bluuebunny/crossref_metadata_2025 huggingface dataset:
A Recurrent Network Implementation of Time Series Classification
Authors: Petridis Vassilios , Kehagias Athanasios
Published: April 2008

An incremental credit assignment (ICRA) scheme is introduced and applied to time series classification. It has been inspired from Bayes' rule, but the Bayesian connection is not necessary either for its development or proof of its convergence properties. The ICRA scheme is implemented by a recurrent, hierarchical, modular neural network, which consists of a bank of predictive modules at the lower level, and a decision module at the higher level. For each predictive module, a credit function is computed; the module that best predicts the observed time series behavior receives highest credit. We prove that the credit functions converge (with probability one) to correct values. Simulation results are also presented.
Temporal Segmentation in a Neural Dynamic System
Authors: Horn David , Opher Irit
Published: April 2008

Oscillatory attractor neural networks can perform temporal segmentation, i.e., separate the joint inputs they receive, through the formation of staggered oscillations. This property, which may be basic to many perceptual functions, is investigated here in the context of a symmetric dynamic system. The fully segmented mode is one type of limit cycle that this system can develop. It can be sustained for only a limited number n of oscillators. This limitation to a small number of segments is a basic phenomenon in such systems. Within our model we can explain it in terms of the limited range of narrow subharmonic solutions of the single nonlinear oscillator. Moreover, this point of view allows us to understand the dominance of three leading amplitudes in solutions of partial segmentation, which are obtained for high n. The latter are also abundant when we replace the common input with a graded one, allowing for different inputs to different oscillators. Switching to an input with fluctuating components, we obtain segmentation dominance for small systems and quite irregular waveforms for large systems.
Circular Nodes in Neural Networks
Authors: Kirby Michael J. , Miranda Rick
Published: April 2008

In the usual construction of a neural network, the individual nodes store and transmit real numbers that lie in an interval on the real line; the values are often envisioned as amplitudes. In this article we present a design for a circular node, which is capable of storing and transmitting angular information. We develop the forward and backward propagation formulas for a network containing circular nodes. We show how the use of circular nodes may facilitate the characterization and parameterization of periodic phenomena in general. We describe applications to constructing circular self-maps, periodic compression, and one-dimensional manifold decomposition. We show that a circular node may be used to construct a homeomorphism between a trefoil knot in ℝ3 and a unit circle. We give an application with a network that encodes the dynamic system on the limit cycle of the Kuramoto-Sivashinsky equation. This is achieved by incorporating a circular node in the bottleneck layer of a three-hidden-layer bottleneck network architecture. Exploiting circular nodes systematically offers a neural network alternative to Fourier series decomposition in approximating periodic or almost periodic functions.
Hierarchical, Unsupervised Learning with Growing via Phase Transitions
Authors: Miller David , Rose Kenneth
Published: April 2008

We address unsupervised learning subject to structural constraints, with particular emphasis placed on clustering with an imposed decision tree structure. Most known methods are greedy, optimizing one node of the tree at a time to minimize a local cost. By constrast, we develop a joint optimization method, derived based on information-theoretic principles and closely related to known methods in statistical physics. The approach is inspired by the deterministic annealing algorithm for unstructured data clustering, which was based on maximum entropy inference. The new approach is founded on the principle of minimum cross-entropy, using informative priors to approximate the unstructured clustering solution while imposing the structural constraint. The resulting method incorporates supervised learning principles applied in an unsupervised problem setting. In our approach, the tree “grows” by a sequence of bifurcations that occur while optimizing an effective free energy cost at decreasing temperature scales. Thus, estimates of the tree size and structure are naturally obtained at each temperature in the process. Examples demonstrate considerable improvement over known methods.
Parameter Extraction from Population Codes: A Critical Assessment
Authors: Snippe Herman P.
Published: April 2008

In perceptual systems, a stimulus parameter can be extracted by determining the center-of-gravity of the response profile of a population of neural sensors. Likewise at the motor end of a neural system, center-of-gravity decoding, also known as vector decoding, generates a movement direction from the neural activation profile. We evaluate these schemes from a statistical perspective, by comparing their statistical variance with the minimum variance possible for an unbiased parameter extraction from the noisy neuronal ensemble activation profile. Center-of-gravity decoding can be statistically optimal. This is the case for regular arrays of sensors with gaussian tuning profiles that have an output described by Poisson statistics, and for arrays of sensors with a sinusoidal tuning profile for the (angular) parameter estimated. However, there are also many cases in which center-of-gravity decoding is highly inefficient. This includes the important case where sensor positions are very irregular. Finally, we study the robustness of center-of-gravity decoding against response nonlinearities at different stages of an information processing hierarchy. We conclude that, in neural systems, instead of representing a parameter explicitly, it is safer to leave the parameter coded implicitly in a neuronal ensemble activation profile.
Coupling the Neural and Physical Dynamics in Rhythmic Movements
Authors: Hatsopoulos Nicholas G.
Published: April 2008

