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  • 1.
    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.

  • 2.
    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. 

  • 3.
    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)].

  • 4.
    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.

  • 5.
    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. 

  • 6.
    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.

  • 7.
    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.

  • 8.
    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.

  • 9.
    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.

  • 10.
    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.

  • 11.
    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.

  • 12.
    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.

  • 13.
    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.

  • 14.
    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.

  • 15.
    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 1520-8524Article 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.

  • 16.
    Heil, Katharina F.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). University of Edinburgh.
    A Systems Biological Approach to Parkinson's Disease2018Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Parkinson’s Disease (PD) is the second most common neurodegenerative disease in the Western world. Itshows a high degree of genetic and phenotypic complexity with many implicated factors, various diseasemanifestations but few clear causal links. Ongoing research has identified a growing number of molecularalterations linked to the disease.Dopaminergic neurons in the substantia nigra, specifically their synapses, are the key-affected region in PD.Therefore, this work focuses on understanding the disease effects on the synapse, aiming to identify potentialgenetic triggers and synaptic PD associated mechanisms. Currently, one of the main challenges in this area isdata quality and accessibility.In order to study PD, publicly available data were systematically retrieved and analysed. 418 PD associatedgenes could be identified, based on mutations and curated annotations. I curated an up-to-date and completesynaptic proteome map containing a total of 6,706 proteins. Region specific datasets describing thepresynapse, postsynapse and synaptosome were also delimited. These datasets were analysed, investigatingsimilarities and differences, including reproducibility and functional interpretations.The use of Protein-Protein-Interaction Network (PPIN) analysis was chosen to gain deeper knowledgeregarding specific effects of PD on the synapse. Thus I generated a customised, filtered, human specificProtein-Protein Interaction (PPI) dataset, containing 211,824 direct interactions, from four public databases.Proteomics data and PPI information allowed the construction of PPINs. These were analysed and a set oflow level statistics, including modularity, clustering coefficient and node degree, explaining the network’stopology from a mathematical point of view were obtained.Apart from low-level network statistics, high-level topology of the PPINs was studied. To identify functionalnetwork subgroups, different clustering algorithms were investigated. In the context of biological networks, theunderlying hypothesis is that proteins in a structural community are more likely to share common functions.Therefore I attempted to identify PD enriched communities of synaptic proteins. Once identified, they werecompared amongst each other. Three community clusters could be identified as containing largely overlappinggene sets. These contain 24 PD associated genes. Apart from the known disease associated genes in thesecommunities, a total of 322 genes was identified. Each of the three clusters is specifically enriched for specificbiological processes and cellular components, which include neurotransmitter secretion, positive regulation ofsynapse assembly, pre- and post-synaptic membrane, scaffolding proteins, neuromuscular junctiondevelopment and complement activation (classical pathway) amongst others.The presented approach combined a curated set of PD associated genes, filtered PPI information andsynaptic proteomes. Various small- and large-scale analytical approaches, including PPIN topology analysis,clustering algorithms and enrichment studies identified highly PD affected synaptic proteins and subregions.Specific disease associated functions confirmed known research insights and allowed me to propose a newlist of so far unknown potential disease associated genes. Due to the open design, this approach can be usedto answer similar research questions regarding other complex diseases amongst others.

  • 17.
    Hoffman, Johan
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Holm, Bärbel
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Richter, Thomas
    The locally adapted parametric finite element method for interface problems on triangular meshes2017In: Fluid-Structure Interaction: Modeling, Adaptive Discretizations and Solvers / [ed] Stefan Frei, Bärbel Holm, Thomas Richter, Thomas, Huidong Yang, Walter de Gruyter, 2017, p. 41-63Chapter in book (Refereed)
  • 18.
    Holm, Bärbel
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Wihler, Thomas P.
    Univ Bern, Math Inst, Sidlerstr 5, CH-3012 Bern, Switzerland..
    Continuous and discontinuous Galerkin time stepping methods for nonlinear initial value problems with application to finite time blow-up2018In: Numerische Mathematik, ISSN 0029-599X, E-ISSN 0945-3245, Vol. 138, no 3, p. 767-799Article in journal (Refereed)
    Abstract [en]

    We consider continuous and discontinuous Galerkin time stepping methods of arbitrary order as applied to first-order initial value ordinary differential equation problems in real Hilbert spaces. Our only assumption is that the nonlinearities are continuous; in particular, we include the case of unbounded nonlinear operators. Specifically, we develop new techniques to prove general Peano-type existence results for discrete solutions. In particular, our results show that the existence of solutions is independent of the local approximation order, and only requires the local time steps to be sufficiently small (independent of the polynomial degree). The uniqueness of (local) solutions is addressed as well. In addition, our theory is applied to finite time blow-up problems with nonlinearities of algebraic growth. For such problems we develop a time step selection algorithm for the purpose of numerically computing the blow-up time, and provide a convergence result.

  • 19. Iatropoulos, G.
    et al.
    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).
    Karlgren, Jussi
    KTH, School of Electrical Engineering and Computer Science (EECS), Theoretical Computer Science, TCS. Gavagai, Slussplan 9, Stockholm, Sweden.
    Larsson, M.
    Olofsson, J. K.
    The language of smell: Connecting linguistic and psychophysical properties of odor descriptors2018In: Cognition, ISSN 0010-0277, E-ISSN 1873-7838, Vol. 178, p. 37-49Article in journal (Refereed)
    Abstract [en]

    The olfactory sense is a particularly challenging domain for cognitive science investigations of perception, memory, and language. Although many studies show that odors often are difficult to describe verbally, little is known about the associations between olfactory percepts and the words that describe them. Quantitative models of how odor experiences are described in natural language are therefore needed to understand how odors are perceived and communicated. In this study, we develop a computational method to characterize the olfaction-related semantic content of words in a large text corpus of internet sites in English. We introduce two new metrics: olfactory association index (OAI, how strongly a word is associated with olfaction) and olfactory specificity index (OSI, how specific a word is in its description of odors). We validate the OAI and OSI metrics using psychophysical datasets by showing that terms with high OAI have high ratings of perceived olfactory association and are used to describe highly familiar odors. In contrast, terms with high OSI have high inter-individual consistency in how they are applied to odors. Finally, we analyze Dravnieks's (1985) dataset of odor ratings in terms of OAI and OSI. This analysis reveals that terms that are used broadly (applied often but with moderate ratings) tend to be olfaction-unrelated and abstract (e.g., “heavy” or “light”; low OAI and low OSI) while descriptors that are used selectively (applied seldom but with high ratings) tend to be olfaction-related (e.g., “vanilla” or “licorice”; high OAI). Thus, OAI and OSI provide behaviorally meaningful information about olfactory language. These statistical tools are useful for future studies of olfactory perception and cognition, and might help integrate research on odor perception, neuroimaging, and corpus-based linguistic models of semantic organization.

  • 20.
    Jansson, Niclas
    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), Computational Science and Technology (CST).
    Towards a Parallel Algebraic Multigrid Solver Using PGAS2018In: 2018 Workshop on High Performance Computing Asia, New York, NY, USA: Association for Computing Machinery (ACM), 2018, p. 31-38Conference paper (Refereed)
    Abstract [en]

    The Algebraic Multigrid (AMG) method has over the years developed into an efficient tool for solving unstructured linear systems. The need to solve large industrial problems discretized on unstructured meshes, has been a key motivation for devising a parallel AMG method. Despite some success, the key part of the AMG algorithm; the coarsening step, is far from trivial to parallelize efficiently. We here introduce a novel parallelization of the inherently sequential Ruge-Stüben coarsening algorithm, that retains most of the good interpolation properties of the original method. Our parallelization is based on the Partitioned Global Address Space (PGAS) abstraction, which greatly simplifies the parallelization as compared to traditional message passing based implementations. The coarsening algorithm and solver is described in detail and a performance study on a Cray XC40 is presented.

