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  • 101. Henricson, Anna
    et al.
    Käll, Lukas
    Sonnhammer, Erik L. L.
    A novel transmembrane topology of presenilin based on reconciling experimental and computational evidence2005Inngår i: The FEBS Journal, ISSN 1742-464X, E-ISSN 1742-4658, Vol. 272, nr 11, s. 2727-2733Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The transmembrane topology of presenilins is still the subject of debate despite many experimental topology studies using antibodies or gene fusions. The results from these studies are partly contradictory and consequently several topology models have been proposed. Studies of presenilin-interacting proteins have produced further contradiction, primarily regarding the location of the C-terminus. It is thus impossible to produce a topology model that agrees with all published data on presenilin. We have analyzed the presenilin topology through computational sequence analysis of the presenilin family and the homologous presenilin-like protein family. Members of these families are intramembrane-cleaving aspartyl proteases. Although the overall sequence homology between the two families is low, they share the conserved putative active site residues and the conserved 'PAL' motif. Therefore, the topology model for the presenilin-like proteins can give some clues about the presenilin topology. Here we propose a novel nine-transmembrane topology with the C-terminus in the extracytosolic space. This model has strong support from published data on gamma-secretase function and presenilin topology. Contrary to most presenilin topology models, we show that hydrophobic region X is probably a transmembrane segment. Consequently, the C-terminus would be located in the extracytosolic space. However, the last C-terminal amino acids are relatively hydrophobic and in conjunction with existing experimental data we cannot exclude the possibility that the extreme C-terminus could be buried within the gamma-secretase complex. This might explain the difficulties in obtaining consistent experimental evidence regarding the location of the C-terminal region of presenilin.

  • 102.
    Herman, Pawel Andrzej
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. Stockholm University.
    Lundqvist, Mikael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. Stockholm University.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. Stockholm University.
    Nested theta to gamma oscillations and precise spatiotemporal firing during memory retrieval in a simulated attractor network2013Inngår i: Brain Research, ISSN 0006-8993, E-ISSN 1872-6240, Vol. 1536, nr SI, s. 68-87Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

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

  • 103.
    Herman, Pawel
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsvetenskap och beräkningsteknik (CST).
    Benjaminsson, S.
    Lansner, A.
    Odor recognition in an attractor network model of the mammalian olfactory cortex2017Inngår i: 2017 International Joint Conference on Neural Networks (IJCNN), Institute of Electrical and Electronics Engineers (IEEE), 2017, s. 3561-3568, artikkel-id 7966304Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Odor recognition constitutes a key functional aspect of olfaction and in real-world scenarios it requires that odorants occurring in complex chemical mixtures are identified irrespective of their concentrations. We investigate this challenging pattern recognition problem in the framework of a three-stage computational model of the mammalian olfactory system. To this end, we first synthesize odor stimuli with the primary representations in the olfactory receptor neuron (ORN) layer and the secondary representations in the output of the olfactory bulb (OB) in the model. Next, sparse olfactory codes are extracted and fed into the recurrent network model, where as a result of Hebbian-like associative learning an attractor memory storage is produced. We demonstrate the capability of the resultant olfactory cortex (OC) model to perform robust odor recognition tasks and offer computational insights into the underlying network mechanisms.

  • 104.
    Herman, Pawel
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Odor recognition framework for evaluating olfactory codes2011Konferansepaper (Annet vitenskapelig)
  • 105.
    Herman, Pawel
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lundqvist, Mikael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Oscillations in a simulated meso-scale memory network: origin and function of theta to gamma rhythmsArtikkel i tidsskrift (Annet vitenskapelig)
  • 106.
    Innocenti, Nicolas
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Data Analysis and Next Generation Sequencing : Applications in Microbiology.2015Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Next Generation Sequencing (NGS) is a new technology that has revolutionized the way we study living organisms. Where previously only a few genes could be studied at a time through targeted direct probing, NGS offers the possibility to perform measurements for a whole genome at once. The drawback is that the amount of data generated in the process is large and extracting useful information from it requires new methods to process and analyze it.

    The main contribution of this thesis is the development of a novel experimental method coined tagRNA-seq, combining 5’tagRACE, a previously developed technique, with RNA-sequencing technology. Briefly, tagRNA-seq makes it possible to identify the 5’ ends of RNAs in bacteria and directly probe for their type, primary or processed, by ligating short RNA sequences, the tags, to the beginnings of RNA molecules. We used the method to directly probe for transcription start and processing sites in two bacterial species, Escherichiacoli and Enterococcus faecalis. It was also used to study polyadenylation in E. coli, where the ability to identify processed RNA molecules proved to be useful to separate direct and indirect regulatory effects of this mechanism. We also demonstrate how data from tagRNA-seq experiments can be used to increase confidence on the discovery of anti-sense transcripts in bacteria. Analyses of RNA-seq data obtained in the context of these experiments revealed subtle artifacts in the coverage signal towards gene ends, that we were able to explain and quantify based Kolmogorov’s broken stick model. We also discovered evidences for circularization of a few RNA transcripts, both in our own data sets and publicly available data.

    Designing the tags used in tagRNA-seq led us to the problem of words absent from a text. We focus on a particular subset of these, the minimal absent words (MAWs), and develop a theory providing a complete description of their size distribution in random text. We also show that MAWs in genomes from viruses and living organisms almost always exhibit a behavior different from random texts in the tail of the distribution, and that MAWs from this tail are closely related to sequences present in the genome that preferentially appear in regions with important regulatory functions.

    Finally, and independently from tagRNA-seq, we propose a new approach to the problem of bacterial community reconstruction in metagenomic, based on techniques from compressed sensing. We provide a novel algorithm competing with state-of-the-art techniques in the field.

  • 107.
    Johansson, Christopher
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Ekeberg, Örjan
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Clustering of stored memories in an attractor network with local competition2006Inngår i: International Journal of Neural Systems, ISSN 0129-0657, E-ISSN 1793-6462, Vol. 16, nr 6, s. 393-403Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In this paper we study an attractor network with units that compete locally for activation and we prove that a reduced version of it has fixpoint dynamics. An analysis, complemented by simulation experiments, of the local characteristics of the network's attractors with respect to a parameter controlling the intensity of the local competition is performed. We find that the attractors are hierarchically clustered when the parameter of the local competition is changed

  • 108.
    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, Skolan för elektroteknik och datavetenskap (EECS), Beräkningsvetenskap och beräkningsteknik (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)2018Inngår i: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 12, artikkel-id 34Artikkel i tidsskrift (Fagfellevurdert)
  • 109.
    Jovanović, Stojan
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. Univ Freiburg, Germany.
    Rotter, Stefan
    Interplay between Graph Topology and Correlations of Third Order in Spiking Neuronal Networks2016Inngår i: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 12, nr 6, artikkel-id e1004963Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The study of processes evolving on networks has recently become a very popular research field, not only because of the rich mathematical theory that underpins it, but also because of its many possible applications, a number of them in the field of biology. Indeed, molecular signaling pathways, gene regulation, predator-prey interactions and the communication between neurons in the brain can be seen as examples of networks with complex dynamics. The properties of such dynamics depend largely on the topology of the underlying network graph. In this work, we want to answer the following question: Knowing network connectivity, what can be said about the level of third-order correlations that will characterize the network dynamics? We consider a linear point process as a model for pulse-coded, or spiking activity in a neuronal network. Using recent results from theory of such processes, we study third-order correlations between spike trains in such a system and explain which features of the network graph (i.e. which topological motifs) are responsible for their emergence. Comparing two different models of network topology-random networks of Erdos-Renyi type and networks with highly interconnected hubs-we find that, in random networks, the average measure of third-order correlations does not depend on the local connectivity properties, but rather on global parameters, such as the connection probability. This, however, ceases to be the case in networks with a geometric out-degree distribution, where topological specificities have a strong impact on average correlations.