A pair of coupled oscillators simulating a central pattern generator (CPG) interacting with a pendular limb were numerically integrated. The CPG was represented as a van der Pol oscillator and the pendular limb was modeled as a linearized, hybrid spring-pendulum system. The CPG oscillator drove the pendular limb while the pendular limb modulated the frequency of the CPG. Three results were observed. First, sensory feedback influenced the oscillation frequency of the coupled system. The oscillation frequency was lower in the absence of sensory feedback. Moreover, if the muscle gain was decreased, thereby decreasing the oscillation amplitude of the pendular limb and indirectly lowering the effect of sensory feedback, the oscillation frequency decreased monotonically. This is consistent with experimental data (Williamson and Roberts 1986). Second, the CPG output usually led the angular displacement of the pendular limb by a phase of 90° regardless of the length of the limb. Third, the frequency of the coupled system tuned itself to the resonant frequency of the pendular limb. Also, the frequency of the coupled system was highly resistant to changes in the endogenous frequency of the CPG. The results of these simulations support the view that motor behavior emerges from the interaction of the neural dynamics of the nervous system and the physical dynamics of the periphery.
The VC Dimension and Pseudodimension of Two-Layer Neural Networks with Discrete Inputs
Authors: Bartlett Peter L. , Williamson Robert C.
Published: April 2008

We give upper bounds on the Vapnik-Chervonenkis dimension and pseudodimension of two-layer neural networks that use the standard sigmoid function or radial basis function and have inputs from {−D, …,D}n. In Valiant's probably approximately correct (pac) learning framework for pattern classification, and in Haussler's generalization of this framework to nonlinear regression, the results imply that the number of training examples necessary for satisfactory learning performance grows no more rapidly than W log (WD), where W is the number of weights. The previous best bound for these networks was O(W4).
A Theoretical and Experimental Account of n-Tuple Classifier Performance
Authors: Rohwer Richard , Morciniec Michał
Published: April 2008

The n-tuple recognition method is briefly reviewed, summarizing the main theoretical results. Large-scale experiments carried out on Stat-Log project datasets confirm this method as a viable competitor to more popular methods due to its speed, simplicity, and accuracy on the majority of a wide variety of classification problems. A further investigation into the failure of the method on certain datasets finds the problem to be largely due to a mismatch between the scales which describe generalization and data sparseness.
The Effects of Adding Noise During Backpropagation Training on a Generalization Performance
Authors: An Guozhong
Published: April 2008

We study the effects of adding noise to the inputs, outputs, weight connections, and weight changes of multilayer feedforward neural networks during backpropagation training. We rigorously derive and analyze the objective functions that are minimized by the noise-affected training processes. We show that input noise and weight noise encourage the neural-network output to be a smooth function of the input or its weights, respectively. In the weak-noise limit, noise added to the output of the neural networks only changes the objective function by a constant. Hence, it cannot improve generalization. Input noise introduces penalty terms in the objective function that are related to, but distinct from, those found in the regularization approaches. Simulations have been performed on a regression and a classification problem to further substantiate our analysis. Input noise is found to be effective in improving the generalization performance for both problems. However, weight noise is found to be effective in improving the generalization performance only for the classification problem. Other forms of noise have practically no effect on generalization.
Stable Encoding of Large Finite-State Automata in Recurrent Neural Networks with Sigmoid Discriminants
Authors: Omlin Christian W. , Giles C. Lee
Published: April 2008

We propose an algorithm for encoding deterministic finite-state automata (DFAs) in second-order recurrent neural networks with sigmoidal discriminant function and we prove that the languages accepted by the constructed network and the DFA are identical. The desired finite-state network dynamics is achieved by programming a small subset of all weights. A worst case analysis reveals a relationship between the weight strength and the maximum allowed network size, which guarantees finite-state behavior of the constructed network. We illustrate the method by encoding random DFAs with 10, 100, and 1000 states. While the theory predicts that the weight strength scales with the DFA size, we find empirically the weight strength to be almost constant for all the random DFAs. These results can be explained by noting that the generated DFAs represent average cases. We empirically demonstrate the existence of extreme DFAs for which the weight strength scales with DFA size.
Unicycling Helps Your French: Spontaneous Recovery of Associations by Learning Unrelated Tasks
Authors: Harvey Inman , Stone James V.
Published: April 2008

We demonstrate that imperfect recall of a set of associations can usually be improved by training on a new, unrelated set of associations. This spontaneous recovery of associations is a consequence of the high dimensionality of weight spaces, and is therefore not peculiar to any single type of neural net. Accordingly, this work may have implications for spontaneous recovery of memory in the central nervous system.
Controlling the Magnification Factor of Self-Organizing Feature Maps
Authors: Bauer H.-U. , Der R.
Published: April 2008