  • 21.
    Jansson, Ylva
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Dynamic Texture Recognition Using Time-Causal and Time-Recursive Spatio-Temporal Receptive Fields2018In: Journal of Mathematical Imaging and Vision, ISSN 0924-9907, E-ISSN 1573-7683, p. 1-30Article in journal (Refereed)
    Abstract [en]

    This work presents a first evaluation of using spatio-temporal receptive fields from a recently proposed time-causal spatiotemporal scale-space framework as primitives for video analysis. We propose a new family of video descriptors based on regional statistics of spatio-temporal receptive field responses and evaluate this approach on the problem of dynamic texture recognition. Our approach generalises a previously used method, based on joint histograms of receptive field responses, from the spatial to the spatio-temporal domain and from object recognition to dynamic texture recognition. The time-recursive formulation enables computationally efficient time-causal recognition. The experimental evaluation demonstrates competitive performance compared to state of the art. In particular, it is shown that binary versions of our dynamic texture descriptors achieve improved performance compared to a large range of similar methods using different primitives either handcrafted or learned from data. Further, our qualitative and quantitative investigation into parameter choices and the use of different sets of receptive fields highlights the robustness and flexibility of our approach. Together, these results support the descriptive power of this family of time-causal spatio-temporal receptive fields, validate our approach for dynamic texture recognition and point towards the possibility of designing a range of video analysis methods based on these new time-causal spatio-temporal primitives.

  • 22.
    Jordan, Jakob
    et al.
    Julich Res Ctr, Inst Neurosci & Med INM 6, Julich, Germany.;Julich Res Ctr, Inst Adv Simulat IAS 6, Julich, Germany.;Julich Res Ctr, JARA Inst Brain Struct Funct Relationships INM 10, Julich, Germany..
    Ippen, Tammo
    Julich Res Ctr, Inst Neurosci & Med INM 6, Julich, Germany.;Julich Res Ctr, Inst Adv Simulat IAS 6, Julich, Germany.;Julich Res Ctr, JARA Inst Brain Struct Funct Relationships INM 10, Julich, Germany.;Norwegian Univ Life Sci, Fac Sci & Technol, As, Norway..
    Helias, Moritz
    Julich Res Ctr, Inst Neurosci & Med INM 6, Julich, Germany.;Julich Res Ctr, Inst Adv Simulat IAS 6, Julich, Germany.;Julich Res Ctr, JARA Inst Brain Struct Funct Relationships INM 10, Julich, Germany.;Rhein Westfal TH Aachen, Dept Phys, Fac 1, Aachen, Germany..
    Kitayama, Itaru
    RIKEN, Adv Inst Computat Sci, Kobe, Hyogo, Japan..
    Sato, Mitsuhisa
    RIKEN, Adv Inst Computat Sci, Kobe, Hyogo, Japan..
    Igarashi, Jun
    RIKEN, Computat Engn Applicat Unit, Wako, Saitama, Japan..
    Diesmann, Markus
    Julich Res Ctr, Inst Neurosci & Med INM 6, Julich, Germany.;Julich Res Ctr, Inst Adv Simulat IAS 6, Julich, Germany.;Julich Res Ctr, JARA Inst Brain Struct Funct Relationships INM 10, Julich, Germany.;Rhein Westfal TH Aachen, Dept Phys, Fac 1, Aachen, Germany.;Rhein Westfal TH Aachen, Med Fac, Dept Psychiat Psychotherapy & Psychosomat, Aachen, Germany..
    Kunkel, Susanne
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). Julich Res Ctr, Simulat Lab Neurosci Bernstein Facil Simulat & Da, Julich, Germany..
    Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers (vol 12, 2, 2018)2018In: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 12, article id 34Article in journal (Refereed)
  • 23.
    Jordanova, V. K.
    et al.
    Los Alamos Natl Lab, Los Alamos, NM 87545 USA..
    Delzanno, G. L.
    Los Alamos Natl Lab, Los Alamos, NM 87545 USA..
    Henderson, M. G.
    Los Alamos Natl Lab, Los Alamos, NM 87545 USA..
    Godinez, H. C.
    Los Alamos Natl Lab, Los Alamos, NM 87545 USA..
    Jeffery, C. A.
    Los Alamos Natl Lab, Los Alamos, NM 87545 USA..
    Lawrence, E. C.
    Los Alamos Natl Lab, Los Alamos, NM 87545 USA..
    Morley, S. K.
    Los Alamos Natl Lab, Los Alamos, NM 87545 USA..
    Moulton, J. D.
    Los Alamos Natl Lab, Los Alamos, NM 87545 USA..
    Vernon, L. J.
    Los Alamos Natl Lab, Los Alamos, NM 87545 USA..
    Woodroffe, J. R.
    Los Alamos Natl Lab, Los Alamos, NM 87545 USA..
    Brito, T. V.
    Los Alamos Natl Lab, Los Alamos, NM 87545 USA..
    Engel, M. A.
    Los Alamos Natl Lab, Los Alamos, NM 87545 USA..
    Meierbachtol, C. S.
    Los Alamos Natl Lab, Los Alamos, NM 87545 USA..
    Svyatsky, D.
    Los Alamos Natl Lab, Los Alamos, NM 87545 USA..
    Yu, Y.
    Los Alamos Natl Lab, Los Alamos, NM 87545 USA.;Beihang Univ, Beijing, Peoples R China..
    Toth, G.
    Univ Michigan, Ann Arbor, MI 48109 USA..
    Welling, D. T.
    Univ Michigan, Ann Arbor, MI 48109 USA..
    Chen, Y.
    Univ Michigan, Ann Arbor, MI 48109 USA..
    Haiducek, J.
    Univ Michigan, Ann Arbor, MI 48109 USA..
    Markidis, Stefano
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Albert, J. M.
    Air Force Res Lab, Kirtland AFB, NM USA..
    Birn, J.
    Los Alamos Natl Lab, Los Alamos, NM 87545 USA.;Space Sci Inst, Boulder, CO USA..
    Denton, M. H.
    Space Sci Inst, Boulder, CO USA.;New Mexico Consortium, Los Alamos, NM USA..
    Horne, R. B.
    British Antarctic Survey, Cambridge, England..
    Specification of the near-Earth space environment with SHIELDS2018In: Journal of Atmospheric and Solar-Terrestrial Physics, ISSN 1364-6826, E-ISSN 1879-1824, Vol. 177, p. 148-159Article in journal (Refereed)
    Abstract [en]

    Predicting variations in the near-Earth space environment that can lead to spacecraft damage and failure is one example of "space weather" and a big space physics challenge. A project recently funded through the Los Alamos National Laboratory (LANL) Directed Research and Development (LDRD) program aims at developing a new capability to understand, model, and predict Space Hazards Induced near Earth by Large Dynamic Storms, the SHIELDS framework. The project goals are to understand the dynamics of the surface charging environment (SCE), the hot (keV) electrons representing the source and seed populations for the radiation belts, on both macro and micro-scale. Important physics questions related to particle injection and acceleration associated with magnetospheric storms and substorms, as well as plasma waves, are investigated. These challenging problems are addressed using a team of world-class experts in the fields of space science and computational plasma physics, and state-of-the-art models and computational facilities. A full two-way coupling of physics-based models across multiple scales, including a global MHD (BATS-R-US) embedding a particle-in-cell (iPIC3D) and an inner magnetosphere (RAM-SCB) codes, is achieved. New data assimilation techniques employing in situ satellite data are developed; these provide an order of magnitude improvement in the accuracy in the simulation of the SCE. SHIELDS also includes a post-processing tool designed to calculate the surface charging for specific spacecraft geometry using the Curvilinear Particle-In-Cell (CPIC) code that can be used for reanalysis of satellite failures or for satellite design.

