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We examine the scaling trends in particle multiplicity and flow observables between SPS, RHIC and LHC, and discuss their compatibility with popular theoretical models. We examine the way scaling trends between SPS and RHIC are broken at LHC energies, and suggest experimental measurements which can further clarify the situation.
We derive the equations of second order dissipative fluid dynamics from the relativistic Boltzmann equation following the method of W. Israel and J. M. Stewart [1]. We present a frame independent calculation of all first- and second-order terms and their coefficients using a linearised collision integral. Therefore, we restore all terms that were previously neglected in the original papers of W. Israel and J. M. Stewart.
We present results on Hanbury Brown-Twiss (HBT) radii extracted from the Ultra-relativistic Molecular Dynamics (UrQMD) approach to relativistic heavy ion collisions. The present investigation provides a comparison of results from pure hadronic transport calculations to a Boltzmann + Hydrodynamic hybrid approach with an intermediate hydrodynamic phase. For the hydrodynamic phase different Equations of State (EoS) have been employed, i.e. bag model, hadron resonance gas and a chiral EoS. The influence of various freeze-out scenarios has been investigated and shown to be negligible if hadronic rescatterings after the hydrodynamic evolution are included. Furthermore, first results of the source tilt from azimuthal sensitive HBT and the direct extraction from the transport model are presented and exhibit a very good agreement with E895 data at AGS.
A mechanism for locally density-dependent dynamic parton rearrangement and fusion has been implemented into the Ultrarelativistic Quantum Molecular Dynamics (UrQMD) approach. The same mechanism has been previously built in the Quark Gluon String Model (QGSM). This rearrangement and fusion approach based on parton coalescence ideas enables the description of multi-particle interactions, namely 3 -> 3 and 3 -> 2, between (pre)hadronic states in addition to standard binary interactions. The UrQMD model (v2.3) extended by these additional processes allows to investigate implications of multi-particle interactions on the reaction dynamics of ultrarelativistic heavy ion collisions. The mechanism, its implementation and first results of this investigation are presented and discussed.
We present the current status of hybrid approaches to describe heavy ion collisions and their future challenges and perspectives. First we present a hybrid model combining a Boltzmann transport model of hadronic degrees of freedom in the initial and final state with an optional hydrodynamic evolution during the dense and hot phase. Second, we present a recent extension of the hydrodynamical model to include fluctuations near the phase transition by coupling a chiral field to the hydrodynamic evolution.
Fast thermalization and a strong build up of elliptic flow of QCD matter were investigated within the pQCD based 3+1 dimensional parton transport model BAMPS including bremsstrahlung 2 <-> 3 processes. Within the same framework quenching of gluonic jets in Au+Au collisions at RHIC can be understood. The development of conical structure by gluonic jets is investigated in a static box for the regimes of small and large dissipation. Furthermore we demonstrate two different approaches to extract the shear viscosity coefficient n from a microscopical picture.
We study the kinetic and chemical equilibration in 'infinite' parton-hadron matter within the Parton-Hadron-String Dynamics transport approach, which is based on a dynamical quasiparticle model for partons matched to reproduce lattice-QCD results – including the partonic equation of state – in thermodynamic equilibrium. The 'infinite' matter is simulated within a cubic box with periodic boundary conditions initialized at different baryon density (or chemical potential) and energy density. The transition from initially pure partonic matter to hadronic degrees of freedom (or vice versa) occurs dynamically by interactions. Different thermody-namical distributions of the strongly-interacting quark-gluon plasma (sQGP) are addressed and discussed.
