Change search
Refine search result
123 1 - 50 of 142
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Aguilar, Xavier
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Performance Monitoring, Analysis, and Real-Time Introspection on Large-Scale Parallel Systems2020Doctoral thesis, monograph (Other academic)
    Abstract [en]

    High-Performance Computing (HPC) has become an important scientific driver. A wide variety of research ranging for example from drug design to climate modelling is nowadays performed in HPC systems. Furthermore, the tremendous computer power of such HPC systems allows scientists to simulate problems that were unimaginable a few years ago. However, the continuous increase in size and complexity of HPC systems is turning the development of efficient parallel software into a difficult task. Therefore, the use of per- formance monitoring and analysis is a must in order to unveil inefficiencies in parallel software. Nevertheless, performance tools also face challenges as a result of the size of HPC systems, for example, coping with huge amounts of performance data generated.

    In this thesis, we propose a new model for performance characterisation of MPI applications that tackles the challenge of big performance data sets. Our approach uses Event Flow Graphs to balance the scalability of profiling techniques (generating performance reports with aggregated metrics) with the richness of information of tracing methods (generating files with sequences of time-stamped events). In other words, graphs allow to encode ordered se- quences of events without storing the whole sequence of such events, and therefore, they need much less memory and disk space, and are more scal- able. We demonstrate in this thesis how our Event Flow Graph model can be used as a trace compression method. Furthermore, we propose a method to automatically detect the structure of MPI applications using our Event Flow Graphs. This knowledge can afterwards be used to collect performance data in a smarter way, reducing for example the amount of redundant data collected. Finally, we demonstrate that our graphs can be used beyond trace compression and automatic analysis of performance data. We propose a new methodology to use Event Flow Graphs in the task of visual performance data exploration.

    In addition to the Event Flow Graph model, we also explore in this thesis the design and use of performance data introspection frameworks. Future HPC systems will be very dynamic environments providing extreme levels of parallelism, but with energy constraints, considerable resource sharing, and heterogeneous hardware. Thus, the use of real-time performance data to or- chestrate program execution in such a complex and dynamic environment will be a necessity. This thesis presents two different performance data introspec- tion frameworks that we have implemented. These introspection frameworks are easy to use, and provide performance data in real time with very low overhead. We demonstrate, among other things, how our approach can be used to reduce in real time the energy consumed by the system.

    The approaches proposed in this thesis have been validated in different HPC systems using multiple scientific kernels as well as real scientific applica- tions. The experiments show that our approaches in performance character- isation and performance data introspection are not intrusive at all, and can be a valuable contribution to help in the performance monitoring of future HPC systems.

  • 2.
    Aguilar, Xavier
    et al.
    KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Jordan, H.
    Heller, T.
    Hirsch, A.
    Fahringer, T.
    Laure, Erwin
    KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    An On-Line Performance Introspection Framework for Task-Based Runtime Systems2019In: 19th International Conference on Computational Science, ICCS 2019, Springer Verlag , 2019, p. 238-252Conference paper (Refereed)
    Abstract [en]

    The expected high levels of parallelism together with the heterogeneity and complexity of new computing systems pose many challenges to current software. New programming approaches and runtime systems that can simplify the development of parallel applications are needed. Task-based runtime systems have emerged as a good solution to cope with high levels of parallelism, while providing software portability, and easing program development. However, these runtime systems require real-time information on the state of the system to properly orchestrate program execution and optimise resource utilisation. In this paper, we present a lightweight monitoring infrastructure developed within the AllScale Runtime System, a task-based runtime system for extreme scale. This monitoring component provides real-time introspection capabilities that help the runtime scheduler in its decision-making process and adaptation, while introducing minimum overhead. In addition, the monitoring component provides several post-mortem reports as well as real-time data visualisation that can be of great help in the task of performance debugging.

  • 3.
    Ahmed, Laeeq
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Scalable Analysis of Large Datasets in Life Sciences2019Doctoral thesis, monograph (Other academic)
    Abstract [en]

    We are experiencing a deluge of data in all fields of scientific and business research, particularly in the life sciences, due to the development of better instrumentation and the rapid advancements that have occurred in information technology in recent times. There are major challenges when it comes to handling such large amounts of data. These range from the practicalities of managing these large volumes of data, to understanding the meaning and practical implications of the data.

    In this thesis, I present parallel methods to efficiently manage, process, analyse and visualize large sets of data from several life sciences fields at a rapid rate, while building and utilizing various machine learning techniques in a novel way. Most of the work is centred on applying the latest Big Data Analytics frameworks for creating efficient virtual screening strategies while working with large datasets. Virtual screening is a method in cheminformatics used for Drug discovery by searching large libraries of molecule structures. I also present a method for the analysis of large Electroencephalography data in real time. Electroencephalography is one of the main techniques used to measure the brain electrical activity.

    First, I evaluate the suitability of Spark, a parallel framework for large datasets, for performing parallel ligand-based virtual screening. As a case study, I classify molecular library using prebuilt classification models to filter out the active molecules. I also demonstrate a strategy to create cloud-ready pipelines for structure-based virtual screening. The major advantages of this strategy are increased productivity and high throughput. In this work, I show that Spark can be applied to virtual screening, and that it is, in general, an appropriate solution for large-scale parallel pipelining. Moreover, I illustrate how Big Data analytics are valuable in working with life sciences datasets.

    Secondly, I present a method to further reduce the overall time of the structured-based virtual screening strategy using machine learning and a conformal-prediction-based iterative modelling strategy. The idea is to only dock those molecules that have a better than average chance of being an inhibitor when searching for molecules that could potentially be used as drugs. Using machine learning models from this work, I built a web service to predict the target profile of multiple compounds against ready-made models for a list of targets where 3D structures are available. These target predictions can be used to understand off-target effects, for example in the early stages of drug discovery projects.

    Thirdly, I present a method to detect seizures in long term Electroencephalography readings - this method works in real time taking the ongoing readings in as live data streams. The method involves tackling the challenges of real-time decision-making, storing large datasets in memory and updating the prediction model with newly produced data at a rapid rate. The resulting algorithm not only classifies seizures in real time, it also learns the threshold in real time. I also present a new feature "top-k amplitude measure" for classifying which parts of the data correspond to seizures. Furthermore, this feature helps to reduce the amount of data that needs to be processed in the subsequent steps.

  • 4.
    Ahmed, Laeeq
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Georgiev, Valentin
    Capuccini, Marco
    Toor, Salman
    Schaal, Wesley
    Laure, Erwin
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Spjuth, Ola
    Efficient iterative virtual screening with Apache Spark and conformal prediction2018In: Journal of Cheminformatics, ISSN 1758-2946, E-ISSN 1758-2946, Vol. 10, article id 8Article in journal (Refereed)
    Abstract [en]

    Background: Docking and scoring large libraries of ligands against target proteins forms the basis of structure-based virtual screening. The problem is trivially parallelizable, and calculations are generally carried out on computer clusters or on large workstations in a brute force manner, by docking and scoring all available ligands. Contribution: In this study we propose a strategy that is based on iteratively docking a set of ligands to form a training set, training a ligand-based model on this set, and predicting the remainder of the ligands to exclude those predicted as 'low-scoring' ligands. Then, another set of ligands are docked, the model is retrained and the process is repeated until a certain model efficiency level is reached. Thereafter, the remaining ligands are docked or excluded based on this model. We use SVM and conformal prediction to deliver valid prediction intervals for ranking the predicted ligands, and Apache Spark to parallelize both the docking and the modeling. Results: We show on 4 different targets that conformal prediction based virtual screening (CPVS) is able to reduce the number of docked molecules by 62.61% while retaining an accuracy for the top 30 hits of 94% on average and a speedup of 3.7. The implementation is available as open source via GitHub (https://github.com/laeeq80/spark-cpvs) and can be run on high-performance computers as well as on cloud resources.

  • 5.
    Akhmetova, Dana
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Cebamanos, L.
    Iakymchuk, Roman
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Rotaru, T.
    Rahn, M.
    Markidis, Stefano
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Laure, Erwin
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Bartsch, V.
    Simmendinger, C.
    Interoperability of GASPI and MPI in large scale scientific applications2018In: 12th International Conference on Parallel Processing and Applied Mathematics, PPAM 2017, Springer Verlag , 2018, p. 277-287Conference paper (Refereed)
    Abstract [en]

    One of the main hurdles of a broad distribution of PGAS approaches is the prevalence of MPI, which as a de-facto standard appears in the code basis of many applications. To take advantage of the PGAS APIs like GASPI without a major change in the code basis, interoperability between MPI and PGAS approaches needs to be ensured. In this article, we address this challenge by providing our study and preliminary performance results regarding interoperating GASPI and MPI on the performance crucial parts of the Ludwig and iPIC3D applications. In addition, we draw a strategy for better coupling of both APIs. 

  • 6.
    Andreozzi, Emilio
    et al.
    Univ Naples Federico II, Dept Elect Engn & Informat Technol DIETI, Naples, Italy.;Ist Clin Sci Maugeri IRCCS, Dept Bioengn, Telese Terme Inst, Telese Terme, BN, Italy..
    Carannante, Ilaria
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    D'Addio, Giovanni
    Ist Clin Sci Maugeri IRCCS, Dept Bioengn, Telese Terme Inst, Telese Terme, BN, Italy..
    Cesarelli, Mario
    Univ Naples Federico II, Dept Elect Engn & Informat Technol DIETI, Naples, Italy.;Ist Clin Sci Maugeri IRCCS, Dept Bioengn, Telese Terme Inst, Telese Terme, BN, Italy..
    Balbi, Pietro
    Ist Clin Sci Maugeri IRCCS, Lab Computat Neurophysiol, Telese Terme Inst, Telese Terme, BN, Italy..
    Phenomenological models of Na(V)1.5. A side by side, procedural, hands-on comparison between Hodgkin-Huxley and kinetic formalisms2019In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 9, article id 17493Article in journal (Refereed)
    Abstract [en]

    Computational models of ion channels represent the building blocks of conductance-based, biologically inspired models of neurons and neural networks. Ion channels are still widely modelled by means of the formalism developed by the seminal work of Hodgkin and Huxley (HH), although the electrophysiological features of the channels are currently known to be better fitted by means of kinetic Markov-type models. The present study is aimed at showing why simplified Markov-type kinetic models are more suitable for ion channels modelling as compared to HH ones, and how a manual optimization process can be rationally carried out for both. Previously published experimental data of an illustrative ion channel (Na(V)1.5) are exploited to develop a step by step optimization of the two models in close comparison. A conflicting practical limitation is recognized for the HH model, which only supplies one parameter to model two distinct electrophysiological behaviours. In addition, a step by step procedure is provided to correctly optimize the kinetic Markov-type model. Simplified Markov-type kinetic models are currently the best option to closely approximate the known complexity of the macroscopic currents of ion channels. Their optimization can be achieved through a rationally guided procedure, and allows to obtain models with a computational burden that is comparable with HH models one.

