Res Ctr, Inst Adv Simulat IAS 6, Julich, Germany.;Julich Res Ctr, JARA BRAIN Inst 1, Julich, Germany.;Ruhr Univ Bochum, Inst Cognit Neurosci, Fac Psychol, Bochum, Germany..
    Reproducing Polychronization: A Guide to Maximizing the Reproducibility of Spiking Network Models2018In: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 12, article id 46Article in journal (Refereed)
    Abstract [en]

    Any modeler who has attempted to reproduce a spiking neural network model from its description in a paper has discovered what a painful endeavor this is. Even when all parameters appear to have been specified, which is rare, typically the initial attempt to reproduce the network does not yield results that are recognizably akin to those in the original publication. Causes include inaccurately reported or hidden parameters (e.g., wrong unit or the existence of an initialization distribution), differences in implementation of model dynamics, and ambiguities in the text description of the network experiment. The very fact that adequate reproduction often cannot be achieved until a series of such causes have been tracked down and resolved is in itself disconcerting, as it reveals unreported model dependencies on specific implementation choices that either were not clear to the original authors, or that they chose not to disclose. In either case, such dependencies diminish the credibility of the model's claims about the behavior of the target system. To demonstrate these issues, we provide a worked example of reproducing a seminal study for which, unusually, source code was provided at time of publication. Despite this seemingly optimal starting position, reproducing the results was time consuming and frustrating. Further examination of the correctly reproduced model reveals that it is highly sensitive to implementation choices such as the realization of background noise, the integration timestep, and the thresholding parameter of the analysis algorithm. From this process, we derive a guideline of best practices that would substantially reduce the investment in reproducing neural network studies, whilst simultaneously increasing their scientific quality. We propose that this guideline can be used by authors and reviewers to assess and improve the reproducibility of future network models.

  • 14. Vlachos, Ioannis
    et al.
    Zaytsev, Yury V
    Spreizer, Sebastian
    Aertsen, Ad
    Kumar, Arvind
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Neural system prediction and identification challenge2013In: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 7, no DEC, p. 43-Article in journal (Refereed)
    Abstract [en]

    Can we infer the function of a biological neural network (BNN) if we know the connectivity and activity of all its constituent neurons? This question is at the core of neuroscience and, accordingly, various methods have been developed to record the activity and connectivity of as many neurons as possible. Surprisingly, there is no theoretical or computational demonstration that neuronal activity and connectivity are indeed sufficient to infer the function of a BNN. Therefore, we pose the Neural Systems Identification and Prediction Challenge (nuSPIC). We provide the connectivity and activity of all neurons and invite participants (1) to infer the functions implemented (hard-wired) in spiking neural networks (SNNs) by stimulating and recording the activity of neurons and, (2) to implement predefined mathematical/biological functions using SNNs. The nuSPICs can be accessed via a web-interface to the NEST simulator and the user is not required to know any specific programming language. Furthermore, the nuSPICs can be used as a teaching tool. Finally, nuSPICs use the crowd-sourcing model to address scientific issues. With this computational approach we aim to identify which functions can be inferred by systematic recordings of neuronal activity and connectivity. In addition, nuSPICs will help the design and application of new experimental paradigms based on the structure of the SNN and the presumed function which is to be discovered.

  • 15. Weidel, Philipp
    et al.
    Djurfeldt, Mikael
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for High Performance Computing, PDC.
    Duarte, Renato C.
    Morrison, Abigail
    Closed Loop Interactions between Spiking Neural Network and Robotic Simulators Based on MUSIC and ROS2016In: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 10, article id 31Article in journal (Refereed)
    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.

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