This thesis investigates the development of early cognition in infancy using neural network models. Fundamental events in visual perception such as caused motion, occlusion, object permanence, tracking of moving objects behind occluders, object unity perception and sequence learning are modeled in a unifying computational framework while staying close to experimental data in developmental psychology of infancy. In the first project, the development of causality and occlusion perception in infancy is modeled using a simple, three-layered, recurrent network trained with error backpropagation to predict future inputs (Elman network). The model unifies two infant studies on causality and occlusion perception. Subsequently, in the second project, the established framework is extended to a larger prediction network that models the development of object unity, object permanence and occlusion perception in infancy. It is shown that these different phenomena can be unified into a single theoretical framework thereby explaining experimental data from 14 infant studies. The framework shows that these developmental phenomena can be explained by accurately representing and predicting statistical regularities in the visual environment. The models assume (1) different neuronal populations processing different motion directions of visual stimuli in the visual cortex of the newborn infant which are supported by neuroscientific evidence and (2) available learning algorithms that are guided by the goal of predicting future events. Specifically, the models demonstrate that no innate force notions, motion analysis modules, common motion detectors, specific perceptual rules or abilities to "reason" about entities which have been widely postulated in the developmental literature are necessary for the explanation of the discussed phenomena. Since the prediction of future events turned out to be fruitful for theoretical explanation of various developmental phenomena and a guideline for learning in infancy, the third model addresses the development of visual expectations themselves. A self-organising, fully recurrent neural network model that forms internal representations of input sequences and maps them onto eye movements is proposed. The reinforcement learning architecture (RLA) of the model learns to perform anticipatory eye movements as observed in a range of infant studies. The model suggests that the goal of maximizing the looking time at interesting stimuli guides infants' looking behavior thereby explaining the occurrence and development of anticipatory eye movements and reaction times. In contrast to classical neural network modelling approaches in the developmental literature, the model uses local learning rules and contains several biologically plausible elements like excitatory and inhibitory spiking neurons, spike-timing dependent plasticity (STDP), intrinsic plasticity (IP) and synaptic scaling. It is also novel from the technical point of view as it uses a dynamic recurrent reservoir shaped by various plasticity mechanisms and combines it with reinforcement learning. The model accounts for twelve experimental studies and predicts among others anticipatory behavior for arbitrary sequences and facilitated reacquisition of already learned sequences. All models emphasize the development of the perception of the discussed phenomena thereby addressing the questions of how and why this developmental change takes place - questions that are difficult to be assessed experimentally. Despite the diversity of the discussed phenomena all three projects rely on the same principle: the prediction of future events. This principle suggests that cognitive development in infancy may largely be guided by building internal models and representations of the visual environment and using those models to predict its future development.
Background: The immune system is a complex adaptive system of cells and molecules that are interwoven in a highly organized communication network. Primary immune deficiencies are disorders in which essential parts of the immune system are absent or do not function according to plan. X-linked agammaglobulinemia is a B-lymphocyte maturation disorder in which the production of immunoglobulin is prohibited by a genetic defect. Patients have to be put on life-long immunoglobulin substitution therapy in order to prevent recurrent and persistent opportunistic infections. Methodology: We formulate an immune response model in terms of stochastic differential equations and perform a systematic analysis of empirical therapy protocols that differ in the treatment frequency. The model accounts for the immunoglobulin reduction by natural degradation and by antigenic consumption, as well as for the periodic immunoglobulin replenishment that gives rise to an inhomogeneous distribution of immunoglobulin specificities in the shape space. Results are obtained from computer simulations and from analytical calculations within the framework of the Fokker-Planck formalism, which enables us to derive closed expressions for undetermined model parameters such as the infection clearance rate. Conclusions: We find that the critical value of the clearance rate, below which a chronic infection develops, is strongly dependent on the strength of fluctuations in the administered immunoglobulin dose per treatment and is an increasing function of the treatment frequency. The comparative analysis of therapy protocols with regard to the treatment frequency yields quantitative predictions of therapeutic relevance, where the choice of the optimal treatment frequency reveals a conflict of competing interests: In order to diminish immunomodulatory effects and to make good economic sense, therapeutic immunoglobulin levels should be kept close to physiological levels, implying high treatment frequencies. However, clearing infections without additional medication is more reliably achieved by substitution therapies with low treatment frequencies. Our immune response model predicts that the compromise solution of immunoglobulin substitution therapy has a treatment frequency in the range from one infusion per week to one infusion per two weeks.
