bluuebunny/crossref_metadata_2025 huggingface dataset:
RANDOM ACTIVITY AT THE MICROSCOPIC NEURAL LEVEL IN CORTEX (“NOISE”) SUSTAINS AND IS REGULATED BY LOW-DIMENSIONAL DYNAMICS OF MACROSCOPIC CORTICAL ACTIVITY (“CHAOS”)
Authors: FREEMAN WALTER J.
Published: October 2004

In this review I posit two levels of neural function. Microscopic activity is generated by neurons, to the extent that they act autonomously or in concert with networks of finite numbers of other neurons. Macroscopic activity is found in neuropil, where it depends on the sustained interaction of innumerable neurons. These levels coexist in cerebral cortex. Microscopic activity is manifested in the fraction of the variance of single neuron pulse trains (>99.9%) that is both random and uncorrelated with pulse trains of other neurons in the neuropil. Macroscopic activity is revealed in the <0.1% of the total variance of each neuron that is covariant with all other neurons in an area of neuropil comprising a population. It is best observed in dendritic potentials recorded as surface EEGs. The “spontaneous” background activity of neuropil at both levels arises from mutual excitation within a population of excitatory neurons. It is governed by a point attractor of the neuropil, which is actualized by the microscopic activity engendering the macroscopic state, and which acts as an order parameter to regulate the contributing neurons. The point attractor manifests a homogeneous field of white noise, into which sensory receptors send their microscopic stimuli. When neuropil comprises both excitatory and inhibitory neurons, the interactions at the macroscopic level lead to oscillations, manifesting a limit cycle attractor. When multiple areas of neuropil comprising a sensory system interact, then owing to their incommensurate characteristic frequencies and the long axonal delays between them, the system maintains a global chaotic attractor having multiple wings, one for each discriminable class of stimuli. Access to each wing is by stimulus-induced state transitions, leading to the construction of macroscopic chaotic patterns, that are carried to targets of cortical transmission by the microscopic noise. The chaotic modulation of the carrier noise is extracted by the targets through spatiotemporal integration, thereby retrieving the small covariance comprising the chaotic signal. Thus, controlled noise is the substrate for the meanings of stimuli that are expressed in chaotic patterns of sensory cortical activity.
SPONTANEOUS AND EVOKED CORTICAL DYNAMICS DURING DEEP ANAESTHESIA
Authors: MÄKINEN SAKU , HARTIKAINEN KAISA , ERIKSSON JARL-THURE , JÄNTTI VILLE
Published: October 2004

In this paper we have studied cortical dynamics as assessed using graphical methods during deep anaesthesia. Graphical analysis was carried out by autocorrelation functions and return maps with different lags.

During moderate and deep anaesthesia, the electroencephalogram (EEG) shows a burst suppression pattern, consisting of abruptly-occurring high amplitude bursts alternating with periods of relative silence. Deep anaesthesia with burst suppression pattern provides a simple model of brain activity when the noise that is usually present in a subject who is awake is suppressed. During anaesthesia-induced EEG suppression, the brain reacts to different external stimuli with bursts. In respect to sensory processing during anaesthesia, it is interesting to know whether these bursts have different dynamics depending on the stimuli used.

We have used graphical analysis to reveal the possible differences in bursts evoked by different stimuli. Externally evoked bursts were induced by auditory, electric and visual stimuli. The EEG studied in this paper consists of 25 bursts from one subject. We have estimated the autocorrelation function for each burst and used the formation gained from such autocorrelation coefficients as the grounds for determining different lags for return maps.

The graphical methods used revealed differences in dynamics and topology of bursts as evoked by different stimuli. Spontaneous bursts clearly had different dynamics from evoked burst; which could not be seen directly from the raw EEG data. This study suggests that graphical analysis is a useful tool to obtain information about the dynamics of neuronal processes behind cortical responses during deep anaesthesia.
REAFFERENCE AND ATTRACTORS IN THE OLFACTORY SYSTEM DURING ODOR RECOGNITION
Authors: KAY LESLIE M. , LANCASTER LARRY R. , FREEMAN WALTER J.
Published: October 2004

Olfactory bulb activity has been postulated to be chaotic, as measured in the EEG, and to be subject to an attractor with many “wings” enabling classification of different learned odor classes. Two parallel questions are thus addressed by the work presented here: (1) what is the evidence for attractors in the olfactory system, which can mediate learned odor classes? and (2) how does the olfactory system enter a specific attractor or attractor wing associated with the learned odor during the classification process? Both of these questions address the wider notion of endogenous activity preparing the system for an expected stimulus, which is at the basis of the reafference principle. By viewing the brain as a distributed complex dynamical system with global attractors, these questions can be answered together.

Rats were implanted with bipolar macroelectrodes in the Olfactory Bulb (OB), Prepyriform Cortex (PPC), Entorhinal Cortex (EC), and Dentate Gyrus (DG), and then trained in an operant paradigm to press a bar for a reward in the presence of one odor and to receive no reward in the presence of another odor. Local Field Potentials (LFP) were recorded simultaneously from the structures during the operant task. We present evidence for three endogenous events: (1) preafference, which is manifested both by the EC entering an attractor and a mid-range signal (15–30 Hz) which appears to be passed from the EC to the OB just before the OB enters an attractor; (2) afference, where the OB enters an attractor during the odor recognition period of the experiment and the LFP recordings indicate that the OB drives the other structures in all frequency bands, especially the high gamma band (65–100 Hz) associated with the OB burst frequency; and (3) reafference or post-afference, which is accompanied by a lower frequency gamma band signal (40–60 Hz) originating in the PPC and passed to both the OB and the EC just before the onset of the motor response to the odor. We use a new method, NECTAR (Nonparametric Exact Contingency Table Association Routine), related to mutual information, to verify what is seen with coherence and phase estimates, the apparent driving of each structure at different times in the odor trials, and to display evidence for non-periodic attractors governing both individual physiological structures and the system of structures.