  • 24.
    Kisner, Alexandre
    et al.
    NIDA, Neuronal Circuits & Behav Unit, Intramural Res Program, NIH, Baltimore, MD 21224 USA..
    Slocomb, Julia E.
    NIDA, Neuronal Circuits & Behav Unit, Intramural Res Program, NIH, Baltimore, MD 21224 USA..
    Sarsfield, Sarah
    NIDA, Neuronal Circuits & Behav Unit, Intramural Res Program, NIH, Baltimore, MD 21224 USA..
    Zuccoli, Maria Laura
    NIDA, Neuronal Circuits & Behav Unit, Intramural Res Program, NIH, Baltimore, MD 21224 USA.;Univ Genoa, Dept Internal Med, Pharmacol & Toxicol Unit, Genoa, Italy..
    Siemian, Justin
    NIDA, Neuronal Circuits & Behav Unit, Intramural Res Program, NIH, Baltimore, MD 21224 USA..
    Gupta, Jay F.
    NIDA, Neuronal Circuits & Behav Unit, Intramural Res Program, NIH, Baltimore, MD 21224 USA..
    Kumar, Arvind
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Aponte, Yeka
    NIDA, Neuronal Circuits & Behav Unit, Intramural Res Program, NIH, Baltimore, MD 21224 USA.;Johns Hopkins Univ, Sch Med, Solomon H Snyder Dept Neurosci, Baltimore, MD 21218 USA..
    Electrophysiological properties and projections of lateral hypothalamic parvalbumin positive neurons2018In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 13, no 6, article id e0198991Article in journal (Refereed)
    Abstract [en]

    Cracking the cytoarchitectural organization, activity patterns, and neurotransmitter nature of genetically-distinct cell types in the lateral hypothalamus (LH) is fundamental to develop a mechanistic understanding of how activity dynamics within this brain region are generated and operate together through synaptic connections to regulate circuit function. However, the precise mechanisms through which LH circuits orchestrate such dynamics have remained elusive due to the heterogeneity of the intermingled and functionally distinct cell types in this brain region. Here we reveal that a cell type in the mouse LH identified by the expression of the calcium-binding protein parvalbumin (PVALB; LHPV) is fast-spiking, releases the excitatory neurotransmitter glutamate, and sends long range projections throughout the brain. Thus, our findings challenge long-standing concepts that define neurons with a fast-spiking phenotype as exclusively GABAergic. Furthermore, we provide for the first time a detailed characterization of the electrophysiological properties of these neurons. Our work identifies LHPV neurons as a novel functional component within the LH glutamatergic circuitry.

  • 25. Lindroos, Robert
    et al.
    Dorst, Matthijs C.
    Du, Kai
    Filipovic, Marko
    Keller, Daniel
    Ketzef, Maya
    Kozlov, Alexander K.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Kumar, Arvind
    Lindahl, Mikael
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Nair, Anu G.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Perez-Fernandez, Juan
    Grillner, Sten
    Silberberg, Gilad
    Hällgren Kotaleski, Jeanette
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Basal Ganglia Neuromodulation Over Multiple Temporal and Structural Scales-Simulations of Direct Pathway MSNs Investigate the Fast Onset of Dopaminergic Effects and Predict the Role of Kv4.22018In: Frontiers in Neural Circuits, ISSN 1662-5110, E-ISSN 1662-5110, Vol. 12, article id 3Article in journal (Refereed)
    Abstract [en]

    The basal ganglia are involved in the motivational and habitual control of motor and cognitive behaviors. Striatum, the largest basal ganglia input stage, integrates cortical and thalamic inputs in functionally segregated cortico-basal ganglia-thalamic loops, and in addition the basal ganglia output nuclei control targets in the brainstem. Striatal function depends on the balance between the direct pathway medium spiny neurons (D1-MSNs) that express D1 dopamine receptors and the indirect pathway MSNs that express D2 dopamine receptors. The striatal microstructure is also divided into striosomes and matrix compartments, based on the differential expression of several proteins. Dopaminergic afferents from the midbrain and local cholinergic interneurons play crucial roles for basal ganglia function, and striatal signaling via the striosomes in turn regulates the midbrain dopaminergic system directly and via the lateral habenula. Consequently, abnormal functions of the basal ganglia neuromodulatory system underlie many neurological and psychiatric disorders. Neuromodulation acts on multiple structural levels, ranging from the subcellular level to behavior, both in health and disease. For example, neuromodulation affects membrane excitability and controls synaptic plasticity and thus learning in the basal ganglia. However, it is not clear on what time scales these different effects are implemented. Phosphorylation of ion channels and the resulting membrane effects are typically studied over minutes while it has been shown that neuromodulation can affect behavior within a few hundred milliseconds. So how do these seemingly contradictory effects fit together? Here we first briefly review neuromodulation of the basal ganglia, with a focus on dopamine. We furthermore use biophysically detailed multi-compartmental models to integrate experimental data regarding dopaminergic effects on individual membrane conductances with the aim to explain the resulting cellular level dopaminergic effects. In particular we predict dopaminergic effects on Kv4.2 in D1-MSNs. Finally, we also explore dynamical aspects of the onset of neuromodulation effects in multi-scale computational models combining biochemical signaling cascades and multi-compartmental neuron models.

  • 26.
    Lundqvist, Mikael
    et al.
    MIT, Picower Inst Learning & Memory, 43 Vassar St, Cambridge, MA 02139 USA.;MIT, Dept Brain & Cognit Sci, 43 Vassar St, Cambridge, MA 02139 USA..
    Herman, Pawel
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH Royal Inst.
    Miller, Earl K.
    MIT, Picower Inst Learning & Memory, 43 Vassar St, Cambridge, MA 02139 USA.;MIT, Dept Brain & Cognit Sci, 43 Vassar St, Cambridge, MA 02139 USA..
    Working Memory: Delay Activity, Yes! Persistent Activity? Maybe Not2018In: Journal of Neuroscience, ISSN 0270-6474, E-ISSN 1529-2401, Vol. 38, no 32, p. 7013-7019Article in journal (Refereed)
    Abstract [en]

    Persistent spiking has been thought to underlie working memory (WM). However, virtually all of the evidence for this comes from studies that averaged spiking across time and across trials, which masks the details. On single trials, activity often occurs in sparse transient bursts. This has important computational and functional advantages. In addition, examination of more complex tasks reveals neural coding in WM is dynamic over the course of a trial. All this suggests that spiking is important for WM, but that its role is more complex than simply persistent spiking.

  • 27. Lundqvist, Mikael
    et al.
    Herman, Pawel
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Warden, Melissa R.
    Brincat, Scott L.
    Miller, Earl K.
    Gamma and beta bursts during working memory readout suggest roles in its volitional control2018In: Nature Communications, ISSN 2041-1723, E-ISSN 2041-1723, Vol. 9, article id 394Article in journal (Refereed)
    Abstract [en]

    Working memory (WM) activity is not as stationary or sustained as previously thought. There are brief bursts of gamma (similar to 50-120 Hz) and beta (similar to 20-35 Hz) oscillations, the former linked to stimulus information in spiking. We examined these dynamics in relation to readout and control mechanisms of WM. Monkeys held sequences of two objects in WM to match to subsequent sequences. Changes in beta and gamma bursting suggested their distinct roles. In anticipation of having to use an object for the match decision, there was an increase in gamma and spiking information about that object and reduced beta bursting. This readout signal was only seen before relevant test objects, and was related to premotor activity. When the objects were no longer needed, beta increased and gamma decreased together with object spiking information. Deviations from these dynamics predicted behavioral errors. Thus, beta could regulate gamma and the information in WM.