  • 7. Aronsson, Sanna
    et al.
    Artman, Henrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Media Technology and Interaction Design, MID.
    Lindquist, Sinna
    Mikael, Mitchell
    Persson, Tomas
    KTH, School of Electrical Engineering and Computer Science (EECS), Media Technology and Interaction Design, MID.
    Ramberg, Robert
    Stockholms Universitet.
    Romero, Mario
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    van de Vehn, Pontus
    Supporting after action review in simulator mission training: Co-creating visualization concepts for training of fast-jet fighter pilots2019In: The Journal of Defence Modeling and Simulation: Applications, Methodology, Technology, ISSN 1548-5129, E-ISSN 1557-380X, Vol. 16, no 3, p. 219-231Article in journal (Refereed)
    Abstract [en]

    This article presents the design and evaluation of visualization concepts supporting After Action Review (AAR) in simulator mission training of fast-jet fighter pilots. The visualization concepts were designed based on three key characteristics of representations: re-representation, graphical constraining, and computational offloading. The visualization concepts represent combined parameters of missile launch and threat range, the former meant to elicit discussions about the prerequisites for launching missiles, and the latter to present details of what threats a certain aircraft is facing at a specific moment. The visualization concepts were designed to: 1) perceptually and cognitively offload mental workload from participants in support of determining relevant situations to discuss; 2) re-represent parameters in a format that facilitates reading-off of crucial information; and 3) graphically constrain plausible interpretations. Through a series of workshop iterations, two visualization concepts were developed and evaluated with 11 pilots and instructors. All pilots were unanimous in their opinion that the visualization concepts should be implemented as part of the AAR. Offloading, in terms of finding interesting events in the dynamic and unique training sessions, was the most important guiding concept, while re-representation and graphical constraining enabled a more structured and grounded collaboration during the AAR.

  • 8.
    Aurell, Erik
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). Aalto Univ, Dept Comp Sci, FIN-00076 Aalto, Finland.;Aalto Univ, Dept Appl Phys, FIN-00076 Aalto, Finland.
    Characteristic functions of quantum heat with baths at different temperatures2018In: Physical review. E, ISSN 2470-0045, E-ISSN 2470-0053, Vol. 97, no 6, article id 062117Article in journal (Refereed)
    Abstract [en]

    This paper is about quantum heat defined as the change in energy of a bath during a process. The presentation takes into account recent developments in classical strong-coupling thermodynamics and addresses a version of quantum heat that satisfies quantum-classical correspondence. The characteristic function and the full counting statistics of quantum heat are shown to be formally similar. The paper further shows that the method can be extended to more than one bath, e.g., two baths at different temperatures, which opens up the prospect of studying correlations and heat flow. The paper extends earlier results on the expected quantum heat in the setting of one bath [E. Aurell and R. Eichhorn, New .J Phys. 17, 065007 (2015); E. Aurell, Entropy 19, 595 (2017)].

  • 9.
    Aurell, Erik
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Dominguez, Eduardo
    Univ Havana, Dept Theoret Phys, Grp Complex Syst & Stat Phys, Havana, Cuba..
    Machado, David
    Univ Havana, Dept Theoret Phys, Grp Complex Syst & Stat Phys, Havana, Cuba..
    Mulet, Roberto
    Univ Havana, Dept Theoret Phys, Grp Complex Syst & Stat Phys, Havana, Cuba..
    Exploring the diluted ferromagnetic p-spin model with a cavity master equation2018In: Physical review. E, ISSN 2470-0045, E-ISSN 2470-0053, Vol. 97, no 5, article id 050103Article in journal (Refereed)
    Abstract [en]

    We introduce an alternative solution to Glauber multispin dynamics on random graphs. The solution is based on the recently introduced cavity master equation (CME), a time-closure turning the, in principle, exact dynamic cavity method into a practical method of analysis and of fast simulation. Running CME once is of comparable computational complexity as one Monte Carlo run on the same problem. We show that CME correctly models the ferromagnetic p-spin Glauber dynamics from high temperatures down to and below the spinoidal transition. We also show that CME allows an alternative exploration of the low-temperature spin-glass phase of the model.

  • 10.
    Aurell, Erik
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Montana, Federica
    KTH, Centres, Nordic Institute for Theoretical Physics NORDITA.
    Thermal power of heat flow through a qubit2019In: Physical review. E, ISSN 2470-0045, E-ISSN 2470-0053, Vol. 99, no 4, article id 042130Article in journal (Refereed)
    Abstract [en]

    In this paper we consider the thermal power of a heat flow through a qubit between two baths. The baths are modeled as a set of harmonic oscillators initially at equilibrium, at two temperatures. Heat is defined as the change of energy of the cold bath, and thermal power is defined as expected heat per unit time, in the long-time limit. The qubit and the baths interact as in the spin-boson model, i.e., through qubit operator sigma(z). We compute thermal power in an approximation analogous to a "noninteracting blip" (NIBA) and express it in the polaron picture as products of correlation functions of the two baths, and a time derivative of a correlation function of the cold bath. In the limit of weak interaction we recover known results in terms of a sum of correlation functions of the two baths, a correlation functions of the cold bath only, and the energy split.

  • 11.
    Barkman, Patrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Grey-box modelling of distributed parameter systems2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Grey-box models are constructed by combining model components that are derived from first principles with components that are identified empirically from data. In this thesis a grey-box modelling method for describing distributed parameter systems is presented. The method combines partial differential equations with a multi-layer perceptron network in order to incorporate prior knowledge about the system while identifying unknown dynamics from data. A gradient-based optimization scheme which relies on the reverse mode of automatic differentiation is used to train the network. The method is presented in the context of modelling the dynamics of a chemical reaction in a fluid. Lastly, the grey-box modelling method is evaluated on a one-dimensional and two-dimensional instance of the reaction system. The results indicate that the grey-box model was able to accurately capture the dynamics of the reaction system and identify the underlying reaction.

  • 12.
    Belic, Jovana
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Automatic detection of exudates in retinal images2010Conference paper (Refereed)
    Abstract [en]

    Nowadays, automatic detection of different diseases plays an important role in early and reliable diagnosis, which leads to faster recovery and significant reduction in health care costs. One such disease is diabetic retinopathy, which is induced by diabetes and is manifested through the gradual loss of eye blood vessels. Exudates are a form of diabetic retinopathy, and the idea of this paper was developing the program which would be used for automatic recognition of places that are potentially exudates in retinal images. The program was made in MatLab and three different methods were used. Also, a method for detection of blind spots was developed, concerning importance of it for appropriate detection of exudates.

  • 13.
    Belic, Jovana
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Untangling Cortico-Striatal Circuitry and its Role in Health and Disease - A computational investigation2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The basal ganglia (BG) play a critical role in a variety of regular motor and cognitive functions. Many brain diseases, such as Parkinson’s diseases, Huntington’s disease and dyskinesia, are directly related to malfunctions of the BG nuclei. One of those nuclei, the input nucleus called the striatum, is heavily connected to the cortex and receives afferents from nearly all cortical areas. The striatum is a recurrent inhibitory network that contains several distinct cell types. About 95% of neurons in the striatum are medium spiny neurons (MSNs) that form the only output from the striatum. Two of the most examined sources of GABAergic inhibition into MSNs are the feedback inhibition (FB) from the axon collaterals of the MSNs themselves, and the feedforward inhibition (FF) via the small population (1-2% of striatal neurons) of fast spiking interneurons (FSIs). The cortex sends direct projections to the striatum, while the striatum can affect the cortex only indirectly through other BG nuclei and the thalamus. Understanding how different components of the striatal network interact with each other and influence the striatal response to cortical inputs has crucial importance for clarifying the overall functions and dysfunctions of the BG.

        In this thesis I have employed advanced experimental data analysis techniques as well as computational modelling, to study the complex nature of cortico-striatal interactions. I found that for pathological states, such as Parkinson’s disease and L-DOPA-induced dyskinesia, effective connectivity is bidirectional with an accent on the striatal influence on the cortex. Interestingly, in the case of L-DOPA-induced dyskinesia, there was a high increase in effective connectivity at ~80 Hz and the results also showed a large relative decrease in the modulation of the local field potential amplitude (recorded in the primary motor cortex and sensorimotor striatum in awake, freely behaving, 6-OHDA lesioned hemi-parkinsonian rats) at ~80 Hz by the phase of low frequency oscillations. These results suggest a lack of coupling between the low frequency activity of a presumably larger neuronal population and the synchronized activity of a presumably smaller group of neurons active at 80 Hz.

        Next, I used a spiking neuron network model of the striatum to isolate the mechanisms underlying the transmission of cortical oscillations to the MSN population. I showed that FSIs play a crucial role in efficient propagation of cortical oscillations to the MSNs that did not receive direct cortical oscillations. Further, I have identified multiple factors such as the number of activated neurons, ongoing activity, connectivity, and synchronicity of inputs that influenced the transfer of oscillations by modifying the levels of FB and FF inhibitions. Overall, these findings reveal a new role of FSIs in modulating the transfer of information from the cortex to striatum. By modulating the activity and properties of the FSIs, striatal oscillations can be controlled very efficiently. Finally, I explored the interactions in the striatal network with different oscillation frequencies and showed that the features of those oscillations, such as amplitude and frequency fluctuations, can be influenced by a change in the input intensities into MSNs and FSIs and that these fluctuations are also highly dependent on the selected frequencies in addition to the phase offset between different cortical inputs.

        Lastly, I investigated how the striatum responds to cortical neuronal avalanches. Recordings in the striatum revealed that striatal activity was also characterized by spatiotemporal clusters that followed a power law distribution albeit, with significantly steeper slope. In this study, an abstract computational model was developed to elucidate the influence of intrastriatal inhibition and cortico-striatal interplay as important factors to understand the experimental findings. I showed that one particularly high activation threshold of striatal nodes can reproduce a power law-like distribution with a coefficient similar to the one found experimentally. By changing the ratio of excitation and inhibition in the cortical model, I saw that increased activity in the cortex strongly influenced striatal dynamics, which was reflected in a less negative slope of cluster size distributions in the striatum.  Finally, when inhibition was added to the model, cluster size distributions had a prominently earlier deviation from the power law distribution compared to the case when inhibition was not present. 