  • 110. Järvstråt, Linnea
    et al.
    Johansson, Mikael
    KTH, Skolan för elektro- och systemteknik (EES), Reglerteknik.
    Gullberg, Urban
    Nilsson, Björn
    Ultranet: efficient solver for the sparse inverse covariance selection problem in gene network modeling2013Inngår i: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 29, nr 4, s. 511-512Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Graphical Gaussian models (GGMs) are a promising approach to identify gene regulatory networks. Such models can be robustly inferred by solving the sparse inverse covariance selection (SICS) problem. With the high dimensionality of genomics data, fast methods capable of solving large instances of SICS are needed. We developed a novel network modeling tool, Ultranet, that solves the SICS problem with significantly improved efficiency. Ultranet combines a range of mathematical and programmatical techniques, exploits the structure of the SICS problem and enables computation of genome-scale GGMs without compromising analytic accuracy.

  • 111.
    Kahles, André
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Sarqume, Fahad
    KTH, Skolan för bioteknologi (BIO).
    Savolainen, Peter
    KTH, Skolan för bioteknologi (BIO), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Arvestad, Lars
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. KTH, Centra, Science for Life Laboratory, SciLifeLab. Stockholms universitet.
    Excap: maximization of haplotypic diversity of linked markers2013Inngår i: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 8, nr 11, s. e79012-Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Genetic markers, defined as variable regions of DNA, can be utilized for distinguishing individuals or populations. As long as markers are independent, it is easy to combine the information they provide. For nonrecombinant sequences like mtDNA, choosing the right set of markers for forensic applications can be difficult and requires careful consideration. In particular, one wants to maximize the utility of the markers. Until now, this has mainly been done by hand. We propose an algorithm that finds the most informative subset of a set of markers. The algorithm uses a depth first search combined with a branch-and-bound approach. Since the worst case complexity is exponential, we also propose some data-reduction techniques and a heuristic. We implemented the algorithm and applied it to two forensic caseworks using mitochondrial DNA, which resulted in marker sets with significantly improved haplotypic diversity compared to previous suggestions. Additionally, we evaluated the quality of the estimation with an artificial dataset of mtDNA. The heuristic is shown to provide extensive speedup at little cost in accuracy.

  • 112.
    Kamali Sarvestani, Iman
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lindahl, Mikael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Hellgren Kotaleski, Jeanette
    Ekeberg, Örjan
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    The arbitration-extension hypothesis: A hierarchical interpretation of the functional organization of the basal ganglia2011Inngår i: Frontiers in Systems Neuroscience, ISSN 1662-5137, E-ISSN 1662-5137, Vol. 5, s. 13-Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Based on known anatomy and physiology, we present a hypothesis where the basal ganglia motor loop is hierarchically organized in two main subsystems: the arbitration system and the extension system. The arbitration system, comprised of the subthalamic nucleus, globus pallidus, and pedunculopontine nucleus, serves the role of selecting one out of several candidate actions as they are ascending from various brain stem motor regions and aggregated in the centromedian thalamus or descending from the extension system or from the cerebral cortex. This system is an action-input/action-output system whose winner-take-all mechanism finds the strongest response among several candidates to execute. This decision is communicated back to the brain stem by facilitating the desired action via cholinergic/glutamatergic projections and suppressing conflicting alternatives via GABAergic connections. The extension system, comprised of the striatum and, again, globus pallidus, can extend the repertoire of responses by learning to associate novel complex states to certain actions. This system is a state-input/action-output system, whose organization enables it to encode arbitrarily complex Boolean logic rules using striatal neurons that only fire given specific constellations of inputs (Boolean AND) and pallidal neurons that are silenced by any striatal input (Boolean OR). We demonstrate the capabilities of this hierarchical system by a computational model where a simulated generic "animal" interacts with an environment by selecting direction of movement based on combinations of sensory stimuli, some being appetitive, others aversive or neutral. While the arbitration system can autonomously handle conflicting actions proposed by brain stem motor nuclei, the extension system is required to execute learned actions not suggested by external motor centers. Being precise in the functional role of each component of the system, this hypothesis generates several readily testable predictions.

  • 113.
    Kaplan, Bernhard
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. Stockholm Brain Institute, Stockholm, Sweden.
    Modeling prediction and pattern recognition in the early visual and olfactory systems2015Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Our senses are our mind's window to the outside world and determine how we perceive our environment.Sensory systems are complex multi-level systems that have to solve a multitude of tasks that allow us to understand our surroundings.However, questions on various levels and scales remain to be answered ranging from low-level neural responses to behavioral functions on the highest level.Modeling can connect different scales and contribute towards tackling these questions by giving insights into perceptual processes and interactions between processing stages.In this thesis, numerical simulations of spiking neural networks are used to deal with two essential functions that sensory systems have to solve: pattern recognition and prediction.The focus of this thesis lies on the question as to how neural network connectivity can be used in order to achieve these crucial functions.The guiding ideas of the models presented here are grounded in the probabilistic interpretation of neural signals, Hebbian learning principles and connectionist ideas.The main results are divided into four parts.The first part deals with the problem of pattern recognition in a multi-layer network inspired by the early mammalian olfactory system with biophysically detailed neural components.Learning based on Hebbian-Bayesian principles is used to organize the connectivity between and within areas and is demonstrated in behaviorally relevant tasks.Besides recognition of artificial odor patterns, phenomena like concentration invariance, noise robustness, pattern completion and pattern rivalry are investigated.It is demonstrated that learned recurrent cortical connections play a crucial role in achieving pattern recognition and completion.The second part is concerned with the prediction of moving stimuli in the visual system.The problem of motion-extrapolation is studied using different recurrent connectivity patterns.The main result shows that connectivity patterns taking the tuning properties of cells into account can be advantageous for solving the motion-extrapolation problem.The third part focuses on the predictive or anticipatory response to an approaching stimulus.Inspired by experimental observations, particle filtering and spiking neural network frameworks are used to address the question as to how stimulus information is transported within a motion sensitive network.In particular, the question if speed information is required to build up a trajectory dependent anticipatory response is studied by comparing different network connectivities.Our results suggest that in order to achieve a dependency of the anticipatory response to the trajectory length, a connectivity that uses both position and speed information seems necessary.The fourth part combines the self-organization ideas from the first part with motion perception as studied in the second and third parts.There, the learning principles used in the olfactory system model are applied to the problem of motion anticipation in visual perception.Similarly to the third part, different connectivities are studied with respect to their contribution to anticipate an approaching stimulus.The contribution of this thesis lies in the development and simulation of large-scale computational models of spiking neural networks solving prediction and pattern recognition tasks in biophysically plausible frameworks.

