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  • 201.
    Innocenti, Nicolas
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Repoila, Francis
    INRA, UMR1319 Micalis, Domaine de Vilvert, F-78352, Jouy-en-Josas, France}\affiliation{AgroParisTech, UMR Micalis, Domaine de Vilvert, F-78350, Jouy-en-Josas, France.
    Aurell, Erik
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Detection and quantitative estimation of spurious double stranded DNA formation during reverse transcription in bateria using tagRNA-seq2015Ingår i: RNA Biology, ISSN 1547-6286, E-ISSN 1555-8584Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Standard RNA-seq has a well know tendency to generate "ghost" antisense reads due to formation of spurious second strand cDNA in the sequencing process. We recently reported on a novel variant of RNA-seq coined "tagRNA-seq" introduced for the purpose of distinguishing primary from processed transcripts in bacteria. Incidentally, the additional information provided by the tag is also very suitable for detection of true anti-sense RNA transcripts and quantification of spurious antisense signals in a sample. We briefly explain how to perform such a detection and illustrate on previously published datasets.

  • 202.
    Jansson, Ylva
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Normalization in a cortical hypercolumn: The modulatory effects of a highly structured recurrent spiking neural network2014Självständigt arbete på avancerad nivå (masterexamen), 20 poäng / 30 hpStudentuppsats (Examensarbete)
    Abstract [sv]

    Normalisering är viktigt för en lång rad fenomen i biologiska nervsystem såsom näthinnans ljusanpassning, kontextberoende beslutsfattande och probabilistisk inferens. I en normaliserande krets skalas aktiviteten hos en nervcell/grupp av nervceller om i relation till aktiviteten hos andra nervceller/grupper. Detta ger neurala svar som är invarianta i förhållande till vissa dimensioner hos stimuli, och anpassar dynamiskt för vilka inputmagnituder ett system kan särskilja mellan stimuli. Den här uppsatsen undersöker huruvida en biologiskt realistisk normal­iserande krets kan implementeras av ett spikande neuronnätverk konstruerat med utgångspunkt från kolumnstrukturen i kortex. Detta gjordes genom att konstruera och utvärdera ett hårt strukturerat rekurrent spikande neuronnätverk, som modellerar lager 2/3 av en kortikal hyperkolumn med en grupp av neuroner som grundläggande beräkningsenhet. Resultaten visar att strukturen i hyperkolumn­modulen inte i sig skapar ett normaliserande nätverk. För de flesta nätverks­versioner implementerar nätverket en modulerande effekt som bättre beskrivs som subtraktiv inhibition. Dock hittades tre mekanismer som skapar ett mer normaliserande nätverk: Ökad membranvarians för större modulerande inputs; variabilitet i excitabilitet och inkommande kopplingar; och korttidsdepression på drivande synapser. Det visas också att genom att kombinera dessa mekanismer är det möjligt att skapa ett spikande neuronnät som approximerar normalisering över ett en åtminstone tio gångers ökning av storleken på input. Detta pekar på möjliga normaliserande mekanismer i en kortikal hyperkolumn, men ytterligare studier är nödvändiga för att avgöra om en eller flera av dessa kan vara en förklaring till hur normalisering är implementerat i biologiska nervsystem.

  • 203.
    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 competition2006Ingår i: International Journal of Neural Systems, ISSN 0129-0657, E-ISSN 1793-6462, Vol. 16, nr 6, s. 393-403Artikel i tidskrift (Refereegranskat)
    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

  • 204.
    Johansson, Christopher
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Numerisk Analys och Datalogi, NADA. KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Numerisk Analys och Datalogi, NADA.
    Attractor Memory with Self-Organizing Input2006Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2006, s. 265-280Konferensbidrag (Refereegranskat)
    Abstract [en]

    We propose a neural network based autoassociative memory system for unsupervised learning. This system is intended to be an example of how a general information processing architecture, similar to that of neocortex, could be organized. The neural network has its units arranged into two separate groups called populations, one input and one hidden population. The units in the input population form receptive fields that sparsely projects onto the units of the hidden population. Competitive learning is used to train these forward projections. The hidden population implements an attractor memory. A back projection from the hidden to the input population is trained with a Hebbian learning rule. This system is capable of processing correlated and densely coded patterns, which regular attractor neural networks are very poor at. The system shows good performance on a number of typical attractor neural network tasks such as pattern completion, noise reduction, and prototype extraction.