  • 14.
    Belic, Jovana
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab. Bernstein Center Freiburg, University of Freiburg, Freiburg, 79104, Germany.
    Kumar, Arvind
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). Bernstein Center Freiburg, University of Freiburg, Freiburg, 79104, Germany.
    Hellgren Kotaleski, Jeanette
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab. Department of Neuroscience, Karolinska Institute, Solna, 17177, Sweden.
    The role of striatal feedforward inhibition in propagation of cortical oscillations2017In: BMC neuroscience (Online), ISSN 1471-2202, E-ISSN 1471-2202, Vol. 18, p. 91-91Article in journal (Refereed)
    Abstract [en]

    Fast spiking interneurons (FSIs) and feedforward (FF) inhibition are a common property of neuronal networks throughout the brain and play crucial role in neural computations. For instance, in the cortex FF inhibition sets the window of temporal integration and spiking and thereby contributes to the control of firing rate and correlations [1]. In the striatum (the main input structure of the basal ganglia) despite their high firing rates and strong synapses, FSIs (comprise 1–2% of striatal neurons) do not seem to play a major role in controlling the firing of medium spiny neurons (MSNs; comprise 95% of striatal neurons) [2] and so far, it has not been possible to attribute a functional role to FSIs in the striatum. Here we use a spiking neuron network model in order to investigate how externally induced oscillations propagate through striatal circuitry. Recordings in the striatum have shown robust oscillatory activity that might be in fact cortical oscillations transmitted by the corticostriatal projections [3–5]. We propose that FSIs can perform an important role in transferring cortical oscillations to the striatum especially to those MSNs that are not directly driven by the cortical oscillations. Strong and divergent connectivity of FSIs implies that even weak oscillations in FSI population activity can be spread to the whole MSN population [6]. Further, we have identified multiple factors that influence the transfer of oscillations to MSNs. The variables such as the number of activated neurons, ongoing activity, connectivity, and synchronicity of inputs influence the transfer of oscillations by modifying the levels of feedforward and feedback inhibitions suggesting that the striatum can exploit different parameters to impact the transfer of oscillatory signals.

    References

    1. Isaacson, J. S., & Scanziani, M. (2011). How inhibition shapes cortical activity. Neuron, 72(2), 231–243. 

    2. Berke, J. D. (2011). Functional properties of striatal fast-spiking interneurons. Frontiers in systems neuroscience, 5.

    3. Belić, J. J., Halje, P., Richter, U., Petersson, P., & Kotaleski, J. H. (2016). Untangling cortico-striatal connectivity and cross-frequency coupling in L-DOPA-induced dyskinesia. Frontiers in systems neuroscience, 10.

    4. Berke, J. D. (2009). Fast oscillations in cortical‐striatal networks switch frequency following rewarding events and stimulant drugs. European Journal of Neuroscience, 30(5), 848–859.

    5. Boraud, T., Brown, P., Goldberg, J. A., Graybiel, A. M., & Magill, P. J. (2005). Oscillations in the basal ganglia: the good, the bad, and the unexpected. In The basal ganglia VIII (pp. 1–24). Springer US.

    6. Belić, J. J., Kumar, A., & Kotaleski, J. H. (2017). Interplay between periodic stimulation and GABAergic inhibition in striatal network oscillations. PloS one, 12(4), e0175135.

  • 15.
    Berglund, Emelie
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology.
    Maaskola, Jonas
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology.
    Schultz, Niklas
    Friedrich, Stefanie
    Marklund, Maja
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology.
    Bergenstrahle, Joseph
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology.
    Tarish, Firas
    Tanoglidi, Anna
    Vickovic, Sanja
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Larsson, Ludvig
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology.
    Salmén, Fredrik
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Ogris, Christoph
    Wallenborg, Karolina
    Lagergren, Jens
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Ståhl, Patrik
    Sonnhammer, Erik
    Helleday, Thomas
    Lundeberg, Joakim
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity2018In: Nature Communications, ISSN 2041-1723, E-ISSN 2041-1723, Vol. 9, article id 2419Article in journal (Refereed)
    Abstract [en]

    Intra-tumor heterogeneity is one of the biggest challenges in cancer treatment today. Here we investigate tissue-wide gene expression heterogeneity throughout a multifocal prostate cancer using the spatial transcriptomics (ST) technology. Utilizing a novel approach for deconvolution, we analyze the transcriptomes of nearly 6750 tissue regions and extract distinct expression profiles for the different tissue components, such as stroma, normal and PIN glands, immune cells and cancer. We distinguish healthy and diseased areas and thereby provide insight into gene expression changes during the progression of prostate cancer. Compared to pathologist annotations, we delineate the extent of cancer foci more accurately, interestingly without link to histological changes. We identify gene expression gradients in stroma adjacent to tumor regions that allow for re-stratification of the tumor microenvironment. The establishment of these profiles is the first step towards an unbiased view of prostate cancer and can serve as a dictionary for future studies.

  • 16.
    Blackwell, Kim T.
    et al.
    George Mason Univ, Krasnow Inst Adv Study, Fairfax, VA 22030 USA. lackwell, Kim T..
    Salinas, Armando G.
    Tewatia, Parul
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    English, Brad
    Hellgren Kotaleski, Jeanette
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lovinger, David M.
    Molecular mechanisms underlying striatal synaptic plasticity: relevance chronic alcohol consumption and seeking2019In: European Journal of Neuroscience, ISSN 0953-816X, E-ISSN 1460-9568, Vol. 49, no 6, p. 768-783Article in journal (Refereed)
    Abstract [en]

    The striatum, the input structure of the basal ganglia, is a major site learning and memory for goal-directed actions and habit formation. iny projection neurons of the striatum integrate cortical, thalamic, d nigral inputs to learn associations, with cortico-striatal synaptic asticity as a learning mechanism. Signaling molecules implicated in naptic plasticity are altered in alcohol withdrawal, which may ntribute to overly strong learning and increased alcohol seeking and nsumption. To understand how interactions among signaling molecules oduce synaptic plasticity, we implemented a mechanistic model of gnaling pathways activated by dopamine D1 receptors, acetylcholine ceptors, and glutamate. We use our novel, computationally efficient mulator, NeuroRD, to simulate stochastic interactions both within and tween dendritic spines. Dopamine release during theta burst and 20-Hz imulation was extrapolated from fast-scan cyclic voltammetry data llected in mouse striatal slices. Our results show that the combined tivity of several key plasticity molecules correctly predicts the currence of either LTP, LTD, or no plasticity for numerous perimental protocols. To investigate spatial interactions, we imulate two spines, either adjacent or separated on a 20-mu m ndritic segment. Our results show that molecules underlying LTP hibit spatial specificity, whereas 2-arachidonoylglycerol exhibits a atially diffuse elevation. We also implement changes in NMDA ceptors, adenylyl cyclase, and G protein signaling that have been asured following chronic alcohol treatment. Simulations under these nditions suggest that the molecular changes can predict changes in naptic plasticity, thereby accounting for some aspects of alcohol use sorder.

  • 17.
    Brocke, Ekaterina
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Method development for co-simulation of electrical-chemical systems in Neuroscience2020Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Multiscale modeling and simulation is a powerful approach for studying such phenomena in nature as learning and memory. In computational neuroscience, historically, methods and tools for neuronal modeling and simulations have been developed for studies focused on a single level of the neuronal organization. Once the community realized that the interaction of multiple systems acting at different temporal and spatial scales can lead to emerging properties of the phenomena under study, the interest in and need for models encompassing processes acting at multiple scales of time and space increased. Such models are called multiscale models.

    Multiscale modeling and simulation can be achieved in different ways. One of the possible solutions is to use an already existing foundation of formalisms and methods, and couple existing numerical algorithms and models during a simulation in a co-simulation, i.e. a joint simulation of subsystems. However, there are several obstacles on the way. First, a lack of interoperability of simulation environments makes it non-trivial to couple existing models in a single environment that supports multiscale simulation. Second, there is a decision to make regarding which variables to communicate between subsystems. The communication signal has a significant impact on the behavior of the whole multiscale system. Last but not least, an absence of a theory or general approach for the numerical coupling of existing mathematical formalisms makes the coupling of the numerical solvers a challenging task.

    The main contribution of this thesis is a numerical framework for multiscale co-simulation of electrical and chemical systems in neuroscience. A multiscale model that integrates a subcellular signaling system with the electrical activity of the neuron was developed. The thesis emphasizes the importance of numerically correct and efficient coupling of the systems of interest. Two coupling algorithms, named singlerate and multirate, differ in the rate of communication between the coupling subsystems, were proposed in the thesis. The algorithms, as well as test cases, were implemented in the MATLAB® environment. MATLAB was used to validate the accuracy and efficiency of the algorithms. Both algorithms showed an expected second order accuracy with the simulated electrical-chemical system. The guaranteed accuracy in the singlerate algorithm can be used as a trade-off for efficiency in the multirate algorithm. Thus, both algorithms can find its application in the proposed numerical framework for multiscale co-simulations. The framework exposes a modular organization with natural interfaces and could be used as a basis for the development of a generic tool for multiscale co-simulations.

    The thesis also presents an implementation of a new numerical method in the NEURON simulation environment, with benchmarks. The method can replace the standard discretization schema for the Hodgkin-Huxley type models. It can be beneficial in a co-simulation of large models where the Jacobian evaluation of the whole system becomes a very expensive operation.

    Finally, the thesis describes an extension of the MUlti-SImulation Coordinator tool (MUSIC). MUSIC is a library that is mainly used for co-simulations of spiking neural networks on a cluster. A series of important developments was done in MUSIC as the first step towards multiscale co-simulations. First, a new algorithm and an improvement of the existing parallel communication algorithms were implemented as described in the thesis. Then, a new communication scheduling algorithm was developed and implemented in the MUSIC library and analyzed. The numerical framework presented in the thesis could be implemented with MUSIC to allow efficient co-simulations of electrical-chemical systems.

  • 18.
    Brocke, Ekaterina
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Djurfeldt, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Efficient Spike Communication in the MUSIC Framework on a Blue Gene/Q SupercomputerManuscript (preprint) (Other (popular science, discussion, etc.))
  • 19.
    Bruce, Neil J.
    et al.
    Heidelberg Inst Theoret Studies, Mol & Cellular Modeling Grp, Schloss Heidelberg, Germany..
    Narzi, Daniele
    Ecole Polytech Fed Lausanne, Inst Sci & Ingn Chim, CH-1015 Lausanne, Switzerland..
    Trpevski, Daniel
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Electrical Engineering and Computer Science (EECS).
    van Keulen, Siri C.
    Ecole Polytech Fed Lausanne, Inst Sci & Ingn Chim, CH-1015 Lausanne, Switzerland.;Stanford Univ, Dept Comp Sci, Stanford, CA 94305 USA..
    Nair, Anu G.
    Univ Zurich, Inst Mol Life Sci, Zurich, Switzerland..
    Rothlisberger, Ursula
    Ecole Polytech Fed Lausanne, Inst Sci & Ingn Chim, CH-1015 Lausanne, Switzerland..
    Wade, Rebecca C.
    Heidelberg Inst Theoret Studies, Mol & Cellular Modeling Grp, Schloss Heidelberg, Germany.;Heidelberg Univ, Ctr Mol Biol ZMBH, DKFZ ZMBH Alliance, Heidelberg, Germany.;Heidelberg Univ, Interdisciplinary Ctr Sci Comp IWR, Heidelberg, Germany..
    Carloni, Paolo
    Rhein Westfal TH Aachen, Dept Phys, Aachen, Germany.;Rhein Westfal TH Aachen, Dept Neurobiol, Aachen, Germany.;Forschungszentrum Julich, Inst Neurosci & Med INM 11, Julich, Germany.;Forschungszentrum Julich, Inst Neurosci & Med INM 9, Julich, Germany.;Forschungszentrum Julich, Inst Adv Simulat IAS 5, Julich, Germany..
    Hällgren Kotaleski, Jeanette
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Regulation of adenylyl cyclase 5 in striatal neurons confers the ability to detect coincident neuromodulatory signals2019In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 15, no 10, article id e1007382Article in journal (Refereed)
    Abstract [en]