  • 114.
    Kaplan, Bernhard A.
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. Stockholm Brain Institute, Karolinska Institute, Sweden .
    Khoei, M. A.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. Stockholm Brain Institute, Karolinska Institute, Sweden .
    Perrinet, L. U.
    Signature of an anticipatory response in area VI as modeled by a probabilistic model and a spiking neural network2014Inngår i: 2014 International Joint Conference on Neural Networks (IJCNN), IEEE , 2014, s. 3205-3212Konferansepaper (Fagfellevurdert)
    Abstract [en]

    As it is confronted to inherent neural delays, how does the visual system create a coherent representation of a rapidly changing environment? In this paper, we investigate the role of motion-based prediction in estimating motion trajectories compensating for delayed information sampling. In particular, we investigate how anisotropic diffusion of information may explain the development of anticipatory response as recorded in a neural populations to an approaching stimulus. We validate this using an abstract probabilistic framework and a spiking neural network (SNN) model. Inspired by a mechanism proposed by Nijhawan [1], we first use a Bayesian particle filter framework and introduce a diagonal motion-based prediction model which extrapolates the estimated response to a delayed stimulus in the direction of the trajectory. In the SNN implementation, we have used this pattern of anisotropic, recurrent connections between excitatory cells as mechanism for motion-extrapolation. Consistent with recent experimental data collected in extracellular recordings of macaque primary visual cortex [2], we have simulated different trajectory lengths and have explored how anticipatory responses may be dependent on the information accumulated along the trajectory. We show that both our probabilistic framework and the SNN model can replicate the experimental data qualitatively. Most importantly, we highlight requirements for the development of a trajectory-dependent anticipatory response, and in particular the anisotropic nature of the connectivity pattern which leads to the motion extrapolation mechanism.

  • 115.
    Kaplan, Bernhard A.
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Khoei, Mina A.
    Aix Marseille Univ, CNRS, UMR 7289, Inst Neurosci Timone, Marseille, France..
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Perrinet, Laurent U.
    Aix Marseille Univ, CNRS, UMR 7289, Inst Neurosci Timone, Marseille, France..
    Signature of an anticipatory response in area V1 as modeled by a probabilistic model and a spiking neural network2014Inngår i: PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), IEEE , 2014, s. 3205-3212Konferansepaper (Fagfellevurdert)
    Abstract [en]

    As it is confronted to inherent neural delays, how does the visual system create a coherent representation of a rapidly changing environment? In this paper, we investigate the role of motion-based prediction in estimating motion trajectories compensating for delayed information sampling. In particular, we investigate how anisotropic diffusion of information may explain the development of anticipatory response as recorded in a neural populations to an approaching stimulus. We validate this using an abstract probabilistic framework and a spiking neural network (SNN) model. Inspired by a mechanism proposed by Nijhawan [1], we first use a Bayesian particle filter framework and introduce a diagonal motion-based prediction model which extrapolates the estimated response to a delayed stimulus in the direction of the trajectory. In the SNN implementation, we have used this pattern of anisotropic, recurrent connections between excitatory cells as mechanism for motion-extrapolation. Consistent with recent experimental data collected in extracellular recordings of macaque primary visual cortex [2], we have simulated different trajectory lengths and have explored how anticipatory responses may be dependent on the information accumulated along the trajectory. We show that both our probabilistic framework and the SNN model can replicate the experimental data qualitatively. Most importantly, we highlight requirements for the development of a trajectory-dependent anticipatory response, and in particular the anisotropic nature of the connectivity pattern which leads to the motion extrapolation mechanism.

  • 116.
    Kaplan, Bernhard A.
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    A spiking neural network model of self-organized pattern recognition in the early mammalian olfactory system2014Inngår i: Frontiers in Neural Circuits, ISSN 1662-5110, E-ISSN 1662-5110, Vol. 8, nr Feb, s. 5-Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Olfactory sensory information passes through several processing stages before an odor percept emerges. The question how the olfactory system learns to create odor representations linking those different levels and how it learns to connect and discriminate between them is largely unresolved. We present a large-scale network model with single and multi-compartmental Hodgkin-Huxley type model neurons representing olfactory receptor neurons (ORNs) in the epithelium, periglomerular cells, mitral/tufted cells and granule cells in the olfactory bulb (OB), and three types of cortical cells in the piriform cortex (PC). Odor patterns are calculated based on affinities between ORNs and odor stimuli derived from physico-chemical descriptors of behaviorally relevant real-world odorants. The properties of ORNs were tuned to show saturated response curves with increasing concentration as seen in experiments. On the level of the OB we explored the possibility of using a fuzzy concentration interval code, which was implemented through dendro-dendritic inhibition leading to winner-take-all like dynamics between mitral/tufted cells belonging to the same glomerulus. The connectivity from mitral/tufted cells to PC neurons was self-organized from a mutual information measure and by using a competitive Hebbian-Bayesian learning algorithm based on the response patterns of mitral/tufted cells to different odors yielding a distributed feed-forward projection to the PC. The PC was implemented as a modular attractor network with a recurrent connectivity that was likewise organized through Hebbian-Bayesian learning. We demonstrate the functionality of the model in a one-sniff-learning and recognition task on a set of 50 odorants. Furthermore, we study its robustness against noise on the receptor level and its ability to perform concentration invariant odor recognition. Moreover, we investigate the pattern completion capabilities of the system and rivalry dynamics for odor mixtures.

  • 117.
    Kaplan, Bernhard
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Anders, Lansner
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Perrinet, Laurent
    Centre National de la Recherche Scientifique & Aix-Marseille Université, Marseille, France.
    Masson, Guillaume
    Centre National de la Recherche Scientifique & Aix-Marseille Université, Marseille, France.
    Anisotropic connectivity implements motion-basedprediction in a spiking neural network2013Inngår i: Frontiers in Computational Neuroscience, ISSN 1662-5188, E-ISSN 1662-5188Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Predictive coding hypothesizes that the brain explicitly infers upcoming sensory inputto establish a coherent representation of the world. Although it is becoming generallyaccepted, it is not clear on which level spiking neural networks may implementpredictive coding and what function their connectivity may have. We present a networkmodel of conductance-based integrate-and-fire neurons inspired by the architectureof retinotopic cortical areas that assumes predictive coding is implemented throughnetwork connectivity, namely in the connection delays and in selectiveness for the tuningproperties of source and target cells. We show that the applied connection pattern leadsto motion-based prediction in an experiment tracking a moving dot. In contrast to ourproposed model, a network with random or isotropic connectivity fails to predict the pathwhen the moving dot disappears. Furthermore, we show that a simple linear decodingapproach is sufficient to transform neuronal spiking activity into a probabilistic estimatefor reading out the target trajectory.

