bluuebunny/crossref_metadata_2025 huggingface dataset:
Mast Cell-Dependent Control ofGiardia lambliaInfections in Mice
Authors: Li Erqiu , Zhou Ping , Petrin Ziva , Singer Steven M.
Published: October 2004

Mast cells are important for protective immunity to intestinal helminth infections and as mediators of allergic disease. Their role in protozoan infections is less well described. We have therefore analyzed mast cell responses and parasite control in mice infected with the protozoanGiardia lamblia. We also measured immunoglobulin A (IgA) responses to the parasite, as IgA can have a protective role in this model. c-kitw/wvmice failed to make parasite-specific IgA, mount a mast cell response, or eliminate the infection. Anti-c-kit-treated C57BL/6 mice had normal IgA responses, lacked mast cell responses, had reduced interleukin-6 (IL-6) mRNA in the small intestine, and failed to control the infection within 10 days. IL-9-deficient mice had a significant but reduced mast cell response and still controlled the infection within 2 weeks. Interestingly, IL-6-deficient mice had enhanced mast cell responses yet failed to rapidly control the infection. However, prevention of mast cell responses in IL-6-deficient mice by anti-c-kit treatment did not lead to parasite elimination. Both IL-6- and IL-9-deficient mice had normal IgA production. IL-6-deficient mice had significant serum levels of mast cell mediators, histamine and mast cell protease 1, following infection. Together, these results show that mast cells are important for the rapid control ofGiardiainfections in mice. Furthermore, they show that IL-6 is not necessary for these mast cell responses. Instead, they suggest that mast cell production of IL-6 appears to be important for control of this infection.
Functional Characterization of Two Type III Secretion Systems of
Vibrio parahaemolyticus
Authors: Park Kwon-Sam , Ono Takahiro , Rokuda Mitsuhiro , Jang Myoung-Ho , Okada Kazuhisa , Iida Tetsuya , Honda Takeshi
Published: October 2004

Vibrio parahaemolyticus
, a gram-negative marine bacterium, is a worldwide cause of food-borne gastroenteritis. Recent genome sequencing of the clinical
V. parahaemolyticus
strain RIMD2210633 identified two sets of genes for the type III secretion system (TTSS), TTSS1 and TTSS2. Here, we constructed a series of mutant strains from RIMD2210633 to determine whether the two putative TTSS apparatus are functional. The cytotoxic activity of mutant strains having a deletion in one of the TTSS1 genes was significantly decreased compared with that of the parent and TTSS2-related mutant strains. In an enterotoxicity assay with the rabbit ileal loop test, intestinal fluid accumulation was diminished by deletion of the TTSS2-related genes while TTSS1-related mutants caused a level of fluid accumulation similar to that of the parent. VopD, a protein encoded in the proximity of the TTSS1 region and a homologue of the
Yersinia
YopD, was secreted in a TTSS1-dependent manner. In contrast, VopP, which is encoded by a pathogenicity island on chromosome 2 and is homologous to the
Yersinia
YopP, was secreted via the TTSS2 pathway. These results provide evidence that
V. parahaemolyticus
TTSSs function as secretion systems and may have a role in the pathogenicity of the organism. This is the first report of functional TTSSs in
Vibrio
species. The presence of TTSS apparatus gene homologues was demonstrated in other vibrios, such as
Vibrio alginolyticus
,
Vibrio harveyi
, and
Vibrio tubiashii
, suggesting that some other vibrios also contain TTSS and that the TTSS has a role in protein secretion in those organisms during interaction with eukaryotic cells.
Both Inducible Nitric Oxide Synthase and NADPH Oxidase Contribute to the Control of Virulent Phase ICoxiella burnetiiInfections
Authors: Brennan Robert E. , Russell Kasi , Zhang Guoquan , Samuel James E.
Published: October 2004