  • 28.
    Ma, Yingjuan
    et al.
    Univ Calif Los Angeles, Dept Earth Planetary & Space Sci, Los Angeles, CA 90095 USA..
    Russell, Christopher T.
    Univ Calif Los Angeles, Dept Earth Planetary & Space Sci, Los Angeles, CA 90095 USA..
    Toth, Gabor
    Univ Michigan, Dept Climate & Space Sci & Engn, Ann Arbor, MI 48109 USA..
    Chen, Yuxi
    Univ Michigan, Dept Climate & Space Sci & Engn, Ann Arbor, MI 48109 USA..
    Nagy, Andrew F.
    Univ Michigan, Dept Climate & Space Sci & Engn, Ann Arbor, MI 48109 USA..
    Harada, Yuki
    Univ Iowa, Dept Phys & Astron, Iowa City, IA 52242 USA..
    McFadden, James
    Univ Calif Berkeley, Space Sci Lab, Berkeley, CA 94720 USA..
    Halekas, Jasper S.
    Univ Iowa, Dept Phys & Astron, Iowa City, IA 52242 USA..
    Lillis, Rob
    Univ Calif Berkeley, Space Sci Lab, Berkeley, CA 94720 USA..
    Connerney, John E. P.
    NASA, Goddard Space Flight Ctr, Greenbelt, MD USA..
    Espley, Jared
    NASA, Goddard Space Flight Ctr, Greenbelt, MD USA..
    DiBraccio, Gina A.
    NASA, Goddard Space Flight Ctr, Greenbelt, MD USA..
    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.
    Peng, Ivy Bo
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Fang, Xiaohua
    Univ Colorado, Lab Atmospher & Space Phys, Boulder, CO 80309 USA..
    Jakosky, Bruce M.
    Univ Colorado, Lab Atmospher & Space Phys, Boulder, CO 80309 USA..
    Reconnection in the Martian Magnetotail: Hall-MHD With Embedded Particle-in-Cell Simulations2018In: Journal of Geophysical Research - Space Physics, ISSN 2169-9380, E-ISSN 2169-9402, Vol. 123, no 5, p. 3742-3763Article in journal (Refereed)
    Abstract [en]

    Mars Atmosphere and Volatile EvolutioN (MAVEN) mission observations show clear evidence of the occurrence of the magnetic reconnection process in the Martian plasma tail. In this study, we use sophisticated numerical models to help us understand the effects of magnetic reconnection in the plasma tail. The numerical models used in this study are (a) a multispecies global Hall-magnetohydrodynamic (HMHD) model and (b) a global HMHD model two-way coupled to an embedded fully kinetic particle-in-cell code. Comparison with MAVEN observations clearly shows that the general interaction pattern is well reproduced by the global HMHD model. The coupled model takes advantage of both the efficiency of the MHD model and the ability to incorporate kinetic processes of the particle-in-cell model, making it feasible to conduct kinetic simulations for Mars under realistic solar wind conditions for the first time. Results from the coupled model show that the Martian magnetotail is highly dynamic due to magnetic reconnection, and the resulting Mars-ward plasma flow velocities are significantly higher for the lighter ion fluid, which are quantitatively consistent with MAVEN observations. The HMHD with Embedded Particle-in-Cell model predicts that the ion loss rates are more variable but with similar mean values as compared with HMHD model results.

  • 29.
    Markidis, Stefano
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Chien, Steven Wei Der
    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).
    Peng, I. B.
    Vetter, J. S.
    NVIDIA tensor core programmability, performance & precision2018In: Proceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2018, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 522-531, article id 8425458Conference paper (Refereed)
    Abstract [en]

    The NVIDIA Volta GPU microarchitecture introduces a specialized unit, called Tensor Core that performs one matrix-multiply-and-accumulate on 4x4 matrices per clock cycle. The NVIDIA Tesla V100 accelerator, featuring the Volta microarchitecture, provides 640 Tensor Cores with a theoretical peak performance of 125 Tflops/s in mixed precision. In this paper, we investigate current approaches to program NVIDIA Tensor Cores, their performances and the precision loss due to computation in mixed precision. Currently, NVIDIA provides three different ways of programming matrix-multiply-and-accumulate on Tensor Cores: the CUDA Warp Matrix Multiply Accumulate (WMMA) API, CUTLASS, a templated library based on WMMA, and cuBLAS GEMM. After experimenting with different approaches, we found that NVIDIA Tensor Cores can deliver up to 83 Tflops/s in mixed precision on a Tesla V100 GPU, seven and three times the performance in single and half precision respectively. A WMMA implementation of batched GEMM reaches a performance of 4 Tflops/s. While precision loss due to matrix multiplication with half precision input might be critical in many HPC applications, it can be considerably reduced at the cost of increased computation. Our results indicate that HPC applications using matrix multiplications can strongly benefit from using of NVIDIA Tensor Cores.

  • 30.
    Nair, Anu G.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Modeling Biochemical Network Involved in Striatal Dopamine Signaling2018Doctoral thesis, monograph (Other academic)
    Abstract [en]

    In this thesis, I studied the molecular integration of reward-learning related neuromodulatory inputs by striatal medium-sized projection neurons (MSNs) using mass-action kinetic modeling.

    It is known that, in reward learning, an unexpected reward results in transient elevation in dopamine (peak) whereas omission of an expected reward leads to transient dopamine decrease (dip). In silico experiments performed in the current study indicated that reward-related transient dopamine signals could act differentially on the cAMP/PKA signaling of the two MSN classes, D1 receptor expressing MSNs (D1 MSNs) and D2 receptor expressing MSNs (D2 MSNs). PKA in D1 MSN responded to dopamine peaks, whereas in D2 MSN it was affected by dopamine dips. Simulations further highlighted the possibility that cAMP/PKA signaling in D1 MSNs is tonically inhibited by acetylcholine by activating muscarinic M4 receptors under the basal condition. In this scenario, the D1 receptor activation by a dopamine peak does not have any downstream effect, unless the dopamine peak is accompanied by an acetylcholine dip that could release the M4-mediated inhibition. Such acetylcholine dips accompany dopamine peaks due to the time-locked dopaminergic bursts and cholinergic pauses observed in reward-learning. Thus, an acetylcholine dip could be viewed as a time window for dopamine signaling in D1 MSN. Similarly, the cAMP/PKA signaling in D2 MSN could be tonically inhibited by the dopamine-dependent D2 receptors. In this case, a dopamine dip results in the cAMP/PKA activation, and the strength of the downstream response depends on the level of basal adenosine, acting via A2a receptors. These results highlight how multiple neuromodulators could be integrated by striatal MSNs to produce effective downstream response. Such signal integration scenarios require that the dopamine and acetylcholine-triggered cAMP signaling be sufficiently powerful and sensitive. However, quantitative information regarding the efficacy of dopamine and acetylcholine on cAMP signaling is virtually nonexistent for living MSNs. Therefore, the effects of dopamine and acetylcholine on cAMP signaling were quantitatively characterized in this study by imaging genetically-encoded FRET-based biosensor expressed in mice brain slices. The measurements confirmed that the cAMP signaling in MSNs is quite sensitive and could strongly be influenced by neuromodulators, thus supporting the underlying model requirements, and thereby predictions.