  • 205.
    Johansson, Christopher
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Numerisk Analys och Datalogi, NADA. KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Implementing Plastic Weights in Neural Networks using Low Precision Arithmetic2009Ingår i: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 72, nr 4-6, s. 968-972Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this letter, we develop a fixed-point arithmetic, low precision, implementation of an exponentially weighted moving average (EWMA) that is used in a neural network with plastic weights. We analyze the proposed design both analytically and experimentally, and we also evaluate its performance in the application of an attractor neural network. The EWMA in the proposed design has a constant relative truncation error, which is important for avoiding round-off errors in applications with slowly decaying processes, e.g. connectionist networks. We conclude that the proposed design offers greatly improved memory and computational efficiency compared to a naive implementation of the EWMA's difference equation, and that it is well suited for implementation in digital hardware.

  • 206.
    Johansson, Christopher
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Numerisk Analys och Datalogi, NADA. KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Imposing Biological Constraints onto an Abstract Neocortical Attractor Network Model2007Ingår i: Neural Computation, ISSN 0899-7667, E-ISSN 1530-888X, Vol. 19, nr 7, s. 1871-1896Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this letter, we study an abstract model of neocortex based on its modularization into mini- and hypercolumns. We discuss a full-scale instance of this model and connect its network properties to the underlying biological properties of neurons in cortex. In particular, we discuss how the biological constraints put on the network determine the network's performance in terms of storage capacity. We show that a network instantiating the model scales well given the biologically constrained parameters on activity and connectivity, which makes this network interesting also as an engineered system. In this model, the minicolumns are grouped into hypercolumns that can be active or quiescent, and the model predicts that only a few percent of the hypercolumns should be active at any one time. With this model, we show that at least 20 to 30 pyramidal neurons should be aggregated into a minicolumn and at least 50 to 60 minicolumns should be grouped into a hypercolumn in order to achieve high storage capacity.

  • 207.
    Johansson, Christopher
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Mapping of the BCPNN onto Cluster Computers2003Rapport (Övrigt vetenskapligt)
  • 208.
    Johansson, Christopher
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    On the Storage Capacity of an Abstract Cortical Model with Silent Hypercolumns2005Rapport (Övrigt vetenskapligt)
  • 209.
    Johansson, Christopher
    et al.
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA. KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Raicevic, Peter
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Lansner, Anders
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Reinforcement Learning Based on a Bayesian Confidence Propagating Neural Network2003Konferensbidrag (Refereegranskat)
    Abstract [en]

    We present a system capable of reinforcement learning (RL) based on the Bayesian confidence propagating neural network (BCPNN). The system is called BCPNNRL and its architecture is somewhat motivated by parallels to biology. We analyze the systems properties and we benchmark it against a simple Monte Carlo (MC) based RL algorithm, pursuit RL methods, and the Associative Reward Penalty (AR-P) algorithm. The system is used to solve the n-armed bandit problem, pattern association, and path finding in a maze.

  • 210.
    Johansson, Christopher
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Numerisk Analys och Datalogi, NADA. KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Rehn, Martin
    KTH, Skolan för datavetenskap och kommunikation (CSC), Numerisk Analys och Datalogi, NADA.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Numerisk Analys och Datalogi, NADA.
    Attractor neural networks with patchy connectivity2006Ingår i: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 69, nr 7-9, s. 627-633Artikel i tidskrift (Refereegranskat)
    Abstract [en]

     The neurons in the mammalian visual cortex are arranged in columnar structures, and the synaptic contacts of the pyramidal neurons in layer II/III are clustered into patches that are sparsely distributed over the surrounding cortical surface. Here, We use an attractor neural-network model of the cortical circuitry and investigate the effects of patchy connectivity, both on the properties of the network and the attractor dynamics. An analysis of the network shows that the signal-to-noise ratio of the synaptic potential sums are improved by the patchy connectivity, which results in a higher storage capacity. This analysis is performed for both the Hopfield and Willshaw learning rules and the results are confirmed by simulation experiments.