Host control ofCoxiella burnetiiinfections is believed to be mediated primarily by activated monocytes/macrophages. The activation of macrophages by cytokines leads to the production of reactive oxygen intermediates (ROI) and reactive nitrogen intermediates (RNI) that have potent antimicrobial activities. The contributions of ROI and RNI to the inhibition ofC. burnetiireplication were examined in vitro by the use of murine macrophage-like cell lines and primary mouse macrophages. A gamma interferon (IFN-γ) treatment of infected cell lines and primary macrophages resulted in an increased production of nitric oxide (NO) and hydrogen peroxide (H2O2) and a significant inhibition ofC. burnetiireplication. The inhibition of replication was reversed in the murine cell line J774.16 upon the addition of either the inducible nitric oxide synthase (iNOS) inhibitor NG-monomethyl-l-arginine (NGMMLA) or the H2O2scavenger catalase. IFN-γ-treated primary macrophages from iNOS−/−and p47phox−/−mice significantly inhibited replication but were less efficient at controlling infection than IFN-γ-treated wild-type macrophages. To investigate the contributions of ROI and RNI to resistance to infection, we performed in vivo studies, using C57BL/6 wild-type mice and knockout mice lacking iNOS or p47phox. Both iNOS−/−and p47phox−/−mice were attenuated in the ability to controlC. burnetiiinfection compared to wild-type mice. Together, these results strongly support a role for both RNI and ROI in the host control ofC. burnetiiinfection.
Shiga Toxin Binding to Isolated Porcine Tissues and Peripheral Blood Leukocytes
Authors: Winter Kellie R. K. , Stoffregen William C. , Dean-Nystrom Evelyn A.
Published: October 2004

Shiga toxin (Stx) binding sites in porcine tissues and leukocytes were identified by the use of Stx overlay and anti-CD77/Gb3 immunoassays. Stx1 and Stx2 bound to similar tissue locations and leukocytes, although some differences were noted. Previously unreported Stx binding sites were identified in kidney tubules, intestinal lymphoid aggregates, sinusoidal liver cells, alveolar macrophages, and peripheral blood leukocytes.
Human Factor inStaphylococcus aureusNasal Carriage
Authors: Nouwen Jan , Boelens Hélène , van Belkum Alex , Verbrugh Henri
Published: October 2004

Persistent nasal carriers and noncarriers ofStaphylococcus aureuswere inoculated with a mixture of differentS. aureusstrains. The majority of noncarriers and nearly all persistent carriers returned to their original carrier state after artificial inoculation. Furthermore, the majority of persistent carriers tested positive again for their original resident strain. Using a human nasal inoculation model, we here demonstrate that the human factor is an important determinant ofS. aureusnasal carriage.
Moraxella catarrhalis
Coaggregates with
Streptococcus pyogenes
and Modulates Interactions of
S. pyogenes
with Human Epithelial Cells
Authors: Lafontaine Eric R. , Wall David , Vanlerberg Serena L. , Donabedian Haig , Sledjeski Darren D.
Published: October 2004

The pathogens
Streptococcus pyogenes
and
Moraxella catarrhalis
colonize overlapping regions of the human nasopharynx. We have found that
M. catarrhalis
can dramatically increase
S. pyogenes
adherence to human epithelial cells and that species-specific coaggregation of these bacteria correlates with this enhanced adherence.
Streptococcus pneumoniae
-Induced Inhibition of Rat Ependymal Cilia Is Attenuated by Antipneumolysin Antibody
Authors: Hirst Robert A. , Mohammed Bashir J. , Mitchell Timothy J. , Andrew Peter W. , O'Callaghan Christopher
Published: October 2004

Ciliated ependymal cells line the ventricular surfaces and aqueducts of the brain. In ex vivo experiments, pneumolysin caused rapid inhibition of the ependymal ciliary beat frequency and caused ependymal cell disruption. Wild-type pneumococci and pneumococci deficient in pneumolysin caused ciliary slowing, but penicillin lysis of wild-type, not pneumolysin-deficient, pneumococci increased the extent of ciliary inhibition. This effect was abolished by antipneumolysin antibody. Ependymal ciliary stasis by purified pneumolysin was also blocked by the addition of antipneumolysin monoclonal antibodies. These data show that antibiotic lysis of
Streptococcus pneumoniae
can be detrimental to the ciliated ependyma and that antipneumolysin antibody may have a therapeutic potential.
Cleavage of the N-Linked Oligosaccharide from the Surfaces of
Chlamydia
Species Affects Attachment and Infectivity of the Organisms in Human Epithelial and Endothelial Cells
Authors: Kuo Cho-chou , Lee Amy , Campbell Lee Ann
Published: October 2004

Previous studies demonstrated that the high-mannose oligosaccharide N linked to the
Chlamydia
major outer membrane protein inhibited the attachment and infectivity of the organism. The present study showed that cleavage of the glycan with N-glycanase decreased the attachment and infectivity of chlamydial organisms in human epithelial and endothelial cells.
Variations in Barbour-Stoenner-Kelly Culture Medium Modulate Infectivity and Pathogenicity of
Borrelia burgdorferi
Clinical Isolates
Authors: Wang Guiqing , Iyer Radha , Bittker Susan , Cooper Denise , Small Jennifer , Wormser Gary P. , Schwartz Ira
Published: October 2004