    Another parameter that is important for effective molecular signal integration is the relative timing between various convergent inputs. For example, studies have shown that LTP in D1 MSNs is produced if corticostriatal glutamate synaptic activity is shortly followed by a dopamine peak. However, there is no LTP if the order of the inputs is reversed. This temporal dependence is believed to result in various aspects of reward learning, such as reward causality, and is theoretically represented by the so-called eligibility trace. However, little is known how such temporal constraints emerge at the level of molecular signaling. I investigated the possible molecular mechanism responsible for the emergence of this temporal constraints, using computational modeling. This study proposes a novel molecular mechanism based on the coordinated activity of two striatally enriched phosphoproteins, DARPP-32 and ARPP-21 that could explain the emergence of the timing-dependence for postsynaptic signal integration, and thus a plausible molecular underpinning for the eligibility trace of reward learning.

    In summary, the results presented in this thesis advance our understanding on how the striatal cAMP respond towards reward-related nueromodulator signals, and the downstream effects on synaptic signaling and reward learning.

  • 31.
    Nguyen, Van Dang
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Jansson, Johan
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Hoffman, Johan
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Li, Jing-Rebecca
    INRIA Saclay-Equipe DEFI, CMAP, Ecole Polytechnique Route de Saclay, 91128, Palaiseau Cedex, France.
    A partition of unity finite element method for computational diffusion MRI2018In: Journal of Computational Physics, ISSN 0021-9991, E-ISSN 1090-2716, Vol. 375, p. 271-290Article in journal (Refereed)
    Abstract [en]

    The Bloch–Torrey equation describes the evolution of the spin (usually water proton) magnetization under the influence of applied magnetic field gradients and is commonly used in numerical simulations for diffusion MRI and NMR. Microscopic heterogeneity inside the imaging voxel is modeled by interfaces inside the simulation domain, where a discontinuity in the magnetization across the interfaces is produced via a permeability coefficient on the interfaces. To avoid having to simulate on a computational domain that is the size of an entire imaging voxel, which is often much larger than the scale of the microscopic heterogeneity as well as the mean spin diffusion displacement, smaller representative volumes of the imaging medium can be used as the simulation domain. In this case, the exterior boundaries of a representative volume either must be far away from the initial positions of the spins or suitable boundary conditions must be found to allow the movement of spins across these exterior boundaries.

    Many approaches have been taken to solve the Bloch–Torrey equation but an efficient high-performance computing framework is still missing. In this paper, we present formulations of the interface as well as the exterior boundary conditions that are computationally efficient and suitable for arbitrary order finite elements and parallelization. In particular, the formulations are based on the partition of unity concept which allows for a discontinuous solution across interfaces conforming with the mesh with weak enforcement of real (in the case of interior interfaces) and artificial (in the case of exterior boundaries) permeability conditions as well as an operator splitting for the exterior boundary conditions. The method is straightforward to implement and it is available in FEniCS for moderate-scale simulations and in FEniCS-HPC for large-scale simulations. The order of accuracy of the resulting method is validated in numerical tests and a good scalability is shown for the parallel implementation. We show that the simulated dMRI signals offer good approximations to reference signals in cases where the latter are available and we performed simulations for a realistic model of a neuron to show that the method can be used for complex geometries.

  • 32.
    Nguyen, Van-Dang
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    High-Performance Finite Element Methods: with Application to Simulation of Diffusion MRI and Vertical Axis Wind Turbines2018Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    The finite element methods (FEM) have been developed over decades, and together with the growth of computer engineering, they become more and more important in solving large-scale problems in science and industry. The objective of this thesis is to develop high-performance finite element methods (HP-FEM), with two main applications in mind: computational diffusion magnetic resonance imaging (MRI), and simulation of the turbulent flow past a vertical axis wind turbine (VAWT). In the first application, we develop an efficient high-performance finite element framework HP-PUFEM based on a partition of unity finite element method to solve the Bloch-Torrey equation in heterogeneous domains. The proposed framework overcomes the difficulties that the standard approaches have when imposing the microscopic heterogeneity of the biological tissues. We also propose artificial jump conditions at the external boundaries to approximate the pseudo-periodic boundary conditions which allows for the water exchange at the external boundaries for non-periodic meshes. The framework is of a high level simplicity and efficiency that well facilitates parallelization. It can be straightforwardly implemented in different FEM software packages and it is implemented in FEniCS for moderate-scale simulations and in FEniCS-HPC for the large-scale simulations. The framework is validated against reference solutions, and implementation shows a strong parallel scalability. Since such a high-performance simulation framework is still missing in the field, it can become a powerful tool to uncover diffusion in complex biological tissues. In the second application, we develop an ALE-DFS method which combines advanced techniques developed in recent years to simulate turbulence. We apply a General Galerkin (G2) method which is continuous piecewise linear in both time and space, to solve the Navier-Stokes equations for a rotating turbine in an Arbitrary Lagrangian-Eulerian (ALE) framework. This method is enhanced with dual-based a posterior error control and automated mesh adaptation. Turbulent boundary layers are modeled by a slip boundary condition to avoid a full resolution which is impossible even with the most powerful computers available today. The method is validated against experimental data of parked turbines with good agreements. The thesis presents contributions in the form of both numerical methods for high-performance computing frameworks and efficient, tested software, published open source as part of the FEniCS-HPC platform.

  • 33.
    Nguyen, Van-Dang
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Jansson, Johan
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Frachon, Thomas
    Degirmenci, Cem
    Hoffman, Johan
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Numerical Analysis, NA.
    A fluid-structure interaction model with weak slip velocity boundary conditions on conforming internal interfaces2018Conference paper (Other (popular science, discussion, etc.))
    Abstract [en]

    We develop a PUFEM–Partition of Unity Finite Element Method to impose slip velocity boundary conditions on conforming internal interfaces for a fluid-structure interaction model. The method facilitates a straightforward implementation on the FEniCS/FEniCS-HPC platform. We show two results for 2D model problems with the implementation on FEniCS: (1) optimal convergence rate is shown for a stationary Navier-Stokes flow problem, and (2) the slip velocity conditions give qualitatively the correct result for the Euler flow. 

  • 34.
    Nguyen, Van-Dang
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Jansson, Johan
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Tran, Hoang Trong An
    CMAP, Polytechnique, France.
    Hoffman, Johan
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Li, Jing-Rebecca
    CMAP, Ecole Polytechnique, France.
    Diffusion MRI simulation in thin-layer and thin-tube media using a discretization on manifoldsManuscript (preprint) (Other academic)
    Abstract [en]

    The Bloch-Torrey partial differential equation describes the evolution of the transverse magnetization under the influence of diffusion-encoding magnetic field gradients inside a three-dimensional medium. The integral of the magnetization inside a voxel gives the simulated diffusion MRI signal. This paper proposes a finite element discretization on manifolds in order to simulate the diffusion MRI signal in domains that have a thin layer or a thin tube geometrical structure. Suppose that the three-dimensional domain has a thin layer geometrical structure: points in the domain can be obtained by starting on the two-dimensional manifold and moving along a depth (thickness) function. For this type of domains, we propose a finite element discretization formulated on a surface triangulation of the manifold. The variable thickness of the domain is included in the weak formulation on the surface triangular elements. A simple modification extends the approach to `thin tube' domains where a manifold in one direction and a two-dimensional variable cross-section describe the points in the domain. This discretization approach was implemented using the finite element platform FEniCS. We conduct a numerical study of the proposed approach by simulating the diffusion MRI signals from the extracellular space (a thin layer medium) and from neurons (a thin tube medium) and compare the results with the reference signals obtained by using a standard three-dimensional finite element discretization. We show good agreement between the simulated signals using our proposed method and the reference signals. The method helps us to significantly reduce both the simulation cost and the complexity of mesh generation.