  • 211.
    Johansson, Christopher
    et al.
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA. KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Sandberg, Anders
    Lansner, Anders
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    A Neural Network with Hypercolumns2002Konferensbidrag (Refereegranskat)
  • 212.
    John, Hertz
    et al.
    KTH.
    Roudi, Yasser
    Thorning, Andreas
    Tyrcha, Joanna
    Aurell, Erik
    KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre. KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Zeng, Hong-Li
    Inferring network connectivity using kinetic Ising models2010Konferensbidrag (Refereegranskat)
  • 213.
    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 Networks2016Ingår i: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 12, nr 6, artikel-id e1004963Artikel i tidskrift (Refereegranskat)
    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.

  • 214.
    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 markers2013Ingår i: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 8, nr 11, s. e79012-Artikel i tidskrift (Refereegranskat)
    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.

  • 215.
    Kalfas, Ioannis
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Dynamics of Cortical Networks Segregated into Layers and Columns2015Självständigt arbete på avancerad nivå (masterexamen), 20 poäng / 30 hpStudentuppsats (Examensarbete)
    Abstract [en]

    The neocortex covers 90% of the human cerebral cortex [41] and is responsible for higher cognitive function and socio-cognitive skills in all mammals. It is known to be structured in layers and in some species or cortical areas, in columns. A balanced network model was built, which incorporated these structural organizations and in particular, the layers, minicolumns and hypercolumns. The dynamics of eight different network models were studied, based on combinations of structural organizations that they have. The eigenvalue spectra of their matrices was calculated showing that layered networks have eigenvalues outside their bulk distribution in contrast to networks with columns and no layers. It was demonstrated, through simulations, that networks with layers are unstable and have a lower threshold to synchronization, thus, making them more susceptible to switch to synchronous and regular activity regimes [10]. Moreover, introduction of minicolumns to these networks was observed to partially counterbalance synchrony and regularity, in the network and neuron activity, respectively. Layered networks, principally the ones without minicolumns, also have higher degree correlations and a reduced size of potential pre- and post- connections, which induces correlations in the neuronal activity and oscillations.