This is the first evidence of an endogenous, limbic event associated with sensory perceptual tuning in a mammal. These results are also the first experimental confirmation that the attractors governing olfactory activity involve multiple sites in the olfactory/limbic system and implement the process of attention.
GLOBAL EFFECTS OF FLUCTUATIONS IN NEURAL INFORMATION PROCESSING
Authors: LILJENSTRÖM HANS
Published: October 2004

We are interested in how the complex dynamics of the brain, which may include oscillations, chaos and noise, can affect the efficiency of neural information processing. Here, we consider the amplification and functional role of fluctuations, expressed as chaos or noise in the system. Using computer simulations of a neural network model of the olfactory cortex, we demonstrate how microscopic fluctuations can result in global effects at the network level. In particular, we show that the rate of information processing in associative memory tasks can be maximized for optimal noise levels. Noise can also induce transitions between different dynamical states, related to learning and memory. A chaotic-like behavior, induced by noise or by an increase in neuronal excitability, can enhance system performance if it is transient and converges to a limit cycle memory state. The level of accuracy required for correct pattern association further affects the rate of information processing. We discuss how neuromodulatory control of the cortical dynamics can shift the balance between rate and accuracy optimization, as well as between sensitivity and stability.
CONTROL OF SPATIOTEMPORAL CHAOS IN NEURONAL NETWORKS
Authors: LOURENÇO CARLOS , BABLOYANTZ AGNESSA
Published: October 2004

Different strategies for the control of chaotic neuronal networks and physical systems are presented. The variables of the dynamics span phase-spaces with very high dimensions. Novel control techniques are introduced that handle these high-dimensional cases. Depending on the system, several orbits with different spatiotemporal symmetries may be stabilized out of the chaotic attractor. The role of chaos control in information processing is pointed out.
MULTICOMPARTMENTAL MODELING OF NEURAL CIRCUITS IN THE OLFACTORY BULB
Authors: ARADI ILDIKÓ , ÉRDI PÉTER
Published: October 2004

The generation and propagation of action potentials in the excitatory and inhibitory cell types of the olfactory bulb are simulated by applying multi-compartmental modeling technique. Detailed models of the main cell types of the olfactory bulb have been presented previously.1 Further simulations on granule and periglomerular cells have been done to find proper parameters matching to the physiological recordings.22 Elementary synaptic interactions and dynamic behaviour of small networks of excitatory and inhibitory neurons have been studied. To investigate the relationship between the anatomical structure of the neural circuits of whole olfactory bulb and the generated firing patterns, further series of simulations have been done.
ROBUST FEEDFORWARD PROCESSING IN SYNFIRE CHAINS
Authors: POSTMA ERIC O. , VAN DEN HERIK H. JAAP , HUDSON PATRICK T.W.
Published: October 2004

The rapidity of time-constrained visual identification suggests a feedforward process in which neural activity is propagated through a number of cortical stages. The process is modeled by using a synfire chain, leading to a neural-network model which involves propagating activation waves through a sequence of layers. Theory and analysis of the model’s behavior, especially in the presence of noise, predict enhancement of wave propagation for a range of noise intensities. Simulation studies confirm this prediction. The results are discussed in terms of (spatio-temporal) stochastic resonance. It is concluded that feedforward processes such as time-constrained visual identification may benefit from moderate levels of noise.
IMPACT OF INFORMATION COMPRESSION ON INTELLECTUAL ACTIVITIES IN THE BRAIN
Authors: ERIKSSON JARL-THURE
Published: October 2004

Imaging and modeling represent an action of information compression, which is inevitable for a complex system in order to process and communicate data. Humans are such complex systems. Sensational and body reaction information is processed by the brain, the overall information rate being 1011–1012 bit/s. Consciousness is the result of massive information compression, the attentional data rate being 10–14 bit/s. The primary purpose of the conscious state is to control the interaction with the environment. In humans, this ability facilitates the projection of the future horizon far away from the next instant. Thinking and reasoning can be thought of as a process of retrospective control. Retrospective in the sense that the sub-conscious has already decided what is to come next. In the long run, however, conscious thought influences the cortical context potentiation, thus providing steering to the associative process. The paper emphasizes that all cognitive operations are restricted to the bioelectrical signal processing of the brain. Accordingly, mental models and scientific theories will always be constrained by this fact. The last subject of this paper deals with the interaction between the brain, the glands and the rest of the body. It is interpreted that qualia are the result of a complex feedback interaction between those parts, a self-supported process close to a singularity point.
NOISE IN THE BRAIN: A PHYSICAL NETWORK MODEL
Authors: HAKEN HERMANN
Published: October 2004