The effects of variations in Barbour-Stoenner-Kelly (BSK) medium on the infectivity and pathogenicity of
Borrelia burgdorferi
clinical isolates were assessed by retrospective and prospective studies using a murine model of Lyme borreliosis. Thirty of 35 (86%) mice infected with any of six virulent
B. burgdorferi
clinical isolates grown in a BSK-H medium developed clinically apparent arthritis. By contrast, arthritis was observed in only 25 of 60 (42%) mice inoculated with two of these
B. burgdorferi
strains grown in a different lot of BSK-H medium (
P
< 0.001). In a prospective study, mice inoculated with a
B. burgdorferi
clinical isolate grown in a BSK medium prepared in-house produced significantly greater disease than those injected with the same isolate cultured in BSK-H medium (
P
< 0.05). The attenuated pathogenicity is not due to the loss of plasmids during in vitro cultivation. The data suggest that variations in BSK medium have a significant impact on the infectivity and pathogenicity of
B. burgdorferi
clinical isolates.
Identification of Genes Transcribed byActinobacillus pleuropneumoniaein Necrotic Porcine Lung Tissue by Using Selective Capture of Transcribed Sequences
Authors: Baltes Nina , Gerlach Gerald F.
Published: October 2004

Genes expressed byActinobacillus pleuropneumoniaein necrotic porcine lung tissue were identified by selective capture of transcribed sequences analysis. In total, 46 genes were identified, 20 of which have been previously reported to be associated with in vivo expression or virulence inA. pleuropneumoniaeor in other organisms.
Human Galectin-3 PromotesTrypanosoma cruziAdhesion to Human Coronary Artery Smooth Muscle Cells
Authors: Kleshchenko Yuliya Y. , Moody Tapria N. , Furtak Vyacheslav A. , Ochieng Josiah , Lima Maria F. , Villalta Fernando
Published: October 2004

Human galectin-3 binds to the surface ofTrypanosoma cruzitrypomastigotes and human coronary artery smooth muscle (CASM) cells. CASM cells express galectin-3 on their surface and secrete it. Exogenous galectin-3 increased the binding ofT. cruzito CASM cells. Trypanosome binding to CASM cells was enhanced when eitherT. cruzior CASM cells were preincubated with galectin-3. Cells stably transfected with galectin-3 antisense show a dramatic decrease in galectin-3 expression and very littleT. cruziadhesion to cells. The addition of galectin-3 to these cells restores their initial capacity to bind to trypanosomes. Thus, host galectin-3 expression is required forT. cruziadhesion to human cells and exogenous galectin-3 enhances this process, leading to parasite entry.
Interleukin-15-Deficient Mice Develop Protective Immunity toToxoplasma gondii
Authors: Lieberman Linda A. , Villegas Eric N. , Hunter Christopher A.
Published: October 2004

Previous studies have suggested an important role for interleukin-15 (IL-15) in resistance to and memory forToxoplasma gondiiinfection. The studies presented here reveal that IL-15 is not required for infection-induced expansion of NK or CD8+T cells. Furthermore, IL-15−/−mice develop long-term protective immunity to this pathogen.
In Vitro and In Vivo Evaluation of Staphylococcal Superantigen Peptide Antagonists
Authors: Rajagopalan Govindarajan , Sen Moon M. , David Chella S.
Published: October 2004

Superantigen peptide antagonists failed to block T-cell activation and cytokine production as well as toxic shock induced by staphylococcal enterotoxin B (SEB) in HLA class II transgenic mice. They also failed to inhibit the binding of SEB to HLA class II molecules as well as activation of human T lymphocytes in vitro.
Identification of
Neisseria meningitidis
Genetic Loci Involved in the Modulation of Phase Variation Frequencies
Authors: Alexander Heather L. , Rasmussen Andrew W. , Stojiljkovic Igor
Published: October 2004

It has been proposed that increased phase variation frequencies in
Neisseria meningitidis
augment transmissibility and invasiveness. A
Himar1 mariner
transposon mutant library was constructed in serogroup A
N. meningitidis
and screened for clones with increased phase variation frequencies. Insertions increasing the frequency of slippage events within mononucleotide repeat tracts were identified in three known phase variation-modulating genes (
mutS
,
mutL
, and
uvrD
), as well as six additional loci (
pilP
,
fbpA
,
fbpB
, NMA1233, and two intergenic regions). The implications of these insertion mutations are discussed.
Streptococcus mutans
Surface α-Enolase Binds Salivary Mucin MG2 and Human Plasminogen
Authors: Ge Jingping , Catt Diana M. , Gregory Richard L.
Published: October 2004