  • 216.
    Kamali Sarvestani, Iman
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Subsystems of the basal ganglia and motor infrastructure2013Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    The motor nervous system is one of the main systems of the body and is our principle means ofbehavior. Some of the most debilitating and wide spread disorders are motor systempathologies. In particular the basal ganglia are complex networks of the brain that control someaspects of movement in all vertebrates. Although these networks have been extensively studied,lack of proper methods to study them on a system level has hindered the process ofunderstanding what they do and how they do it. In order to facilitate this process I have usedcomputational models as an approach that can faithfully take into account many aspects of ahigh dimensional multi faceted system.In order to minimize the complexity of the system, I first took agnathan fish and amphibians asmodeling animals. These animals have rather simple neuronal networks and have been wellstudied so that developing their biologically plausible models is more feasible. I developedmodels of sensory motor transformation centers that are capable of generating basic behaviorsof approach, avoidance and escape. The networks in these models used a similar layeredstructure having a sensory map in one layer and a motor map on other layers. The visualinformation was received as place coded information, but was converted into population codedand ultimately into rate coded signals usable for muscle contractions.In parallel to developing models of visuomotor centers, I developed a novel model of the basalganglia. The model suggests that a subsystem of the basal ganglia is in charge of resolvingconflicts between motor programs suggested by different motor centers in the nervous system.This subsystem that is composed of the subthalamic nucleus and pallidum is called thearbitration system. Another subsystem of the basal ganglia called the extension system which iscomposed of the striatum and pallidum can bias decisions made by an animal towards theactions leading to lower cost and higher outcome by learning to associate proper actions todifferent states. Such states are generally complex states and the novel hypothesis I developedsuggests that the extension system is capable of learning such complex states and linking themto appropriate actions. In this framework, striatal neurons play the role of conjunction (BooleanAND) neurons while pallidal neurons can be envisioned as disjunction (Boolean OR) neurons.In the next set of experiments I tried to take the idea of basal ganglia subsystems to a new levelby dividing the rodent arbitration system into two functional subunits. A rostral group of ratpallidal neurons form dense local inhibition among themselves and even send inhibitoryprojections to the caudal segment. The caudal segment does not project back to its rostralcounterpart, but both segments send inhibitory projections to the output nuclei of the rat basalganglia i.e. the entopeduncular nucleus and substantia nigra. The rostral subsystems is capableof precisely detecting one (or several) components of a rudimentary action and suppress othercomponents. The components that are reinforced are those which lead to rewarding stateswhereas those that are suppressed are those which do not. The hypothesis explains neuronalmechanisms involved in this process and suggests that this subsystem is a means of generatingsimple but precise movements (such as using a single digit) from innate crude actions that theanimal can perform even at birth (such as general movement of the whole limb). In this way, therostral subsystem may play important role in exploration based learning.In an attempt to more precisely describe the relation between the arbitration and extensionsystems, we investigated the effect of dynamic synapses between subthalamic, pallidal andstriatal neurons and output neurons of the basal ganglia. The results imply that output neuronsare sensitive to striatal bursts and pallidal irregular firing. They also suggest that few striatalneurons are enough to fully suppress output neurons. Finally the results show that the globuspallidus exerts its effect on output neurons by direct inhibition rather than indirect influence viathe subthalamic nucleus.

  • 217.
    Kamali Sarvestani, Iman
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Ekeberg, Örjan
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Internal Connectivity of the GlobusPallidus and the Arbitration System2013Manuskript (preprint) (Övrigt vetenskapligt)
    Abstract [en]

    The rodent globus pallidus (homologue of primate external globus pallidus) has been shown to be composed of two types neuronal groups based on their location and local axon collaterals. The rostral outer layer near the striatopallidal border (GPr) has shorter but more dense local axon collaterals while the caudal inner layer (GPc) has wider and less dense axon collaterals. Moreover, the connection between the two segments is unidirectional with outer layer neurons sending inhibitory projections to the inner layer. Both segments inhibit the substantia nigra and the entopeduncular nucleus (homologue of primate internal globus pallidus). We have created a model of the basal ganglia arbitration subsystem composed of the subthalamic nucleus, the two segments of the pallidus as well as the entopeduncular nucleus and the substantia nigra in order to assess functional roles of the two pallidal segments. The simulations reveal that both segments of the pallidum are involved in winner-take-all structure of the arbitration system but the type of information competing is different in the two subsystems. In the STN-GPr network, strong lateral inhibition between pallidal neurons representing muscles leads to selection of a muscle which has been (due to noise or other reasons) randomly overactivated. In contrast, in STN-GPc network actions (each utilizing many muscles) compete. Our simulations suggest that both networks are active during selection and execution of movements. If overactivation of a muscle is accompanied with dopamine flow, the GPr-GPc connection together with local axonal network of GPc suppress other muscles and reinforce the muscle whose overactivity has caused the dopaminergic flow. Simulated lesions of these neuronal groups also show different results. Lesioning GPr results in synchronous activity in GPc and SNr but the mean firing rate of these nuclei remains untouched. Lesioning GPc on the other hand lifts the activity in the SNr drastically but does not create synchrony in any of the nuclei. The results suggest that STN-GPc and STN-GPr can be considered as two different subsystems working both in synergy and in competition.