In the brain as in any other open physical systems, noise is inevitable. We present an explicit model of an active physical system that is borrowed from laser physics and allows us to establish the properties of the fluctuating forces that cause noise in the system. It is shown how the cooperation of the individual parts of a system (atoms or neurons) can considerably reduce the noise level. In particular we determine the correlation function between the individual parts. The basic equations can be transformed in such a way that a close analogy with typical equations of neural nets are obtained. In particular, the nonlinear properties of neurons described by the sigmoid function are well captured. Propagation of excitation in axons and dendrites is represented by a linear equation, where we consider both a bandwidth filter and more or less free propagation. In the latter case, a close analogy with an equation for neural activity in the sense of Nunez and established by Jirsa and Haken is pointed out.
IDENTIFICATION OF COHERENT STRUCTURES IN TURBULENT SHEAR FLOWS WITH A FUZZY ARTMAP NEURAL NETWORK
Authors: FERRE-GINE J. , RALLO R. , ARENAS A. , GIRALT FRANCE
Published: October 2004

An implementation of a Fuzzy Artmap neural network is used to detect and to identify (recognise) structures (patterns) embedded in the velocity field of a turbulent wake behind a circular cylinder. The net is trained to recognise both clockwise and anticlockwise eddies present in the u and v velocity fields at 420 diameters downstream of the cylinder that generates the wake, using a pre-processed part of the recorded velocity data. The phase relationship that exists between the angles of the velocity vectors of an eddy pattern is used to reduce the number of classes contained in the data, before the start of the training procedure. The net was made stricter by increasing the vigilance parameter within the interval [0.90, 0.95] and a set of net-weights were obtained for each value. Full data files were scanned with the net classifying patterns according to their phase characteristics. The net classifies about 27% of the recorded signals as eddy motions, with the strictest vigilance parameter and without the need to impose external initial templates. Spanwise distances (homogeneous direction of the flow) within the centres of the eddies identified suggest that they form pairs of counter-rotating vortices (double rollers). The number of patterns selected with Fuzzy Artmap is lower than that reported for template matching because the net classifies eddies according to the recirculating pattern present at the core or central region, while template matching extends the region over which correlation between data and template is performed. In both cases, the topology of educed patterns is in agreement.
FREQUENCY-SPATIAL TRANSFORMATION: A PROPOSAL FOR PARSIMONIOUS INTRA-CORTICAL COMMUNICATION
Authors: LEVI REGEV , RUPPIN EYTAN , MATIAS YOSSI , REGGIA JAMES A.
Published: October 2004

This work examines a neural network model of a cortical module, where neurons are organized on a 2-dimensional sheet and are connected with higher probability to their spatial neighbors. Motivated by recent findings that cortical neurons have a resonant peak in their impedance magnitude function, we present a frequency-spatial transformation scheme that is schematically described as follows: An external input signal, applied to a small input subset of the neurons, spreads along the network. Due to a stochastic component in the dynamics of the neurons, the frequency of the spreading signal decreases as it propagates through the network. Depending on the input signal frequency, different neural assemblies will hence fire at their specific resonance frequency. We show analytically that the resulting frequency-spatial transformation is well-formed; an injective, fixed, mapping is obtained. Extensive numerical simulations demonstrate that a homogeneous, well-formed transformation may also be obtained in neural networks with cortical-like “Mexican-hat” connectivity. We hypothesize that a frequency-spatial transformation may serve as a basis for parsimonious cortical communication.
SYNCHRONY IN BINARY-OSCILLATOR NETWORKS WITH LOCAL COUPLINGS
Authors: LU ZIYI , WANG BAOYUN , YANG LUXI , HE ZHENYA
Published: October 2004

Since synchronous oscillations in the visual cortex may be responsible for some features of visual scene, many works have been done to study the dynamics of oscillatory neural networks. Most of the oscillator network models rely on a global connection to reach sychronization. Here we propose a class of neural network models based on a simplified binary-oscillator, and find that the neurons of networks with local couplings will be in global synchrony under some criteria.
A COMPETITIVE ACTIVATION NEURAL NETWORK MODEL FOR THE WEIGHTED MINIMUM VERTEX COVERING
Authors: LE GALL A. , ZISSIMOPOULOS V.
Published: October 2004

We give a generalization of a neural network model originally developed to solve the minimum cardinality vertex covering problem, in order to solve the weighted version of the problem. The model is governed by a modified activation rule and we show that it has some important properties, namely convergence and irredundant covers at stable states. We present experimental results that confirm the effectiveness of the model.
USING NEURAL NETWORKS TO SOLVE THE MULTICAST ROUTING PROBLEM IN PACKET RADIO NETWORKS
Authors: HEMMINGER THOMAS L. , POMALAZA-RAEZ CARLOS A.
Published: October 2004

The primary function of a packet radio network is the efficient transfer of information between source and destination nodes using minimal bandwidth and end-to-end delay. Many researchers have investigated the problem of minimizing the end-to-end delay from a single source to a single destination for a variety of networks; however, very little work is reported about routing mechanisms for the common case where a particular information packet is intended to be sent to more than one destination in the network. This is known as multicasting. A simplified version of the problem is to ignore the packet delay at each node, then the problem becomes one of finding solutions which require the least number of transmissions. Determination of an optimal solution is NP-complete meaning that suboptimal solutions are frequently tolerated. The problem becomes more rigorous if packet delays are included in the network topology. This paper describes a practical technique for the computation of optimum or near optimum solutions to the multicasting problem with and without packet delay. The method is based on the Hopfield neural network and experiment has shown this method to yield near optimal solutions while requiring a minimum of CPU time.
PROBABILISTIC INTERPRETATION OF FEEDFORWARD NETWORK OUTPUTS, WITH RELATIONSHIPS TO STATISTICAL PREDICTION OF ORDINAL QUANTITIES
Authors: COSTA MARIO
Published: October 2004