  • 35.
    Otero, Evelyn
    et al.
    KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering.
    Gong, Jing
    KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Min, Misun
    Argonne National Laboratory.
    Fischer, Paul
    Argonne National Laboratory.
    Schlatter, Philipp
    KTH, School of Engineering Sciences (SCI), Mechanics. KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.
    Laure, Erwin
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, Centres, SeRC - Swedish e-Science Research Centre.
    OpenACC accelerator for the Pn-Pn-2 algorithm in Nek50002018In: Proceedings of the 5th International Conference on Exascale Applications and Software, 2018Conference paper (Refereed)
  • 36.
    Pauli, Robin
    et al.
    Julich Res Ctr, Inst Neurosci & Med INM 6, Julich, Germany.;Julich Res Ctr, Inst Adv Simulat IAS 6, Julich, Germany.;Julich Res Ctr, JARA BRAIN Inst 1, Julich, Germany..
    Weidel, Philipp
    Julich Res Ctr, Inst Neurosci & Med INM 6, Julich, Germany.;Julich Res Ctr, Inst Adv Simulat IAS 6, Julich, Germany.;Julich Res Ctr, JARA BRAIN Inst 1, Julich, Germany..
    Kunkel, Susanne
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). Norwegian Univ Life Sci, Fac Sci & Technol, As, Norway.
    Morrison, Abigail
    Julich Res Ctr, Inst Neurosci & Med INM 6, Julich, Germany.;Julich Res Ctr, Inst Adv Simulat IAS 6, Julich, Germany.;Julich Res Ctr, JARA BRAIN Inst 1, Julich, Germany.;Ruhr Univ Bochum, Inst Cognit Neurosci, Fac Psychol, Bochum, Germany..
    Reproducing Polychronization: A Guide to Maximizing the Reproducibility of Spiking Network Models2018In: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 12, article id 46Article in journal (Refereed)
    Abstract [en]

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

  • 37. Peng, I. B.
    et al.
    Gioiosa, R.
    Kestor, G.
    Vetter, J. S.
    Cicotti, P.
    Laure, Erwin
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Markidis, Stefano
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Characterizing the performance benefit of hybrid memory system for HPC applications2018In: Parallel Computing, ISSN 0167-8191, E-ISSN 1872-7336, Vol. 76, p. 57-69Article in journal (Refereed)
    Abstract [en]

    Heterogenous memory systems that consist of multiple memory technologies are becoming common in high-performance computing environments. Modern processors and accelerators, such as the Intel Knights Landing (KNL) CPU and NVIDIA Volta GPU, feature small-size high-bandwidth memory near the compute cores and large-size normal-bandwidth memory that is connected off-chip. Theoretically, HBM can provide about four times higher bandwidth than conventional DRAM. However, many factors impact the actual performance improvement that an application can achieve on such system. In this paper, we focus on the Intel KNL system and identify the most important factors on the application performance, including the application memory access pattern, the problem size, the threading level and the actual memory configuration. We use a set of representative applications from both scientific and data-analytics domains. Our results show that applications with regular memory access benefit from MCDRAM, achieving up to three times performance when compared to the performance obtained using only DRAM. On the contrary, applications with irregular memory access pattern are latency-bound and may suffer from performance degradation when using only MCDRAM. Also, we provide memory-centric analysis of four applications, identify their major data objects, correlate their characteristics to the performance improvement on the testbed.

  • 38.
    Ravichandran, Naresh Balaji
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Modelling homeostatic regulation in multi-objective decision-making2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    This thesis attempts to model homeostatic regulation, a behavioural phenomenon ubiquitousin animals, in the domain of reinforcement learning. We specifically look at multi-objectivereinforcement learning that can facilitate multi-variate regulation. When multiple objectivesare to be handled, the current framework of Multi-objective Reinforcement Learning provesto be unsuitable without information on some preference over the objectives. We thereforemodel homeostatic regulation as a motivational process, that selectively activates some ob-jectives over others, and implements cognitive control. In doing so, we utilize cognitive con-trol not as behavioural principle, but as a control mechanism that arises as a natural necessityfor homeostatic regulation.

    We utilize a recent framework for drive reduction theory of reinforcement learning, andattempt to provide a normative account of arbitration of objectives from drives. We showthat a purely reactive agent can face difficulties in achieving this regulation, and would re-quire a persistence-flexibility mechanism. This could be handled effectively in our model byincorporating a progress metric. We attempt to build this model with the intention of actingas a natural extension to the current reinforcement learning framework, while also showingappropriate behavioural properties.

  • 39.
    Rivas-Gomez, Sergio
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Gioiosa, Roberto
    Oak Ridge Natl Lab, Oak Ridge, TN 37830 USA..
    Peng, Ivy Bo
    Oak Ridge Natl Lab, Oak Ridge, TN 37830 USA..
    Kestor, Gokcen
    Oak Ridge Natl Lab, Oak Ridge, TN 37830 USA..
    Narasimhamurthy, Sai
    Seagate Syst UK, Havant PO9 1SA, England..
    Laure, Erwin
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Markidis, Stefano
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    MPI windows on storage for HPC applications2018In: Parallel Computing, ISSN 0167-8191, E-ISSN 1872-7336, Vol. 77, p. 38-56Article in journal (Refereed)
    Abstract [en]

    Upcoming HPC clusters will feature hybrid memories and storage devices per compute node. In this work, we propose to use the MPI one-sided communication model and MPI windows as unique interface for programming memory and storage. We describe the design and implementation of MPI storage windows, and present its benefits for out-of-core execution, parallel I/O and fault-tolerance. In addition, we explore the integration of heterogeneous window allocations, where memory and storage share a unified virtual address space. When performing large, irregular memory operations, we verify that MPI windows on local storage incurs a 55% performance penalty on average. When using a Lustre parallel file system, "asymmetric" performance is observed with over 90% degradation in writing operations. Nonetheless, experimental results of a Distributed Hash Table, the HACC I/O kernel mini-application, and a novel MapReduce implementation based on the use of MPI one-sided communication, indicate that the overall penalty of MPI windows on storage can be negligible in most cases in real-world applications.

  • 40.
    Rivas-Gomez, Sergio
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Pena, A. J.
    Moloney, D.
    Laure, Erwin
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Markidis, Stefano
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Exploring the vision processing unit as co-processor for inference2018In: Proceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2018, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 589-598, article id 8425465Conference paper (Refereed)
    Abstract [en]

    The success of the exascale supercomputer is largely debated to remain dependent on novel breakthroughs in technology that effectively reduce the power consumption and thermal dissipation requirements. In this work, we consider the integration of co-processors in high-performance computing (HPC) to enable low-power, seamless computation offloading of certain operations. In particular, we explore the so-called Vision Processing Unit (VPU), a highly-parallel vector processor with a power envelope of less than 1W. We evaluate this chip during inference using a pre-trained GoogLeNet convolutional network model and a large image dataset from the ImageNet ILSVRC challenge. Preliminary results indicate that a multi-VPU configuration provides similar performance compared to reference CPU and GPU implementations, while reducing the thermal-design power (TDP) up to 8x in comparison.