  • 218.
    Kamali Sarvestani, Iman
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Kozlov, Alexander
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Harischandra, Nalin
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Grillner, Sten
    Karolinska Institutet.
    Ekeberg, Örjan
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    A computational model of visually guided locomotion in lamprey2013Ingår i: Biological Cybernetics, ISSN 0340-1200, E-ISSN 1432-0770, Vol. 107, nr 5, s. 497-512Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This study addresses mechanisms for the generation and selection of visual behaviors in anamniotes. To demonstrate the function of these mechanisms, we have constructed an experimental platform where a simulated animal swims around in a virtual environment containing visually detectable objects. The simulated animal moves as a result of simulated mechanical forces between the water and its body. The undulations of the body are generated by contraction of simulated muscles attached to realistic body components. Muscles are driven by simulated motoneurons within networks of central pattern generators. Reticulospinal neurons, which drive the spinal pattern generators, are in turn driven directly and indirectly by visuomotor centers in the brainstem. The neural networks representing visuomotor centers receive sensory input from a simplified retina. The model also includes major components of the basal ganglia, as these are hypothesized to be key components in behavior selection. We have hypothesized that sensorimotor transformation in tectum and pretectum transforms the place-coded retinal information into rate-coded turning commands in the reticulospinal neurons via a recruitment network mimicking the layered structure of tectal areas. Via engagement of the basal ganglia, the system proves to be capable of selecting among several possible responses, even if exposed to conflicting stimuli. The anatomically based structure of the control system makes it possible to disconnect different neural components, yielding concrete predictions of how animals with corresponding lesions would behave. The model confirms that the neural networks identified in the lamprey are capable of responding appropriately to simple, multiple, and conflicting stimuli.

  • 219.
    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 ganglia2011Ingår i: Frontiers in Systems Neuroscience, ISSN 1662-5137, E-ISSN 1662-5137, Vol. 5, s. 13-Artikel i tidskrift (Refereegranskat)
    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.

  • 220.
    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 systems2015Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
    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.

  • 221.
    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 network2014Ingår i: 2014 International Joint Conference on Neural Networks (IJCNN), IEEE , 2014, s. 3205-3212Konferensbidrag (Refereegranskat)
    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.

  • 222.
    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 network2014Ingår i: PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), IEEE , 2014, s. 3205-3212Konferensbidrag (Refereegranskat)
    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.

  • 223.
    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 system2014Ingår i: Frontiers in Neural Circuits, ISSN 1662-5110, E-ISSN 1662-5110, Vol. 8, nr Feb, s. 5-Artikel i tidskrift (Refereegranskat)
    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.

  • 224.
    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 network2013Ingår i: Frontiers in Computational Neuroscience, ISSN 1662-5188, E-ISSN 1662-5188Artikel i tidskrift (Refereegranskat)
    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.

  • 225.
    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 phylogenetics2013Ingår i: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 14, nr 1, s. 334-Artikel i tidskrift (Refereegranskat)
    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.

  • 226.
    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) (Övrigt vetenskapligt)
  • 227. Klaus, A.
    et al.
    Hellgren Kotaleski, Jeanette
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Synchronization effects between striatal fast-spiking interneurons forming networks with different topologies2008Ingår i: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196Artikel i tidskrift (Övrigt vetenskapligt)
    Abstract [en]