    Author summary Adenylyl cyclases (ACs) are enzymes that can translate extracellular signals into the intracellular molecule cAMP, which is thus a 2nd messenger of extracellular events. The brain expresses nine membrane-bound AC variants, and AC5 is the dominant form in the striatum. The striatum is the input stage of the basal ganglia, a brain structure involved in reward learning, i.e. the learning of behaviors that lead to rewarding stimuli (such as food, water, sugar, etc). During reward learning, cAMP production is crucial for strengthening the synapses from cortical neurons onto the striatal principal neurons, and its formation is dependent on several neuromodulatory systems such as dopamine and acetylcholine. It is, however, not understood how AC5 is activated by transient (subsecond) changes in the neuromodulatory signals. Here we combine several computational tools, from molecular dynamics and Brownian dynamics simulations to bioinformatics approaches, to inform and constrain a kinetic model of the AC5-dependent signaling system. We use this model to show how the specific molecular properties of AC5 can detect particular combinations of co-occuring transient changes in the neuromodulatory signals which thus result in a supralinear/synergistic cAMP production. Our results also provide insights into the computational capabilities of the different AC isoforms. Long-term potentiation and depression of synaptic activity in response to stimuli is a key factor in reinforcement learning. Strengthening of the corticostriatal synapses depends on the second messenger cAMP, whose synthesis is catalysed by the enzyme adenylyl cyclase 5 (AC5), which is itself regulated by the stimulatory G alpha(olf) and inhibitory G alpha(i) proteins. AC isoforms have been suggested to act as coincidence detectors, promoting cellular responses only when convergent regulatory signals occur close in time. However, the mechanism for this is currently unclear, and seems to lie in their diverse regulation patterns. Despite attempts to isolate the ternary complex, it is not known if G alpha(olf) and G alpha(i) can bind to AC5 simultaneously, nor what activity the complex would have. Using protein structure-based molecular dynamics simulations, we show that this complex is stable and inactive. These simulations, along with Brownian dynamics simulations to estimate protein association rates constants, constrain a kinetic model that shows that the presence of this ternary inactive complex is crucial for AC5's ability to detect coincident signals, producing a synergistic increase in cAMP. These results reveal some of the prerequisites for corticostriatal synaptic plasticity, and explain recent experimental data on cAMP concentrations following receptor activation. Moreover, they provide insights into the regulatory mechanisms that control signal processing by different AC isoforms.

  • 20.
    Carlsson, Stefan
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Azizpour, Hossein
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Razavian, Ali Sharif
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Sullivan, Josephine
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Smith, Kevin
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    The Preimage of Rectifier Network Activities2017In: International Conference on Learning Representations (ICLR), 2017Conference paper (Refereed)
    Abstract [en]

    The preimage of the activity at a certain level of a deep network is the set of inputs that result in the same node activity. For fully connected multi layer rectifier networks we demonstrate how to compute the preimages of activities at arbitrary levels from knowledge of the parameters in a deep rectifying network. If the preimage set of a certain activity in the network contains elements from more than one class it means that these classes are irreversibly mixed. This implies that preimage sets which are piecewise linear manifolds are building blocks for describing the input manifolds specific classes, ie all preimages should ideally be from the same class. We believe that the knowledge of how to compute preimages will be valuable in understanding the efficiency displayed by deep learning networks and could potentially be used in designing more efficient training algorithms.

  • 21.
    Chen, Guang
    et al.
    Tongji Univ, Coll Automot Engn, Shanghai, Peoples R China.;Tech Univ Munich, Robot & Embedded Syst, Munich, Germany..
    Cao, Hu
    Hunan Univ, State Key Lab Adv Design & Mfg Vehicle Body, Changsha, Hunan, Peoples R China..
    Aafaque, Muhammad
    Tech Univ Munich, Robot & Embedded Syst, Munich, Germany..
    Chen, Jieneng
    Tongji Univ, Coll Elect & Informat Engn, Shanghai, Peoples R China..
    Ye, Canbo
    Tongji Univ, Coll Automot Engn, Shanghai, Peoples R China..
    Roehrbein, Florian
    Tech Univ Munich, Robot & Embedded Syst, Munich, Germany..
    Conradt, Jörg
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Chen, Kai
    Tongji Univ, Coll Automot Engn, Shanghai, Peoples R China..
    Bing, Zhenshan
    Tech Univ Munich, Robot & Embedded Syst, Munich, Germany..
    Liu, Xingbo
    Tongji Univ, Coll Automot Engn, Shanghai, Peoples R China..
    Hinz, Gereon
    Tech Univ Munich, Robot & Embedded Syst, Munich, Germany..
    Stechele, Walter
    Tech Univ Munich, Integrated Syst, Munich, Germany..
    Knoll, Alois
    Tech Univ Munich, Robot & Embedded Syst, Munich, Germany..
    Neuromorphic Vision Based Multivehicle Detection and Tracking for Intelligent Transportation System2018In: Journal of Advanced Transportation, ISSN 0197-6729, E-ISSN 2042-3195, article id 4815383Article in journal (Refereed)
    Abstract [en]

    Neuromorphic vision sensor is a new passive sensing modality and a frameless sensor with a number of advantages over traditional cameras. Instead of wastefully sending entire images at fixed frame rate, neuromorphic vision sensor only transmits the local pixel-level changes caused by the movement in a scene at the time they occur. This results in advantageous characteristics, in terms of low energy consumption, high dynamic range, sparse event stream, and low response latency, which can be very useful in intelligent perception systems for modern intelligent transportation system (ITS) that requires efficient wireless data communication and low power embedded computing resources. In this paper, we propose the first neuromorphic vision based multivehicle detection and tracking system in ITS. The performance of the system is evaluated with a dataset recorded by a neuromorphic vision sensor mounted on a highway bridge. We performed a preliminary multivehicle tracking-by-clustering study using three classical clustering approaches and four tracking approaches. Our experiment results indicate that, by making full use of the low latency and sparse event stream, we could easily integrate an online tracking-by-clustering system running at a high frame rate, which far exceeds the real-time capabilities of traditional frame-based cameras. If the accuracy is prioritized, the tracking task can also be performed robustly at a relatively high rate with different combinations of algorithms. We also provide our dataset and evaluation approaches serving as the first neuromorphic benchmark in ITS and hopefully can motivate further research on neuromorphic vision sensors for ITS solutions.

  • 22.
    Chen, Guang
    et al.
    Tongji Univ, Coll Automot Engn, Shanghai, Peoples R China.;Tech Univ Munich, Robot Artificial Intelligence & Real Time Syst, Munich, Germany..
    Cao, Hu
    Hunan Univ, State Key Lab Adv Design & Mfg Vehicle Body, Changsha, Hunan, Peoples R China..
    Ye, Canbo
    Tongji Univ, Coll Automot Engn, Shanghai, Peoples R China..
    Zhang, Zhenyan
    Tongji Univ, Coll Automot Engn, Shanghai, Peoples R China..
    Liu, Xingbo
    Tongji Univ, Coll Automot Engn, Shanghai, Peoples R China..
    Mo, Xuhui
    Hunan Univ, State Key Lab Adv Design & Mfg Vehicle Body, Changsha, Hunan, Peoples R China..
    Qu, Zhongnan
    Swiss Fed Inst Technol, Comp Engn & Networks Lab, Zurich, Switzerland..
    Conradt, Jörg
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Roehrbein, Florian
    Tech Univ Munich, Robot Artificial Intelligence & Real Time Syst, Munich, Germany..
    Knoll, Alois
    Tech Univ Munich, Robot Artificial Intelligence & Real Time Syst, Munich, Germany..
    Multi-Cue Event Information Fusion for Pedestrian Detection With Neuromorphic Vision Sensors2019In: Frontiers in Neurorobotics, ISSN 1662-5218, Vol. 13, article id 10Article in journal (Refereed)
    Abstract [en]

    Neuromorphic vision sensors are bio-inspired cameras that naturally capture the dynamics of a scene with ultra-low latency, filtering out redundant information with low power consumption. Few works are addressing the object detection with this sensor. In this work, we propose to develop pedestrian detectors that unlock the potential of the event data by leveraging multi-cue information and different fusion strategies. To make the best out of the event data, we introduce three different event-stream encoding methods based on Frequency, Surface of Active Event (SAE) and Leaky Integrate-and-Fire (LIF). We further integrate them into the state-of-the-art neural network architectures with two fusion approaches: the channel-level fusion of the raw feature space and decision-level fusion with the probability assignments. We present a qualitative and quantitative explanation why different encoding methods are chosen to evaluate the pedestrian detection and which method performs the best. We demonstrate the advantages of the decision-level fusion via leveraging multi-cue event information and show that our approach performs well on a self-annotated event-based pedestrian dataset with 8,736 event frames. This work paves the way of more fascinating perception applications with neuromorphic vision sensors.

  • 23.
    Chen, Guang
    et al.
    Tongji Univ, Coll Automot Engn, Shanghai, Peoples R China.;Tech Univ Munich, Chair Robot Artificial Intelligence & Real Time S, Munich, Germany..
    Chen, Jieneng
    Tongji Univ, Coll Elect & Informat Engn, Shanghai, Peoples R China..
    Lienen, Marten
    Tech Univ Munich, Chair Robot Artificial Intelligence & Real Time S, Munich, Germany..
    Conradt, Jörg
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Roehrbein, Florian
    Tech Univ Munich, Chair Robot Artificial Intelligence & Real Time S, Munich, Germany..
    Knoll, Alois C.
    Tech Univ Munich, Chair Robot Artificial Intelligence & Real Time S, Munich, Germany..
    FLGR: Fixed Length Gists Representation Learning for RNN-HMM Hybrid-Based Neuromorphic Continuous Gesture Recognition2019In: Frontiers in Neuroscience, ISSN 1662-4548, E-ISSN 1662-453X, Vol. 13, article id 73Article in journal (Refereed)
    Abstract [en]

    A neuromorphic vision sensors is a novel passive sensing modality and frameless sensors with several advantages over conventional cameras. Frame-based cameras have an average frame-rate of 30 fps, causing motion blur when capturing fast motion, e.g., hand gesture. Rather than wastefully sending entire images at a fixed frame rate, neuromorphic vision sensors only transmit the local pixel-level changes induced by the movement in a scene when they occur. This leads to advantageous characteristics, including low energy consumption, high dynamic range, a sparse event stream and low response latency. In this study, a novel representation learning method was proposed: Fixed Length Gists Representation (FLGR) learning for event-based gesture recognition. Previous methods accumulate events into video frames in a time duration (e.g., 30 ms) to make the accumulated image-level representation. However, the accumulated-frame-based representation waives the friendly event-driven paradigm of neuromorphic vision sensor. New representation are urgently needed to fill the gap in non-accumulated-frame-based representation and exploit the further capabilities of neuromorphic vision. The proposed FLGR is a sequence learned from mixture density autoencoder and preserves the nature of event-based data better. FLGR has a data format of fixed length, and it is easy to feed to sequence classifier. Moreover, an RNN-HMM hybrid was proposed to address the continuous gesture recognition problem. Recurrent neural network (RNN) was applied for FLGR sequence classification while hidden Markov model (HMM) is employed for localizing the candidate gesture and improving the result in a continuous sequence. A neuromorphic continuous hand gestures dataset (Neuro ConGD Dataset) was developed with 17 hand gestures classes for the community of the neuromorphic research. Hopefully, FLGR can inspire the study on the event-based highly efficient, high-speed, and high-dynamic-range sequence classification tasks.