At present, there is a huge lag between the artificial and the biological information processing systems in terms of their capability to learn. This lag could be certainly reduced by gaining more insight into the higher functions of the brain like learning and memory. For instance, primate visual cortex is thought to provide the long-term memory for the visual objects acquired by experience. The visual cortex handles effortlessly arbitrary complex objects by decomposing them rapidly into constituent components of much lower complexity along hierarchically organized visual pathways. How this processing architecture self-organizes into a memory domain that employs such compositional object representation by learning from experience remains to a large extent a riddle. The study presented here approaches this question by proposing a functional model of a self-organizing hierarchical memory network. The model is based on hypothetical neuronal mechanisms involved in cortical processing and adaptation. The network architecture comprises two consecutive layers of distributed, recurrently interconnected modules. Each module is identified with a localized cortical cluster of fine-scale excitatory subnetworks. A single module performs competitive unsupervised learning on the incoming afferent signals to form a suitable representation of the locally accessible input space. The network employs an operating scheme where ongoing processing is made of discrete successive fragments termed decision cycles, presumably identifiable with the fast gamma rhythms observed in the cortex. The cycles are synchronized across the distributed modules that produce highly sparse activity within each cycle by instantiating a local winner-take-all-like operation. Equipped with adaptive mechanisms of bidirectional synaptic plasticity and homeostatic activity regulation, the network is exposed to natural face images of different persons. The images are presented incrementally one per cycle to the lower network layer as a set of Gabor filter responses extracted from local facial landmarks. The images are presented without any person identity labels. In the course of unsupervised learning, the network creates simultaneously vocabularies of reusable local face appearance elements, captures relations between the elements by linking associatively those parts that encode the same face identity, develops the higher-order identity symbols for the memorized compositions and projects this information back onto the vocabularies in generative manner. This learning corresponds to the simultaneous formation of bottom-up, lateral and top-down synaptic connectivity within and between the network layers. In the mature connectivity state, the network holds thus full compositional description of the experienced faces in form of sparse memory traces that reside in the feed-forward and recurrent connectivity. Due to the generative nature of the established representation, the network is able to recreate the full compositional description of a memorized face in terms of all its constituent parts given only its higher-order identity symbol or a subset of its parts. In the test phase, the network successfully proves its ability to recognize identity and gender of the persons from alternative face views not shown before. An intriguing feature of the emerging memory network is its ability to self-generate activity spontaneously in absence of the external stimuli. In this sleep-like off-line mode, the network shows a self-sustaining replay of the memory content formed during the previous learning. Remarkably, the recognition performance is tremendously boosted after this off-line memory reprocessing. The performance boost is articulated stronger on those face views that deviate more from the original view shown during the learning. This indicates that the off-line memory reprocessing during the sleep-like state specifically improves the generalization capability of the memory network. The positive effect turns out to be surprisingly independent of synapse-specific plasticity, relying completely on the synapse-unspecific, homeostatic activity regulation across the memory network. The developed network demonstrates thus functionality not shown by any previous neuronal modeling approach. It forms and maintains a memory domain for compositional, generative object representation in unsupervised manner through experience with natural visual images, using both on- ("wake") and off-line ("sleep") learning regimes. This functionality offers a promising departure point for further studies, aiming for deeper insight into the learning mechanisms employed by the brain and their consequent implementation in the artificial adaptive systems for solving complex tasks not tractable so far.