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

  • 228. Klaus, A.
    et al.
    Hjorth, Johannes
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Hellgren Kotaleski, Jeanette
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    The influence of subthreshold membrane potential oscillations and GABAergic input on firing activity in striatal fast-spiking neurons2009Ingår i: BMC Neuroscience, ISSN 1471-2202, Vol. 10, nr Suppl.1, s. P244-Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The striatum is the main input stage of the basal ganglia system, which is involved in executive functions of the forebrain, such as the planning and the selection of motor behavior. Feedforward inhibition of medium-sized spiny projection neurons in the striatum by fast-spiking interneurons is supposed to be an important determinant of controlling striatal output to later stages of the basal ganglia[1]. Striatal fast-spiking interneurons, which constitute approximately 1–2% of all striatal neurons, show many similarities to cortical fast-spiking cells. In response to somatic current injection, for example, some of these neurons exhibit spike bursts with a variable number of action potentials (so called stuttering)[2-4]. Interestingly, the membrane potential between such stuttering episodes oscillates in the range of 20–100 Hz[3,5]. The first spike of each stuttering episode invariably occurs at a peak of the underlying subthreshold oscillation. In both cortex and striatum, fast-spiking cells are inter-connected by gap junctions[6,7]. In vitro measurements as well as theoretical studies indicate that electrical coupling via gap junctions might be able to promote synchronous activity among these neurons[6,8]. Here we investigate the possible role of subthreshold oscillations on the synchronization of sub- and suprathreshold activity in a model of electrically coupled fast-spiking neurons. We use the model of Golomb et al.[3], which we extended with a dendritic tree so as to be able to simulate distal synaptic input. We show that gap junctions are able to synchronize subthreshold membrane potential fluctuations in response to somatic current injection. However, the oscillations are only prevalent in the subthreshold range and therefore require enough membrane potential depolarization[5]. In response to synaptic input, our model neuron only enters the subthreshold oscillatory regime with AMPA and NMDA synapses located at distal dendrites. Proximal synaptic input leads to more random fluctuations of the membrane potential, reflecting a smaller extent of dendritic filtering of the Poisson-distributed postsynaptic potentials. We furthermore investigate the effect of GABAergic (i.e. inhibitory) input to the model of the fast-spiking neuron and predict that inhibitory input is able to induce a stuttering episode in these cells. We finally discuss our results in the context of the feedforward inhibitory network, which is likely to play an important role in striatal and basal ganglia function.

  • 229. Klaus, A.
    et al.
    Planert, H.
    Hjorth, Johannes
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Berke, J.D.
    Silberberg, G.
    Hellgren Kotaleski, Jeanette
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Striatal fast-spiking interneurons: from firing patterns to postsynaptic impact2011Ingår i: Frontiers in Systems Neuroscience, ISSN 1662-5137, E-ISSN 1662-5137, Vol. 5, nr July, s. 57-Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In the striatal microcircuit, fast-spiking (FS) interneurons have an important role in mediating inhibition onto neighboring medium spiny (MS) projection neurons. In this study, we combined computational modeling with in vitro and in vivo electrophysiological measurements to investigate FS cells in terms of their discharge properties and their synaptic efficacies onto MS neurons. In vivo firing of striatal FS interneurons is characterized by a high firing variability. It is not known, however, if this variability results from the input that FS cells receive, or if it is promoted by the stuttering spike behavior of these neurons. Both our model and measurements in vitro show that FS neurons that exhibit random stuttering discharge in response to steady depolarization do not show the typical stuttering behavior when they receive fluctuating input. Importantly, our model predicts that electrically coupled FS cells show substantial spike synchronization only when they are in the stuttering regime. Therefore, together with the lack of synchronized firing of striatal FS interneurons that has been reported in vivo, these results suggest that neighboring FS neurons are not in the stuttering regime simultaneously and that in vivo FS firing variability is more likely determined by the input fluctuations. Furthermore, the variability in FS firing is translated to variability in the postsynaptic amplitudes in MS neurons due to the strong synaptic depression of the FS-to-MS synapse. Our results support the idea that these synapses operate over a wide range from strongly depressed to almost fully recovered. The strong inhibitory effects that FS cells can impose on their postsynaptic targets, and the fact that the FS-to-MS synapse model showed substantial depression over extended periods of time might indicate the importance of cooperative effects of multiple presynaptic FS interneurons and the precise orchestration of their activity.

  • 230. 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 tomography2013Ingår i: Ultramicroscopy, ISSN 0304-3991, E-ISSN 1879-2723, Vol. 132, s. 70-74Artikel i tidskrift (Refereegranskat)
    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.

  • 231.
    Kozlov, Alexander
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Full-scale simulations of the lamprey spinal central pattern generator2005Konferensbidrag (Refereegranskat)
  • 232.
    Kozlov, Alexander
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Locking response in coupled phase systems1994Ingår i: Radiophysics and Quantum Electronics, ISSN 0033-8443, E-ISSN 1573-9120, Vol. 36, nr 8, s. 552-554Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Some distinctive features of the locking response to a chaotic action are pointed out using the example of coupled phase systems of the delay type: a) the possibility of locking for any values of the parameters of the acting subsystem in the case of precise matching of the response-subsystem parameters; b) relatively low sensitivity to mismatching of the subsystem parameters; and c) mild loss of locking in a nonuniform chain of phase systems.