Several problems require the estimation of discrete random variables whose values can be put in a one-to-one ordered correspondence with a finite subset of the natural numbers. This happens whenever quantities are involved that represent integer items, or have been quantized on a fixed number of levels, or correspond to “graded” linguistic values. Here we propose a correct probabilistic approach to such kind of problems that fully exploits all the available prior knowledge about their own structure. In spite of the very stringent constraints induced in output space, the method can be directly applied to standard feed-forward networks while keeping local computation of both outputs and error signals. According to these guidelines, we devised a neural implementation of a complex image pre-processing algorithm by using very poor resolution on the computing elements in the network.
SIMPLE NEURON MODELS FOR INDEPENDENT COMPONENT ANALYSIS
Authors: HYVÄRINEN AAPO , OJA ERKKI
Published: October 2004

Recently, several neural algorithms have been introduced for Independent Component Analysis. Here we approach the problem from the point of view of a single neuron. First, simple Hebbian-like learning rules are introduced for estimating one of the independent components from sphered data. Some of the learning rules can be used to estimate an independent component which has a negative kurtosis, and the others estimate a component of positive kurtosis. Next, a two-unit system is introduced to estimate an independent component of any kurtosis. The results are then generalized to estimate independent components from non-sphered (raw) mixtures. To separate several independent components, a system of several neurons with linear negative feedback is used. The convergence of the learning rules is rigorously proven without any unnecessary hypotheses on the distributions of the independent components.
DILATION AND TRANSLATION FOR INCOMPLETE SIGMA-PI NEURAL NETWORKS OF HOPFIELD-TYPE — A CASE STUDY
Authors: LENZE BURKHARD
Published: October 2004

In this paper we show how dilation and translation of bipolar signals can be used to increase the capacity of sigma-pi Hopfield neural networks while keeping their complexity in check. We propose a generalization of the standard Hebb learning scheme which gives rise to proper dilation and translation parameters and apply these generalized Hopfield-type neural networks to some pattern recognition problems. We will see that the new approach especially makes sense for highly correlated information and in cases where the number of multiplicative synaptic correlations of neural activities is limited for some theoretical or practical reason and a so-called incomplete situation appears.
VLSI NEURAL SYSTEM ARCHITECTURE FOR FINITE RING RECURSIVE REDUCTION
Authors: ZHANG D. , JULLIEN G.A.
Published: October 2004

The use of neural-like networks to implement finite ring computations has been presented in a previous paper.1 This paper develops efficient VLSI neural system architecture for the finite ring recursive reduction (FRRR), including modulo reduction, MSB carry iteration and feedforward processing. These techniques deal with the basic principles involved in constructing a FRRR, and their implementations are efficiently matched to the VLSI medium. Compared with the other structure models for finite ring computation (e.g. modification of binary arithmetic logic and bit-steered ROM’s), the FRRR structure has the lowest area complexity in silicon while maintaining a high throughput rate. Examples of several implementations are used to illustrate the effectiveness of the FRRR architecture.
ATTRACTION BASIN OF BIDIRECTIONAL ASSOCIATIVE MEMORIES
Authors: LEUNG CHI SING , CHAN LAI WAN , SUM JOHN
Published: October 2004

In this paper, we investigate the attraction basin of the bidirectional associative memory (BAM) model. The BAM is a two-layer heteroassociator that stores a prescribed set of bipolar library pairs. It consists of two layers of neurons. One layer has n neurons and the other has p neurons. We will first point out why the conventional energy approach cannot tell us about the attraction basin of each library pair. We then rigorously derive the statistical dynamics of the BAM, which shows how the upper bound on the number of errors changes during recalling for an arbitrary error pattern in the initial state. From the dynamics, we can estimate the attraction basin for the worst case errors, as well as the memory capacity and the number of errors in the retrieved pairs. The memory capacity is αrn, where αr (0<αr<1) depends on the ratio [Formula: see text]. The number of errors in the retrieved pairs is [Formula: see text] when the number of library pairs is αn. When r=1, the lower bound on the attraction basin for the worst case errors is about 0.0068n.
A NEURAL NETWORK FOR POSITION INVARIANT PATTERN RECOGNITION COMBINING SPIKING NEURONS WITH THE FOURIER-TRANSFORM
Authors: STOECKER MICHAEL , REITBOECK HERBERT J.
Published: October 2004

We present an approach for position invariant recognition of individual objects in composite scenes, combining neural networks and algorithmic methods. A dynamic network of spiking neurons is used to generate object definition and figure/ground separation via temporal signal correlations. A shift invariant representation of the network spike activity distribution is subsequently realized via the amplitude spectrum of the Fourier-transform. Objects and their transformed representations are therefore linked in the time domain. The model segregates scenes and classifies individual patterns independent of their position in the input scene.
MANIPULATOR ADAPTIVE CONTROL BY NEURAL NETWORKS IN AN ORANGE PICKING ROBOT
Authors: CAVALIERI S. , PLEBE A.
Published: October 2004