Matrix-assisted laser desorption ionization-time of flight mass spectrometry analysis identified enolase as a cell surface component of
Streptococcus mutans
, which was confirmed by enzyme-linked immunosorbent assay, Western blotting, and transmission electron microscopy. Surface enolase was demonstrated to bind to human plasminogen and salivary mucin MG2. The results suggested a role for enolase in
S. mutans
attachment, clearance, or breach of the bloodstream barrier.
FROM REGULAR WEIGHTED EXPRESSIONS TO FINITE AUTOMATA
Authors: CHAMPARNAUD JEAN-MARC , LAUGEROTTE ÉRIC , OUARDI FAISSAL , ZIADI DJELLOUL
Published: October 2004

In this article we generalize concepts of the position automaton and ZPC-structure to the regular [Formula: see text]-expressions. We show that the extended ZPC-structure can be built in linear time w.r.t. the size of the [Formula: see text]-expression and that the associated position automaton can be deduced from it in quadratic time.
ON THE VERTEX-CONNECTIVITY PROBLEM FOR GRAPHS WITH SHARPENED TRIANGLE INEQUALITY
Authors: FERRANTE ALESSANDRO , PARENTE MIMMO
Published: October 2004

Given a graph with the edge costs satisfying the β-sharpened triangle inequality: cost(u,v)≤β(cost(u,x)+cost(x,v)), for l/2≤β<1, we study the NP-hard problem of finding a minimum cost spanning subgraph which is k-vertex-connected, k≥2. We analyze an approximation quadratic-time algorithm whose performance ratio is [Formula: see text]. The main motivation of this study is to provide an algorithm with a good performance ratio and a practical worst case running time for significative subclasses of the metric Travelling Salesman Problem and the naturally related to it connectivity problems.
FAST PURSUIT OF MOBILE NODES USING TPR TREES
Authors: IDWAN SAHAR , MEHTA DINESH P. , LOPEZ MARIO A.
Published: October 2004

In this paper, we consider a set of mobile nodes, each of which moves with constant velocity. We present algorithms to determine which of these mobile nodes can be captured the soonest by a set of one or more pursuers. We use a time parameterized R-tree (TPR-tree) to index these moving points. We then develop algorithms that operate on the TPR-tree to answer three query variations: (1) a single pursuer that is faster than all of the mobile nodes, (2) a single pursuer that is slower than some of the mobile nodes and (3) multiple pursuers. Experimental results show that our algorithms are simpler and faster than other approaches to solve the problem.
OPTIMAL RANGE MAX DATACUBE FOR FIXED DIMENSIONS
Authors: POON CHUNG KEUNG
Published: October 2004

We present a new data structure to support orthogonal range max queries on a datacube. For a d-dimensional datacube with size n in each dimension where d≤c3 log log n/ log ( log * n), our structure requires [Formula: see text] query time and O((c2n)d) storage where c1, c2 and c3 are constants independent of d and n; and log * n is the minimum number of repeated logarithms it takes to reduce the value n to at most 2. Hence our data structure is asymptotically optimal when d is constant independent of n.
A NOTE ON REBOUND TURING MACHINES
Authors: INOUE KATSUSHI , ITO AKIRA , KAMIURA TAKASHI , PETERSEN HOLGER , ZHANG LAN
Published: October 2004

This paper continues the investigation of rebound Turing machines (RTM's). We first investigate a relationship between the accepting powers of simple one-way 2-head finite automata and simultaneously space-bounded and leaf-size bounded alternating RTM's, and show that for any functions L(n) and Z(n) such that L(n)Z(n)=o( log n) and [Formula: see text], simple one-way 2-head finite automata are incomparable with simultaneously L(n) space-bounded and Z(n) leaf-size bounded alternating RTM's. We then investigate a relationship between Las Vegas and determinism for space-bounded RTM's, and show that there is a language accepted by a Las Vegas rebound automaton, but not accepted by any weakly o( log log n) space-bounded deterministic RTM. This is the first separation result between Las Vegas and determinism for space-bounded computing models over strings.
AN ATTRACTOR NEURAL NETWORK MODEL OF CLASSICAL CONDITIONING
Authors: SERULNIK SERGIO D. , GUR MOSHE
Published: October 2004

Living beings learn to associate known stimuli that exhibit specific temporal correlations. This kind of learning is called associative learning, and the process by which animals change their responses according to the schedule of arriving stimuli is called “classical conditioning”. In this paper, a conditionable neural network which exhibits features like forward conditioning, dependency on the interstimulus interval, and absence of backward and reverse conditioning is presented. An asymmetric neural network was used and its ability to retrieve a sequence of embedded patterns using a single recalling input was exploited. The main assumption was that synapses that respond with different time constants coexist in the system. These synapses induce transitions between different embedded patterns. The appearance of a correct transition when only the first stimulus is applied, is interpreted as a realization of the conditioning process. The model also allows the analytical description of the conditioning process in terms of internal and external or researcher-controlled variables.
ANALYTICAL INTERPRETATION OF FEED-FORWARD NETS OUTPUTS AFTER TRAINING
Authors: GARRIDO LLUIS , GÓMEZ SERGIO
Published: October 2004