  • 24. Chien, Steven W. D.
    et al.
    Markidis, Stefano
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Sishtla, Chaitanya Prasad
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Santos, Luis
    Herman, Pawel
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Nrasimhamurthy, Sai
    Laure, Erwin
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Characterizing Deep-Learning I/O Workloads in TensorFlow2018In: Proceedings of PDSW-DISCS 2018: 3rd Joint International Workshop on Parallel Data Storage and Data Intensive Scalable Computing Systems, Held in conjunction with SC 2018: The International Conference for High Performance Computing, Networking, Storage and Analysis, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 54-63Conference paper (Refereed)
    Abstract [en]

    The performance of Deep-Learning (DL) computing frameworks rely on the rformance of data ingestion and checkpointing. In fact, during the aining, a considerable high number of relatively small files are first aded and pre-processed on CPUs and then moved to accelerator for mputation. In addition, checkpointing and restart operations are rried out to allow DL computing frameworks to restart quickly from a eckpoint. Because of this, I/O affects the performance of DL plications. this work, we characterize the I/O performance and scaling of nsorFlow, an open-source programming framework developed by Google and ecifically designed for solving DL problems. To measure TensorFlow I/O rformance, we first design a micro-benchmark to measure TensorFlow ads, and then use a TensorFlow mini-application based on AlexNet to asure the performance cost of I/O and checkpointing in TensorFlow. To prove the checkpointing performance, we design and implement a burst ffer. find that increasing the number of threads increases TensorFlow ndwidth by a maximum of 2.3 x and 7.8 x on our benchmark environments. e use of the tensorFlow prefetcher results in a complete overlap of mputation on accelerator and input pipeline on CPU eliminating the fective cost of I/O on the overall performance. The use of a burst ffer to checkpoint to a fast small capacity storage and copy ynchronously the checkpoints to a slower large capacity storage sulted in a performance improvement of 2.6x with respect to eckpointing directly to slower storage on our benchmark environment.

  • 25.
    Chien, Steven Wei Der
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Sishtla, Chaitanya Prasad
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Markidis, Stefano
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Jun, Zhang
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Peng, Ivy Bo
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC. KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Laure, Erwin
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC. KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    An Evaluation of the TensorFlow Programming Model for Solving Traditional HPC Problems2018In: Proceedings of the 5th International Conference on Exascale Applications and Software, The University of Edinburgh , 2018, p. 34-Conference paper (Refereed)
    Abstract [en]

    Computational intensive applications such as pattern recognition, and natural language processing, are increasingly popular on HPC systems. Many of these applications use deep-learning, a branch of machine learning, to determine the weights of artificial neural network nodes by minimizing a loss function. Such applications depend heavily on dense matrix multiplications, also called tensorial operations. The use of Graphics Processing Unit (GPU) has considerably speeded up deep-learning computations, leading to a Renaissance of the artificial neural network. Recently, the NVIDIA Volta GPU and the Google Tensor Processing Unit (TPU) have been specially designed to support deep-learning workloads. New programming models have also emerged for convenient expression of tensorial operations and deep-learning computational paradigms. An example of such new programming frameworks is TensorFlow, an open-source deep-learning library released by Google in 2015. TensorFlow expresses algorithms as a computational graph where nodes represent operations and edges between nodes represent data flow. Multi-dimensional data such as vectors and matrices which flows between operations are called Tensors. For this reason, computation problems need to be expressed as a computational graph. In particular, TensorFlow supports distributed computation with flexible assignment of operation and data to devices such as GPU and CPU on different computing nodes. Computation on devices are based on optimized kernels such as MKL, Eigen and cuBLAS. Inter-node communication can be through TCP and RDMA. This work attempts to evaluate the usability and expressiveness of the TensorFlow programming model for traditional HPC problems. As an illustration, we prototyped a distributed block matrix multiplication for large dense matrices which cannot be co-located on a single device and a Conjugate Gradient (CG) solver. We evaluate the difficulty of expressing traditional HPC algorithms using computational graphs and study the scalability of distributed TensorFlow on accelerated systems. Our preliminary result with distributed matrix multiplication shows that distributed computation on TensorFlow is extremely scalable. This study provides an initial investigation of new emerging programming models for HPC.

  • 26.
    Chien, Wei Der
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    An Evaluation of TensorFlow as a Programming Framework for HPC Applications2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In recent years, deep-learning, a branch of machine learning gained increasing popularity due to their extensive applications and performance. At the core of these application is dense matrix-matrix multiplication. Graphics Processing Units (GPUs) are commonly used in the training process due to their massively parallel computation capabilities. In addition, specialized low-precision accelerators have emerged to specifically address Tensor operations. Software frameworks, such as TensorFlow have also emerged to increase the expressiveness of neural network model development. In TensorFlow computation problems are expressed as Computation Graphs where nodes of a graph denote operation and edges denote data movement between operations. With increasing number of heterogeneous accelerators which might co-exist on the same cluster system, it became increasingly difficult for users to program efficient and scalable applications. TensorFlow provides a high level of abstraction and it is possible to place operations of a computation graph on a device easily through a high level API. In this work, the usability of TensorFlow as a programming framework for HPC application is reviewed. We give an introduction of TensorFlow as a programming framework and paradigm for distributed computation. Two sample applications are implemented on TensorFlow: tiled matrix multiplication and conjugate gradient solver for solving large linear systems. We try to illustrate how such problems can be expressed in computation graph for distributed computation. We perform scalability tests and comment on performance scaling results and quantify how TensorFlow can take advantage of HPC systems by performing micro-benchmarking on communication performance. Through this work, we show that TensorFlow is an emerging and promising platform which is well suited for a particular class of problem which requires very little synchronization.

  • 27.
    Chien, Wei Der
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Theoretical Computer Science, TCS.
    Peng, Ivy
    Markidis, Stefano
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Performance evaluation of advanced features in CUDA unified memory2019In: Proceedings of MCHPC 2019: Workshop on Memory Centric High Performance Computing - Held in conjunction with SC 2019: The International Conference for High Performance Computing, Networking, Storage and Analysis, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 50-57Conference paper (Refereed)
    Abstract [en]

    CUDA Unified Memory improves the GPU pro- grammability and also enables GPU memory oversubscription. Recently, two advanced memory features, memory advises and asynchronous prefetch, have been introduced. In this work, we evaluate the new features on two platforms that feature different CPUs, GPUs, and interconnects. We derive a benchmark suite for the experiments and stress the memory system to evaluate both in-memory and oversubscription performance. The results show that memory advises on the Intel-Volta/Pascal- PCIe platform bring negligible improvement for in-memory exe- cutions. However, when GPU memory is oversubscribed by about 50%, using memory advises results in up to 25% performance improvement compared to the basic CUDA Unified Memory. In contrast, the Power9-Volta-NVLink platform can substantially benefit from memory advises, achieving up to 34% performance gain for in-memory executions. However, when GPU memory is oversubscribed on this platform, using memory advises increases GPU page faults and results in considerable performance loss. The CUDA prefetch also shows different performance impact on the two platforms. It improves performance by up to 50% on the Intel-Volta/Pascal-PCI-E platform but brings little benefit to the Power9-Volta-NVLink platform.

  • 28.
    Chrysanthidis, Nikolaos
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). Aristotle University of Thessaloniki, Faculty of Engineering, School of Electrical and Computer Engineering, 54124, Thessaloniki, Greece.
    Fiebig, Florian
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). Institute for Adaptive and Neural Computation, Edinburgh University, EH8 9AB Edinburgh, Scotland.
    Lansner, Anders
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). Department of Numerical Analysis and Computer Science, Stockholm University, 10691 Stockholm, Sweden.
    Introducing double bouquet cells into a modular cortical associative memory modelManuscript (preprint) (Other academic)
    Abstract [en]

    We present an electrophysiological model of double bouquet cells and integrate them into an established cortical columnar microcircuit model that has previously been used as a spiking attractor model for memory. Learning in that model relies on a Bayesian-Hebbian learning rule to condition recurrent connectivity between pyramidal cells. We here demonstrate that the inclusion of a biophysically plausible double bouquet cell model can solve earlier concerns about learning rules that simultaneously learn excitation and inhibition and might thus violate Dale's Principle. We show that learning ability and resulting effective connectivity between functional columns of previous network models is preserved when pyramidal synapses onto double-bouquet cells are plastic under the same Hebbian-Bayesian learning rule. The proposed architecture draws on experimental evidence on double bouquet cells and effectively solves the problem of duplexed learning of inhibition and excitation by replacing recurrent inhibition between pyramidal cells in functional columns of different stimulus selectivity with a plastic disynaptic pathway. We thus show that the resulting change to the microcircuit architecture improves the model's biological plausibility without otherwise impacting the models spiking activity, basic operation, and learning abilities.

  • 29.
    Chrysanthidis, Nikolaos
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST). Faculty of Engineering, School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece.
    Fiebig, Florian
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST). Institute for Adaptive and Neural Computation, Edinburgh University, Edinburgh, EH8 9AB, United Kingdom.
    Lansner, Anders
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST). Department of Mathematics, Stockholm University, Stockholm, 10691, Sweden.
    Introducing double bouquet cells into a modular cortical associative memory model2019In: Journal of Computational Neuroscience, ISSN 0929-5313, E-ISSN 1573-6873, Vol. 47, no 2-3, p. 223-230Article in journal (Refereed)
    Abstract [en]

    We present an electrophysiological model of double bouquet cells and integrate them into an established cortical columnar microcircuit model that has previously been used as a spiking attractor model for memory. Learning in that model relies on a Hebbian-Bayesian learning rule to condition recurrent connectivity between pyramidal cells. We here demonstrate that the inclusion of a biophysically plausible double bouquet cell model can solve earlier concerns about learning rules that simultaneously learn excitation and inhibition and might thus violate Dale's principle. We show that learning ability and resulting effective connectivity between functional columns of previous network models is preserved when pyramidal synapses onto double bouquet cells are plastic under the same Hebbian-Bayesian learning rule. The proposed architecture draws on experimental evidence on double bouquet cells and effectively solves the problem of duplexed learning of inhibition and excitation by replacing recurrent inhibition between pyramidal cells in functional columns of different stimulus selectivity with a plastic disynaptic pathway. We thus show that the resulting change to the microcircuit architecture improves the model's biological plausibility without otherwise impacting the model's spiking activity, basic operation, and learning abilities.