  • 118.
    Khan, Mehmood Alam
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsvetenskap och beräkningsteknik (CST).
    Computational Problems in Modeling Evolution and Inferring Gene Families.2016Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Over the last few decades, phylogenetics has emerged as a very promising field, facilitating a comparative framework to explain the genetic relationships among all the living organisms on earth. These genetic relationships are typically represented by a bifurcating phylogenetic tree — the tree of life. Reconstructing a phylogenetic tree is one of the central tasks in evolutionary biology. The different evolutionary processes, such as gene duplications, gene losses, speciation, and lateral gene transfer events, make the phylogeny reconstruction task more difficult. However, with the rapid developments in sequencing technologies and availability of genome-scale sequencing data, give us the opportunity to understand these evolutionary processes in a more informed manner, and ultimately, enable us to reconstruct genes and species phylogenies more accurately. This thesis is an attempt to provide computational methods for phylogenetic inference and give tools to conduct genome-scale comparative evolutionary studies, such as detecting homologous sequences and inferring gene families.

    In the first project, we present FastPhylo as a software package containing fast tools for reconstructing distance-based phylogenies. It implements the previously published efficient algorithms for estimating a distance matrix from the input sequences and reconstructing an un-rooted Neighbour Joining tree from a given distance matrix. Results on simulated datasets reveal that FastPhylo can handles hundred of thousands of sequences in a minimum time and memory efficient manner. The easy to use, well-defined interfaces, and the modular structure of FastPhylo allows it to be used in very large Bioinformatic pipelines.

    In the second project, we present a synteny-aware gene homology method, called GenFamClust (GFC) that uses gene content and gene order conservation to detect homology. Results on simulated and biological datasets suggest that local synteny information combined with the sequence similarity improves the detection of homologs.

    In the third project, we introduce a novel phylogeny-based clustering method, PhyloGenClust, which partitions a very large gene family into smaller subfamilies. ROC (receiver operating characteristics) analysis on synthetic datasets show that PhyloGenClust identify subfamilies more accurately. PhyloGenClust can be used as a middle tier clustering method between raw clustering methods, such as sequence similarity methods, and more sophisticated Bayesian-based phylogeny methods.

    Finally, we introduce a novel probabilistic Bayesian method based on the DLTRS model, to sample reconciliations of a gene tree inside a species tree. The method uses MCMC framework to integrate LGTs, gene duplications, gene losses and sequence evolution under a relaxed molecular clock for substitution rates. The proposed sampling method estimates the posterior distribution of gene trees and provides the temporal information of LGT events over the lineages of a species tree. Analysis on simulated datasets reveal that our method performs well in identifying the true temporal estimates of LGT events. We applied our method to the genome-wide gene families for mollicutes and cyanobacteria, which gave an interesting insight into the potential LGTs highways. 

  • 119.
    Khan, Mehmood Alam
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsvetenskap och beräkningsteknik (CST).
    Arvestad, Lars
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsvetenskap och beräkningsteknik (CST).
    Phylogenetic Partitioning of Gene FamiliesManuskript (preprint) (Annet vitenskapelig)
    Abstract [en]

    Clustering and organizing molecular sequences is one of the central tasks in Bioinformatics. It is a common first step in, for example, phylogenomic analysis. For some tasks, a large gene family needs to be partitioned into more manageable subfamilies. In particular, Bayesian phylogenetic analysis can be very expensive. There is a need for easy and natural means of breaking up a gene family, with moderate computational requirements, to enable careful analysis of subfamilies with computationally expensive tools. We devised and implemented a method that infer and reconcile gene trees to species trees and identifies putative orthogroups as subfamilies. To achieve reasonable speed, approximate ML phylogenies are inferred using the FastTree method and combined with a subfamily-centered bootstrapping procedure to ensure robustness. Using the new method, very large clusters of sequences are now easier to manage in pipelines containing computationally expensive steps. The implementation of PhyloGenClust is available at a public repository, https://github.com/malagori/PhyloGenClust, under the GNU General Public License version 3. 

  • 120.
    Khan, Mehmood Alam
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Elias, Isaac
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Sjölund, Erik
    Stockholms universitet.
    Nylander, Kristina
    KTH, Skolan för datavetenskap och kommunikation (CSC).
    Guimera, Roman Valls
    Stockholms univetsitet.
    Schobesberger, Richard
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. University of Applied Sciences Upper Austria.
    Schmitzberger, Peter
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. University of Applied Sciences Upper Austria.
    Lagergren, Jens
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Arvestad, Lars
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    fastphylo: Fast tools for phylogenetics2013Inngår i: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 14, nr 1, s. 334-Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background: Distance methods are ubiquitous tools in phylogenetics. Their primary purpose may be to reconstruct evolutionary history, but they are also used as components in bioinformatic pipelines. However, poor computational efficiency has been a constraint on the applicability of distance methods on very large problem instances. Results: We present fastphylo, a software package containing implementations of efficient algorithms for two common problems in phylogenetics: estimating DNA/protein sequence distances and reconstructing a phylogeny from a distance matrix. We compare fastphylo with other neighbor joining based methods and report the results in terms of speed and memory efficiency. Conclusions: Fastphylo is a fast, memory efficient, and easy to use software suite. Due to its modular architecture, fastphylo is a flexible tool for many phylogenetic studies.

  • 121.
    Khan, Mehmood Alam
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC). KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Mahmudi, Owais
    KTH, Skolan för datavetenskap och kommunikation (CSC). KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Ulah, Ikram
    KTH, Skolan för datavetenskap och kommunikation (CSC). KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Arvestad, Lars
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Centra, SeRC - Swedish e-Science Research Centre. Stockholm Univ, Sweden.
    Lagergren, Jens
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsvetenskap och beräkningsteknik (CST). KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Probabilistic inference of lateral gene transfer events2016Inngår i: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 17, artikkel-id 431Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background: Lateral gene transfer (LGT) is an evolutionary process that has an important role in biology. It challenges the traditional binary tree-like evolution of species and is attracting increasing attention of the molecular biologists due to its involvement in antibiotic resistance. A number of attempts have been made to model LGT in the presence of gene duplication and loss, but reliably placing LGT events in the species tree has remained a challenge. Results: In this paper, we propose probabilistic methods that samples reconciliations of the gene tree with a dated species tree and computes maximum a posteriori probabilities. The MCMC-based method uses the probabilistic model DLTRS, that integrates LGT, gene duplication, gene loss, and sequence evolution under a relaxed molecular clock for substitution rates. We can estimate posterior distributions on gene trees and, in contrast to previous work, the actual placement of potential LGT, which can be used to, e.g., identify "highways" of LGT. Conclusions: Based on a simulation study, we conclude that the method is able to infer the true LGT events on gene tree and reconcile it to the correct edges on the species tree in most cases. Applied to two biological datasets, containing gene families from Cyanobacteria and Molicutes, we find potential LGTs highways that corroborate other studies as well as previously undetected examples.