  • 41.
    Silverstein, David N.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Investigations of neural attractor dynamics in human visual awareness2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    What we see, how we see it and what emotions may arise from stimuli has long been studied by philosophers, psychologists, medical doctors and neuroscientists. This thesis work investigates a particular view on the possible dynamics, utilizing computational models of spiking neural attractor networks. From neurological studies on humans and other primates, we know visual perception and recognition of objects occur partly along the visual ventral stream, from V1 to V2, V4, IT and downstream to other areas. This visual awareness can be both conscious and unconscious and may also trigger an emotional response. As seen from many psychophysical experiments in backward masking (BM) and attentional blink (AB), some spatial and temporal dynamics can determine what becomes visually conscious and what does not. To explore this computationally, biophysical models of BM and AB were implemented and simulated to mimic human experiments, with the assumption that neural assemblies as attractor networks activate and propagate along the ventral stream and beyond. It was observed that attractor interference between percepts in sensory and associative cortex can occur during this activity. During typical human AB experimental trials in which two expected target symbols amongst distractors are presented less than 500 ms apart, the second target is often not reported as seen. When simulating this paradigm as two expected target neural attractors amongst distractors, it was observed in the present work that an initial attractor in associative cortex can impede the activation and propagation of a following attractor, which mimics missing conscious perception of the second target. It was also observed that simulating the presence of benzodiazepines (GABA agonists) will slow cortical dynamics and increase the AB, as previously shown in human experiments.

    During typical human BM experimental trials in which a brief target stimulus is followed by a masking stimulus after a short interval of less than 100 ms, recognition of the target can be impaired when in close spatial proximity. When simulating this paradigm using a biophysical model of V1 and V2 with feedforward and feedback connections, attractor targets were activated in V1 before imposition of a proximal metacontrast mask. If an activating target attractor in V1 is quiesced enough with lateral inhibition from a mask, or not reinforced by recurrent feedback from feedforward activation in V2, it is more likely to burn out before becoming fully active and progressing through V2 and beyond. BM was also simulated with an increasing stimulus interval and with the presence and absence of feedback activity. This showed that recurrent feedback diminishes BM effects and can make conscious perception more likely.

    To better understand possible emotional components of visual perception and early regulation, visual signaling pathways to the amygdala were investigated and proposed for emotional salience and the possible onset of fear. While one subcortical and likely unconscious pathway (before amydala efferent signaling) was affirmed via the superior colliculus and pulvinar, four others traversed through the ventral stream. One traversed though IT on recognition, another via the OFC on conditioning, and two other possibly conscious pathways traversed though the parietal and then prefrontal cortex, one excitatory pathway via the ventral-medial area and one regulatory pathway via the ventral-lateral area. Predicted latencies were determined for these signaling pathways, which can be experimentally testable. The conscious feeling of fear itself may not occur until after interoceptive inspection.

    A pathology of attractor dynamics was also investigated, which can occur from the presence of a brain tumor in white matter. Due to degradation from tumor invasion of white matter projections between two simulated neocortical patches, information transfer between separate neural attractors degraded, leading first to recall errors and later to epileptic-like activity. Neural plasticity could partially compensate up to a point, before transmission failure. This suggests that once epileptic seizures start in glioma patients, compensatory plasticity may already be exhausted. Interestingly, the presence of additional noise could also partially compensate for white matter loss.

  • 42.
    Smith, Kevin
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Piccinini, Filippo
    IRCCS, Ist Sci Romagnolo Studio & Cura Tumori IRST, Via P Maroncelli 40, I-47014 Meldola, FC, Italy..
    Balassa, Tamas
    Hungarian Acad Sci, Synthet & Syst Biol Unit, BRC, Temesvari Krt 62, H-6726 Szeged, Hungary..
    Koos, Krisztian
    Hungarian Acad Sci, Synthet & Syst Biol Unit, BRC, Temesvari Krt 62, H-6726 Szeged, Hungary..
    Danka, Tivadar
    Hungarian Acad Sci, Synthet & Syst Biol Unit, BRC, Temesvari Krt 62, H-6726 Szeged, Hungary..
    Azizpour, Hossein
    KTH, School of Electrical Engineering and Computer Science (EECS). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Horvath, Peter
    Hungarian Acad Sci, Synthet & Syst Biol Unit, BRC, Temesvari Krt 62, H-6726 Szeged, Hungary.;Univ Helsinki, Inst Mol Med Finland, Tukholmankatu 8, FIN-00014 Helsinki, Finland..
    Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays2018In: CELL SYSTEMS, ISSN 2405-4712, Vol. 6, no 6, p. 636-653Article, review/survey (Refereed)
    Abstract [en]

    Phenotypic image analysis is the task of recognizing variations in cell properties using microscopic image data. These variations, produced through a complex web of interactions between genes and the environment, may hold the key to uncover important biological phenomena or to understand the response to a drug candidate. Today, phenotypic analysis is rarely performed completely by hand. The abundance of high-dimensional image data produced by modern high-throughput microscopes necessitates computational solutions. Over the past decade, a number of software tools have been developed to address this need. They use statistical learning methods to infer relationships between a cell's phenotype and data from the image. In this review, we examine the strengths and weaknesses of non-commercial phenotypic image analysis software, cover recent developments in the field, identify challenges, and give a perspective on future possibilities.

  • 43.
    Sullivan, Devin P.
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Winsnes, Casper F.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Åkesson, Lovisa
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hjelmare, Martin
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Wiking, Mikaela
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Schutten, Rutger
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Campbell, Linzi
    CCP Hf, Reyjkavik, Iceland..
    Leifsson, Hjalti
    CCP Hf, Reyjkavik, Iceland..
    Rhodes, Scott
    CCP Hf, Reyjkavik, Iceland..
    Nordgren, Andie
    CCP Hf, Reyjkavik, Iceland..
    Smith, Kevin
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Revaz, Bernard
    MMOS Sarl, Monthey, Switzerland..
    Finnbogason, Bergur
    CCP Hf, Reyjkavik, Iceland..
    Szantner, Attila
    MMOS Sarl, Monthey, Switzerland..
    Lundberg, Emma
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics.
    Deep learning is combined with massive-scale citizen science to improve large-scale image classification2018In: Nature Biotechnology, ISSN 1087-0156, E-ISSN 1546-1696, Vol. 36, no 9, p. 820-+Article in journal (Refereed)
    Abstract [en]

    Pattern recognition and classification of images are key challenges throughout the life sciences. We combined two approaches for large-scale classification of fluorescence microscopy images. First, using the publicly available data set from the Cell Atlas of the Human Protein Atlas (HPA), we integrated an image-classification task into a mainstream video game (EVE Online) as a mini-game, named Project Discovery. Participation by 322,006 gamers over 1 year provided nearly 33 million classifications of subcellular localization patterns, including patterns that were not previously annotated by the HPA. Second, we used deep learning to build an automated Localization Cellular Annotation Tool (Loc-CAT). This tool classifies proteins into 29 subcellular localization patterns and can deal efficiently with multi-localization proteins, performing robustly across different cell types. Combining the annotations of gamers and deep learning, we applied transfer learning to create a boosted learner that can characterize subcellular protein distribution with F1 score of 0.72. We found that engaging players of commercial computer games provided data that augmented deep learning and enabled scalable and readily improved image classification.