  • 30.
    Degirmenci, Niyazi Cem
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Adaptive Finite Element Methods for Fluid Structure Interaction Problems with Applications to Human Phonation2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    This work presents a unified framework for numerical solution of Fluid Structure Interaction (FSI) and acoustics problems with focus on human phonation. The Finite Element Method is employed for numerical investigation of partial differential equations that model conservation of momentum and mass. Since the resulting system of equations is very large, an efficient open source high performance implementation is constructed and provided. In order to gain accuracy for the numerical solutions, an adaptive mesh refinement strategy is employed which reduces the computational cost in comparison to a uniform refinement. Adaptive refinement of the mesh relies on computable error indicators which appear as a combination of a computable residual and the solution of a so-called dual problem acting as weights on computed residuals. The first main achievement of this thesis is to apply this strategy to numerical simulations of a benchmark problem for FSI. This FSI model is further extended for contact handling and applied to a realistic vocal folds geometry where the glottic wave formation was captured in the numerical simulations. This is the second achievement in the presented work. The FSI model is further coupled to an acoustics model through an acoustic analogy, for vocal folds with flow induced oscillations for a domain constructed to create the vowel /i/. The comparisons of the obtained pressure signal at specified points with respect to results from literature for the same vowel is reported, which is the final main result presented.

  • 31. Dembrower, K.
    et al.
    Liu, Yue
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Azizpour, Hossein
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Eklund, M.
    Smith, Kevin
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lindholm, P.
    Strand, F.
    Comparison of a deep learning risk score and standard mammographic density score for breast cancer risk prediction2020In: Radiology, ISSN 0033-8419, E-ISSN 1527-1315, Vol. 294, no 2, p. 265-272Article in journal (Refereed)
    Abstract [en]

    Background: Most risk prediction models for breast cancer are based on questionnaires and mammographic density assessments. By training a deep neural network, further information in the mammographic images can be considered. Purpose: To develop a risk score that is associated with future breast cancer and compare it with density-based models. Materials and Methods: In this retrospective study, all women aged 40-74 years within the Karolinska University Hospital uptake area in whom breast cancer was diagnosed in 2013-2014 were included along with healthy control subjects. Network development was based on cases diagnosed from 2008 to 2012. The deep learning (DL) risk score, dense area, and percentage density were calculated for the earliest available digital mammographic examination for each woman. Logistic regression models were fitted to determine the association with subsequent breast cancer. False-negative rates were obtained for the DL risk score, age-adjusted dense area, and age-adjusted percentage density. Results: A total of 2283 women, 278 of whom were later diagnosed with breast cancer, were evaluated. The age at mammography (mean, 55.7 years vs 54.6 years; P< .001), the dense area (mean, 38.2 cm2 vs 34.2 cm2; P< .001), and the percentage density (mean, 25.6% vs 24.0%; P< .001) were higher among women diagnosed with breast cancer than in those without a breast cancer diagnosis. The odds ratios and areas under the receiver operating characteristic curve (AUCs) were higher for age-adjusted DL risk score than for dense area and percentage density: 1.56 (95% confidence interval [CI]: 1.48, 1.64; AUC, 0.65), 1.31 (95% CI: 1.24, 1.38; AUC, 0.60), and 1.18 (95% CI: 1.11, 1.25; AUC, 0.57), respectively (P< .001 for AUC). The false-negative rate was lower: 31% (95% CI: 29%, 34%), 36% (95% CI: 33%, 39%; P = .006), and 39% (95% CI: 37%, 42%; P< .001); this difference was most pronounced for more aggressive cancers. Conclusion: Compared with density-based models, a deep neural network can more accurately predict which women are at risk for future breast cancer, with a lower false-negative rate for more aggressive cancers.

  • 32.
    Divin, A.
    et al.
    St Petersburg State Univ, Dept Earths Phys, St Petersburg 198504, Russia..
    Semenov, V.
    St Petersburg State Univ, Dept Earths Phys, St Petersburg 198504, Russia..
    Zaitsev, I.
    St Petersburg State Univ, Dept Earths Phys, St Petersburg 198504, Russia..
    Korovinskiy, D.
    Austrian Acad Sci, Space Res Inst, A-8042 Graz, Austria..
    Deca, J.
    Univ Colorado Boulder, LASP, Boulder, CO 80303 USA..
    Lapenta, G.
    Katholieke Univ Leuven, Dept Math, B-3001 Leuven, Belgium..
    Olshevsky, Viacheslav
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Markidis, Stefano
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Inner and outer electron diffusion region of antiparallel collisionless reconnection: Density dependence2019In: Physics of Plasmas, ISSN 1070-664X, E-ISSN 1089-7674, Vol. 26, no 10, article id 102305Article in journal (Refereed)
    Abstract [en]

    We study inflow density dependence of substructures within electron diffusion region (EDR) of collisionless symmetric magnetic reconnection. We perform a set of 2.5D particle-in-cell simulations which start from a Harris current layer with a uniform background density n(b). A scan of n(b) ranging from 0:02 n(0) to 2 n(0) of the peak current layer density (n(0)) is studied keeping other plasma parameters the same. Various quantities measuring reconnection rate, EDR spatial scales, and characteristic velocities are introduced. We analyze EDR properties during quasisteady stage when the EDR length measures saturate. Consistent with past kinetic simulations, electrons are heated parallel to the B field in the inflow region. The presence of the strong parallel anisotropy acts twofold: (1) electron pressure anisotropy drift gets important at the EDR upstream edge in addition to the E x B drift speed and (2) the pressure anisotropy term -del.P-(e)/(ne) modifies the force balance there. We find that the width of the EDR demagnetization region and EDR current are proportional to the electron inertial length similar to d(e) and similar to d(e)n(b)(0.22), respectively. Magnetic reconnection is fast with a rate of similar to 0.1 but depends weakly on density as similar to n(b)(-1/8). Such reconnection rate proxies as EDR geometrical aspect or the inflow-to-outflow electron velocity ratio are shown to have different density trends, making electric field the only reliable measure of the reconnection rate. Published under license by AIP Publishing.

  • 33.
    Eisenkolb, I.
    et al.
    Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart, Germany.
    Jensch, A.
    Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart, Germany.
    Eisenkolb, K.
    Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart, Germany.
    Kramer, Andrei
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Buchholz, P. C. F.
    Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart, Germany.
    Pleiss, J.
    Institute for Biochemistry and Technical Biochemistry, University of Stuttgart, Stuttgart, Germany.
    Spiess, A.
    Institute for Biochemical Engineering, Technical University Braunschweig, Braunschweig, Germany.
    Radde, N. E.
    Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart, Germany.
    Modeling of biocatalytic reactions: A workflow for model calibration, selection, and validation using Bayesian statistics2019In: AIChE Journal, article id e16866Article in journal (Refereed)
    Abstract [en]

    We present a workflow for kinetic modeling of biocatalytic reactions which combines methods from Bayesian learning and uncertainty quantification for model calibration, model selection, evaluation, and model reduction in a consistent statistical framework. Our workflow is particularly tailored to sparse data settings in which a considerable variability of the parameters remains after the models have been adapted to available data, a ubiquitous problem in many real‐world applications. Our workflow is exemplified on an enzyme‐catalyzed two‐substrate reaction mechanism describing the symmetric carboligation of 3,5‐dimethoxy‐benzaldehyde to (R)‐3,3′,5,5′‐tetramethoxybenzoin catalyzed by benzaldehyde lyase from Pseudomonas fluorescens. Results indicate a substrate‐dependent inactivation of enzyme, which is in accordance with other recent studies.

  • 34.
    Elgarf, Maha
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Exploring Eye-Tracking driven Sonification for the Visually Impaired2016In: PROCEEDINGS OF THE 7TH AUGMENTED HUMAN INTERNATIONAL CONFERENCE (AUGMENTED HUMAN 2016), 2016Conference paper (Other academic)
    Abstract [en]

    Most existing sonification approaches for the visually impaired restrict the user to the perception of static scenes by performing sequential scans and transformations of visual information to acoustic signals. This takes away the user's freedom to explore the environment and to decide which information is relevant at a given point in time. As a solution, we propose an eye tracking system to allow the user to choose which elements of the field of view should be sonified. More specifically, we enhance the sonification approaches for color, text and facial expressions with eye tracking mechanisms. To find out how visually impaired people might react to such a system we applied a user centered design approach. Finally, we explored the effectiveness of our concept in a user study with seven visually impaired persons. The results show that eye tracking is a very promising input method to control the sonification, but the large variety of visual impairment conditions restricts the applicability of the technology.

  • 35.
    Elgarf, Maha
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Peters, Christopher
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Rock your story: Effects of adapting personality behavior through body movement on story recall2019In: HAI 2019 - Proceedings of the 7th International Conference on Human-Agent Interaction, Association for Computing Machinery (ACM), 2019, p. 241-243Conference paper (Refereed)
    Abstract [en]

    In order to design social agents for long term interactions, it is important to enable them to adapt to the users. In this paper, we chose personality as a medium for adaptation. We conducted a study with 20 participants who watched a story presented by a virtual character in one of two conditions: extroverted or introverted. The study aimed at assessing the impacts of matching the personality of the user with the virtual character through body language on the likability of the character and the information recall of the story. Our findings do not appear to coincide with theoretical expectations since the extroverted character had higher ratings of likability regardless of the personality of the user. Results have also shown a marginal positive effect of the encounter with the introverted character in terms of memory recall. We discuss the important implications that these results may have in the future for human agent interaction design.