The minimization quadratic error criterion which gives rise to the back-propagation algorithm is studied using functional analysis techniques. With them, we recover easily the well-known statistical result which states that the searched global minimum is a function which assigns, to each input pattern, the expected value of its corresponding output patterns. Its application to classification tasks shows that only certain output class representations can be used to obtain the optimal Bayesian decision rule. Finally, our method permits the study of other error criterions, finding out, for instance, that absolute value errors lead to medians instead of mean values.
SPEAKER IDENTIFICATION USING TIME-DELAY HMEs
Authors: CHEN KE , XIE DAHONG , CHI HUISHENG
Published: October 2004

In this paper, we extend the Hierarchical Mixture of Experts (HME) to temporal processing and explore it for a substantial problem, that of text-dependent speaker identification. For a specific multiway classification, we propose a generalized Bernoulli density instead of the multinomial logit density to avoid the instability during training. Time-delay technique is applied for spatio-temporal processing in the HME and a combining scheme is presented for combining multiple time-delay HMEs in order to complete a multi-scale analysis for the temporal data. Using the time-delay HME along with the EM algorithm as well as the combination of multiple time-delay HMEs, the speaker identification system has a good performance and yields significantly fast training. We have also addressed some issues about the time-delay techniques in the HME.
HARDWARE PROTOTYPES OF A BOOLEAN NEURAL NETWORK AND THE SIMULATED ANNEALING OPTIMIZATION METHOD
Authors: NIITTYLAHTI JARKKO
Published: October 2004

Boolean Neural Network is a neural network that operates with binary weight values of “1” and “0”. Otherwise it is formally analogous to the Multilayer Perceptron (MLP).1 Simulated Annealing is a stochastic optimization method that is suitable for performing nonlinear multivariable optimization tasks. Training a Boolean Neural Network is a well-suited problem to this algorithm. However, the Simulated Annealing method is computationally heavy, which makes the training procedure slow. The training speed can be improved by using custom designed hardware for the whole system including the optimization method and the neural network.

Hardware prototypes of a Boolean Neural Network and the Simulated Annealing optimization method have been designed using discrete components. The Boolean Neural Network implementation is basically a dynamically configurable feedforward network of Boolean logic gates of two inputs. The Simulated Annealing implementation is a general purpose hardware tool for multivariable optimization tasks. Here it is applied to do supervised training of the Boolean Neural Network hardware.
ON THE CHARACTERISTICS OF THE QUADRATIC ORDER ASSOCIATIVE MEMORY THAT USES SYNCHRONOUS UPDATE AND DIRECT CONVERGENCE
Authors: WANG JUNG-HUA
Published: October 2004

A statistical method is applied to explore the characteristics of a certain class of quadratic order associative memories: the Synchronous Update Direct Convergence Memory (SQDM). The initial input vector is required to converge, with a given value of probability Pdc, to a stored codeword in just one synchronous update. The memory capacity ms is derived by using a figure of merit NΦ, and its tight asymptotic bound is found by using Tchebycheff's inequality. When the input contains erroneous bits, the maximum allowable number of attractors and their attraction radii are determined. The existence of principal connections Tpr useful in solving the problem of proliferation of connections in the SQDM is introduced. We prove that Tpr is a set of which an arbitrary connection Tijk satisfies[Formula: see text]
ROLE OF CHAOS IN TRIAL-AND-ERROR PROBLEM SOLVING BY AN ARTIFICIAL NEURAL NETWORK
Authors: OBANA ICHIRO , FUKUI YASUHIRO
Published: October 2004

One role of chaotic neural activity is illustrated by means of computer simulations of an imaginary agent’s goal-oriented behavior. The agent has a simplified neural network with seven neurons and three legs. The neural network consists of one photosensory neuron and three pairs of inter- and motor neurons. The three legs whose movements are governed by the three motor neurons allow the agent to walk in six concentric radial directions on a plane. It is intended that the neural network causes the agent to walk in a direction of greater brightness, to reach finally the most brightly lit place on the plane. The presence of only one sensory neuron has an important meaning. That is, no immediate information on directions of greater brightness is sensed by the agent. In other words, random walking in the manner of trial-and-error problem solving must be involved in the agent’s walking. Chaotic firing of the motor neurons is intended to play a crucial role in generating the random walking. Brief random walking and rapid straight walking in a direction of greater brightness were observed to occur alternately in the computer simulation. Controlled chaos in naturally occurring neural networks may play a similar role.
LEARNING ACTIVATION RULES RATHER THAN CONNECTION WEIGHTS
Authors: GRUNDSTROM ERIC L. , REGGIA JAMES A.
Published: October 2004