The magnification exponents μ occurring in adaptive map formation algorithms like Kohonen's self-organizing feature map deviate for the information theoretically optimal value μ = 1 as well as from the values that optimize, e.g., the mean square distortion error (μ = 1/3 for one-dimensional maps). At the same time, models for categorical perception such as the "perceptual magnet" effect, which are based on topographic maps, require negative magnification exponents μ < 0. We present an extension of the self-organizing feature map algorithm, which utilizes adaptive local learning step sizes to actually control the magnification properties of the map. By change of a single parameter, maps with optimal information transfer, with various minimal reconstruction errors, or with an inverted magnification can be generated. Analytic results on this new algorithm are complemented by numerical simulations.
Using Neural Networks to Model Conditional Multivariate Densities
Authors: Williams Peter M.
Published: April 2008

Neural network outputs are interpreted as parameters of statistical distributions. This allows us to fit conditional distributions in which the parameters depend on the inputs to the network. We exploit this in modeling multivariate data, including the univariate case, in which there may be input-dependent (e.g., time-dependent) correlations between output components. This provides a novel way of modeling conditional correlation that extends existing techniques for determining input-dependent (local) error bars.
A Novel Optimizing Network Architecture with Applications
Authors: Rangarajan Anand , Gold Steven , Mjolsness Eric
Published: April 2008

We present a novel optimizing network architecture with applications in vision, learning, pattern recognition, and combinatorial optimization. This architecture is constructed by combining the following techniques: (1) deterministic annealing, (2) self-amplification, (3) algebraic transformations, (4) clocked objectives, and (5) softassign. Deterministic annealing in conjunction with self-amplification avoids poor local minima and ensures that a vertex of the hypercube is reached. Algebraic transformations and clocked objectives help partition the relaxation into distinct phases. The problems considered have doubly stochastic matrix constraints or minor variations thereof. We introduce a new technique, softassign, which is used to satisfy this constraint. Experimental results on different problems are presented and discussed.
Online Steepest Descent Yields Weights with Nonnormal Limiting Distribution
Authors: Mukherjee Sayandev , Fine Terrence L.
Published: April 2008

We study the asymptotic properties of the sequence of iterates of weight-vector estimates obtained by training a feedforward neural network with a basic gradient-descent method using a fixed learning rate and no batch-processing. Earlier results based on stochastic approximation techniques (Kuan and Hornik 1991; Finnoff 1993; Bucklew et al. 1993) have established the existence of a gaussian limiting distribution for the weights, but they apply only in the limiting case of a zero learning rate. We here prove, from an exact analysis of the one-dimensional case and constant learning rate, weak convergence to a distribution that is not gaussian in general. We also run simulations to compare and contrast the results of our analysis with those of stochastic approximation.
A Numerical Study on Learning Curves in Stochastic Multilayer Feedforward Networks
Authors: Müller K.-R. , Finke M. , Murata N. , Schulten K. , Amari S.
Published: April 2008

The universal asymptotic scaling laws proposed by Amari et al. are studied in large scale simulations using a CM5. Small stochastic multilayer feedforward networks trained with backpropagation are investigated. In the range of a large number of training patterns t, the asymptotic generalization error scales as 1/t as predicted. For a medium range t a faster 1/t2 scaling is observed. This effect is explained by using higher order corrections of the likelihood expansion. It is shown for small t that the scaling law changes drastically, when the network undergoes a transition from strong overfitting to effective learning.
Modeling Conditional Probability Distributions for Periodic Variables
Authors: Bishop Christopher M. , Nabney Ian T.
Published: April 2008

Most conventional techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we introduce three related techniques for tackling such problems, and investigate their performance using synthetic data. We then apply these techniques to the problem of extracting the distribution of wind vector directions from radar scatterometer data gathered by a remote-sensing satellite.
Biologically Plausible Error-Driven Learning Using Local Activation Differences: The Generalized Recirculation Algorithm
Authors: O'Reilly Randall C.
Published: April 2008

The error backpropagation learning algorithm (BP) is generally considered biologically implausible because it does not use locally available, activation-based variables. A version of BP that can be computed locally using bidirectional activation recirculation (Hinton and McClelland 1988) instead of backpropagated error derivatives is more biologically plausible. This paper presents a generalized version of the recirculation algorithm (GeneRec), which overcomes several limitations of the earlier algorithm by using a generic recurrent network with sigmoidal units that can learn arbitrary input/output mappings. However, the contrastive Hebbian learning algorithm (CHL, also known as DBM or mean field learning) also uses local variables to perform error-driven learning in a sigmoidal recurrent network. CHL was derived in a stochastic framework (the Boltzmann machine), but has been extended to the deterministic case in various ways, all of which rely on problematic approximations and assumptions, leading some to conclude that it is fundamentally flawed. This paper shows that CHL can be derived instead from within the BP framework via the GeneRec algorithm. CHL is a symmetry-preserving version of GeneRec that uses a simple approximation to the midpoint or second-order accurate Runge-Kutta method of numerical integration, which explains the generally faster learning speed of CHL compared to BI. Thus, all known fully general error-driven learning algorithms that use local activation-based variables in deterministic networks can be considered variations of the GeneRec algorithm (and indirectly, of the backpropagation algorithm). GeneRec therefore provides a promising framework for thinking about how the brain might perform error-driven learning. To further this goal, an explicit biological mechanism is proposed that would be capable of implementing GeneRec-style learning. This mechanism is consistent with available evidence regarding synaptic modification in neurons in the neocortex and hippocampus, and makes further predictions.
Effects of Nonlinear Synapses on the Performance of Multilayer Neural Networks
Authors: Dündar G. , Hsu F-C. , Rose K.
Published: April 2008

The problems arising from the use of nonlinear multipliers in multilayer neural network synapse structures are discussed. The errors arising from the neglect of nonlinearities are shown and the effect of training in eliminating these errors is discussed. A method for predicting the final errors resulting from nonlinearities is described. Our approximate results are compared with the results from circuit simulations of an actual multiplier circuit.
Neuronal-Based Synaptic Compensation: A Computational Study in Alzheimer's Disease
Authors: Horn David , Levy Nir , Ruppin Eytan
Published: April 2008

In the framework of an associative memory model, we study the interplay between synaptic deletion and compensation, and memory deterioration, a clinical hallmark of Alzheimer's disease. Our study is motivated by experimental evidence that there are regulatory mechanisms that take part in the homeostasis of neuronal activity and act on the neuronal level. We show that following synaptic deletion, synaptic compensation can be carried out efficiently by a local, dynamic mechanism, where each neuron maintains the profile of its incoming post-synaptic current. Our results open up the possibility that the primary factor in the pathogenesis of cognitive deficiencies in Alzheimer's disease (AD) is the failure of local neuronal regulatory mechanisms. Allowing for neuronal death, we observe two pathological routes in AD, leading to different correlations between the levels of structural damage and functional decline.
The Error Surface of the Simplest XOR Network Has Only Global Minima
Authors: Sprinkhuizen-Kuyper Ida G. , Boers Egbert J. W.
Published: April 2008

The artificial neural network with one hidden unit and the input units connected to the output unit is considered. It is proven that the error surface of this network for the patterns of the XOR problem has minimum values with zero error and that all other stationary points of the error surface are saddlepoints. Also, the volume of the regions in weight space with saddlepoints is zero, hence training this network on the four patterns of the XOR problem using, e.g., backpropagation with momentum, the correct solution with error zero will be reached in the limit with probability one.
Statistical Approach to Shape from Shading: Reconstruction of Three-Dimensional Face Surfaces from Single Two-Dimensional Images
Authors: Atick Joseph J. , Griffin Paul A. , Redlich A. Norman
Published: April 2008

The human visual system is proficient in perceiving three-dimensional shape from the shading patterns in a two-dimensional image. How it does this is not well understood and continues to be a question of fundamental and practical interest. In this paper we present a new quantitative approach to shape-from-shading that may provide some answers. We suggest that the brain, through evolution or prior experience, has discovered that objects can be classified into lower-dimensional object-classes as to their shape. Extraction of shape from shading is then equivalent to the much simpler problem of parameter estimation in a low-dimensional space. We carry out this proposal for an important class of three-dimensional (3D) objects: human heads. From an ensemble of several hundred laser-scanned 3D heads, we use principal component analysis to derive a low-dimensional parameterization of head shape space. An algorithm for solving shape-from-shading using this representation is presented. It works well even on real images where it is able to recover the 3D surface for a given person, maintaining facial detail and identity, from a single 2D image of his face. This algorithm has applications in face recognition and animation.
No Free Lunch for Cross-Validation
Authors: Zhu Huaiyu , Rohwer Richard
Published: April 2008

It is known theoretically that an algorithm cannot be good for an arbitrary prior. We show that in practical terms this also applies to the technique of “cross-validation,” which has been widely regarded as defying this general rule. Numerical examples are analyzed in detail. Their implications to researches on learning algorithms are discussed.
A Self-Organizing Model of “Color Blob” Formation
Authors: Barrow Harry G. , Bray Alistair J. , Budd Julian M. L.
Published: April 2008

This paper explores the possibility that the formation of color blobs in primate striate cortex can be partly explained through the process of activity-based self-organization. We present a simulation of a highly simplified model of visual processing along the parvocellular pathway, that combines precortical color processing, excitatory and inhibitory cortical interactions, and Hebbian learning. The model self-organizes in response to natural color images and develops islands of unoriented, color-selective cells within a sea of contrast-sensitive, orientation-selective cells. By way of understanding this topography, a principal component analysis of the color inputs presented to the network reveals that the optimal linear coding of these inputs keeps color information and contrast information separate.
Learning Perceptually Salient Visual Parameters Using Spatiotemporal Smoothness Constraints
Authors: Stone James V.
Published: April 2008

A model is presented for unsupervised learning of low level vision tasks, such as the extraction of surface depth. A key assumption is that perceptually salient visual parameters (e.g., surface depth) vary smoothly over time. This assumption is used to derive a learning rule that maximizes the long-term variance of each unit's outputs, whilst simultaneously minimizing its short-term variance. The length of the half-life associated with each of these variances is not critical to the success of the algorithm. The learning rule involves a linear combination of anti-Hebbian and Hebbian weight changes, over short and long time scales, respectively. This maximizes the information throughput with respect to low-frequency parameters implicit in the input sequence. The model is used to learn stereo disparity from temporal sequences of random-dot and gray-level stereograms containing synthetically generated subpixel disparities. The presence of temporal discontinuities in disparity does not prevent learning or generalization to previously unseen image sequences. The implications of this class of unsupervised methods for learning in perceptual systems are discussed.
Learning and Generalization in Cascade Network Architectures
Authors: Littmann Enno , Ritter Helge
Published: April 2008

Incrementally constructed cascade architectures are a promising alternative to networks of predefined size. This paper compares the direct cascade architecture (DCA) proposed in Littmann and Ritter (1992) to the cascade-correlation approach of Fahlman and Lebiere (1990) and to related approaches and discusses the properties on the basis of various benchmark results. One important virtue of DCA is that it allows the cascading of entire subnetworks, even if these admit no error-backpropagation. Exploiting this flexibility and using LLM networks as cascaded elements, we show that the performance of the resulting network cascades can be greatly enhanced compared to the performance of a single network. Our results for the Mackey-Glass time series prediction task indicate that such deeply cascaded network architectures achieve good generalization even on small data sets, when shallow, broad architectures of comparable size suffer from overfitting. We conclude that the DCA approach offers a powerful and flexible alternative to existing schemes such as, e.g., the mixtures of experts approach, for the construction of modular systems from a wide range of subnetwork types.
Hybrid Modeling, HMM/NN Architectures, and Protein Applications
Authors: Baldi Pierre , Chauvin Yves
Published: April 2008

We describe a hybrid modeling approach where the parameters of a model are calculated and modulated by another model, typically a neural network (NN), to avoid both overfitting and underfitting. We develop the approach for the case of Hidden Markov Models (HMMs), by deriving a class of hybrid HMM/NN architectures. These architectures can be trained with unified algorithms that blend HMM dynamic programming with NN backpropagation. In the case of complex data, mixtures of HMMs or modulated HMMs must be used. NNs can then be applied both to the parameters of each single HMM, and to the switching or modulation of the models, as a function of input or context. Hybrid HMM/NN architectures provide a flexible NN parameterization for the control of model structure and complexity. At the same time, they can capture distributions that, in practice, are inaccessible to single HMMs. The HMM/NN hybrid approach is tested, in its simplest form, by constructing a model of the immunoglobulin protein family. A hybrid model is trained, and a multiple alignment derived, with less than a fourth of the number of parameters used with previous single HMMs.
Synchronized Action of Synaptically Coupled Chaotic Model Neurons
Authors: Abarbanel Henry D. I. , Huerta R. , Rabinovich M. I. , Rulkov N. F. , Rowat P. F. , Selverston A. I.
Published: April 2008

Experimental observations of the intracellular recorded electrical activity in individual neurons show that the temporal behavior is often chaotic. We discuss both our own observations on a cell from the stom-atogastric central pattern generator of lobster and earlier observations in other cells.

In this paper we work with models of chaotic neurons, building on models by Hindmarsh and Rose for bursting, spiking activity in neurons. The key feature of these simplified models of neurons is the presence of coupled slow and fast subsystems. We analyze the model neurons using the same tools employed in the analysis of our experimental data.

We couple two model neurons both electrotonically and electrochemically in inhibitory and excitatory fashions. In each of these cases, we demonstrate that the model neurons can synchronize in phase and out of phase depending on the strength of the coupling. For normal synaptic coupling, we have a time delay between the action of one neuron and the response of the other. We also analyze how the synchronization depends on this delay. A rich spectrum of synchronized behaviors is possible for electrically coupled neurons and for inhibitory coupling between neurons. In synchronous neurons one typically sees chaotic motion of the coupled neurons. Excitatory coupling produces essentially periodic voltage trajectories, which are also synchronized. We display and discuss these synchronized behaviors using two “distance” measures of the synchronization.
Binocular Receptive Field Models, Disparity Tuning, and Characteristic Disparity
Authors: Zhu Yu-Dong , Qian Ning
Published: April 2008

Disparity tuning of visual cells in the brain depends on the structure of their binocular receptive fields (RFs). Freeman and coworkers have found that binocular RFs of a typical simple cell can be quantitatively described by two Gabor functions with the same gaussian envelope but different phase parameters in the sinusoidal modulations (Freeman and Ohzawa 1990). This phase-parameter-based RF description has recently been questioned by Wagner and Frost (1993) based on their identification of a so-called characteristic disparity (CD) in some cells' disparity tuning curves. They concluded that their data favor the traditional binocular RF model, which assumes an overall positional shift between a cell's left and right RFs. Here we set to resolve this issue by studying the dependence of cells' disparity tuning on their underlying RF structures through mathematical analyses and computer simulations. We model the disparity tuning curves in Wagner and Frost's experiments and demonstrate that the mere existence of approximate CDs in real cells cannot be used to distinguish the phase-parameter-based RF description from the traditional position-shift-based RF description. Specifically, we found that model simple cells with either type of RF description do not have a CD. Model complex cells with the position-shift-based RF description have a precise CD, and those with the phase-parameter-based RF description have an approximate CD. We also suggest methods for correctly distinguishing the two types of RF descriptions. A hybrid of the two RF models may be required to fit the behavior of some real cells, and we show how to determine the relative contributions of the two RF models.
Response Characteristics of a Low-Dimensional Model Neuron
Authors: Cartling Bo
Published: April 2008

It is shown that a low-dimensional model neuron with a response time constant smaller than the membrane time constant closely reproduces the activity and excitability behavior of a detailed conductance-based model of Hodgkin-Huxley type. The fast response of the activity variable also makes it possible to reduce the model to a one-dimensional model, in particular for typical conditions. As an example, the reduction to a single-variable model from a multivariable conductance-based model of a neocortical pyramidal cell with somatic input is demonstrated. The conditions for avoiding a spurious damped oscillatory response to a constant input are derived, and it is shown that a limit-cycle response cannot occur. The capability of the low-dimensional model to approximate higher-dimensional models accurately makes it useful for describing complex dynamics of nets of interconnected neurons. The simplicity of the model facilitates analytic studies, elucidation of neurocomputational mechanisms, and applications to large-scale systems.
Singular Perturbation Analysis of Competitive Neural Networks with Different Time Scales
Authors: Meyer-Bäse Anke , Ohl Frank , Scheich Henning
Published: April 2008

The dynamics of complex neural networks must include the aspects of long- and short-term memory. The behavior of the network is characterized by an equation of neural activity as a fast phenomenon and an equation of synaptic modification as a slow part of the neural system. The main idea of this paper is to apply a stability analysis method of fixed points of the combined activity and weight dynamics for a special class of competitive neural networks. We present a quadratic-type Lyapunov function for the flow of a competitive neural system with fast and slow dynamic variables as a global stability method and a modality of detecting the local stability behavior around individual equilibrium points.
Assessment of Early Changes in the Segmental Functions of the Left and the Right Ventricles After Biventricular Pacing in Heart Failure: A Study With Tissue Doppler Imaging
Authors: Bilge Ahmet Kaya , Ozben Beste , Ozyigit Tolga , Acar Deniz , Hunerel Dilek , Adalet Kamil , Nisanci Yilmaz
Published: April 2008

Tissue Doppler imaging allows assessment of systolic and diastolic regional ventricular function. The aim of this study was to assess early changes in regional systolic and diastolic functions and differences in transition time to contraction between the ventricles after cardiac resynchronization therapy. Fourteen patients were included, who underwent echocardiography before and 1 month after resynchronization. The difference between transition time to contraction of left and right ventricles decreased to 24.4 ± 10.7 milliseconds from 65.3 ± 18.2 milliseconds after resynchronization therapy ( P = .001). There was a significant relation between the decrease in difference between transition time and increase in ejection fraction (r = 0.80, P = .002). Early or late diastolic myocardial motion increased in 7 segments of left and 2 segments of right ventricles. Systolic myocardial motion increased in 7 segments of left and in all segments of right ventricles. Resynchronization therapy improved systolic and diastolic functions in both ventricles. The difference between transition time to contraction of ventricles might be helpful in estimating optimal resynchronization.
Relation of Serum Uric Acid Levels With the Presence and Severity of Angiographic Coronary Artery Disease
Authors: Gur Mustafa , Yilmaz Remzi , Demirbag Recep , Aksoy Nurten
Published: April 2008

The aim of this study is to investigate the association between uric acid level and severity of coronary artery disease. Consecutive 495 patients with coronary artery disease and 356 individuals with normal coronary angiograms were included in the study. Severity of coronary artery disease was evaluated using the Gensini score index. For both groups, conventional risk factors, the levels of uric acid, and other biochemical markers were assessed. The mean uric acid levels of the patient group were significantly higher than those of the control group ( P = .002). Mean uric acid levels were higher in men than in women ( P < .001). In the coronary artery disease group, there was no significant correlation between uric acid levels and Gensini score in both sexes. Thus, it can be concluded that uric acid level is associated with the presence but not with the severity of coronary artery disease.
Fate of Incidental, Asymptomatic Lesions Discovered During Percutaneous Coronary Intervention
Authors: Caymaz Hüseyin Oğuz , Yüksel Güvenç
Published: April 2008

The authors aimed to determine the incidence and angiographic features associated with plaque progression requiring nontarget lesion percutaneous coronary intervention after culprit lesion percutaneous coronary intervention. Of 945 consecutive percutaneous coronary interventions reviewed, 100 patients who required nontarget lesion percutaneous coronary intervention in the following year of the index percutaneous coronary intervention were found and compared with 100 consecutive patients who did not require nontarget lesion percutaneous coronary intervention. Patients with restenosis were excluded. Incidence of clinical plaque progression leading to additional nontarget lesion percutaneous coronary intervention in the year after an index percutaneous coronary intervention was found to be 10.5%. In multivariable logistic regression analyses, the predictors of plaque progression were multivessel disease, unstable angina pectoris, diabetes mellitus, prior percutaneous coronary intervention, and lack of statin use. Initially, lesions that lead to repercutaneous coronary intervention were mostly nonsignificant. Clinical presentation of plaque progression was mostly acute coronary syndrome. Results emphasize the need for further study to refine the methods to identify potentially vulnerable but clinically silent plaques.
Assessment of Left Ventricular Functions in Patients With Isolated Coronary Artery Ectasia by Conventional and Tissue Doppler Imaging
Authors: Saglam Mustafa , Barutcu Irfan , Karakaya Osman , Esen Ali Metin , Akgun Taylan , Karavelioglu Yusuf , Karapinar Hekim , Turkmen Muhsin , Ozdemir Nihal , Kaymaz Cihangir
Published: April 2008

The authors sought to determine left ventricular functions by conventional and tissue Doppler imaging in patients with isolated coronary artery ectasia and controls. Peak early (E) and late (A) mitral inflow velocity, E/A ratio, E deceleration time, and isovolumetric relaxation time were obtained. Peak systolic velocity (Sm), diastolic early (Em), and late (Am) velocities were measured by tissue Doppler imaging. Interventricular septum velocities, including peak systolic (Ss), diastolic early (Es), and late (As) velocities, were recorded. Peak early (E) velocity, E/A ratio, and E deceleration time were different in both groups. Isovolumetric relaxation time was prolonged in patients with coronary artery ectasia than controls. Em and Em/Am ratio were lower in patients with coronary artery ectasia than controls. Diastolic early and Es/As velocities were lower in patients with coronary artery ectasia compared with controls. The authors showed that mitral inflow-lateral annulus and interventricular septum velocities were lower in patients with coronary artery ectasia than controls indicating left ventricular diastolic dysfunction.
Acute Pancreatitis Mimicking Acute Inferior Myocardial Infarction
Authors: Tejada Julio G. , Hernández Felipe , Chimeno Javier , Alonso Maria A. , Martin Roberto , Bastante Teresa
Published: April 2008

A 56-year-old man presented with acute pancreatitis and electrocardiographic abnormalities, suggesting acute inferior myocardial infarction. An emergent coronary angiogram showed normal coronary arteries. The clinical significance and therapeutic implications of this rare finding are discussed.
Effect of Acute Cigarette Smoking on Left and Right Ventricle Filling Parameters: A Conventional and Tissue Doppler Echocardiographic Study in Healthy Participants
Authors: Barutcu Irfan , Esen Ali Metin , Kaya Dayimi , Onrat Ersel , Melek Mehmet , Celik Atac , Kilit Celal , Turkmen Muhsin , Karakaya Osman , Esen Ozlem Batukan , Saglam Mustafa , Kirma Cevat
Published: April 2008

Acute effects of smoking on left and right ventricular function is determined by conventional and tissue Doppler imaging methods in this study. Pulsed-wave Doppler indices of the left and right ventricle diastolic function, including mitral and tricuspid inflow peak early and late velocity and their ratio were obtained from 20 healthy subjects by conventional Doppler and tissue Doppler imaging. Echocardiographic indices of left and right ventricles, including isovolumetric relaxation time, deceleration time, isovolumetric contraction time, ejection time, and myocardial performance index of right ventricle were measured before and 30 minutes after smoking a cigarette. Mitral and tricuspid inflow parameters and right ventricular myocardial performance index significantly altered after smoking a cigarette. Among the tissue Doppler imaging parameters, mitral and tricuspid lateral annulus diastolic, but not systolic, velocities altered after smoking a cigarette. Acute cigarette smoking alters left and right ventricular diastolic functions in healthy nonsmokers.
Imaging Investigation of a Giant Splenic Artery Aneurysm
Authors: Vlychou Marianna , Kokkinis Constantinos , Stathopoulou Sotiria , Tsilikas Constantinos , Lazoura Olga , Petinelli Adamantia , Papadaki Paraskevi , Fezoulidis Ioannis
Published: April 2008

Splenic artery aneurysms represent approximately 60% of visceral arterial aneurysms. Their incidence at autopsy is 0.02 — 2 % in all age groups and rises to 10.4% above the age of 60. Although splenic artery aneurysms are the most common visceral aneurysms, giant splenic artery aneurysms >10 cm in diameter have rarely been reported. A recent review of the literature came up with 12 true giant splenic artery aneurysms >10 cm. A case of 12-cm splenic artery aneurysms is presented in this study and the imaging findings are described.
Catastrophic Antiphospholipid Syndrome Presenting With Multiorgan Failure and Gangrenous Lesions of the Skin
Authors: Antonelli Incalzi Raffaele , Gemma Antonella , Moro Leo , Antonelli Massimo
Published: April 2008

A 38-year-old woman with a 20-year history of systemic lupus erythematosus and positive anticardiolipin antibodies developed anuria and hypotension. 20 days before, she had discontinued 25 mg prednisone, but not warfarin, on medical advice. 3 days before admission, she developed extensive necrosis and blisters involving both arms and legs and a multiorgan failure. She improved after immunosuppressive and antibiotic therapy, 3 sessions of dialysis and 5 sessions of plasmapheresis. It was decided that she could be discharged after 45 days. Her skin lesions were complicated by several abscesses requiring surgical drainage and finally healed almost completely within 9 months. Catastrophic antiphospholipid syndrome is a distinctly rare dramatic condition characterized by widespread thrombosis of small vessels, which in the present case was likely triggered by the abrupt withdrawal of steroid therapy. It should be borne in mind in cases of multiorgan failure, which does not recognize a well-defined etiology.
Impact of Percutaneous Transluminal Septal Myocardial Ablation on Refractory Paroxysmal Atrial Fibrillation in Patients With Hypertrophic Obstructive Cardiomyopathy
Authors: Hosokawa Yusuke , Takano Hitoshi , Ohno Tadaaki , Takayama Morimasa , Takano Teruo
Published: April 2008

Atrial fibrillation is commonly observed in patients with hypertrophic obstructive cardiomyopathy. Episodes of paroxysmal atrial fibrillation are often torturous and limit the quality of life by causing congestive heart failure, transient hypotension, or bradycardia. Control of paroxysmal atrial fibrillation in patients with hypertrophic obstructive cardiomyopathy is considered to be important for symptomatic improvement and prevention of the development to chronic atrial fibrillation. The authors report on 3 patients with hypertrophic obstructive cardiomyopathy who suffered from paroxysmal atrial fibrillation despite receiving medical treatment using antiarrhythmic agents. However, after undergoing percutaneous transluminal septal myocardial ablation, the incidence of episodes became significantly less frequent. Percutaneous transluminal septal myocardial ablation is normally performed for attenuating left ventricular obstruction by reducing the systolic anterior motion of the mitral leaflet. However, in these patients, this procedure was also effective in preventing supraventricular arrhythmia, probably by improving left ventricular diastolic dysfunction, smooth blood inflow into the left ventricular, and decreasing the pressure stress against the left atrial wall.
Age and Blood Pressure Levels Modify the Functional Properties of Central but Not Peripheral Arteries
Authors: Ruitenbeek Astrid G. , van der Cammen Tischa J.M. , van den Meiracker Anton H. , Mattace-Raso Francesco U.S.
Published: April 2008

The effect of age and blood pressure on the carotid and the radial artery distensibilities was investigated. Patients referred to the outpatient clinic of the Department of Internal Medicine and Geriatric Medicine were asked to participate in the study. The carotid and radial artery distensibility coefficients were measured. Linear regression analyses were performed to investigate the associations between determinants and arterial distensibility. The mean age of the participants was 72.3 years, and 41.5% were men. Carotid distensibility decreased with age in adjusted models (β = −0.317; 95% confidence interval [CI], −0.241, −0.055), whereas the radial distensibility did not decrease. Levels of systolic blood pressure and mean arterial pressure were associated with decreasing levels of carotid distensibility, whereas the diastolic blood pressure and pulse pressure were not associated (β = −0.571; 95% CI, −0.404, −0.007; β = −0.410; 95% CI, −0.308, −0.101, respectively). In conclusion, age and blood pressure levels are associated with the distensibility of the central arteries but not with that of the peripheral arteries.
“Perilous Pearl”—Papillary Fibroelastoma of Aortic Valve: A Case Report and Literature Review
Authors: Kumbala Damodar , Sharp Thomas , Kamalesh Masoor
Published: April 2008

Papillary fibroelastomas of the heart valves are benign, slow-growing, rare tumors of the heart. These lesions are primarily responsible for embolic events that can clinically manifest with neurological and cardiovascular symptoms. Early diagnosis is very important, as surgical excision of these tumors can prevent cerebrovascular and cardiovascular complications. The case of a 60-year-old man who presented with a neurological deficit caused by a papillary fibroelastoma of the noncoronary cusp of the aortic valve is described. Diagnosis was made by transesophageal echocardiogram, and the tumor was resected surgically.
Primary Myxofibrosarcoma of the Left Atrium: Case Report and Review of the Literature
Authors: Lazaros George A. , Matsakas Evangelos P. , Madas John S. , Toli Dina I. , Nikas Dimitris J. , Kershaw Melissa A. , Alpert Martin A.
Published: April 2008

A 63-year-old woman with progressive dyspnea underwent transthoracic echocardiography and was found to have a large multilobed mass in the left atrium that was attached to lateral wall. On inspection during surgery, the tumor was found to infiltrate the posterior mitral annulus and leaflet. The patient underwent surgical resection of the tumor and mitral valve replacement. Histologic and cytochemical evaluation confirmed that the tumor was a myxofibrosarcoma. Despite chemotherapy, the tumor recurred and the patient died 3 months after surgery.