  • 122.
    Khan, Mehmood
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. KTH, Centra, SeRC - Swedish e-Science Research Centre. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Mahmudi, Owais
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. KTH, Centra, SeRC - Swedish e-Science Research Centre. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Ulah, Ikram
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. KTH, Centra, SeRC - Swedish e-Science Research Centre. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Arvestad, Lars
    Lagergren, Jens
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. KTH, Centra, SeRC - Swedish e-Science Research Centre. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Probabilistic inference of lataral gene transfer eventsManuskript (preprint) (Annet vitenskapelig)
  • 123. Koelling, S.
    et al.
    Innocenti, Nicolas
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Bogdanowicz, J.
    Vandervorst, W.
    Optimal laser positioning for laser-assisted atom probe tomography2013Inngår i: Ultramicroscopy, ISSN 0304-3991, E-ISSN 1879-2723, Vol. 132, s. 70-74Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Laser-assisted atom probe tomography is a material analysis method based on field evaporating ions from a tip-shaped sample by a combination of a standing electric held and a short (pico- or lemtosecond) laser pulse. The laser-pulse thereby acts as a starting signal for a time-of-flight mass analysis of the ions whereby the thermal energy deposited in the tip by the laser pulse temporarily enables the evaporation of ions from the surface of the tip. Here we will use simulations of the laser absorption on a silicon tip to find the optimal position of the laser spot in order to maximize the mass resolution achieved during the experiments. We will confirm our simulations by showing that the experimentally observed mass resolution indeed changes as predicted by the simulations.

  • 124.
    Kozlov, Alexander K.
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Grillner, S.
    Hellgren Kotaleski, Jeanette
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    A hemicord locomotor network of excitatory interneurons: a simulation study2007Inngår i: Biological Cybernetics, ISSN 0340-1200, E-ISSN 1432-0770, Vol. 96, nr 2, s. 229-243Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Locomotor burst generation is simulated using a full-scale network model of the unilateral excitatory interneuronal population. Earlier small-scale models predicted that a population of excitatory neurons would be sufficient to produce burst activity, and this has recently been experimentally confirmed. Here we simulate the hemicord activity induced under various experimental conditions, including pharmacological activation by NMDA and AMPA as well as electrical stimulation. The model network comprises a realistic number of cells and synaptic connectivity patterns. Using similar distributions of cellular and synaptic parameters, as have been estimated experimentally, a large variation in dynamic characteristics like firing rates, burst, and cycle durations were seen in single cells. On the network level an overall rhythm was generated because the synaptic interactions cause partial synchronization within the population. This network rhythm not only emerged despite the distributed cellular parameters but relied on this variability, in particular, in reproducing variations of the activity during the cycle and showing recruitment in interneuronal populations. A slow rhythm (0.4-2 Hz) can be induced by tonic activation of NMDA-sensitive channels, which are voltage dependent and generate depolarizing plateaus. The rhythm emerges through a synchronization of bursts of the individual neurons. A fast rhythm (4-12 Hz), induced by AMPA, relies on spike synchronization within the population, and each burst is composed of single spikes produced by different neurons. The dynamic range of the fast rhythm is limited by the ability of the network to synchronize oscillations and depends on the strength of synaptic connections and the duration of the slow after hyperpolarization. The model network also produces prolonged bouts of rhythmic activity in response to brief electrical activations, as seen experimentally. The mutual excitation can sustain long-lasting activity for a realistic set of synaptic parameters. The bout duration depends on the strength of excitatory synaptic connections, the level of persistent depolarization, and the influx of Ca2+ ions and activation of Ca2+-dependent K+ current.

  • 125.
    Kozlov, Alexander
    et al.
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Hellgren Kotaleski, Jeanette
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Aurell, Erik
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Grillner, S.
    Lansner, Anders
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Modeling of plasticity of the synaptic connections in the lamprey spinal CPG - consequences for network behavior2000Inngår i: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 32, s. 441-446Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Consequences of synaptic plasticity in the lamprey spinal CPG are analyzed. This is motivated by the experimentally found effects substance P and 5-hydroxytryptamin (5-HT) have on the inhibitory and excitatory synaptic transmission. The effects can be a change of the amplitude of the postsynaptic potentials as well as induction of an activity-dependent facilitation or depression during repetitive activation. Simulations show that network level effects (i.e. swimming frequency) of substance P and 5-HT can to a substantial part be explained based on their effects on the plasticity of the synaptic transmission.

  • 126.
    Kozlov, Alexander
    et al.
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Hellgren Kotaleski, Jeanette
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Aurell, Erik
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Grillner, S.
    Lansner, Anders
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Modeling of substance P and 5-HT induced synaptic plasticity in the lamprey spinal CPG: Consequences for network pattern generation2001Inngår i: Journal of Computational Neuroscience, ISSN 0929-5313, E-ISSN 1573-6873, Vol. 11, nr 2, s. 183-200Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Consequences of synaptic plasticity in the lamprey spinal CPG are analyzed by means of simulations. This is motivated by the effects substance P (a tachykinin) and serotonin (5-hydroxytryptamin; 5-HT) have on synaptic transmission in the locomotor network. Activity-dependent synaptic depression and potentiation have recently been shown experimentally using paired intracellular recordings. Although normally activity-dependent plasticity presumably does not contribute to the patterning of network activity, this changes in the presence of the neuromodulators substance P and 5-HT, which evoke significant plasticity. Substance P can induce a faster and larger depression of inhibitory connections but potentiation of excitatory inputs, whereas 5-HT induces facilitation of both inhibitory and excitatory inputs. Changes in the amplitude of the first postsynaptic potential are also seen. These changes could thus be a potential mechanism underlying the modulatory role these substances have on the rhythmic network activity. The aim of the present study has been to implement the activity dependent synaptic depression and facilitation induced by substance P and 5-HT into two alternative models of the lamprey spinal locomotor network, one relying on reciprocal inhibition for bursting and one in which each hemicord is capable of oscillations. The consequences of the plasticity of inhibitory and excitatory connections are then explored on the network level. In the intact spinal cord, tachykinins and 5-HT, which can be endogenously released, increase and decrease the frequency of the alternating left-right burst pattern, respectively. The frequency decreasing effect of 5-HT has previously been explained based on its conductance decreasing effect on K underlying the postspike afterhyperpolarization (AHP). The present simulations show that short-term synaptic plasticity may have strong effects on frequency regulation in the lamprey spinal CPG. In the network model relying on reciprocal inhibition, the observed effects substance P and 5-HT have on network behavior (i.e., a frequency increase and decrease respectively) can to a substantial part be explained by their effects on the total extent and time dynamics of synaptic depression and facilitation. The cellular effects of these substances will in the 5-HT case further contribute to its network effect.

  • 127.
    Kozlov, Alexander
    et al.
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Hellgren Kotaleski, Jeanette
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Wallén, Peter
    Karolinska institutet, Neuroscience.
    Grillner, Sten
    Karolinska institutet, Neuroscience.
    Lansner, Anders
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Detailed reduced models excitatory hemi-cord locomotor network lamprey2003Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    Rhythmic locomotor-related activity can be induced in the isolated hemi-spinal cord of lamprey during bath application of D-glutamate or NMDA (Cangiano and Grillner, 2003). This bursting activity is not dependent on glycinergic inhibition but relies on mutual glutamatergic excitation among network interneurons. The possibility of such oscillatory activity was suggested by earlier simulations (Hellgren-Kotaleski et al. 1999). Here the underlying mechanisms are further examined using both detailed and reduced mathematical models. The detailed network model comprises a population of compartmental excitatory interneurones with Na+, K+, Ca2+, KCa channels as well as two Ca-pools. The synaptic interactions are mediated by AMPA receptors and voltage-dependent NMDA receptors, as established experimentally. This model reproduces the main experimental observations on both cell and network level, including the slow (NMDA/Mg2+ dependent) and the fast rhythm. Burst frequency can be modulated by changing the AMPA and/or NMDA drive, the latter providing only a narrow dynamic range. Further, the distributed network of the entire hemi-cord has been simulated. A weakly asymmetric rostro-caudal connectivity (stronger descending) could support a uniform intersegmental phase lag along most of the spinal cord, whereas a symmetric connectivity could not. The intersegmental phase lag is effectively controlled (forward and backward direction) by adding excitation or inhibition to the most rostral segments. The detailed model was progressively reduced until only the most important (slow) currents remained. The dynamics of the reduced model followed that of the detailed model. Ca influx and activation of KCa currents was shown to play a key role in the burst generation.