Relying on the existing estimates for the production cross sections of mini black holes in models with large extra dimensions, we review strategies for identifying those objects at collider experiments. We further consider a possible stable final state of such black holes and discuss their characteristic signatures. Keywords: Black holes
We discuss the present collective flow signals for the phase transition to the quark-gluon plasma (QGP) and the collective flow as a barometer for the equation of state (EoS). We emphasize the importance of the flow excitation function from 1 to 50A GeV: here the hydrodynamicmodel has predicted the collapse of the v1-flow at ~ 10A GeV and of the v2-flow at ~ 40A GeV. In the latter case, this has recently been observed by the NA49 collaboration. Since hadronic rescattering models predict much larger flow than observed at this energy, we interpret this observation as potential evidence for a first order phase transition at high baryon density pB.
We study various fluctuation and correlation signals of the deconfined state using a dynamical recombination approach (quark Molecular Dynamics, qMD). We analyse charge ratio fluctuations, charge transfer fluctuations and baryon-strangeness correlations as a function of the center of mass energy with a set of central Pb+Pb/Au+Au events from AGS energies on (Elab = 4 AGeV) up to the highest RHIC energy available (V sNN = 200 GeV) and as a function of time with a set of central Au+Au qMD events at V sNN = 200 GeV with and without applying our hadronization procedure. For all studied quantities, the results start from values compatible with a weakly coupled QGP in the early stage and end with values compatible with the hadronic result in the final state. We show that the loss of the signal occurs at the same time as hadronization and trace it back to the dynamical recombination process implemented in our model.
To investigate the formation and the propagation of relativistic shock waves in viscous gluon matter we solve the relativistic Riemann problem using a microscopic parton cascade. We demonstrate the transition from ideal to viscous shock waves by varying the shear viscosity to entropy density ratio n/s. Furthermore we compare our results with those obtained by solving the relativistic causal dissipative fluid equations of Israel and Stewart (IS), in order to show the validity of the IS hydrodynamics. Employing the parton cascade we also investigate the formation of Mach shocks induced by a high-energy gluon traversing viscous gluon matter. For n/s = 0.08 a Mach cone structure is observed, whereas the signal smears out for n/s >=0.32.
Gamma synchronization has generally been associated with grouping processes in the visual system. Here, we examine in monkey V1 whether gamma oscillations play a functional role in segmenting surfaces of plaid stimuli. Local field potentials (LFPs) and spiking activity were recorded simultaneously from multiple sites in the opercular and calcarine regions while the monkeys were presented with sequences of single and superimposed components of plaid stimuli. In accord with the previous studies, responses to the single components (gratings) exhibited strong and sustained gamma-band oscillations (30–65 Hz). The superposition of the second component, however, led to profound changes in the temporal structure of the responses, characterized by a drastic reduction of gamma oscillations in the spiking activity and systematic shifts to higher frequencies in the LFP (~10% increase). Comparisons between cerebral hemispheres and across monkeys revealed robust subject-specific spectral signatures. A possible interpretation of our results may be that single gratings induce strong cooperative interactions among populations of cells that share similar response properties, whereas plaids lead to competition. Overall, our results suggest that the functional architecture of the cortex is a major determinant of the neuronal synchronization dynamics in V1. Key words: attention , gamma , gratings , oscillation , visual cortex
Human Transformer2-beta (hTra2-beta) is an important member of the serine/arginine-rich protein family, and contains one RNA recognition motif (RRM). It controls the alternative splicing of several pre-mRNAs, including those of the calcitonin/calcitonin gene-related peptide (CGRP), the survival motor neuron 1 (SMN1) protein and the tau protein. Accordingly, the RRM of hTra2-beta specifically binds to two types of RNA sequences [the CAA and (GAA)2 sequences]. We determined the solution structure of the hTra2-beta RRM (spanning residues Asn110–Thr201), which not only has a canonical RRM fold, but also an unusual alignment of the aromatic amino acids on the beta-sheet surface. We then solved the complex structure of the hTra2-beta RRM with the (GAA)2 sequence, and found that the AGAA tetra-nucleotide was specifically recognized through hydrogen-bond formation with several amino acids on the N- and C-terminal extensions, as well as stacking interactions mediated by the unusually aligned aromatic rings on the beta-sheet surface. Further NMR experiments revealed that the hTra2-beta RRM recognizes the CAA sequence when it is integrated in the stem-loop structure. This study indicates that the hTra2-beta RRM recognizes two types of RNA sequences in different RNA binding modes.