In the construction of neural networks involving associative recall, information is sometimes best encoded with a local representation. Moreover, a priori knowledge can lead to a natural selection of connection weights for these networks. With predetermined and fixed weights, standard learning algorithms that work by altering connection strengths are unable to train such networks. To address this problem, this paper derives a supervised learning rule based on gradient descent, where connection weights are fixed and a network is trained by changing the activation rule. It incorporates both traditional and competitive activation mechanisms, the latter being an efficient method for instilling competition in a network. The learning rule has been implemented, and the results from several test networks demonstrate that it works effectively.
Corrigendum: NEURONAL MECHANISMS UNDERLYING THE INTERACTION BETWEEN VISUAL LANDMARKS AND PATH INTEGRATION IN THE RAT
Authors: Knierim James J. , Kudrimoti Hemant S. , McNaughton Bruce L.
Published: October 2004

Place- and direction-specific firing properties of hippocampal and thalamic neurons are not strongly tied to visual landmarks when a rat is disoriented. These results suggest that rats rely more on path integration mechanisms than on landmarks, until they have learned that the landmarks are stable directional references.
A LOW-DIMENSIONAL, TIME-RESOLVED AND ADAPTING MODEL NEURON
Authors: CARTLING BO
Published: October 2004

A low-dimensional, time-resolved and adapting model neuron is formulated and evaluated. The model is an extension of the integrate-and-fire type of model with respect to adaptation and of a recent adapting firing-rate model with respect to time-resolution. It is obtained from detailed conductance-based models by a separation of fast and slow ionic processes of action potential generation. The model explicitly includes firing-rate regulation via the slow afterhyperpolarization phase of action potentials, which is controlled by calcium-sensitive potassium channels. It is demonstrated that the model closely reproduces the firing pattern and excitability behaviour of a detailed multicompartment conductance-based model of a neocortical pyramidal cell. The inclusion of adaptation in a model neuron is important for its capability to generate complex dynamics of networks of interconnected neurons. The time-resolution is required for studies of systems in which the temporal aspects of neural coding are important. The simplicity of the model facilitates analytical studies, insight into neurocomputational mechanisms and simulations of large-scale systems. The capability to generate complex network computations may also make the model useful in practical applications of artificial neural networks.
EMPIRICAL ERROR-CONFIDENCE CURVES FOR NEURAL NETWORK AND GAUSSIAN CLASSIFIERS
Authors: WOLFF GREGORY J. , STORK DAVID G. , OWEN ART
Published: October 2004

“Error-Confidence” measures the probability that the proportion of errors made by a classifier will be within ∊ of EB, the optimal (Bayes) error. Probably Almost Bayes (PAB) theory attempts to quantify how this confidence increases with the number of training samples. We investigate the relationship empirically by comparing average error versus number of training patterns (m) for linear and neural network classifiers. On Gaussian problems, the resulting EC curves demonstrate that the PAB bounds are extremely conservative. Asymptotic statistics predicts a linear relationship between the logarithms of the average error and the number of training patterns. For low Bayes error rates we found excellent agreement between the prediction and the linear discriminant performance. At higher Bayes error rates we still found a linear relationship, but with a shallower slope than the predicted -1. When the underlying true model is a three-layer network, the EC curves show a greater dependence on classifier capacity, and the linear predictions no longer seem to hold.
USING A FEED-FORWARD NETWORK TO INCORPORATE THE RELATION BETWEEN ATTRACTEES AND ATTRACTORS IN A GENERALIZED DISCRETE HOPFIELD NETWORK
Authors: BROUWER ROELOF K.
Published: October 2004

This paper demonstrates how a feedforward network with constant connection matrices may be used to train a Hopfield style network for pattern recognition. The connection matrix of the Hopfield style network is asymmetric and its diagonal is non-zero. The Hopfield style network referred to as a GDHN is trained to incorporate a relation between attractees and attractors. The attractees represent class samples and the attractors represent class prototypes. The feedforward network is trained using a gradient descent method. Gradients are fed forward in the network to obtain a gradient for a cost function.
CONCEPT HIERARCHY MEMORY MODEL: A NEURAL ARCHITECTURE FOR CONCEPTUAL KNOWLEDGE REPRESENTATION, LEARNING, AND COMMONSENSE REASONING
Authors: TAN AH-HWEE , SOON HUI-SHIN VIVIEN
Published: October 2004