  • 128.
    Kozlov, Alexander K.
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Kardamakis, Andreas A.
    Hällgren Kotaleski, Jeanette
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Grillner, Sten
    Gating of steering signals through phasic modulation of reticulospinal neurons during locomotion2014Inngår i: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 111, nr 9, s. 3591-3596Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The neural control of movements in vertebrates is based on a set of modules, like the central pattern generator networks (CPGs) in the spinal cord coordinating locomotion. Sensory feedback is not required for the CPGs to generate the appropriate motor pattern and neither a detailed control from higher brain centers. Reticulospinal neurons in the brainstem activate the locomotor network, and the same neurons also convey signals from higher brain regions, such as turning/steering commands from the optic tectum (superior colliculus). A tonic increase in the background excitatory drive of the reticulospinal neurons would be sufficient to produce coordinated locomotor activity. However, in both vertebrates and invertebrates, descending systems are in addition phasically modulated because of feedback from the ongoing CPG activity. We use the lamprey as a model for investigating the role of this phasic modulation of the reticulospinal activity, because the brainstem-spinal cord networks are known down to the cellular level in this phylogenetically oldest extant vertebrate. We describe how the phasic modulation of reticulospinal activity from the spinal CPG ensures reliable steering/turning commands without the need for a very precise timing of on-or offset, by using a biophysically detailed large-scale (19,600 model neurons and 646,800 synapses) computational model of the lamprey brainstem-spinal cord network. To verify that the simulated neural network can control body movements, including turning, the spinal activity is fed to a mechanical model of lamprey swimming. The simulations also predict that, in contrast to reticulospinal neurons, tectal steering/turning command neurons should have minimal frequency adaptive properties, which has been confirmed experimentally.

  • 129.
    Kozlov, Alexander K.
    et al.
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Ullén, F.
    Fagerstedt, P.
    Aurell, Erik
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Lansner, Anders
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Grillner, S.
    Mechanisms for lateral turns in lamprey in response to descending unilateral commands: a modeling study2002Inngår i: Biological Cybernetics, ISSN 0340-1200, E-ISSN 1432-0770, Vol. 86, nr 1, s. 1-14Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Straight locomotion in the lamprey is, at the segmental level, characterized by alternating bursts of motor activity with equal duration and spike frequency on the left and the right sides of the body. Lateral turns are characterized by three main changes in this pattern: (1) in the turn cycle, the spike frequency, burst duration, and burst proportion (burst duration/cycle duration) increase on the turning side; (2) the cycle duration increases in both the turn cycle and the succeeding cycle; and (3) in the cycle succeeding the turn cycle, the burst duration increases on the non-turning side (rebound). We investigated mechanisms for the generation of turns in single-segment models of the lamprey locomotor spinal network. Activation of crossing inhibitory neurons proved a sufficient mechanism to explain all three changes in the locomotor rhythm during a fictive turn. Increased activation of these cells inhibits the activity of the opposite side during the prolonged burst of the turn cycle, and slows down the locomotor rhythm. Secondly, this activation of the crossing inhibitory neurons is accompanied by an increased calcium influx into the cells. This gives a suppressed activity on the turning side and a contralateral rebound after the turn, through activation of calcium-dependent potassium channels.

  • 130.
    Kozlov, Alexander
    et al.
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Lansner, Anders
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Grillner, S.
    Burst dynamics under mixed NMDA and AMPA drive in the models of the lamprey spinal CPG2003Inngår i: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 52-54, s. 65-71Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The spinal CPG of the lamprey is modeled using a chain of nonlinear oscillators. Each oscillator represents a small neuron population capable of bursting under mixed NMDA and AMPA drive. Parameters of the oscillator are derived from detailed conductance-based neuron models. Analysis and simulations of dynamics of a single oscillator, a chain of locally coupled excitatory oscillators and a chain of two pairs of excitatory and inhibitory oscillators in each segment are done. The roles of asymmetric couplings and additional rostral drive for generation of a traveling wave with one cycle per chain length in a realistic frequency range are studied.

  • 131.
    Krishnamurthy, Pradeep
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Silberberg, G.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    A Cortical Attractor Network with Martinotti Cells Driven by Facilitating Synapses2012Inngår i: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 7, nr 4, s. e30752-Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

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

  • 132.
    Krishnamurthy, Pradeep
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. Stockholm University, Sweden.
    Silberberg, Gilad
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. Stockholm University, Sweden.
    Long-range recruitment of Martinotti cells causes surround suppression and promotes saliency in an attractor network model2015Inngår i: Frontiers in Neural Circuits, ISSN 1662-5110, E-ISSN 1662-5110, Vol. 9, artikkel-id 60Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

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

  • 133.
    Kumar, Arvind
    et al.
    University of Freiburg, Germany .
    Singh, Harinder Pal
    Information homeostasis as a fundamental principle governing the cell division and death2011Inngår i: Medical Hypotheses, ISSN 0306-9877, E-ISSN 1532-2777, Vol. 77, nr 3, s. 318-322Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    To express the genetic information with minimal error is one of the key functions of a cell. Here we propose an information theory based, phenomenological model for the expression of genetic information. Based on the model we propose the concept of 'information homeostasis' which ensures that genetic information is expressed with minimal error. We suggest that together with energy homeostasis, information homeostasis is a fundamental working principle of a biological cell. This model proposes a novel explanation of why a cell divides and why it stops to divide and, thus, provides novel insights into oncogenesis and various neuro-degenerative diseases. Moreover, the model suggests a theoretical framework to understand cell division and death, beyond specific biochemical pathways.

  • 134.
    Käll, Lukas
    Department of Biochemistry and Biophysics, Center for Biomembrane Research and Stockholm Bioinformatics Center, Stockholm University.
    Prediction of transmembrane topology and signal peptide given a protein's amino acid sequence2010Inngår i: Vol. 673, s. 53-62Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Here, we describe transmembrane topology and signal peptide predictors and highlight their advantages and shortcomings. We also discuss the relation between these two types of prediction.

  • 135.
    Käll, Lukas
    et al.
    Department of Genome Sciences, University of Washington.
    Canterbury, Jesse D.
    Weston, Jason
    Noble, William Stafford
    MacCoss, Michael J.
    Semi-supervised learning for peptide identification from shotgun proteomics datasets2007Inngår i: Nature Methods, ISSN 1548-7091, E-ISSN 1548-7105, Vol. 4, nr 11, s. 923-925Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Shotgun proteomics uses liquid chromatography-tandem mass spectrometry to identify proteins in complex biological samples. We describe an algorithm, called Percolator, for improving the rate of confident peptide identifications from a collection of tandem mass spectra. Percolator uses semi-supervised machine learning to discriminate between correct and decoy spectrum identifications, correctly assigning peptides to 17% more spectra from a tryptic Saccharomyces cerevisiae dataset, and up to 77% more spectra from non-tryptic digests, relative to a fully supervised approach.