The goal of this project is to develop a framework for a cell that takes in consideration its internal structure, using an agent-based approach. In this framework, a cell was simulated as many sub-particles interacting to each other. This sub-particles can, in principle, represent any internal structure from the cell (organelles, etc). In the model discussed here, two types of sub-particles were used: membrane sub-particles and cytosolic elements. A kinetic and dynamic Delaunay triangulation was used in order to define the neighborhood relations between the sub-particles. However, it was soon noted that the relations defined by the Delaunay triangulation were not suitable to define the interactions between membrane sub-particles. The cell membrane is a lipid bilayer, and does not present any long range interactions between their sub-particles. This means that the membrane particles should not be able to interact in a long range. Instead, their interactions should be confined to the two-dimensional surface supposedly formed by the membrane. A method to select, from the original three-dimensional triangulations, connections restricted to the two-dimensional surface formed by the cell membrane was then developed. The algorithm uses as starting point the three-dimensional Delaunay triangulation involving both internal and membrane sub-particles. From this triangulation, only the subset of connections between membrane sub-particles was considered. Since the cell is full of internal particles, the collection of the membrane particles' connections will resemble the surface to be obtained, even though it will still have many connections that do not belong to the restricted triangulation on the surface. This "thick surface" was called a quasi-surface. The following step was to refine the quasi-surface, cutting out some of the connections so that the ones left made a proper surface triangulation with the membrane points. For that, the quasi-surface was separated in clusters. Clusters are defined as areas on the quasi-surface that are not yet properly triangulated on a two-dimensional surface. Each of the clusters was then re-triangulated independently, using re-triangulation methods also developed during this work. The interactions between cytosolic elements was given by a Lennard-Jones potential, as well as the interactions between cytosolic elements and membrane particles. Between only membrane particles, the interactions were given by an elastic interaction. For each particle, the equation of motion was written. The algorithm chosen to solve the equations of motion was the Verlet algorithm. Since the cytosol can be approximated as a gel, it is reasonable to suppose that the sub-cellular particles are moving in an overdamped environment. Therefore, an overdamped approximation was used for all interactions. Additionally, an adaptive algorithm was used in order to define the size of the time step used in each interaction. After the method to re-triangulate the membrane points was implemented, the time needed to re-triangulate a single cluster was studied, followed by an analysis on how the time needed to re-triangulate each point in a cluster varied with the cluster size. The frequency of appearance for each cluster size was also compared, as this information is necessary to guarantee that the total time needed by to re-triangulate a cell is convergent. At last, the total time spent re-triangulating a surface was plotted, as well as a scaling for the total re-triangulation time with the variation. Even though there is still a lot to be done, the work presented here is an important step on the way to the main goal of this project: to create an agent-based framework that not only allows the simulation of any sub-cellular structure of interest but also provides meaningful interaction relations to particles belonging to the cell membrane.
Although models based on independent component analysis (ICA) have been successful in explaining various properties of sensory coding in the cortex, it remains unclear how networks of spiking neurons using realistic plasticity rules can realize such computation. Here, we propose a biologically plausible mechanism for ICA-like learning with spiking neurons. Our model combines spike-timing dependent plasticity and synaptic scaling with an intrinsic plasticity rule that regulates neuronal excitability to maximize information transmission. We show that a stochastically spiking neuron learns one independent component for inputs encoded either as rates or using spike-spike correlations. Furthermore, different independent components can be recovered, when the activity of different neurons is decorrelated by adaptive lateral inhibition.