  • 36.
    Eriksson, Olivia
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, Centres, SeRC - Swedish e-Science Research Centre.
    Jauhiainen, Alexandra
    AstraZeneca, IMED Biotech Unit, Early Clin Dev, Biometr, Gothenburg, Sweden..
    Sasane, Sara Maad
    Lund Univ, Ctr Math Sci, Lund, Sweden..
    Kramer, Andrei
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Nair, Anu G.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Sartorius, Carolina
    Lund Univ, Ctr Math Sci, Lund, Sweden..
    Hellgren Kotaleski, Jeanette
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, Centres, SeRC - Swedish e-Science Research Centre.
    Uncertainty quantification, propagation and characterization by Bayesian analysis combined with global sensitivity analysis applied to dynamical intracellular pathway models2019In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 35, no 2, p. 284-292Article in journal (Refereed)
    Abstract [en]

    Motivation: Dynamical models describing intracellular phenomena are increasing in size and complexity as more information is obtained from experiments. These models are often over-parameterized with respect to the quantitative data used for parameter estimation, resulting in uncertainty in the individual parameter estimates as well as in the predictions made from the model. Here we combine Bayesian analysis with global sensitivity analysis (GSA) in order to give better informed predictions; to point out weaker parts of the model that are important targets for further experiments, as well as to give guidance on parameters that are essential in distinguishing different qualitative output behaviours. Results: We used approximate Bayesian computation (ABC) to estimate the model parameters from experimental data, as well as to quantify the uncertainty in this estimation (inverse uncertainty quantification), resulting in a posterior distribution for the parameters. This parameter uncertainty was next propagated to a corresponding uncertainty in the predictions (forward uncertainty propagation), and a GSA was performed on the predictions using the posterior distribution as the possible values for the parameters. This methodology was applied on a relatively large model relevant for synaptic plasticity, using experimental data from several sources. We could hereby point out those parameters that by themselves have the largest contribution to the uncertainty of the prediction as well as identify parameters important to separate between qualitatively different predictions. This approach is useful both for experimental design as well as model building.

  • 37.
    Eriksson, Olivia
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Laure, Erwin
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Lindahl, Erik
    KTH, School of Engineering Sciences (SCI), Applied Physics, Biophysics.
    Henningson, Dan S.
    KTH, School of Engineering Sciences (SCI), Mechanics, Stability, Transition and Control.
    Ynnerman, Anders
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    e-Science in Scandinavia2018In: Informatik-Spektrum, ISSN 0170-6012, E-ISSN 1432-122X, Vol. 41, no 6, p. 398-404Article in journal (Refereed)
  • 38.
    Fiebig, Florian
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Active Memory Processing on Multiple Time-scales in Simulated Cortical Networks with Hebbian Plasticity2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis examines declarative memory function, and its underlying neural activity and mechanisms in simulated cortical networks. The included simulation models utilize and synthesize proposed universal computational principles of the brain, such as the modularity of cortical circuit organization, attractor network theory, and Hebbian synaptic plasticity, along with selected biophysical detail from the involved brain areas to implement functional models of known cortical memory systems. The models hypothesize relations between neural activity, brain area interactions, and cognitive memory functions such as sleep-dependent memory consolidation, or specific working memory tasks. In particular, this work addresses the acutely relevant research question if recently described fast forms of Hebbian synaptic plasticity are a possible mechanism behind working memory. The proposed models specifically challenge the “persistent activity hypothesis of working memory”, an established but increasingly questioned paradigm in working memory theory. The proposed alternative is a novel synaptic working memory model that is arguably more defensible than the existing paradigm as it can better explain memory function and important aspects of working memory-linked activity (such as the role of long-term memory in working memory tasks), while simultaneously matching experimental data from behavioral memory testing and important evidence from electrode recordings.

  • 39.
    Fiebig, Florian
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Herman, Pawel
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Lansner, Anders
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). Department of Mathematics, Stockholm University, 10691 Stockholm, Swed.
    An Indexing Theory for Working Memory based on Fast Hebbian PlasticityManuscript (preprint) (Other academic)
    Abstract [en]

    Working memory (WM) is a key component of human memory and cognitive function. Computational models have been used to uncover the underlying neural mechanisms. However, these studies have mostly focused on the short-term memory aspects of WM and neglected the equally important role of interactions between short- and long-term memory (STM, LTM). Here, we concentrate on these interactions within the framework of our new computational model of WM, which accounts for three cortical patches in macaque brain, corresponding to networks in prefrontal cortex (PFC) together with parieto-temporal cortical areas. In particular, we propose a cortical indexing theory that explains how PFC could associate, maintain and update multi-modal LTM representations. Our simulation results demonstrate how simultaneous, brief multi-modal memory cues could build a temporary joint memory representation linked via an "index" in the prefrontal cortex by means of fast Hebbian synaptic plasticity. The latter can then activate spontaneously and thereby reactivate the associated long-term representations. Cueing one long-term memory item rapidly pattern-completes the associated un-cued item via prefrontal cortex. The STM network updates flexibly as new stimuli arrive thereby gradually over-writing older representations. In a wider context, this WM model suggests a novel explanation for "variable binding", a long-standing and fundamental phenomenon in cognitive neuroscience, which is still poorly understood in terms of detailed neural mechanisms.

  • 40.
    Filipovic, Marko
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST). Univ Freiburg, Bernstein Ctr Freiburg, Freiburg, Germany;Univ Freiburg, Fac Biol, Freiburg, Germany.
    Ketzef, Maya
    Karolinska Inst, Dept Neurosci, Stockholm, Sweden..
    Reig, Ramon
    CSIC, Inst Neurociencias, Alacant, Spain.;Univ Miguel Hernandez, Alacant, Spain..
    Aertsen, Ad
    Univ Freiburg, Bernstein Ctr Freiburg, Freiburg, Germany.;Univ Freiburg, Fac Biol, Freiburg, Germany..
    Silberberg, Gilad
    Karolinska Inst, Dept Neurosci, Stockholm, Sweden..
    Kumar, Arvind
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Direct pathway neurons in mouse dorsolateral striatum in vivo receive stronger synaptic input than indirect pathway neurons2019In: Journal of Neurophysiology, ISSN 0022-3077, E-ISSN 1522-1598, Vol. 122, no 6, p. 2294-2303Article in journal (Refereed)
    Abstract [en]

    Striatal projection neurons, the medium spiny neurons (MSNs), play a crucial role in various motor and cognitive functions. MSNs express either D1- or D2-type dopamine receptors and initiate the direct-pathway (dMSNs) or indirect pathways (iMSNs) of the basal ganglia, respectively. dMSNs have been shown to receive more inhibition than iMSNs from intrastriatal sources. Based on these findings, computational modeling of the suiatal network has predicted that under healthy conditions dMSNs should receive more total input than iMSNs. To test this prediction, we analyzed in vivo whole cell recordings from dMSNs and iMSNs in healthy and dopamine-depleted (60HDA) anaesthetized mice. By comparing their membrane potential fluctuations, we found that dMSNs exhibited considerably larger membrane potential fluctuations over a wide frequency range. Furthermore, by comparing the spike-triggered average membrane potentials. we found that dMSNs depolarized toward the spike threshold significantly faster than iMSNs did. Together, these findings (in particular the STA analysis) corroborate the theoretical prediction that direct-pathway MSNs receive stronger total input than indirect-pathway neurons. Finally, we found that dopamine-depleted mice exhibited no difference between the membrane potential fluctuations of dMSNs and iMSNs. These data provide new insights into the question of how the lack of dopamine may lead to behavioral deficits associated with Parkinson's disease. NEW & NOTEWORTHY The direct and indirect pathways of the basal ganglia originate from the D1- and D2-type dopamine receptor expressing medium spiny neurons (dMSNs and iMSNs). Theoretical results have predicted that dMSNs should receive stronger synaptic input than iMSNs. Using in vivo intracellular membrane potential data, we provide evidence that dMSNs indeed receive stronger input than iMSNs, as has been predicted by the computational model.

  • 41.
    Filipović, Marko
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST). Bernstein Center Freiburg and Faculty of Biology, University of Freiburg, Germany.
    Characterisation of inputs and outputs of striatal medium spiny neurons in health and disease2019Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Striatal medium spiny neurons (MSNs) play a crucial role in various motor and cognitive functions. They are separated into those belonging to the direct pathway (dMSNs) and the indirect pathway (iMSNs) of the basal ganglia, depending on whether they express D1 or D2 type dopamine receptors, respectively. In this thesis I investigated the input processing of both MSN types, the characteristics of dMSN outputs, and the effect that aberrant iMSN activity has on the subthalamic nucleus-globus pallidus externa (STN-GPe) network.In order to verify a previous result from a computational study claiming that dMSNs should receive either more or stronger total input than iMSNs, I performed an analysis of in vivo whole-cell MSN recordings in healthy and dopamine (DA) depleted (6OHDA) anesthetized mice. To test this prediction, I compared subthreshold membrane potential fluctuations and spike-triggered average membrane potentials of the two MSN types. I found that dMSNs in healthy mice exhibited considerably larger fluctuations over a wide frequency range, as well as significantly faster  depolarization towards the spiking threshold than iMSNs. However, these effects were not present in recordings from 6OHDA animals. Together, these findings strongly suggest that dMSNs do  receive stronger total input than iMSNs in healthy condition.I also examined how different concentrations of dopamine affect neural trial-by-trial (or response) variability in a biophysically detailed compartmental model of a direct-pathway MSN.  Some of the sources of trial-by-trial variability include synaptic noise, neural refractory period, and ongoing neural activity. The focus of this study was on the effects of two particular  properties of the synaptic input: correlations of synaptic input rates, and the balance between excitatory and inhibitory inputs (E-I balance). The model demonstrates that dopamine is in  general a significant diminisher of trial-by-trial variability, but that its efficacy depends on the properties of synaptic input. Moreover, input rate correlations and changes in the E-I balance by themselves also proved to have a marked impact on the response variability.Finally, I investigated the beta-band phase properties of the STN-GPe network, known for its exaggerated beta-band oscillations during Parkinson’s disease (PD). The current state-of-the-art  computational model of the network can replicate both transient and persistent beta oscillations, but fails to capture the beta-band phase alignment between the two nuclei as seen in human  recordings. This was particularly evident during simulations of the PD condition, where STN or GPe were receiving additional stimulation in order to induce pathological levels of beta-band  activity. Here I show that by manipulating the percentage of the neurons in either population that receives stimulation it is possible to increase STN-GPe phase difference heterogeneity.  Furthermore, a similar effect can be achieved by adjusting synaptic transmission delays between the two populations. Quantifying the difference between human recordings and network  simulations, I provide the set of parameters for which the model produces the greatest correspondence with experimental results.

  • 42.
    Finnveden, Lukas
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Jansson, Ylva
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Lindeberg, Tony
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    The problems with using STNs to align CNN feature maps2020Conference paper (Other academic)
    Abstract [en]

    Spatial transformer networks (STNs) were designed to enable CNNs to learn invariance to image transformations. STNs were originally proposed to transform CNN feature maps as well as input images. This enables the use of more complex features when predicting transformation parameters. However, since STNs perform a purely spatial transformation, they do not, in the general case, have the ability to align the feature maps of a transformed image and its original. We present a theoretical argument for this and investigate the practical implications, showing that this inability is coupled with decreased classification accuracy. We advocate taking advantage of more complex features in deeper layers by instead sharing parameters between the classification and the localisation network.