This article introduces a neural network based cognitive architecture termed Concept Hierarchy Memory Model (CHMM) for conceptual knowledge representation and commonsense reasoning. CHMM is composed of two subnetworks: a Concept Formation Network (CFN), that acquires concepts based on their sensory representations; and a Concept Hierarchy Network (CHN), that encodes hierarchical relationships between concepts. Based on Adaptive Resonance Associative Map (ARAM), a supervised Adaptive Resonance Theory (ART) model, CHMM provides a systematic treatment for concept formation and organization of a concept hierarchy. Specifically, a concept can be learned by sampling activities across multiple sensory fields. By chunking relations between concepts as cognitive codes, a concept hierarchy can be learned/modified through experience. Also, fuzzy relations between concepts can now be represented in terms of the weights on the links connecting them. Using a unified inferencing mechanism based on code firing, CHMM performs an important class of commonsense reasoning, including concept recognition and property inheritance.
WHAT CAUSES ION CHANNEL PROTEINS TO FLUCTUATE OPEN AND CLOSED?
Authors: LIEBOVITCH LARRY S. , TODOROV ANGELO T.
Published: October 2004

Ion channels in the cell membrane spontaneously switch from states that are closed to the flow of ions such as sodium, potassium, and chloride to states that are open to the flow of these ions. The durations of times that an individual ion channel protein spends in the closed and open states can be measured by the patch clamp technique. We explore two basic issues about the molecular properties of ion channels:

1) If the switching between the closed and open state is an inherently random event, what does the patch clamp data tell us about the structure or motions in the ion channel protein?

2) Is this switching random?
A DYNAMICAL MODEL OF THE DISTRIBUTED INTERACTION OF INTRACELLULAR SIGNALS
Authors: JAMES ADRIENNE , SWANN KARL , RECCE MICHAEL
Published: October 2004

The behaviour of cells is coordinated by a large array of different types of intracellular protein kinases, protein phosphatases and second messenger molecules. Current experimental evidence suggests that these kinases, phosphatases and other messengers form a vast complicated interconnected signalling network inside the cell. A generic model of this signalling network is presented which is specifically designed to examine the global properties that can be expected from a model based on the available knowledge of these molecular interactions. Different protein types are represented as nodes in a network that interact via a connection matrix. During interactions, idealised kinases and phosphatases activate and deactivate other protein types by altering the level of phosphorylation of their regulatory sites. The occupancy of regulatory sites on protein kinases and phosphatases in turn determines their activity. Monte Carlo simulations are carried out on ensembles of networks. Steady states and periodic behaviour are observed in these networks. We discuss the potential of this type of model for understanding cell behaviour.
CAN QUANTUM PROCESSES CONTROL SYNAPTIC EMISSION?
Authors: BECK FRIEDRICH
Published: October 2004

The role of quantum processes in brain activity has been discussed intensively in recent years. Some authors regard the indeterminacy of quantum events as key property to relate brain activity and consciousness on a scientific basis. Here we ask the question where quantum events could be effective in the complex cortical structure. We argue that only elementary microscopic processes like electron transfer in protein complexes are able to survive thermal fluctuations. A model for the quantal emission process at the synaptic cleft is presented and related to the mechanism of electron transfer between different molecular states, as described by Marcus theory.
NOISE IN MODELS OF NEUROLOGICAL AND PSYCHIATRIC DISORDERS
Authors: SPITZER MANFRED , NEUMANN MANFRED
Published: October 2004

The concept of noise has only recently been applied to modelling neuropsychiatric disorders. Two examples of such models are presented.

1. A phantom limb is a neurological condition after the amputation of an extremity. It consists of sensations of the presence of the lost limb and has been attributed to cortical as well as non-cortical mechanisms. A neural network model of phantom limbs is proposed which can parsimoniously account for a large number of clinical features and recent findings of cortical map plasticity after deafferentation. In trained self-organizing feature maps, deafferentation was simulated. Reorganization is shown to be driven by input noise. According to the model, the production of input noise by the deafferented primary sensory neuron drives cortical reorganization in amputees. No such noise is generated and/or conducted to the cortex in paraplegics.