Understanding the dynamics of recurrent neural networks is crucial for explaining how the brain processes information. In the neocortex, a range of different plasticity mechanisms are shaping recurrent networks into effective information processing circuits that learn appropriate representations for time-varying sensory stimuli. However, it has been difficult to mimic these abilities in artificial neural network models. Here we introduce SORN, a self-organizing recurrent network. It combines three distinct forms of local plasticity to learn spatio-temporal patterns in its input while maintaining its dynamics in a healthy regime suitable for learning. The SORN learns to encode information in the form of trajectories through its high-dimensional state space reminiscent of recent biological findings on cortical coding. All three forms of plasticity are shown to be essential for the network's success. Keywords: synaptic plasticity, intrinsic plasticity, recurrent neural networks, reservoir computing, time series prediction
In the present work, the problem of protein folding is addressed from the point of view of equilibrium thermodynamics. The conformation of a globular protein in solution at common temperatures is quite complicated without any geometrical symmetry, but it is an ordered state in the sense of its biological activity. This complicated conformation of a single protein molecule is destroyed upon increasing the temperature or by the addition of appropriate chemical agents, as is revealed by the loss of its activity and change of the physical properties, and so on. Once the complicated native structures having biological activity are lost, it would be natural to suppose that the native structure could hardly be restored. Nevertheless, pioneers, such as Anson and Mirsky, recognized as early as in 1925 that this was not always the case. If one defines the folded and unfolded states of a protein as two distinct phases of a system, then under the variation of temperature the system is transformed from one phase state into another and vice versa. The process of protein folding is accompanied by the release or absorption of a certain amount of energy, corresponding to the first-oder-type phase transitions in the bulk. Knowing the partition function of the system one can evaluate its energy and heat capacity under different temperatures. This task was performed in this work. The results of the developed statistical mechanics model were compared with the results of molecular dynamic simulations of alanine poylpeptides. In particular, the dependencies on temperature of the total energy of the system and heat capacity were compared for alanine polypeptides consisting of 21, 30, 40, 50 and 100 amino acids. The good correspondence of the results of the theoretical model with the results of molecular dynamics simulations allowed to validate the assumptions made about the system and to establish the accuracy range of the theory. In order to perform the comparison of the results of theoretical model and the molecular dynamics simulations it is necessary to perform the efficient analysis of the results of molecular dynamics simulations. This task was also addressed in the present work. In particular, different ways to obtain dependence of the heat capacity on temperature from molecular dynamics simulations are discussed and the most efficient one is proposed. The present thesis reports the result of molecular dynamic simulations for not only alanine polypeptides by also for valine and leucine polypeptides. In valine and leucine polypeptides, it is also possible to observe the helix↔random coil transitions with the increase of temperature. The current thesis presents a work that starts with the investigation of the fundamental degrees of freedom in polypeptides that are responsible for the conformational transitions. Then this knowledge is applied for the statistical mechanics description of helix↔coil transitions in polypeptides. Finally, the theoretical formalism is generalized for the case of proteins in water environment and the comparison of the results of the statistical mechanics model with the experimental measurements of the heat capacity on temperature dependencies for two globular proteins is performed. The presented formalism is based on fundamental physical properties of the system and provides the possibility to describe the folding↔unfolding transitions quantitatively. The combination of these two facts is the major novelty of the presented approach in comparison to the existing ones. The “transparent” physical nature of the formalism provides a possibility to further apply it to a large variety of systems and processes. For instance, it can be used for investigation of the influence of the mutations in the proteins on their stability. This task is of primary importance for design of novel proteins and drug delivering molecules in medicine. It can provide further insights into the problem of protein aggregation and formation of amyloids. The problem of protein aggregation is closely associated with various illnesses such as Alzheimer and mad cow disease. With certain modifications, the presented theoretical method can be applied to the description of the protein crystallization process, which is important for the determination of the structure of proteins with X-Rays. There many other possible applications of the ideas described in the thesis. For instance, the similar formalism can be developed for the description of melting and unzipping of DNA, growth of nanotubes, formation of fullerenes, etc.