  • 43.
    Friberg, Anders
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Speech, Music and Hearing, TMH.
    Lindeberg, Tony
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Hellwagner, Martin
    KTH, School of Electrical Engineering and Computer Science (EECS), Speech, Music and Hearing, TMH.
    Helgason, Pétur
    KTH, School of Electrical Engineering and Computer Science (EECS), Speech, Music and Hearing, TMH.
    Salomão, Gláucia Laís
    KTH, School of Electrical Engineering and Computer Science (EECS), Speech, Music and Hearing, TMH.
    Elovsson, Anders
    KTH, School of Electrical Engineering and Computer Science (EECS), Speech, Music and Hearing, TMH.
    Lemaitre, Guillaume
    Institute for Research and Coordination in Acoustics and Music, Paris, France.
    Ternström, Sten
    KTH, School of Electrical Engineering and Computer Science (EECS), Speech, Music and Hearing, TMH.
    Prediction of three articulatory categories in vocal sound imitations using models for auditory receptive fields2018In: Journal of the Acoustical Society of America, ISSN 0001-4966, E-ISSN 1520-8524, Vol. 144, no 3, p. 1467-1483Article in journal (Refereed)
    Abstract [en]

    Vocal sound imitations provide a new challenge for understanding the coupling between articulatory mechanisms and the resulting audio. In this study, we have modeled the classification of three articulatory categories, phonation, supraglottal myoelastic vibrations, and turbulence from audio recordings. Two data sets were assembled, consisting of different vocal imitations by four professional imitators and four non-professional speakers in two different experiments. The audio data were manually annotated by two experienced phoneticians using a detailed articulatory description scheme. A separate set of audio features was developed specifically for each category using both time-domain and spectral methods. For all time-frequency transformations, and for some secondary processing, the recently developed Auditory Receptive Fields Toolbox was used. Three different machine learning methods were applied for predicting the final articulatory categories. The result with the best generalization was found using an ensemble of multilayer perceptrons. The cross-validated classification accuracy was 96.8 % for phonation, 90.8 % for supraglottal myoelastic vibrations, and 89.0 % for turbulence using all the 84 developed features. A final feature reduction to 22 features yielded similar results.

  • 44.
    Friederici, Anke
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Köpp, Wiebke
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Atzori, Marco
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Fluid Mechanics and Engineering Acoustics. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.
    Vinuesa, Ricardo
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Fluid Mechanics and Engineering Acoustics. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.
    Schlatter, Philipp
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Fluid Mechanics and Engineering Acoustics. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.
    Weinkauf, Tino
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Distributed Percolation Analysis for Turbulent Flows2019In: 2019 IEEE 9th Symposium on Large Data Analysis and Visualization, LDAV 2019, Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 42-51, article id 8944383Conference paper (Refereed)
    Abstract [en]

    Percolation analysis is a valuable tool to study the statistical properties of turbulent flows. It is based on computing the percolation function for a derived scalar field, thereby quantifying the relative volume of the largest connected component in a superlevel set for a decreasing threshold. We propose a novel memory-distributed parallel algorithm to finely sample the percolation function. It is based on a parallel version of the union-find algorithm interleaved with a global synchronization step for each threshold sample. The efficiency of this algorithm stems from the fact that operations in-between threshold samples can be freely reordered, are mostly local and thus require no inter-process communication. Our algorithm is significantly faster than previous algorithms for this purpose, and is neither constrained by memory size nor number of compute nodes compared to the conceptually related algorithm for extracting augmented merge trees. This makes percolation analysis much more accessible in a large range of scenarios. We explore the scaling of our algorithm for different data sizes, number of samples and number of MPI processes. We demonstrate the utility of percolation analysis using large turbulent flow data sets.

  • 45.
    Frånberg, Mattias
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS). KTH, Centres, Science for Life Laboratory, SciLifeLab. Cardiovascular Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden ; Department of Numerical Analysis and Computer Science, Stockholm University, Stockholm, Sweden.
    Lagergren, Jens
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, Centres, SeRC - Swedish e-Science Research Centre.
    Sennblad, Bengt
    Cardiovascular Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden ; Dept of Cell and Molecular Biology, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
    BESIQ: A tool for discovering gene-gene and gene-environment interactions in genome-wide association studiesManuscript (preprint) (Other academic)
  • 46. Galal-Edeen, G. H.
    et al.
    Abdrabou, Y.
    Elgarf, Maha
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Hassan, H. M.
    HCI of Arabia: The challenges of HCI research in Egypt2019In: interactions, ISSN 1072-5520, E-ISSN 1558-3449, Vol. 26, no 3, p. 55-59Article in journal (Refereed)
  • 47.
    Gao, Chen-Yi
    et al.
    Chinese Acad Sci, Inst Theoret Phys, Key Lab Theoret Phys, Beijing 100190, Peoples R China.;Univ Chinese Acad Sci, Sch Phys Sci, Beijing 100049, Peoples R China..
    Cecconi, Fabio
    Sapienza Univ Roma, CNR ISC, Ple Moro 2, I-00185 Rome, Italy.;Sapienza Univ Roma, Dipartimento Fis, Ple Moro 2, I-00185 Rome, Italy..
    Vulpiani, Angelo
    Sapienza Univ Roma, CNR ISC, Ple Moro 2, I-00185 Rome, Italy.;Sapienza Univ Roma, Dipartimento Fis, Ple Moro 2, I-00185 Rome, Italy.;Accademia Lincei, Ctr Interdisciplinare B Segre, Rome, Italy..
    Zhou, Hai-Jun
    Chinese Acad Sci, Inst Theoret Phys, Key Lab Theoret Phys, Beijing 100190, Peoples R China.;Univ Chinese Acad Sci, Sch Phys Sci, Beijing 100049, Peoples R China..
    Aurell, Erik
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). Aalto Univ, Dept Appl Phys, FIN-00076 Aalto, Finland.;Aalto Univ, Dept Comp Sci, FIN-00076 Aalto, Finland.;PSL Res Univ, Lab Phys Chim Theor, UMR CNRS Gulliver 7083, ESPCI, 10 Rue Vauquelin, F-75231 Paris, France..
    DCA for genome-wide epistasis analysis: the statistical genetics perspective2019In: Physical Biology, ISSN 1478-3967, E-ISSN 1478-3975, Vol. 16, no 2, article id 026002Article in journal (Refereed)
    Abstract [en]

    Direct coupling analysis (DCA) is a now widely used method to leverage statistical information from many similar biological systems to draw meaningful conclusions on each system separately. DCA has been applied with great success to sequences of homologous proteins, and also more recently to whole-genome population-wide sequencing data. We here argue that the use of DCA on the genome scale is contingent on fundamental issues of population genetics. DCA can be expected to yield meaningful results when a population is in the quasi-linkage equilibrium (QLE) phase studied by Kimura and others, but not, for instance, in a phase of clonal competition. We discuss how the exponential (Potts model) distributions emerge in QLE, and compare couplings to correlations obtained in a study of about 3000 genomes of the human pathogen Streptococcus pneumoniae.

  • 48.
    Görnerup, Olof
    et al.
    RISE.
    Gillblad, Daniel
    RISE.
    Vasiloudis, Theodore
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). RISE.
    Domain-agnostic discovery of similarities and concepts at scale2017In: Knowledge and Information Systems, ISSN 0219-1377, E-ISSN 0219-3116, Vol. 51, no 2, p. 531-560Article in journal (Refereed)
    Abstract [en]

    Appropriately defining and efficiently calculating similarities from large data sets are often essential in data mining, both for gaining understanding of data and generating processes and for building tractable representations. Given a set of objects and their correlations, we here rely on the premise that each object is characterized by its context, i.e., its correlations to the other objects. The similarity between two objects can then be expressed in terms of the similarity between their contexts. In this way, similarity pertains to the general notion that objects are similar if they are exchangeable in the data. We propose a scalable approach for calculating all relevant similarities among objects by relating them in a correlation graph that is transformed to a similarity graph. These graphs can express rich structural properties among objects. Specifically, we show that concepts—abstractions of objects—are constituted by groups of similar objects that can be discovered by clustering the objects in the similarity graph. These principles and methods are applicable in a wide range of fields and will be demonstrated here in three domains: computational linguistics, music, and molecular biology, where the numbers of objects and correlations range from small to very large.

  • 49.
    Görnerup, Olof
    et al.
    RISE.
    Gillblad, Daniel
    RISE.
    Vasiloudis, Theodore
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). RISE.
    Knowing an Object by the Company It Keeps: A Domain-Agnostic Scheme for Similarity Discovery2015Conference paper (Refereed)
    Abstract [en]

    Appropriately defining and then efficiently calculating similarities from large data sets are often essential in data mining, both for building tractable representations and for gaining understanding of data and generating processes. Here we rely on the premise that given a set of objects and their correlations, each object is characterized by its context, i.e. its correlations to the other objects, and that the similarity between two objects therefore can be expressed in terms of the similarity between their respective contexts. Resting on this principle, we propose a data-driven and highly scalable approach for discovering similarities from large data sets by representing objects and their relations as a correlation graph that is transformed to a similarity graph. Together these graphs can express rich structural properties among objects. Specifically, we show that concepts -- representations of abstract ideas and notions -- are constituted by groups of similar objects that can be identified by clustering the objects in the similarity graph. These principles and methods are applicable in a wide range of domains, and will here be demonstrated for three distinct types of objects: codons, artists and words, where the numbers of objects and correlations range from small to very large.

  • 50.
    Hahn, Gerald
    et al.
    Univ Pompeu Fabra, Ctr Brain & Cognit, Computat Neurosci Grp, Dept Informat & Commun Technol, Barcelona, Spain..
    Ponce-Alvarez, Adrian
    Univ Pompeu Fabra, Ctr Brain & Cognit, Computat Neurosci Grp, Dept Informat & Commun Technol, Barcelona, Spain..
    Deco, Gustavo
    Univ Pompeu Fabra, Ctr Brain & Cognit, Computat Neurosci Grp, Dept Informat & Commun Technol, Barcelona, Spain.;Univ Pompeu Fabra, Inst Catalana Recerca & Estudis Avancats, Barcelona, Spain..
    Aertsen, Ad
    Univ Freiburg, Fac Biol, Freiburg, Germany.;Univ Freiburg, Bernstein Ctr Freiburg, Freiburg, Germany..
    Kumar, Arvind
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Portraits of communication in neuronal networks2019In: Nature Reviews Neuroscience, ISSN 1471-003X, E-ISSN 1471-0048, Vol. 20, no 2, p. 117-127Article, review/survey (Refereed)
    Abstract [en]

    The brain is organized as a network of highly specialized networks of spiking neurons. To exploit such a modular architecture for computation, the brain has to be able to regulate the flow of spiking activity between these specialized networks. In this Opinion article, we review various prominent mechanisms that may underlie communication between neuronal networks. We show that communication between neuronal networks can be understood as trajectories in a two-dimensional state space, spanned by the properties of the input. Thus, we propose a common framework to understand neuronal communication mediated by seemingly different mechanisms. We also suggest that the nesting of slow (for example, alpha-band and theta-band) oscillations and fast (gamma-band) oscillations can serve as an important control mechanism that allows or prevents spiking signals to be routed between specific networks. We argue that slow oscillations can modulate the time required to establish network resonance or entrainment and, thereby, regulate communication between neuronal networks.

123 1 - 50 of 142
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf