bluuebunny/crossref_metadata_2025 huggingface dataset:
Long-Term Doxycycline Is More Effective Than Atenolol to Prevent Thoracic Aortic Aneurysm in Marfan Syndrome Through the Inhibition of Matrix Metalloproteinase-2 and -9
Authors: Chung Ada W.Y. , Yang H. H. Clarice , Radomski Marek W. , van Breemen Cornelis
Published: April 2008

β-Blockers, eg, atenolol, are the cornerstone therapy for thoracic aortic aneurysm (TAA) in patients with Marfan syndrome; however, continued aortic dilatation has been reported. We have demonstrated that matrix metalloproteinase (MMP)-2 and -9 were upregulated during progression of TAA in Marfan syndrome, accompanied with degenerated elastic fibers and vasomotor dysfunction. We hypothesized that doxycycline, a nonspecific inhibitor of MMPs, would ameliorate TAA by attenuating elastic fiber degeneration and improving vasomotor function. A well-characterized mouse model of Marfan syndrome (Fbn1
C1039G/+
) was used. Mice were untreated (n=40), given doxycycline (0.24g/L, n=30), or given atenolol (0.5g/L, n=30) in drinking water at 6 weeks of age. The Fbn1
+/+
mice served as control (n=40). At 3, 6, and 9 months, aortic segments from the ascending, arch, and descending portions were used to obtain the “average” value of the whole thoracic aorta. TAA was prevented in the doxycycline group, whereas mild aneurysm was evident in the atenolol group. Doxycycline improved elastic fiber integrity, normalized aortic stiffness, and prevented vessel weakening. The impairment of vasocontraction and endothelium-dependent relaxation in the untreated and atenolol groups were improved by doxycycline. The upregulation of transforming growth factor-β in the Marfan aorta was suppressed by doxycycline. Doxycycline augmented expression ratios of tissue inhibitors of MMP to MMPs. Intraperitoneally injected neutralizing antibodies against MMP-2 and -9 yielded similar effects to doxycycline. We concluded that long-term treatment with doxycycline, through the inhibition of MMP-2 and -9, is more effective than atenolol in preventing TAA in Marfan syndrome by preserving elastic fiber integrity, normalizing vasomotor function, and reducing transforming growth factor-β activation.
Proteomic and Metabolomic Analysis of Smooth Muscle Cells Derived From the Arterial Media and Adventitial Progenitors of Apolipoprotein E–Deficient Mice
Authors: Mayr Manuel , Zampetaki Anna , Sidibe Anissa , Mayr Ursula , Yin Xiaoke , De Souza Ayesha I. , Chung Yuen-Li , Madhu Basetti , Quax Paul H. , Hu Yanhua , Griffiths John R. , Xu Qingbo
Published: April 2008

We have recently demonstrated that stem cell antigen 1–positive (Sca-1
+
) progenitors exist in the vascular adventitia of apolipoprotein E–deficient (apoE
−/−
) mice and contribute to smooth muscle cell (SMC) accumulation in vein graft atherosclerosis. Using a combined proteomic and metabolomic approach, we now characterize these local progenitors, which participate in the formation of native atherosclerotic lesions in chow-fed apoE
−/−
mice. Unlike Sca-1
+
progenitors from embryonic stem cells, the resident Sca-1
+
stem cell population from the vasculature acquired a mature aortic SMC phenotype after platelet-derived growth factor-BB stimulation. It shared proteomic and metabolomic characteristics of apoE
−/−
SMCs, which were clearly distinct from wild-type SMCs under normoxic and hypoxic conditions. Among the differentially expressed proteins were key enzymes in glucose metabolism, resulting in faster glucose consumption and a compensatory reduction in baseline interleukin-6 secretion. The latter was associated with a marked upregulation of insulin-like growth factor binding proteins (IGFBPs) 3 and 6. Notably, reconstitution of interleukin-6 to levels measured in the conditioned medium of wild-type SMCs attenuated the elevated IGFBP expression in apoE
−/−
SMCs and their vascular progenitors. This coregulation of apoE, interleukin-6, and IGFBPs was replicated in wild-type SMCs from hypercholesterolemic mice and confirmed by silencing apoE expression in SMCs from normocholesterolemic mice. In summary, we provide evidence that Sca-1
+
progenitors contribute to native atherosclerosis in apoE
−/−
mice, that apoE deficiency and hypercholesterolemia alter progenitor cell behavior, and that inflammatory cytokines such as interleukin-6 act as metabolic regulators in SMCs of hyperlipidemic mice.
Importance of In-Hospital Initiation of Therapies and Therapeutic Inertia in Secondary Stroke Prevention
Authors: Touzé Emmanuel , Coste Joël , Voicu Magdalena , Kansao Jamal , Masmoudi Rafik , Doumenc Benoît , Durieux Pierre , Mas Jean-Louis
Published: April 2008

Background and Purpose—

Many patients do not receive prevention consistent with recommendations after stroke, but the relative importance of patient- and physician-related factors is uncertain.

Methods—

We prospectively assessed factors associated with blood pressure (BP) <140/90 mm Hg and low-density lipoprotein (LDL) cholesterol <1 g/L in a collaborative cohort of 240 consecutive patients experiencing stroke/transient ischemic attack (Rankin <4; ≤80 years; no major comorbidity) from a stroke unit and 3 emergency departments. A standardized assessment of risk factors was performed 6 and 12 months after the initial event by an investigator who was not involved in the usual follow-up of patients.

Results—

At 6 months, 41% of patients with diagnosed hypertension at inclusion had their BP <140/90 mm Hg and 55% of those with diagnosed hypercholesterolemia had their LDL <1 g/L. Adherence to treatment was excellent in 81% of patients. In univariate and multivariate analyses, initiation or reinforcement of appropriate treatments during hospitalization were the main factors associated with BP <140/90 mm Hg (OR=2.44; 95% CI: 1.20 to 4.97) and LDL <1 g/L (OR=3.36; 1.27 to 8.89) or with decrease in BP and LDL. Patients’ sociodemographic characteristics, education, income, knowledge of disease, and risk factors were not associated with control of BP or LDL. Among patients with BP ≥140/90 mm Hg, approximately 40% received either no treatment or one drug only, and treatment was reinforced in 20% of them only. Results were similar at 12 months with no improvement in the rate of control of risk factors.

Conclusion—

Therapeutic inertia is an important impediment to achieve BP and LDL control goals after stroke, even in fairly motivated/adherent patients. In-hospital initiation of preventive therapies could improve quality of secondary stroke prevention in the long term.
Outpatient Practice Patterns After Stroke Hospitalization Among Neurologists
Authors: Ovbiagele Bruce , Drogan Oksana , Koroshetz Walter J. , Fayad Pierre , Saver Jeffrey L.
Published: April 2008

Background and Purpose—

Care after stroke hospitalization can provide several opportunities to optimize vascular risk reduction. However, not much is known about poststroke practice patterns among neurologists. Such knowledge may help direct specific efforts to improve the impact of practicing neurologists on clinical outcomes after stroke.

Methods—

A survey soliciting information on processes of care in the outpatient setting after recent hospitalization for ischemic stroke or transient ischemic attack was mailed to a random sample of 833 US and Canadian neurologist–members of the American Academy of Neurology.

Results—

A total of 475 (57%) responses were received. Practice demographics of survey responders and nonresponders were largely similar. Fourteen percent of respondents identified themselves as vascular neurologists. Overall, respondents reported frequently checking for medication adherence and counseling patients on lifestyle modification. However, neurologists reported screening more frequently for diabetes, hypertension, and dyslipidemia than actually treating these conditions (all
P
<0.0001) Vascular neurologists were more likely than general neurologists to screen for hypertension (97% versus 86%,
P
=0.016), dyslipidemia (94% versus 68%,
P
<0.001), diabetes (89% versus 62%,
P
<0.001), and sleep apnea (94% versus 79%,
P
=0.007) as well as to treat hypertension (71% versus 45%,
P
<0.001), dyslipidemia (82% versus 50%,
P
<0.001), diabetes (45% versus 21%,
P
<0.001), and current smoking (77% versus 59%,
P
=0.005). Neurologists with mostly government-insured and uninsured patients were significantly more likely to engage in vascular risk reduction treatment than neurologists with mostly commercially insured patients.

Conclusions—

Self-reported rates of screening and treatment of major vascular risk factors by most neurologists after stroke hospitalization are substantial but not universal. Bridging knowledge gaps or adopting a systematic management approach in coordination with primary care physicians could help optimize poststroke care.
Sarpogrelate-Aspirin Comparative Clinical Study for Efficacy and Safety in Secondary Prevention of Cerebral Infarction (S-ACCESS)
Authors: Shinohara Yukito , Nishimaru Katsuya , Sawada Tohru , Terashi Akiro , Handa Shunnosuke , Hirai Shunsaku , Hayashi Kunihiko , Tohgi Hideo , Fukuuchi Yasuo , Uchiyama Shinichiro , Yamaguchi Takenori , Kobayashi Shotai , Kondo Kazuoki , Otomo Eiichi , Gotoh Fumio
Published: April 2008

Background and Purpose—

The antiplatelet agent sarpogrelate is a selective inhibitor of 5-hydroxytryptamine receptors. The purpose of this study was to compare the efficacy and safety of sarpogrelate with those of aspirin in Japanese ischemic stroke patients.

Methods—

In total, 1510 patients with recent cerebral infarction (1 week to 6 months after onset) were randomly assigned to receive either sarpogrelate (100 mg TID) or aspirin (81 mg/d). Mean follow-up period was 1.59 years. The primary efficacy end point was recurrence of cerebral infarction. Clusters of serious vascular events (stroke, acute coronary syndrome, or vascular event–related death) were selected as secondary end points. The aim of the primary efficacy analysis was to demonstrate the noninferiority of sarpogrelate with respect to aspirin, with the criterion that the upper limit of the 95% CI of the hazard ratio (sarpogrelate vs aspirin) for recurrence of cerebral infarction should not exceed 1.33.

Results—

Cerebral infarction recurred in 72 patients (6.09%/y) in the sarpogrelate group and in 58 (4.86%/y) in the aspirin group (hazard ratio=1.25; 95% CI, 0.89 to 1.77;
P
=0.19). A serious vascular event occurred in 90 (7.61%/y) and in 85 (7.12%/y) patients, respectively (hazard ratio=1.07; 95% CI, 0.80 to 1.44;
P
=0.65). The overall incidences of bleeding events were 89 (11.9%) and 131 (17.3%), respectively (
P
<0.01).

Conclusions—

Sarpogrelate was not noninferior to aspirin for prevention of recurrence of cerebral infarction. Bleeding events were significantly fewer with sarpogrelate than aspirin. The effect of aspirin in Japanese patients was similar to that in Western studies.
Regional Differences in Diabetes as a Possible Contributor to the Geographic Disparity in Stroke Mortality
Authors: Voeks Jenifer H. , McClure Leslie A. , Go Rodney C. , Prineas Ronald J. , Cushman Mary , Kissela Brett M. , Roseman Jeffrey M.
Published: April 2008

Background and Purpose—

Diabetes and hypertension impart approximately the same increased relative risk for stroke, although hypertension has a larger population-attributable risk because of its higher population prevalence. With a growing epidemic of obesity and associated increasing prevalence of diabetes that disproportionately impacts the southeastern Stroke Belt states, any potential contribution of diabetes to the geographic disparity in stroke mortality will only increase.

Methods—

Racial and geographic differences in diabetes prevalence and diabetes awareness, treatment, and control were assessed in the REasons for Geographic And Racial Differences in Stroke study, a national population-based cohort of black and white participants older than 45 years of age. At the time of this report, 21 959 had been enrolled.

Results—

The odds of diabetes were significantly increased in both white and black residents of the stroke buckle (OR, 1.26; [1.10, 1.44]; OR, 1.45 [1.26, 1.66], respectively) and Stroke Belt (OR, 1.22; [1.09, 1.36]; OR, 1.13 [1.02, 1.26]) compared to the rest of the United States. In the buckle, regional differences were not fully mediated and remained significant when controlling for socioeconomic status and risk factors. Addition of hypertension to the models did not reduce the magnitude of the associations. There were no significant differences by region with regard to awareness, treatment, or control for either race.

Conclusions—

These analyses support a possible role of regional variation in the prevalence of diabetes as, in part, an explanation for the regional variation in stroke mortality but fail to support the potential for a contribution of regional differences in diabetes management.
Statistical Theory of Learning Curves under Entropic Loss Criterion
Authors: Amari Shun-ichi , Murata Noboru
Published: April 2008

The present paper elucidates a universal property of learning curves, which shows how the generalization error, training error, and the complexity of the underlying stochastic machine are related and how the behavior of a stochastic machine is improved as the number of training examples increases. The error is measured by the entropic loss. It is proved that the generalization error converges to H0, the entropy of the conditional distribution of the true machine, as H0 + m*/(2t), while the training error converges as H0 - m*/(2t), where t is the number of examples and m* shows the complexity of the network. When the model is faithful, implying that the true machine is in the model, m* is reduced to m, the number of modifiable parameters. This is a universal law because it holds for any regular machine irrespective of its structure under the maximum likelihood estimator. Similar relations are obtained for the Bayes and Gibbs learning algorithms. These learning curves show the relation among the accuracy of learning, the complexity of a model, and the number of training examples.
Three-Dimensional Object Recognition Using an Unsupervised BCM Network: The Usefulness of Distinguishing Features
Authors: Intrator Nathan , Gold Joshua I.
Published: April 2008

We propose an object recognition scheme based on a method for feature extraction from gray level images that corresponds to recent statistical theory, called projection pursuit, and is derived from a biologically motivated feature extracting neuron. To evaluate the performance of this method we use a set of very detailed psychophysical three-dimensional object recognition experiments (Bülthoff and Edelman 1992).
Complexity Optimized Data Clustering by Competitive Neural Networks
Authors: Buhmann Joachim , Kühnel Hans
Published: April 2008

Data clustering is a complex optimization problem with applications ranging from vision and speech processing to data transmission and data storage in technical as well as in biological systems. We discuss a clustering strategy that explicitly reflects the tradeoff between simplicity and precision of a data representation. The resulting clustering algorithm jointly optimizes distortion errors and complexity costs. A maximum entropy estimation of the clustering cost function yields an optimal number of clusters, their positions, and their cluster probabilities. Our approach establishes a unifying framework for different clustering methods like K-means clustering, fuzzy clustering, entropy constrained vector quantization, or topological feature maps and competitive neural networks.
The Variance of Covariance Rules for Associative Matrix Memories and Reinforcement Learning
Authors: Dayan Peter , Sejnowski Terrence J.
Published: April 2008

Hebbian synapses lie at the heart of most associative matrix memories (Kohonen 1987; Hinton and Anderson 1981) and are also biologically plausible (Brown et al. 1990; Baudry and Davis 1991). Their analytical and computational tractability make these memories the best understood form of distributed information storage. A variety of Hebbian algorithms for estimating the covariance between input and output patterns has been proposed. This note points out that one class of these involves stochastic estimation of the covariance, shows that the signal-to-noise ratios of the rules are governed by the variances of their estimates, and considers some parallels in reinforcement learning.
Computational Diversity in a Formal Model of the Insect Olfactory Macroglomerulus
Authors: Linster C. , Masson C. , Kerszberg M. , Personnaz L. , Dreyfus G.
Published: April 2008

We present a model of the specialist olfactory system of selected moth species and the cockroach. The model is built in a semirandom fashion, constrained by biological (physiological and anatomical) data. We propose a classification of the response patterns of individual neurons, based on the temporal aspects of the observed responses. Among the observations made in our simulations a number relate to data about olfactory information processing reported in the literature; others may serve as predictions and as guidelines for further investigations. We discuss the effect of the stochastic parameters of the model on the observed model behavior and on the ability of the model to extract features of the input stimulation. We conclude that a formal network, built with random connectivity, can suffice to reproduce and to explain many aspects of olfactory information processing at the first level of the specialist olfactory system of insects.
Approximation and Radial-Basis-Function Networks
Authors: Park Jooyoung , Sandberg Irwin W.
Published: April 2008

This paper concerns conditions for the approximation of functions in certain general spaces using radial-basis-function networks. It has been shown in recent papers that certain classes of radial-basis-function networks are broad enough for universal approximation. In this paper these results are considerably extended and sharpened.
A Polynomial Time Algorithm for Generating Neural Networks for Pattern Classification: Its Stability Properties and Some Test Results
Authors: Mukhopadhyay Somnath , Roy Asim , Kim Lark Sang , Govil Sandeep
Published: April 2008

Polynomial time training and network design are two major issues for the neural network community. A new algorithm has been developed that can learn in polynomial time and also design an appropriate network. The algorithm is for classification problems and uses linear programing models to design and train the network. This paper summarizes the new algorithm, proves its stability properties, and provides some computational results to demonstrate its potential.
Neural Networks for Fingerprint Recognition
Authors: Baldi Pierre , Chauvin Yves
Published: April 2008

After collecting a data base of fingerprint images, we design a neural network algorithm for fingerprint recognition. When presented with a pair of fingerprint images, the algorithm outputs an estimate of the probability that the two images originate from the same finger. In one experiment, the neural network is trained using a few hundred pairs of images and its performance is subsequently tested using several thousand pairs of images originated from a subset of the database corresponding to 20 individuals. The error rate currently achieved is less than 0.5%. Additional results, extensions, and possible applications are also briefly discussed.
Centered-Object Integrated Segmentation and Recognition of Overlapping Handprinted Characters
Authors: Martin Gale L.
Published: April 2008

Visual object recognition is often conceived of as a final step in a visual processing system, First, physical information in the raw image is used to isolate and enhance to-be-recognized clumps and then each of the resulting preprocessed representations is fed into the recognizer. This general conception fails when there are no reliable physical cues for isolating the objects, such as when objects overlap. This paper describes an approach, called centered object integrated segmentation and recognition (COISR), for integrating object segmentation and recognition within a single neural network. The application is handprinted character recognition. The approach uses a backpropagation network that scans a field of characters and is trained to recognize whether it is centered over a single character or between characters. When it is centered over a character, the net classifies the character. The approach is tested on a dataset of handprinted digits and high accuracy rates are reported.
Robustness in Multilayer Perceptrons
Authors: Kerlirzin P. , Vallet F.
Published: April 2008

In this paper, we study the robustness of multilayer networks versus the destruction of neurons. We show that the classical backpropagation algorithm does not lead to optimal robustness and we propose a modified algorithm that improves this capability.
The Use of Neural Networks in High-Energy Physics
Authors: Denby Bruce
Published: April 2008

In the past few years a wide variety of applications of neural networks to pattern recognition in experimental high-energy physics has appeared. The neural network solutions are in general of high quality, and, in a number of cases, are superior to those obtained using "traditional'' methods. But neural networks are of particular interest in high-energy physics for another reason as well: much of the pattern recognition must be performed online, that is, in a few microseconds or less. The inherent parallelism of neural network algorithms, and the ability to implement them as very fast hardware devices, may make them an ideal technology for this application.
Stimulus-Dependent Synchronization of Neuronal Assemblies
Authors: Grannan E. R. , Kleinfeld D. , Sompolinsky H.
Published: April 2008

We study theoretically how an interaction between assemblies of neuronal oscillators can be modulated by the pattern of external stimuli. It is shown that spatial variations in the stimuli can control the magnitude and phase of the synchronization between the output of neurons with different receptive fields. This modulation emerges from cooperative dynamics in the network, without the need for specialized, activity-dependent synapses. Our results further suggest that the modulation of neuronal interactions by extended features of a stimulus may give rise to complex spatiotemporal fluctuations in the phases of neuronal oscillations.
Artificial Dendritic Trees
Authors: Elias John G.
Published: April 2008

The electronic architecture and dynamic signal processing capabilities of an artificial dendritic tree that can be used to process and classify dynamic signals is described. The electrical circuit architecture is modeled after neurons that have spatially extensive dendritic trees. The artificial dendritic tree is a hybrid VLSI circuit and is sensitive to both temporal and spatial signal characteristics. It does not use the conventional neural network concept of weights, and as such it does not use multipliers, adders, look-up-tables, microprocessors, or other complex computational units to process signals. The weights of conventional neural networks, which take the form of numerical, resistive, voltage, or current values, but do not have any spatial or temporal content, are replaced with connections whose spatial location have both a temporal and scaling significance.
Patterns of Local Connectivity in the Neocortex
Authors: Nicoll Andrew , Blakemore Colin
Published: April 2008

Dual intracellular recording of nearby pairs of pyramidal cells in slices of rat visual cortex has shown that there are significant differences in functional connectivity between the superficial and deep layers (Mason et al. 1991; Nicoll and Blakemore 1993). For pairs of cells no farther than 300 μm apart, synaptic connections between layer 2/3 pyramidal neurons were individually weaker (median peak amplitude, A, of single-fiber excitatory postsynaptic potentials, EPSPs, = 0.4 mV) but more frequent (connection probability, p = 0.087) than those between layer 5 pyramidal neurons (mean A = 0.8 mV, p < 0.015). Taken in combination with plausible estimates of the density of pyramidal cells, the total numbers of synapses on them and the number of synapses formed on their intracortical axons, the present analysis of the above data suggests that roughly 70% of the excitatory synapses on any layer 2/3 pyramid, but fewer than 1% of those on a layer 5 pyramidal neuron, are derived from neighboring pyramidal neurons in its near vicinity. Even assuming very extreme values for some parameters, chosen to erode this difference, the calculated proportion of "local synapses" for layer 5 pyramids was always markedly lower than for layer 2/3 pyramidal neurons. These results imply that local excitatory connections are much more likely to provide significant "intracortical amplification" of afferent signals in layer 2/3 than in layer 5 of rat visual cortex.
Neural Network Modeling of Memory Deterioration in Alzheimer's Disease
Authors: Horn D. , Ruppin E. , Usher M. , Herrmann M.
Published: April 2008

The clinical course of Alzheimer's disease (AD) is generally characterized by progressive gradual deterioration, although large clinical variability exists. Motivated by the recent quantitative reports of synaptic changes in AD, we use a neural network model to investigate how the interplay between synaptic deletion and compensation determines the pattern of memory deterioration, a clinical hallmark of AD. Within the model we show that the deterioration of memory retrieval due to synaptic deletion can be much delayed by multiplying all the remaining synaptic weights by a common factor, which keeps the average input to each neuron at the same level. This parallels the experimental observation that the total synaptic area per unit volume (TSA) is initially preserved when synaptic deletion occurs. By using different dependencies of the compensatory factor on the amount of synaptic deletion one can define various compensation strategies, which can account for the observed variation in the severity and progression rate of AD.
Supervised Factorial Learning
Authors: Redlich A. Norman
Published: April 2008

Factorial learning, finding a statistically independent representation of a sensory “image”—a factorial code—is applied here to solve multilayer supervised learning problems that have traditionally required backpropagation. This lends support to Barlow's argument for factorial sensory processing, by demonstrating how it can solve actual pattern recognition problems. Two techniques for supervised factorial learning are explored, one of which gives a novel distributed solution requiring only positive examples. Also, a new nonlinear technique for factorial learning is introduced that uses neural networks based on almost reversible cellular automata. Due to the special functional connectivity of these networks—which resemble some biological microcircuits—learning requires only simple local algorithms. Also, supervised factorial learning is shown to be a viable alternative to backpropagation. One significant advantage is the existence of a measure for the performance of intermediate learning stages.
On Learning Perceptrons with Binary Weights
Authors: Golea Mostefa , Marchand Mario
Published: April 2008

We present an algorithm that PAC learns any perceptron with binary weights and arbitrary threshold under the family of product distributions. The sample complexity of this algorithm is of O[(n/ε)4 ln(n/δ)] and its running time increases only linearly with the number of training examples. The algorithm does not try to find an hypothesis that agrees with all of the training examples; rather, it constructs a binary perceptron based on various probabilistic estimates obtained from the training examples. We show that, under the restricted case of the uniform distribution and zero threshold, the algorithm reduces to the well known clipped Hebb rule. We calculate exactly the average generalization rate (i.e., the learning curve) of the algorithm, under the uniform distribution, in the limit of an infinite number of dimensions. We find that the error rate decreases exponentially as a function of the number of training examples. Hence, the average case analysis gives a sample complexity of O[n ln(1/ε)], a large improvement over the PAC learning analysis. The analytical expression of the learning curve is in excellent agreement with the extensive numerical simulations. In addition, the algorithm is very robust with respect to classification noise.
Recurrent and Feedforward Polynomial Modeling of Coupled Time Series
Authors: López Vicente , Huerta Ramón , Dorronsoro José R.
Published: April 2008

We present two methods for the prediction of coupled time series. The first one is based on modeling the series by a dynamic system with a polynomial format. This method can be formulated in terms of learning in a recurrent network, for which we give a computationally effective algorithm. The second method is a purely feedforward σ-π network procedure whose architecture derives from the recurrence relations for the derivatives of the trajectories of a Ricatti format dynamic system. It can also be used for the modeling of discrete series in terms of nonlinear mappings. Both methods have been tested successfully against chaotic series.
Analysis of Neuron Models with Dynamically Regulated Conductances
Authors: Abbott L. F. , LeMasson Gwendal
Published: April 2008

We analyze neuron models in which the maximal conductances of membrane currents are slowly varying dynamic variables regulated by the intracellular calcium concentration. These models allow us to study possible activity-dependent effects arising from processes that maintain and modify membrane channels in real neurons. Regulated model neurons maintain a constant average level of activity over a wide range of conditions by appropriately adjusting their conductances. The intracellular calcium concentration acts as a feedback element linking maximal conductances to electrical activity. The resulting plasticity of intrinsic characteristics has important implications for network behavior. We first study a simple two-conductance model, then introduce techniques that allow us to analyze dynamic regulation with an arbitrary number of conductances, and finally illustrate this method by studying a seven-conductance model. We conclude with an analysis of spontaneous differentiation of identical model neurons in a two-cell network.
Fast Recognition of Noisy Digits
Authors: Kidder Jeffrey N. , Seligson Daniel
Published: April 2008

We describe a hardware solution to a high-speed optical character recognition (OCR) problem. Noisy 15 × 10 binary images of machine written digits were processed and applied as input to Intel's Electrically Trainable Analog Neural Network (ETANN). In software simulation, we trained an 80 × 54 × 10 feedforward network using a modified version of backprop. We then downloaded the synaptic weights of the trained network to ETANN and tweaked them to account for differences between the simulation and the chip itself. The best recognition error rate was 0.9% in hardware with a 3.7% rejection rate on a 1000-character test set.
Local Algorithms for Pattern Recognition and Dependencies Estimation
Authors: Vapnik V. , Bottou L.
Published: April 2008

In previous publications (Bottou and Vapnik 1992; Vapnik 1992) we described local learning algorithms, which result in performance improvements for real problems. We present here the theoretical framework on which these algorithms are based. First, we present a new statement of certain learning problems, namely the local risk minimization. We review the basic results of the uniform convergence theory of learning, and extend these results to local risk minimization. We also extend the structural risk minimization principle for both pattern recognition problems and regression problems. This extended induction principle is the basis for a new class of algorithms.
On the Geometry of Feedforward Neural Network Error Surfaces
Authors: Chen An Mei , Lu Haw-minn , Hecht-Nielsen Robert
Published: April 2008

Many feedforward neural network architectures have the property that their overall input-output function is unchanged by certain weight permutations and sign flips. In this paper, the geometric structure of these equioutput weight space transformations is explored for the case of multilayer perceptron networks with tanh activation functions (similar results hold for many other types of neural networks). It is shown that these transformations form an algebraic group isomorphic to a direct product of Weyl groups. Results concerning the root spaces of the Lie algebras associated with these Weyl groups are then used to derive sets of simple equations for minimal sufficient search sets in weight space. These sets, which take the geometric forms of a wedge and a cone, occupy only a minute fraction of the volume of weight space. A separate analysis shows that large numbers of copies of a network performance function optimum weight vector are created by the action of the equioutput transformation group and that these copies all lie on the same sphere. Some implications of these results for learning are discussed.
On an Unsupervised Learning Rule for Scalar Quantization following the Maximum Entropy Principle
Authors: Hulle Marc M. Van , Martinez Dominique
Published: April 2008

A novel unsupervised learning rule, called Boundary Adaptation Rule (BAR), is introduced for scalar quantization. It is shown that the rule maximizes information-theoretic entropy and thus yields equiprobable quantizations of univariate probability density functions. It is shown by simulations that BAR outperforms other unsupervised competitive learning rules in generating equiprobable quantizations. It is also shown that our rule can do better or worse than the Lloyd I algorithm in minimizing average mean square error, depending on the input distribution. Finally, an application to adaptive non-uniform analog to digital (A/D) conversion is considered.
Cortical Map Reorganization as a Competitive Process
Authors: Sutton Granger G. , Reggia James A. , Armentrout Steven L. , D'Autrechy C. Lynne
Published: April 2008

Past models of somatosensory cortex have successfully demonstrated map formation and subsequent map reorganization following localized repetitive stimuli or deafferentation. They provide an impressive demonstration that fairly simple assumptions about cortical connectivity and synaptic plasticity can account for several observations concerning cortical maps. However, past models have not successfully demonstrated spontaneous map reorganization following cortical lesions. Recently, an assumption universally used in these and other cortex models, that peristimulus inhibition is due solely to horizontal intracortical inhibitory connections, has been questioned and an additional mechanism, the competitive distribution of activity, has been proposed. We implemented a computational model of somatosensory cortex based on competitive distribution of activity. This model exhibits spontaneous map reorganization in response to a cortical lesion, going through a two-phase reorganization process. These results make a testable prediction that can be used to experimentally support or refute part of the competitive distribution hypothesis, and may lead to practically useful computational models of recovery following stroke.
Fast Exact Multiplication by the Hessian
Authors: Pearlmutter Barak A.
Published: April 2008

Just storing the Hessian H (the matrix of second derivatives δ2E/δwiδwj of the error E with respect to each pair of weights) of a large neural network is difficult. Since a common use of a large matrix like H is to compute its product with various vectors, we derive a technique that directly calculates Hv, where v is an arbitrary vector. To calculate Hv, we first define a differential operator Rv{f(w)} = (δ/δr)f(w + rv)|r=0, note that Rv{▽w} = Hv and Rv{w} = v, and then apply Rv{·} to the equations used to compute ▽w. The result is an exact and numerically stable procedure for computing Hv, which takes about as much computation, and is about as local, as a gradient evaluation. We then apply the technique to a one pass gradient calculation algorithm (backpropagation), a relaxation gradient calculation algorithm (recurrent backpropagation), and two stochastic gradient calculation algorithms (Boltzmann machines and weight perturbation). Finally, we show that this technique can be used at the heart of many iterative techniques for computing various properties of H, obviating any need to calculate the full Hessian.
Polyhedral Combinatorics and Neural Networks
Authors: Gee Andrew H. , Prager Richard W.
Published: April 2008

The often disappointing performance of optimizing neural networks can be partly attributed to the rather ad hoc manner in which problems are mapped onto them for solution. In this paper a rigorous mapping is described for quadratic 0-1 programming problems with linear equality and inequality constraints, this being the most general class of problem such networks can solve. The problem's constraints define a polyhedron P containing all the valid solution points, and the mapping guarantees strict confinement of the network's state vector to P. However, forcing convergence to a 0-1 point within P is shown to be generally intractable, rendering the Hopfield and similar models inapplicable to the vast majority of problems. A modification of the tabu learning technique is presented as a more coherent approach to general problem solving with neural networks. When tested on a collection of knapsack problems, the modified dynamics produced some very encouraging results.
Computational Aspects of the Respiratory Pattern Generator
Authors: Gottschalk Allan , Ogilvie Malcolm D. , Richter Diethelm W. , Pack Allan I.
Published: April 2008

To help evaluate the hypothesis that the central respiratory rhythm is generated by a network of interacting neurons, a network model of respiratory rhythmogenesis is formulated and examined computationally. The neural elements of the network are driven by tonic inputs and generate a continuous variable representing firing rate. Each neural element in the model can be described by an activation time constant, an adaptation time constant, and a step nonlinearity. Initial network connectivity was based on an earlier proposed model of the central respiratory pattern generator. These connections were adjusted interactively until the model trajectories resembled those observed electrophysiologically. The properties of the resulting network were examined computationally by simulation, determination of the phase resetting behavior of the network oscillator, and examination of the localized eigenstructure of the network. These results demonstrate that the network model can account for a number of diverse physiological observations, and, thus, support the network hypothesis of respiratory rhymogenesis.
Subharmonic Coordination in Networks of Neurons with Slow Conductances
Authors: LoFaro Thomas , Kopell Nancy , Marder Eve , Hooper Scott L.
Published: April 2008

We study the properties of a network consisting of two model neurons that are coupled by reciprocal inhibition. The study was motivated by data from a pair of cells in the crustacean stomatogastric ganglion. One of the model neurons is an endogenous burster; the other is excitable but not bursting in the absence of phasic input. We show that the presence of a hyperpolarization activated inward current (ih) in the excitable neuron allows these neurons to fire in integer subharmonics, with the excitable cell firing once for every N ≥ 1 bursts of the oscillator. The value of N depends on the amount of hyperpolarizing current injected into the excitable cell as well as the voltage activation curve of ih. For a fast synapse, these parameter changes do not affect the characteristic point in the oscillator cycle at which the excitable cell bursts; for slower synapses, such a relationship is maintained within small windows for each N. The network behavior in the current work contrasts with the activity of a pair of coupled oscillators for which the interaction is through phase differences; in the latter case, subharmonics exist if the uncoupled oscillators have near integral frequency relationships, but the phase relationships of the oscillators in general change significantly with parameters. The mechanism of this paper provides a potential means of coordinating subnetworks acting on different time scales but maintaining fixed relationships between characteristic points of the cycles.
Setting the Activity Level in Sparse Random Networks
Authors: Minai Ali A. , Levy William B.
Published: April 2008

We investigate the dynamics of a class of recurrent random networks with sparse, asymmetric excitatory connectivity and global shunting inhibition mediated by a single interneuron. Using probabilistic arguments and a hyperbolic tangent approximation to the gaussian, we develop a simple method for setting the average level of firing activity in these networks. We demonstrate through simulations that our technique works well and extends to networks with more complicated inhibitory schemes. We are interested primarily in the CA3 region of the mammalian hippocampus, and the random networks investigated here are seen as modeling the a priori dynamics of activity in this region. In the presence of external stimuli, a suitable synaptic modification rule could shape this dynamics to perform temporal information processing tasks such as sequence completion and prediction.
What Is the Goal of Sensory Coding?
Authors: Field David J.
Published: April 2008

A number of recent attempts have been made to describe early sensory coding in terms of a general information processing strategy. In this paper, two strategies are contrasted. Both strategies take advantage of the redundancy in the environment to produce more effective representations. The first is described as a “compact” coding scheme. A compact code performs a transform that allows the input to be represented with a reduced number of vectors (cells) with minimal RMS error. This approach has recently become popular in the neural network literature and is related to a process called Principal Components Analysis (PCA). A number of recent papers have suggested that the optimal “compact” code for representing natural scenes will have units with receptive field profiles much like those found in the retina and primary visual cortex. However, in this paper, it is proposed that compact coding schemes are insufficient to account for the receptive field properties of cells in the mammalian visual pathway. In contrast, it is proposed that the visual system is near to optimal in representing natural scenes only if optimality is defined in terms of “sparse distributed” coding. In a sparse distributed code, all cells in the code have an equal response probability across the class of images but have a low response probability for any single image. In such a code, the dimensionality is not reduced. Rather, the redundancy of the input is transformed into the redundancy of the firing pattern of cells. It is proposed that the signature for a sparse code is found in the fourth moment of the response distribution (i.e., the kurtosis). In measurements with 55 calibrated natural scenes, the kurtosis was found to peak when the bandwidths of the visual code matched those of cells in the mammalian visual cortex. Codes resembling “wavelet transforms” are proposed to be effective because the response histograms of such codes are sparse (i.e., show high kurtosis) when presented with natural scenes. It is proposed that the structure of the image that allows sparse coding is found in the phase spectrum of the image. It is suggested that natural scenes, to a first approximation, can be considered as a sum of self-similar local functions (the inverse of a wavelet). Possible reasons for why sensory systems would evolve toward sparse coding are presented.
Design Principles of Columnar Organization in Visual Cortex
Authors: Niebur Ernst , Wörgötter Florentin
Published: April 2008

Visual space is represented by cortical cells in an orderly manner. Only little variation in the cell behavior is found with changing depth below the cortical surface, that is, all cells in a column with axis perpendicular to the cortical plane have approximately the same properties (Hubel and Wiesel 1962, 1963, 1968). Therefore, the multiple features of the visual space (e.g., position in visual space, preferred orientation, and orientation tuning strength) are mapped on a two-dimensional space, the cortical plane. Such a dimension reduction leads to complex maps (Durbin and Mitchison 1990) that so far have evaded an intuitive understanding. Analyzing optical imaging data (Blasdel 1992a, b; Blasdel and Salama 1986; Grinvald et al. 1986) using a theoretical approach we will show that the most salient features of these maps can be understood from a few basic design principles: local correlation, modularity, isotropy, and homogeneity. These principles can be defined in a mathematically exact sense in the Fourier domain by a rather simple annulus-like spectral structure. Many of the models that have been developed to explain the mapping of the preferred orientations (Cooper et al. 1979; Legendy 1978; Linsker 1986a, b; Miller 1992; Nass and Cooper 1975; Obermayer et al. 1990, 1992; Soodak 1987; Swindale 1982, 1985, 1992; von der Malsburg 1973; von der Malsburg and Cowan 1982) are quite successful in generating maps that are close to experimental maps. We suggest that this success is due to these principles, which are common properties of the models and of biological maps.
The Effect of Synchronized Inputs at the Single Neuron Level
Authors: Bernander Öjvind , Koch Christof , Usher Marius
Published: April 2008

It is commonly assumed that temporal synchronization of excitatory synaptic inputs onto a single neuron increases its firing rate. We investigate here the role of synaptic synchronization for the leaky integrate-and-fire neuron as well as for a biophysically and anatomically detailed compartmental model of a cortical pyramidal cell. We find that if the number of excitatory inputs, N, is on the same order as the number of fully synchronized inputs necessary to trigger a single action potential, Nt, synchronization always increases the firing rate (for both constant and Poisson-distributed input). However, for large values of N compared to Nt, “overcrowding” occurs and temporal synchronization is detrimental to firing frequency. This behavior is caused by the conflicting influence of the low-pass nature of the passive dendritic membrane on the one hand and the refractory period on the other. If both temporal synchronization as well as the fraction of synchronized inputs (Murthy and Fetz 1993) is varied, synchronization is only advantageous if either N or the average input frequency, fin, are small enough.
Analysis of the Effects of Noise on a Model for the Neural Mechanism of Short-Term Active Memory
Authors: McAuley J. Devin , Stampfli Joseph
Published: April 2008

Zipser (1991) showed that the hidden unit activity of a fully recurrent neural network model, trained on a simple memory task, matched the temporal activity patterns of memory-associated neurons in monkeys performing delayed saccade or delayed match-to-sample tasks. When noise, simulating random fluctuations in neural firing rate, is added to the unit activations of this model, the effect on the memory dynamics is to slow the rate of information loss. In this paper, we show that the dynamics of the iterated sigmoid function, with gain and bias parameters, is qualitatively very similar to the tonic response properties of Zipser's multiunit model. Analysis of the simpler system provides an explanation for the effect of noise that is missing from the description of the multiunit model.