  • 233.
    Kozlov, Alexander
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    The use of synchronized chaos oscillators for transmitting an information signal1994Ingår i: Technical physics letters, ISSN 1063-7850, E-ISSN 1090-6533, Vol. 20, nr 9, s. 710-712Artikel i tidskrift (Refereegranskat)
  • 234.
    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 study2007Ingår i: Biological Cybernetics, ISSN 0340-1200, E-ISSN 1432-0770, Vol. 96, nr 2, s. 229-243Artikel i tidskrift (Refereegranskat)
    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.

  • 235.
    Kozlov, Alexander
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Aurell, E
    Deliagina, T.G
    Grillner, S
    Hellgren-Kotaleski, J
    Orlovsky, G.N
    Zelenin, P.V
    Modeling control of body orientation in the lamprey1999Konferensbidrag (Refereegranskat)
    Abstract [en]

    A phenomenological model of the mechanism of stabilization of the dorsal-side-up orientation in the lamprey is suggested. Mathematical modeling is based on the experimental results on investigation of postural control in lampreys using combined in vivo and robotics approaches. Dynamics of the model agrees qualitatively with the experiment. It is shown by computer simulations that postural correction commands from one or several reticulospinal neurons provide information which may be sufficient for stabilization of body orientation in the lamprey. (C) 2000 Elsevier Science B.V. All rights reserved.

  • 236.
    Kozlov, Alexander
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Bazhenov, M.V.
    Huerta, R
    Rabinovich, M.I.
    Multistability in neuronal ensembles with balanced couplings1998Ingår i: Bulletin of University of Nizhny Novgorod. RadiofizikaArtikel i tidskrift (Refereegranskat)
  • 237.
    Kozlov, Alexander
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Fagerstedt, P
    Ullén, F
    Aurell, E
    Turning behavior in lamprey in response to descending unilateral commands: experiments and modeling2000Konferensbidrag (Refereegranskat)
    Abstract [en]

    Steering maneuvers in vertebrates are characterized by asymmetric modulation of the cycle duration and the intensity of the symmetric rhythmic locomotor activity. In the lamprey in vitro model system, turns can be evoked by electrical skin stimuli applied to one side of the head, which give rise to descending unilateral excitatory commands. Turns are observed as increased activity on one side of the spinal cord, followed by a rebound on the other. We investigated the generation of turns in single-segment models of the lamprey locomotor spinal network, and were able to reproduce all main experimental results. Sufficient mechanisms to explain changes in the locomotor rhythm, including rebound, are asymmetric activation of crossing inhibitory neurons, accompanied by a calcium influx in these neurons. (C) 2001 Elsevier Science B.V. All rights reserved.

  • 238.
    Kozlov, Alexander
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Hellgren Kotaleski, Jeanette
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Aurell, Erik
    KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre. KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Grillner, S
    Lansner, A
    Modeling of plasticity of the synaptic connections in the lamprey spinal CPG - consequences for network behavior2000Ingår i: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 32-33, s. 441-446Artikel i tidskrift (Refereegranskat)
    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.

  • 239.
    Kozlov, Alexander
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Hellgren Kotaleski, Jeanette
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Aurell, Erik
    KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre. KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Grillner, S
    Lansner, A
    Modeling of substance P and 5-HT induced synaptic plasticity in the lamprey spinal CPG - consequences for network pattern generation2001Ingår i: Journal of Computational Neuroscience, ISSN 0929-5313, E-ISSN 1573-6873, Vol. 11, nr 2, s. 183-200Artikel i tidskrift (Refereegranskat)
    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(Ca) 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.

  • 240.
    Kozlov, Alexander
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Hellgren-Kotaleski, J
    Aurell, E
    Grillner, S
    Lansner, A
    Modeling of metaplasticity of the synaptic connections in the lamprey spinal CPG---consequences for network behavior1999Konferensbidrag (Refereegranskat)
    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.

  • 241.
    Kozlov, Alexander
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Huerta, R
    Rabinovich, M.I.
    Abarbanel, H.D.I.
    Bazhenov, M.V.
    Neuronal ensembles with balanced interconnection as receptors of information1997Ingår i: Doklady physics (Print), ISSN 1028-3358, E-ISSN 1562-6903, Vol. 45, nr 12, s. 664-669Artikel i tidskrift (Refereegranskat)
  • 242.
    Kozlov, Alexander
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Huss, Mikael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Numerisk Analys och Datalogi, NADA.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Numerisk Analys och Datalogi, NADA.
    Hellgren Kotaleski, Jeanette
    KTH, Skolan för datavetenskap och kommunikation (CSC), Numerisk Analys och Datalogi, NADA.
    Grillner, Sten
    Central and local control principles for vertebrate locomotionManuskript (Övrigt vetenskapligt)
  • 243.
    Kozlov, Alexander
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Huss, Mikael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Hellgren Kotaleski, Jeanette
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Grillner, Sten
    Simple cellular and network control principles govern complex patterns of motor behavior2009Ingår i: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 106, nr 47, s. 20027-20032Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The vertebrate central nervous system is organized in modules that independently execute sophisticated tasks. Such modules are flexibly controlled and operate with a considerable degree of autonomy. One example is locomotion generated by spinal central pattern generator networks (CPGs) that shape the detailed motor output. The level of activity is controlled from brainstem locomotor command centers, which in turn, are under the control of the basal ganglia. By using a biophysically detailed, full-scale computational model of the lamprey CPG (10,000 neurons) and its brainstem/forebrain control, we demonstrate general control principles that can adapt the network to different demands. Forward or backward locomotion and steering can be flexibly controlled by local synaptic effects limited to only the very rostral part of the network. Variability in response properties within each neuronal population is an essential feature and assures a constant phase delay along the cord for different locomotor speeds.

  • 244.
    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 locomotion2014Ingår 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. 3591-3596Artikel i tidskrift (Refereegranskat)
    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.

  • 245.
    Kozlov, Alexander
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lansner, A
    Fagerstedt, P
    Grillner, S
    Collective phenomena in large-scale models of the locomotor spinal network of lamprey2000Konferensbidrag (Refereegranskat)
  • 246.
    Kozlov, Alexander
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lansner, A
    Grillner, S
    Large-scale models of the locomotor spinal network of lamprey2001Konferensbidrag (Refereegranskat)
  • 247.
    Kozlov, Alexander
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Osipov, G.V
    Shalfeev, V.D
    impulse suppression of chaotic oscillations1996Ingår i: Nonlinear dynamics, ISSN 0924-090X, E-ISSN 1573-269X, Vol. 1, s. 113-120Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The methods of nonconstant feedback impulse control of chaos are introduced. the approach is based on the similarity of the return of the dissipative continuoustime systems with one dimensional maps. The metods are illustrated for the chua´s circuit Rössler oscillator, and phase-locked loop system.

  • 248.
    Kozlov, Alexander
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Osipov, G.V
    Shalfeev, V.D
    impulse suppression of chaotic oscillations in chua's circuit and phase-locked loop1996Konferensbidrag (Refereegranskat)
  • 249.
    Kozlov, Alexander
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Osipov, G.V
    Shalfeev, V.D
    Suppressing chaos in continuous systems by impulse control1997Konferensbidrag (Refereegranskat)
    Abstract [en]

    The methods of nonconstant feedback impulse control of chaos are introduced. The approach is based on the similarity of the return maps of dissipative continuous-time systems with one dimensional maps. The methods are illustrated for the Chua's circuit (1992), Rossler oscillator, and phase-locked loop system

  • 250.
    Kozlov, Alexander
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Osipov, G.V
    Shalfeev, V.D
    Suppression of chaotic oscillations by external impulse force1996Konferensbidrag (Refereegranskat)
2345678 201 - 250 av 533
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