The paper focuses on the use of neural networks for process identification in an orange-picking robot adaptive control system. The results that will be shown in the paper refer to a study carried out under the European Community ESPRIT project “CONNY”, dealing with the application of neural networks to robotics. The aim of the research is to verify the possibility of integrating a neural identification module in a traditional system to control the movement of the manipulators of the robot. The paper illustrates integration of neural identification in the existing orange-picking robot control system, highlighting the improvement of performance obtainable. Although the proposal refers to a specific robot, it can be applied to any system with the same dynamic features.
EXTENDED APPLICATION OF CONSTRUCTIVE CRITERIA FOR ERGODICITY OF INTERACTING PARTICLE SYSTEMS
Authors: DE JONG HANS , MAES CHRISTIAN
Published: October 2004

We discuss computational aspects of verifying constructive criteria for ergodicity of interacting particle systems. Both discrete time (probabilistic cellular automata) and continuous time spin flip dynamics are considered. We also investigate how the criteria have to be adapted if stirring is added to the dynamics.
REPLICA SYMMETRY BREAKING IN NEURAL NETWORKS WITH NON-MONOTONE ACTIVATION FUNCTION
Authors: LAMURA A. , MARANGI C. , NARDULLI G.
Published: October 2004

In this paper we analyze replica symmetry breaking in attractor neural networks with non-monotone activation function. We study the non-monotone version of the Edinburgh model, which allows the control of the domains of attraction by the stability parameter K, and we compute, at one step of symmetry breaking, storage capacity and, for the strongly dilute model, the domains of attraction of the stable fixed points.
A NEW NUMEROV-TYPE METHOD FOR COMPUTING EIGENVALUES AND RESONANCES OF THE RADIAL SCHRÖDINGER EQUATION
Authors: SIMOS T. E.
Published: October 2004

A two-step method is developed for computing eigenvalues and resonances of the radial Schrödinger equation. Numerical results obtained for the integration of the eigenvalue and the resonance problem for several potentials show that this new method is better than other similar methods.
INFLUENCE OF REFRACTORY PERIODS IN THE HOPFIELD MODEL
Authors: DA SILVA CRISÓGONO R. , TAMARIT FRANCISCO A. , CURADO EVALDO M. F.
Published: October 2004

We study both analytically and numerically the effects of including refractory periods in the Hopfield model for associative memory. These periods are introduced in the dynamics of the network as thresholds that depend on the state of the neuron at the previous time. Both the retrieval properties and the dynamical behavior are analyzed, and we found that, depending on the value of the thresholds and on the the ratio α between the number of stored memories (p) and the total number of neurons (N), the system presents not only fixed points but also chaotic or cyclic orbits. The recognizing capability of the network in the cyclic region is also studied.
DYNAMICAL EFFECTS AND VACANCY MOTION IN SILICON AT HIGH TEMPERATURE
Authors: SMARGIASSI ENRICO , CAR ROBERTO
Published: October 2004

We report a first-principles Molecular Dynamics investigation of the atomic motion in Silicon in the presence of an artificially created vacancy at high temperature (T ≥ 1200 K ). We observe that atomic diffusion events are affected by strong dynamical correlations. At temperatures close to the melting point we discover characteristic premelting phenomena which involve simultaneous jumps of several atoms and introduce a large amount of disorder in the structure.
REPRODUCTION RISK AND SELF-ORGANIZATION OF FERTILITY
Authors: ITO NOBUYASU
Published: October 2004

The reproduction risk is introduced into the Penna model of reproducing systems. And the minimum reproduction age is made variable. It is observed that the survival state selects an adequate distribution of the minimum reproduction age autonomously.
SAND PILE AS A UNIVERSAL COMPUTER
Authors: GOLES ERIC , MARGENSTERN MAURICE
Published: October 2004

We show that the sand pile model is able to simulate, by specific configurations, logic gates and registers and, therefore any computer program. Further, we give its interpretation in terms of a set of several one-dimensional interacting avalanches.
SIMULATION OF WESSLING MODEL FOR NETWORK FORMATION IN DISPERSED POLYMER PHASES
Authors: ROSELIEB C.
Published: October 2004

The model, developed by Wessling to simulate the formation of network structures formed in dispersed polymer phases, was investigated with new programs and systems of up to 109 sites. The results of Wessling are confirmed. A new model is presented by the author, which is forming stable networks.
PARALLEL REAL-TIME IMPLEMENTATION OF LARGE-SCALE, ROUTE-PLAN-DRIVEN TRAFFIC SIMULATION
Authors: RICKERT M. , WAGNER P.
Published: October 2004

This work is part of our ongoing effort to design and implement a traffic simulation application capable of handling realistic problem sizes in multiple real-time. Our traffic simulation model includes multi-lane vehicular traffic and individual route-plans. On a 16-CPU SGI Power Challenger and a 12-CPU SUN workstation-cluster we have reached real-time for the whole German Autobahn network.
ELECTROPHORESIS OF TOPOLOGICALLY NONTRIVIAL MACROMOLECULES: MATHEMATICAL AND COMPUTATIONAL STUDIES
Authors: LIM HWA A.
Published: October 2004

Mathematical and numerical models for studying the electrophoresis of topologically nontrivial molecules in two and three dimensions are presented. The molecules are modeled as polygons residing on a square lattice and a cubic lattice whereas the electrophoretic media of obstacle network are simulated by removing vertices from the lattices at random. The dynamics of the polymeric molecules are modeled by configurational readjustments of segments of the polygons. Configurational readjustments arise from thermal fluctuations and they correspond to piecewise reptation in the simulations. A Metropolis algorithm is introduced to simulate these dynamics, and the algorithms are proven to be reversible and ergodic. Monte Carlo simulations of steady field random obstacle electrophoresis are performed and the results are presented.
CRITICAL EXPONENTS OF THE 3-D ISING MODEL
Authors: Gupta Rajan , Tamayo Pablo
Published: October 2004

We present a status report on the ongoing analysis of the 3D Ising model with nearest-neighbor interactions using the Monte Carlo Renormalization Group (MCRG) and finite size scaling (FSS) methods on 643, 1283, and 2563 simple cubic lattices. Our MCRG estimates are [Formula: see text] and ν = 0.625(1). The FSS results for Kc are consistent with those from MCRG but the value of ν is not. Our best estimate η = 0.025(6) covers the spread in the MCRG and FSS values. A surprise of our calculation is the estimate ω ≈ 0.7 for the correction-to-scaling exponent. We also present results for the renormalized coupling gR along the MCRG flow and argue that the data support the validity of hyperscaling for the 3D Ising model.
THE D = 3 ISING MODEL REVISITED: ON THE ROAD TO HIGHER RESOLUTION
Authors: FERRENBERG ALAN M.
Published: October 2004

With recent improvements in simulation methods and data analysis techniques, Monte Carlo simulations, used in conjunction with finite-size scaling techniques, have become the most powerful and flexible tools for studying critical phenomena. In particular, there has been a dramatic increase in resolution in studies of the three-dimensional Ising model. This paper discusses motivations for performing high-resolution studies, reviews past and current work on the model and points out some potential potholes on the road to higher resolution.
RECENT DEVELOPMENTS IN ISING SPIN GLASSES
Authors: YOUNG A. P. , KAWASHIMA N.
Published: October 2004

We have studied the three-dimensional Ising spin glass with a ± J distribution by Monte Carlo simulations. Using larger sizes and much better statistics than in earlier work, a finite size scaling analysis shows quite strong evidence for a finite transition temperature, Tc, with ordering below Tc. Our estimate of the transition temperature is rather lower than in earlier work, and the value of the correlation length exponent, ν, is somewhat higher. Because there may be (unknown) corrections to finite size scaling, we do not completely rule out the possibility that Tc = 0 or that Tc is finite but with no order below Tc. However, from our data, these possibilities seem less likely.
APPLICATION OF AN EXTENDED ENSEMBLE METHOD TO SPIN GLASSES
Authors: HUKUSHIMA KOJI , TAKAYAMA HAJIME , NEMOTO KOJI
Published: October 2004

An efficient Monte Carlo algorithm for simulating hardly-relaxing systems is proposed. By using this algorithm the three-dimensional ± J Ising spin glass model is studied. The result shows that reasonable values of the critical temperature and of the critical exponents can be obtained within Monte Carlo steps much shorter than the observation time a conventional simulation usually requires.
CHIRAL ORDER IN SPIN GLASSES
Authors: KAWAMURA HIKARU
Published: October 2004

The results of the recent numerical simulations on vector spin glasses are presented. Numerical evidence of the novel chiral-glass state, accompanied with broken spin-reflection symmetry with preserving spin-rotation symmetry, is presented. Implication to experiments on spin-glass transitions is discussed.
MATHEMATICAL BASIS OF COMPUTATIONAL PHYSICS
Authors: SUZUKI M.
Published: October 2004

The present paper explains some general basic formulas concerning quantum Monte Carlo simulations, symplectic integration and other numerical calculations. A generalization of the BCH formula is given with an application to the decomposition of exponential operators in the presence of small parameters.
CROSS-OVER AND EFFECTIVE SCALING DIMENSIONS
Authors: Novotny M. A.
Published: October 2004

Although cross over between fixed points associated with different universality classes is a ubiquitious phenomena, there is little theoretical insight and less numerical evidence about exactly how cross-over phenomena occur. Here numerical evidence for the picture that cross-over phenomena are governed by the underlying structure of the associated renormalization group is given. In particular, it is shown how the cross-over is related to the effective dimension from scaling of the system. For the ferromagnetic Ising model in two and three dimensions numerical evidence will be presented that the cross-over is governed by the line of fixed points in non-integer dimensions. This allows for successful comparisons between the effective dimension from scaling for finite systems and expansions in dimension of universal quantities such as critical exponents.
SLOW DYNAMICS IN THE 'GRIFFITHS PHASE' OF THE 3D ± J ISING SPIN GLASS
Authors: KOMORI TATSUO , TAKAYAMA HAJIME
Published: October 2004

Slow dynamics in the 'Griffiths phase' of the 3D ± J Ising spin glass is studied by means of Monte Carlo simulation, in which the spectra of (logarithms of) relaxation times, P (x) with x = ln τ, are analyzed in detail. At each temperature P (x) exhibits a characteristic crossover behavior at around a certain value xc which in turn tends to diverge toward the spin-glass critical temperature Tc. The contributions to the branch x ≲ xc are interpreted due to the ordinary critical slowing down process associated with Tc (critical dynamics). The behavior of P (x) at x ≲ xc, on the other hand, is consistent with the picture of relaxation processes of large, rare clusters which are related to the Griffiths singularity at the Curie temperature of the corresponding pure ferromagnet (cluster dynamics).
STATISTICAL DEPENDENCE ANALYSIS
Authors: KIKUCHI MACOTO , ITO NOBUYASU , OKABE YUTAKA
Published: October 2004

We review our recent studies on the dynamical correlations in MC simulations from the view point of the statistical dependence. Attentions are paid to the reduction of the statistical degrees of freedom for correlated data. Possible biases on several cumulants, such as the susceptibility and the Binder number due to finite MC length are discussed. A new method for calculating the equilibrium relaxation time from the analysis of the statistical dependence is presented. We apply it to the critical dynamics of the Ising model to estimate the dynamical critical exponent accurately.
SURPRISES IN SIMPLE DRIVEN SYSTEMS: PHASE TRANSITIONS IN A THREE-STATE MODEL
Authors: ZIA R.K.P. , SCHMITTMANN B.
Published: October 2004

Motivated by several physical systems, driven models with three states (i.e., lattice gases with two species of particles) are considered. We study a non-interacting system in which the two species are driven in opposite directions. As the overall particle density is varied, a transition occurs, from a disordered phase with high particle current, to ordered, locked phases with minimal current. Further, there are several locked states, with the frequency of their occurrence depending on the aspect ratio of the system. Many of the novel phenomena can be understood in terms of a simple continuum theory. Variations and generalizations, as well as the physical systems they model, are discussed.
HYBRID MONTE CARLO SPIN DYNAMICS SIMULATION OF DILUTE MAGNETIC ALLOY
Authors: MATSUBARA F. , MORISHITA K.
Published: October 2004

We make a hybrid Monte Carlo spin dynamics simulation of a dilute system of the classical Heisenberg spins coupled via the Ruderman-Kittel-Kasuya-Yoshida interaction in a wide range of spin concentration x. We find the critical concentration of the ferromagnetism xc ~0.15. For x < xc, the data predict the spin glass phase transition at a finite temperature Tg. For x ≳ xc, those predict the reentrance to a mixed phase of the ferromagnetism and a transverse spin glass at a low temperature Tm. The obtained phase diagram is seen to be quite similar to those of dilute alloys Au1 − xFex and Pt1 − xMnx.
MONTE CARLO STUDIES ON FRUSTRATED QUANTUM SPIN SYSTEMS BY A NEW APPROACH TO THE NEGATIVE-SIGN PROBLEM: TRANSFER-MATRIX MONTE CARLO METHOD
Authors: MIYASHITA Seiji , NAKAMURA Tota
Published: October 2004

A new technique for the negative sign problem in the quantum Monte Carlo method using the Suzuki-Trotter decomposition is introduced. In order to reduce the cancellation between between samples with positive and negative weights, we make use of the transfer matrix method, which has been named the Transfer-Matrix Monte Carlo method. Applications to the Heisenberg antiferromagnet on the ∆-chain and on the kagome lattice, and also to the Kondo lattice system also are given.
CRITICAL DYNAMICS IN THE 3D SPIN-1/2 HEISENBERG MODEL: A DECOUPLED CELL MONTE CARLO STUDY
Authors: SISSON CYNTHIA J.
Published: October 2004

The three-dimensional spin-1/2 Heisenberg model on a simple cubic lattice is studied for ferromagnetic and antiferromagnetic interactions using the Decoupled Cell Method for quantum Monte Carlo. Results for the relaxation time τL are determined for both ferromagnetic and antiferromagnetic systems and found to be similar to those found for the classical (s → ∞) Heisenberg model. The scaling of τL with system size is used to extract the dynamical critical exponent z for the two systems. The values of z = 1.98 ± 0.12 for the ferromagnet and z = 1.94 ± 0.09 for the antiferromagnet are in good agreement with theoretical predictions and previous Monte Carlo studies of the classical Heisenberg model.
MONTE CARLO SIMULATION OF RANDOM BOSON HUBBARD MODEL
Authors: HATANO NAOMICHI
Published: October 2004

A Monte Carlo algorithm for the random Boson Hubbard model is reported. The analytic expression of the matrix elements is presented, and the ergodicity of the Monte Carlo flips is discussed. The results in one dimension supports a previously proposed perturbational scaling argument.
IMPURITY BOND EFFECT ON THE ANTIFERROMAGNETIC ISING-LIKE CHAIN
Authors: KABURAGI MAKOTO , MORIWAKI TAKAO , NISHINO TOMOTOSHI , TONEGAWA TAKASHI
Published: October 2004

We investigate the effect of an impurity bond on low-lying excited states of the antiferromagnetic Ising-like chain in terms of domain-wall excitations, using an analytical method and a numerical method. The fundamental difference between the chains with even and odd numbers of spins is discussed.
ON THE LOCAL-FIELD DISTRIBUTION IN ATTRACTOR NEURAL NETWORKS
Authors: KORUTCHEVA E. , KOROUTCHEV K.
Published: October 2004

In this paper a simple two-layer neural network's model, similar to that studied by D. Amit and N. Brunel,11 is investigated in the frames of the mean-field approximation. The distributions of the local fields are analytically derived and compared to those obtained in Ref. 11. The dynamic properties are discussed and the basin of attraction in some parametric space is found. A procedure for driving the system into a basin of attraction by using a regulation imposed on the network is proposed. The effect of outer stimulus is shown to have a destructive influence on the attractor, forcing the latter to disappear if the distribution of the stimulus has high enough variance or if the stimulus has a spatial structure with sufficient contrast.

The techniques, used in this paper, for obtaining the analytical results can be applied to more complex topologies of linked recurrent neural networks.
HYPER-SYSTOLIC PROCESSING ON APE100/QUADRICS: n2-LOOP COMPUTATIONS
Authors: LIPPERT THOMAS , RITZENHÖFER GERO , GLAESSNER UWE , HOEBER HENNING , SEYFRIED ARMIN , SCHILLING KLAUS
Published: October 2004

We investigate the performance gains from hyper-systolic implementations of n2-loop problems on the massively parallel computer Quadrics, exploiting its three-dimensional interprocessor connectivity. For illustration we study the communication aspects of an exact molecular dynamics simulation of n particles with Coulomb (or gravitational) interactions.

We compare the interprocessor communication costs of the standard-systolic and the hyper-systolic approaches for various granularities. We predict gain factors as large as three on the Q4 and eight on the QH4 and measure actual performances on these machine configurations. We conclude that it appears feasible to investigate the thermodynamics of a full gravitating n-body problem with O(16.000) particles using the new method on a QH4 system.
THE PRINCIPLE OF INDIRECT ELIMINATION
Authors: BÄKER MARTIN
Published: October 2004

The principle of indirect elimination states that an algorithm for solving discretized differential equations can be used to identify its own bad-converging modes. When the number of bad-converging modes of the algorithm is not too large, the modes thus identified can be used to strongly improve the convergence. The method presented here is applicable to any linear algorithm like relaxation or multigrid. An example from theoretical physics, the Dirac equation in the presence of almost-zero-modes arising from instantons, is studied. Using the principle, bad-converging modes are removed efficiently. It is sketched how the method can be used for a Conjugate Gradient algorithm. Applied locally, the principle is one of the main ingredients of the Iteratively Smoothing Unigrid algorithm.
FILTER METHODS IN TRACK AND VERTEX RECONSTRUCTION
Authors: REGLER M. , FRÜHWIRTH R. , MITAROFF W.
Published: October 2004

After a review of widely used pattern recognition methods we present the Kalman filter and the associated smoother as a recursive variant of conventional least squares estimators. We first discuss its application to the reconstruction of charged tracks, including simultaneous track finding and track fitting and a robustification of the filter. This section is concluded by a case study of track reconstruction strategy in the DELPHI experiment. The second part deals with vertex reconstruction, including the detection of outlying tracks. It is shown that the detection of secondary vertices can be further improved by a robustification of the vertex fit via the M-estimator.
ON THE APPLICATION OF A NOVEL ALGORITHM TO HYDRODYNAMIC DIFFUSION AND VELOCITY FLUCTUATIONS IN SEDIMENTING SYSTEMS
Authors: KALTHOFF WOLFGANG , SCHWARZER STEFAN , RISTOW GERALD , HERRMANN HANS J.
Published: October 2004

We present a numerical method to deal efficiently with large numbers of particles in incompressible fluids. The interactions between particles and fluid are taken into account by a physically motivated ansatz based on locally defined drag forces. We demonstrate the validity of our approach by performing numerical simulations of sedimenting non-Brownian spheres in two spatial dimensions and compare our results with experiments. Our method reproduces qualitatively important aspects of the experimental findings, in particular the strong anisotropy of the hydrodynamic bulk self-diffusivities.
NUMERICAL CALCULATIONS OF THE SMALL RELATIVISTIC CORRECTIONS OF ANISOTROPIC QUANTUM HARMONIC OSCILLATOR ENERGY EIGENVALUES: THE TWO- AND THREE-DIMENSIONAL CASE
Authors: ARDELEAN GHEORGHE , COTĂESCU ION I.
Published: October 2004

In this paper the small relativistic correction for the energy eigenvalues of the two- and three-dimensional anisotropic quantum harmonic oscillator are calculated, using as eigenstates [Formula: see text], for different values of the relativistic parameters βi ≡ ħwi / m0c2 with i = 1, 2 and 3.
NONEQUILIBRIUM RELAXATION OF THE 3D ±J EA MODEL
Authors: WANG FUGAO , KAWASHIMA NAOKI , SUZUKI MASUO
Published: October 2004

By studying correlation functions of two replicas with the damage spreading method, the spin glass transition temperature of the 3D ±J EA model is evaluated by using Campbell's criterion D (Tg, t → ∞) = 1/2 or [Formula: see text], where D and [Formula: see text] are the Hamming distance and the overlap of the two replicas, respectively. The two replicas evolve with the same series of the random numbers and with initial spin-configurations related to each other by [Formula: see text] (for all i). From our simulational data for system sizes up to L = 64, the spin glass transition temperature is estimated as Tg = 1.18 ± 0.06. The replica simulational method and the damage spreading method are compared with each other. The relaxation exponents of the magnetization and the correlation functions are also estimated in the vicinity of Tg on a large lattice and the dynamical critical exponent z is estimated as z ≃ 6.0.
NON-UNIQUENESS OF SPANNING CLUSTERS IN TWO TO FIVE DIMENSIONS
Authors: SEN PARONGAMA
Published: October 2004

We study the probability of getting more than one spanning cluster at pc for site percolation in dimensions of two to five. The probability increases from ~0.003 in two dimensions to ~0.16 in five. This shows that although there can be only one infinite cluster of finite density, there can be several spanning clusters at the same time.
A CORRELATION BETWEEN PERCOLATION MODEL AND ISING MODEL
Authors: HACKL R. , MORGENSTERN I.
Published: October 2004

In this article we will expose a connection between critical values of percolation and Ising model, i.e., the percolation threshold pc, and the critical temperature Tc and energy Ec, respectively, by the approximation [Formula: see text]. For the two-dimensional square lattice even the identity holds. For higher dimensions — up to d = 7 — and other lattice types we find remarkably small differences from one to five percent.