  • 136.
    Käll, Lukas
    et al.
    Ctr. for Genomics and Bioinformatics, Karolinska Institutet.
    Krogh, Anders
    Sonnhammer, Erik L. L.
    A combined transmembrane topology and signal peptide prediction method2004Inngår i: Journal of Molecular Biology, ISSN 0022-2836, E-ISSN 1089-8638, Vol. 338, nr 5, s. 1027-1036Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    An inherent problem in transmembrane protein topology prediction and signal peptide prediction is the high similarity between the hydrophobic regions of a transmembrane helix and that of a signal peptide, leading to cross-reaction between the two types of predictions. To improve predictions further, it is therefore important to make a predictor that aims to discriminate between the two classes. In addition, topology information can be gained when successfully predicting a signal Peptide leading a trans' membrane protein since it dictates that the N terminus of the mature protein must be on the non-cytoplasmic side of the membrane. Here, we present Phobius, a combined transmembrane protein topology and signal peptide predictor. The predictor is based on a hidden Markov model (HMM) that models the different sequence regions of a signal peptide and the different regions of a transmembrane protein in a series of interconnected states. Training was done on a newly assembled and curated dataset. Compared to TMHMM and SignalP, errors coming from cross-prediction between transmembrane segments and signal peptides were reduced substantially by Phobius. False classifications of signal peptides were reduced from 26.1% to 3.9% and false classifications of transmembrane helices were reduced from 19.0%, to 7.7%. Phobius was applied to the proteomes of Honzo sapiens and Escherichia coli. Here we also noted a drastic reduction of false classifications compared to TMHMM/SignalP, suggesting that Phobius is well suited for whole-genome annotation of signal peptides and transmembrane regions. The method is available at http://phobius.cgb.ki.se/ as well as at http://phobius.binf.ku.dk/.

  • 137.
    Käll, Lukas
    et al.
    KTH, Skolan för bioteknologi (BIO), Genteknologi. KTH, Centra, SeRC - Swedish e-Science Research Centre. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Krogh, Anders
    Sonnhammer, Erik L. L.
    Advantages of combined transmembrane topology and signal peptide prediction - the Phobius web server2007Inngår i: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 35, nr Web Server issue, 1, s. W429-W432Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    When using conventional transmembrane topology and signal peptide predictors, such as TMHMM and SignalP, there is a substantial overlap between these two types of predictions. Applying these methods to five complete proteomes, we found that 30-65% of all predicted signal peptides and 25-35% of all predicted transmembrane topologies overlap. This impairs predictions of 5-10% of the proteome, hence this is an important issue in protein annotation. To address this problem, we previously designed a hidden Markov model, Phobius, that combines transmembrane topology and signal peptide predictions. The method makes an optimal choice between transmembrane segments and signal peptides, and also allows constrained and homology-enriched predictions. We here present a web interface (http://phobius.cgb.ki.se and http://phobius.binf.ku.dk) to access Phobius.

  • 138.
    Käll, Lukas
    et al.
    Center for Genomics and Bioinformatics, Karolinska Institutet.
    Krogh, Anders
    Sonnhammer, Erik L. L.
    An HMM posterior decoder for sequence feature prediction that includes homology information2005Inngår i: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 21, nr Suppl.1, s. i251-i257Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Motivation: When predicting sequence features like transmembrane topology, signal peptides, coil-coil structures, protein secondary structure or genes, extra support can be gained from homologs. Results: We present here a general hidden Markov model (HMM) decoding algorithm that combines probabilities for sequence features of homologs by considering the average of the posterior label probability of each position in a global sequence alignment. The algorithm is an extension of the previously described 'optimal accuracy' decoder, allowing homology information to be used. It was benchmarked using an HMM for transmembrane topology and signal peptide prediction, Phobius. We found that the performance was substantially increased when incorporating information from homologs.

  • 139.
    Käll, Lukas
    et al.
    Ctr. for Genomics and Bioinformatics, Karolinska Institutet.
    Sonnhammer, Erik L. L.
    Reliability of transmembrane predictions in whole-genome data2002Inngår i: FEBS Letters, ISSN 0014-5793, E-ISSN 1873-3468, Vol. 532, nr 3, s. 415-418Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Transmembrane prediction methods are generally benchmarked on a set of proteins with experimentally verified topology. We have investigated if the accuracy measured on such datasets can be expected in an unbiased genomic analysis, or if there is a bias towards 'easily predictable' proteins in the benchmark datasets. As a measurement of accuracy, the concordance of the results from five different prediction methods was used (TMHMM, PHD, HMMTOP, MEMSAT, and TOPPRED). The benchmark dataset showed significantly higher levels (up to five times) of agreement between different methods than in 10 tested genomes. We have also analyzed which programs are most prone to make mispredictions by measuring the frequency of one-out-of-five disagreeing predictions.

  • 140. Käll, Lukas
    et al.
    Storey, John D.
    MacCoss, Michael J.
    Noble, William Stafford
    Assigning significance to peptides identified by tandem mass spectrometry using decoy databases2008Inngår i: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 7, nr 1, s. 29-34Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Automated methods for assigning peptides to observed tandem mass spectra typically return a list of peptide-spectrum matches, ranked according to an arbitrary score. In this article, we describe methods for converting these arbitrary scores into more useful statistical significance measures. These methods employ a decoy sequence database as a model of the null hypothesis, and use false discovery rate (FDR) analysis to correct for multiple testing. We first describe a simple FDR inference method and then describe how estimating and taking into account the percentage of incorrectly identified spectra in the entire data set can lead to increased statistical power.

  • 141. Käll, Lukas
    et al.
    Storey, John D.
    MacCoss, Michael J.
    Noble, William Stafford
    Posterior error probabilities and false discovery rates: two sides of the same coin2008Inngår i: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 7, nr 1, s. 40-44Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    A variety of methods have been described in the literature for assigning statistical significance to peptides identified via tandem mass spectrometry. Here, we explain how two types of scores, the q-value and the posterior error probability, are related and complementary to one another.

  • 142. Käll, Lukas
    et al.
    Storey, John D.
    Noble, William Stafford
    Non-parametric estimation of posterior error probabilities associated with peptides identified by tandem mass spectrometry2008Inngår i: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 24, nr 16, s. i42-i48Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Motivation: A mass spectrum produced via tandem mass spectrometry can be tentatively matched to a peptide sequence via database search. Here, we address the problem of assigning a posterior error probability (PEP) to a given peptide-spectrum match (PSM). This problem is considerably more difficult than the related problem of estimating the error rate associated with a large collection of PSMs. Existing methods for estimating PEPs rely on a parametric or semiparametric model of the underlying score distribution. Results: We demonstrate how to apply non-parametric logistic regression to this problem. The method makes no explicit assumptions about the form of the underlying score distribution; instead, the method relies upon decoy PSMs, produced by searching the spectra against a decoy sequence database, to provide a model of the null score distribution. We show that our non-parametric logistic regression method produces accurate PEP estimates for six different commonly used PSM score functions. In particular, the estimates produced by our method are comparable in accuracy to those of PeptideProphet, which uses a parametric or semiparametric model designed specifically to work with SEQUEST. The advantage of the non-parametric approach is applicability and robustness to new score functions and new types of data.