  • 44.
    Teye, Mattias
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Azizpour, Hossein
    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). Science for Life Laboratory.
    Bayesian Uncertainty Estimation for Batch Normalized Deep Networks2018Conference paper (Refereed)
    Abstract [en]

    We show that training a deep network using batch normalization is equivalent to approximate inference in Bayesian models. We further demonstrate that this finding allows us to make meaningful estimates of the model uncertainty using conventional architectures, without modifications to the network or the training procedure. Our approach is thoroughly validated by measuring the quality of uncertainty in a series of empirical experiments on different tasks. It outperforms baselines with strong statistical significance, and displays competitive performance with recent Bayesian approaches

  • 45.
    Wang, Ruoli
    et al.
    KTH, School of Engineering Sciences (SCI), Centres, BioMEx. Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden.
    Gäverth, J.
    Herman, Pawel
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Changes in the neural and non-neural related properties of the spastic wrist flexors after treatment with botulinum toxin a in post-stroke subjects: An optimization study2018In: Frontiers in Bioengineering and Biotechnology, E-ISSN 2296-4185, Vol. 9, no June, article id 73Article in journal (Refereed)
    Abstract [en]

    Quantifying neural and non-neural contributions to the joint resistance in spasticity is essential for a better evaluation of different intervention strategies such as botulinum toxin A (BoTN-A). However, direct measurement of muscle mechanical properties and spasticity-related parameters in humans is extremely challenging. The aim of this study was to use a previously developed musculoskeletal model and optimization scheme to evaluate the changes of neural and non-neural related properties of the spastic wrist flexors during passive wrist extension after BoTN-A injection. Data of joint angle and resistant torque were collected from 21 chronic stroke patients before, and 4 and 12 weeks post BoTN-A injection using NeuroFlexor, which is a motorized force measurement device to passively stretch wrist flexors. The model was optimized by tuning the passive and stretch-related parameters to fit the measured torque in each participant. It was found that stroke survivors exhibited decreased neural components at 4 weeks post BoNT-A injection, which returned to baseline levels after 12 weeks. The decreased neural component was mainly due to the increased motoneuron pool threshold, which is interpreted as a net excitatory and inhibitory inputs to the motoneuron pool. Though the linear stiffness and viscosity properties of wrist flexors were similar before and after treatment, increased exponential stiffness was observed over time which may indicate a decreased range of motion of the wrist joint. Using a combination of modeling and experimental measurement, valuable insights into the treatment responses, i.e., transmission of motoneurons, are provided by investigating potential parameter changes along the stretch reflex pathway in persons with chronic stroke.

  • 46.
    Yu, Yiqun
    et al.
    Beihang Univ, Sch Space & Environm, Beijing, Peoples R China..
    Delzanno, Gian Luca
    Los Alamos Natl Lab, Los Alamos, NM USA..
    Jordanova, Vania
    Los Alamos Natl Lab, Los Alamos, NM USA..
    Peng, Ivy Bo
    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).
    PIC simulations of wave-particle interactions with an initial electron velocity distribution from a kinetic ring current model2018In: Journal of Atmospheric and Solar-Terrestrial Physics, ISSN 1364-6826, E-ISSN 1879-1824, Vol. 177, p. 169-178Article in journal (Refereed)
    Abstract [en]

    Whistler wave-particle interactions play an important role in the Earth inner magnetospheric dynamics and have been the subject of numerous investigations. By running a global kinetic ring current model (RAM-SCB) in a storm event occurred on Oct 23-24 2002, we obtain the ring current electron distribution at a selected location at MLT of 9 and L of 6 where the electron distribution is composed of a warm population in the form of a partial ring in the velocity space (with energy around 15 keV) in addition to a cool population with a Maxwellian-like distribution. The warm population is likely from the injected plasma sheet electrons during substorm injections that supply fresh source to the inner magnetosphere. These electron distributions are then used as input in an implicit particle-in-cell code (iPIC3D) to study whistler-wave generation and the subsequent wave-particle interactions. We find that whistler waves are excited and propagate in the quasi-parallel direction along the background magnetic field. Several different wave modes are instantaneously generated with different growth rates and frequencies. The wave mode at the maximum growth rate has a frequency around 0.62 omega(ce), which corresponds to a parallel resonant energy of 2.5 keV. Linear theory analysis of wave growth is in excellent agreement with the simulation results. These waves grow initially due to the injected warm electrons and are later damped due to cyclotron absorption by electrons whose energy is close to the resonant energy and can effectively attenuate waves. The warm electron population overall experiences net energy loss and anisotropy drop while moving along the diffusion surfaces towards regions of lower phase space density, while the cool electron population undergoes heating when the waves grow, suggesting the cross-population interactions.

  • 47.
    Zhu, Fei
    et al.
    Univ Edinburgh, Genes Cognit Program, Ctr Clin Brain Sci, Edinburgh EH16 4SB, Midlothian, Scotland.;UCL Inst Neurol, Queen Sq, London WC1N 3BG, England..
    Cizeron, Melissa
    Univ Edinburgh, Genes Cognit Program, Ctr Clin Brain Sci, Edinburgh EH16 4SB, Midlothian, Scotland.;Univ Claude Bernard Lyon 1, Univ Lyon, Inst NeuroMyoGene, CNRS,UMR 5310,INSERM,U1217, F-69008 Lyon, France..
    Qiu, Zhen
    Univ Edinburgh, Genes Cognit Program, Ctr Clin Brain Sci, Edinburgh EH16 4SB, Midlothian, Scotland..
    Benavides-Piccione, Ruth
    CSIC, Inst Cajal, E-28002 Madrid, Spain.;UPM, Ctr Tecnol Biomed, Madrid 28223, Spain.;ISCIII, CIBERNED, Madrid 28031, Spain..
    Kopanitsa, Maksym V.
    Synome Ltd, Babraham Res Campus, Cambridge CB22 3AT, England.;Imperial Coll London, UK Dementia Res Inst, London W12 0NN, England..
    Skene, Nathan G.
    Univ Edinburgh, Genes Cognit Program, Ctr Clin Brain Sci, Edinburgh EH16 4SB, Midlothian, Scotland.;UCL Inst Neurol, Queen Sq, London WC1N 3BG, England.;Karolinska Inst, Dept Med Biochem & Biophys, Lab Mol Neurobiol, S-17177 Stockholm, Sweden..
    Koniaris, Babis
    Univ Edinburgh, Genes Cognit Program, Ctr Clin Brain Sci, Edinburgh EH16 4SB, Midlothian, Scotland..
    DeFelipe, Javier
    CSIC, Inst Cajal, E-28002 Madrid, Spain.;UPM, Ctr Tecnol Biomed, Madrid 28223, Spain.;ISCIII, CIBERNED, Madrid 28031, Spain..
    Fransén, Erik
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Komiyama, Noboru H.
    Univ Edinburgh, Genes Cognit Program, Ctr Clin Brain Sci, Edinburgh EH16 4SB, Midlothian, Scotland..
    Grant, Seth G. N.
    Univ Edinburgh, Genes Cognit Program, Ctr Clin Brain Sci, Edinburgh EH16 4SB, Midlothian, Scotland..
    Architecture of the Mouse Brain Synaptome2018In: Neuron, ISSN 0896-6273, E-ISSN 1097-4199, Vol. 99, no 4, p. 781-+Article in journal (Refereed)
    Abstract [en]

    Synapses are found in vast numbers in the brain and contain complex proteomes. We developed genetic labeling and imaging methods to examine synaptic proteins in individual excitatory synapses across all regions of the mouse brain. Synapse catalogs were generated from the molecular and morphological features of a billion synapses. Each synapse subtype showed a unique anatomical distribution, and each brain region showed a distinct signature of synapse subtypes. Whole-brain synaptome cartography revealed spatial architecture from dendritic to global systems levels and previously unknown anatomical features. Synaptome mapping of circuits showed correspondence between synapse diversity and structural and functional connectomes. Behaviorally relevant patterns of neuronal activity trigger spatio-temporal postsynaptic responses sensitive to the structure of synaptome maps. Areas controlling higher cognitive function contain the greatest synapse diversity, and mutations causing cognitive disorders reorganized synaptome maps. Synaptome technology and resources have wide-ranging application in studies of the normal and diseased brain.

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