  • 143. Käll, Lukas
    et al.
    Storey, John D.
    Noble, William Stafford
    QVALITY: non-parametric estimation of q-values and posterior error probabilities2009Inngår i: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 25, nr 7, s. 964-966Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Qvality is a C++ program for estimating two types of standard statistical confidence measures: the q-value, which is an analog of the p-value that incorporates multiple testing correction, and the posterior error probability (PEP, also known as the local false discovery rate), which corresponds to the probability that a given observation is drawn from the null distribution. In computing q-values, qvality employs a standard bootstrap procedure to estimate the prior probability of a score being from the null distribution; for PEP estimation, qvality relies upon non-parametric logistic regression. Relative to other tools for estimating statistical confidence measures, qvality is unique in its ability to estimate both types of scores directly from a null distribution, without requiring the user to calculate p-values.

  • 144. Lan, Yueheng
    et al.
    Aurell, Erik
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre. Aalto University, Finland.
    The stochastic thermodynamics of a rotating Brownian particle in a gradient flow2015Inngår i: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 5, artikkel-id 12266Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    We compute the entropy production engendered in the environment from a single Brownian particle which moves in a gradient flow, and show that it corresponds in expectation to classical near-equilibrium entropy production in the surrounding fluid with specific mesoscopic transport coefficients. With temperature gradient, extra terms are found which result from the nonlinear interaction between the particle and the non-equilibrated environment. The calculations are based on the fluctuation relations which relate entropy production to the probabilities of stochastic paths and carried out in a multi-time formalism.

  • 145.
    Lansner, Anders
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Diesmann, Markus
    Forschungzentrum Jülich and Aachen University .
    Virtues, Pitfalls, and Methodology of Neuronal Network Modeling and Simulations on Supercomputers2012Inngår i: Computational Systems Neurobiology / [ed] Nicolas Le Novére, Springer, 2012, s. 283-315Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

    The number of neurons and synapses in biological brains is very large, on the order of millions and billions respectively even in small animals like insects and mice. By comparison most neuronal network models developed and simulated up to now have been tiny, comprising many orders of magnitude less neurons than their real counterpart, with an even more dramatic difference when it comes to the number of synapses. In this chapter we discuss why and when it may be important to work with large-scale, if not full-scale, neuronal network and brain models and to run simulations on supercomputers. We describe the state-of-the-art in large-scale neural simulation technology and methodology as well as ways to analyze and visualize output from such simulations. Finally we discuss the challenges and future trends in this field.

  • 146.
    Lansner, Anders
    et al.
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Fransén, Erik
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Sandberg, Anders
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Cell assembly dynamics in detailed and abstract attractor models of cortical associative memory2003Inngår i: Theory in biosciences, ISSN 1431-7613, E-ISSN 1611-7530, Vol. 122, nr 1, s. 19-36Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    During the last few decades we have seen a convergence among ideas and hypotheses regarding functional principles underlying human memory. Hebb's now more than fifty years old conjecture concerning synaptic plasticity and cell assemblies, formalized mathematically as attractor neural networks, has remained among the most viable and productive theoretical frameworks. It suggests plausible explanations for Gestalt aspects of active memory like perceptual completion, reconstruction and rivalry. We review the biological plausibility of these theories and discuss some critical issues concerning their associative memory functionality in the light of simulation studies of models with palimpsest memory properties. The focus is on memory properties and dynamics of networks modularized in terms of cortical minicolumns and hypercolumns. Biophysical compartmental models demonstrate attractor dynamics that support cell assembly operations with fast convergence and low firing rates. Using a scaling model we obtain reasonable relative connection densities and amplitudes. An abstract attractor network model reproduces systems level psychological phenomena seen in human memory experiments as the Sternberg and von Restorff effects. We conclude that there is today considerable substance in Hebb's theory of cell assemblies and its attractor network formulations, and that they have contributed to increasing our understanding of cortical associative memory function. The criticism raised with regard to biological and psychological plausibility as well as low storage capacity, slow retrieval etc has largely been disproved. Rather, this paradigm has gained further support from new experimental data as well as computational modeling.

  • 147.
    Lansner, Anders
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Hemani, Ahmed
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Elektroniksystem.
    Farahini, Nasim
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Elektroniksystem.
    Spiking brain models: Computation, memory and communication constraints for custom hardware implementation2014Inngår i: 2014 19th Asia and South Pacific Design Automation Conference (ASP-DAC), IEEE , 2014, s. 556-562Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We estimate the computational capacity required to simulate in real time the neural information processing in the human brain. We show that the computational demands of a detailed implementation are beyond reach of current technology, but that some biologically plausible reductions of problem complexity can give performance gains between two and six orders of magnitude, which put implementations within reach of tomorrow's technology.

  • 148.
    Lansner, Anders
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lundqvist, Mikael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Modeling Coordination in the Neocortex at the Microcircuit and Global Network Level2010Inngår i: Dynamic Coordination in the Brain: From Neurons to Mind / [ed] von der Malsburg, C., Phillips W. A., Singer W., MIT Press, 2010, s. 83-99Kapittel i bok, del av antologi (Annet vitenskapelig)
  • 149.
    Lansner, Anders
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Marklund, Petter
    Sikström, Sverker
    Nilsson, Lars-Göran
    Reactivation in Working Memory: An Attractor Network Model of Free Recall2013Inngår i: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 8, nr 8, s. e73776-Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The dynamic nature of human working memory, the general-purpose system for processing continuous input, while keeping no longer externally available information active in the background, is well captured in immediate free recall of supraspan word-lists. Free recall tasks produce several benchmark memory phenomena, like the U-shaped serial position curve, reflecting enhanced memory for early and late list items. To account for empirical data, including primacy and recency as well as contiguity effects, we propose here a neurobiologically based neural network model that unifies short- and long-term forms of memory and challenges both the standard view of working memory as persistent activity and dual-store accounts of free recall. Rapidly expressed and volatile synaptic plasticity, modulated intrinsic excitability, and spike-frequency adaptation are suggested as key cellular mechanisms underlying working memory encoding, reactivation and recall. Recent findings on the synaptic and molecular mechanisms behind early LTP and on spiking activity during delayed-match-to-sample tasks support this view.

  • 150.
    Lara, Antonio J.
    et al.
    University of Malaga.
    Perez-Trabado, Guillermo
    University of Malaga.
    Villalobos, David P.
    University of Malaga.
    Diaz-Moreno, Sara M.
    University of Malaga.
    Canton, Francisco R.
    University of Malaga.
    Claros, M. Gonzalo
    University of Malaga.
    A Web Tool to Discover Full-Length Sequences: Full-Lengther2007Inngår i: Innovations in Hybrid Intelligent Systems / [ed] Emilio Corchado, Juan M Corchado, Ajith Abraham, Springer Berlin/Heidelberg, 2007, s. 361-368Kapittel i bok, del av antologi (Fagfellevurdert)
123456 101 - 150 of 290
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