2. Several clinical features of schizophrenia have been related to the ratio of signal to noise in neuronal information processing. In particular, dopamine — which has been implicated in the causation of schizophrenia for decades — has been proposed to modulate signal-to-noise ratio. Data are presented which suggest that schizophrenic thought disorder is the result of a hypodopaminergic state and concomitant increased effects of noise in semantic information processing. Possible functions of noise in the nervous systems are discussed.
THE RECEPTOR MOSAIC HYPOTHESIS OF THE ENGRAM: POSSIBLE RELEVANCE OF BOOLEAN NETWORK MODELING
Authors: ZOLI MICHELE , GUIDOLIN DIEGO , FUXE KJELL , AGNATI LUIGI F.
Published: October 2004

In the past 15 years, several lines of evidence have shown that receptors for chemical signals can interact in domains of the plasma membrane and possibly form molecular circuits encoding logical operators. In this frame, the receptor mosaic hypothesis of the engram was advanced. According to this proposal, aggregates of different receptor species (mosaics) may form in neuronal membranes (typically synapses) and constitute a memory trace (engram) of its activity. In the present paper, we present an attempt to model the functioning of aggregates of interacting receptors in membrane domains by means of random Boolean networks.
SPONTANEOUS SIGNALLING IN SMALL CENTRAL NEURONS: MECHANISMS AND ROLES OF SPIKE-AMPLITUDE AND SPIKE-INTERVAL FLUCTUATIONS
Authors: ÅRHEM PETER , JOHANSSON STAFFAN
Published: October 2004

Spontaneous brain activity is essential for normal brain function. We are studying spontaneous activity in hippocampus at several complexity levels: at the microscopic level by analyzing the role of ion channels, at the mesoscopic level by analyzing the neuronal impulse activity, and at the macroscopic level by computational studies of mean electric fields of cortical network models. We have focused on the role of a subset of hippocampal neurons in the rat — neurons of small size (diameter <10 µ m ). The analysis of spontaneous impulse trains in these neurons, both isolated and in slices, show (i) that impulses vary in amplitude, the magnitude depending on the input signal, suggesting that the amplitude variability may play a role in the information processing of the brain, and (ii) that single ion channel events can trigger neuronal impulses, suggesting that these neurons can function as cellular random generators. The possible role of random generators are investigated by simulating spontaneous activity in a cortical network model, based on a simplified description of the architecture of the CA1 area of hippocampus. The simulations show that such random generators can induce synchronous oscillations in cortical networks. These findings highlight the role of microfluctuations for the global macroactivity of the brain, and stress the importance of the study of channel kinetics for brain physiology.
SIMULTANEOUS DETECTION OF ACTION POTENTIAL CURRENT WAVEFORMS AND SINGLE ION CHANNEL OPENINGS IN RAT CEREBELLAR GRANULE CELLS
Authors: LINNE M.-L. , OJA S.S. , JALONEN T.O.
Published: October 2004

Spontaneous Action Potential Current waveforms (APCs) and single ion channel openings were recorded in cultured rat cerebellar granule cells using the cell-attached configuration of the patch-clamp technique. APCs were activated without any externally applied stimuli. Both mono- and biphasic APCs were detected with a typical amplitude (positive waveform component) of 25–60 pA. Correlation between cell APCs and inward and outward single ion channel openings immediately before and after the capacitive transients was analyzed. In a detailed analysis it was found that the opening of 1.5 pA inward channel before the waveform was associated with 16%, and the opening of 1.0 pA outward channel after the waveform with 88% of APCs (of total 327 APCs analyzed). The modulation of APC activity by bath application of the amino acid taurine was found to increase the frequency of APCs, transform the APCs from mixed monophasic/biphasic to completely biphasic, and enhance the activated inward current deflections seemingly connected to the increased firing frequency.
STOCHASTIC RESONANCE AND TIME ADVANCE CODING IN CHEMICAL REACTIONS
Authors: FÖRSTER A. , GUDERIAN A. , ZEYER K.-P. , DECHERT G. , SCHNEIDER F.W.
Published: October 2004

Stochastic Resonance (SR) is a phenomenon wich may be found in nonlinear systems close to an excitation threshold. SR is a means for enhancing a weak periodic subthreshold signal from its noisy background by adding stochastic fluctuations, i.e. in biological and physical systems. It has been proposed that SR is important for the ability of neural systems to detect weak periodic signals. In the present work we show experimentally that SR occurs in two nonlinear chemical reactions, namely in the enzymatic Peroxidase-Oxidase (PO) reaction and in the Belousov–Zhabotinskii (BZ) reaction. A small sinusoidal signal with increasing noise is imposed on the focal steady state near a subcritical Hopf bifurcation. When the threshold is crossed beyond a certain noise amplitude, the system responds with spikes. The resulting interspike histogram and the plot of the signal to noise ratio, which is evaluated from the respective Fourier spectra, pass through a maximum at an optimal external noise level.

An alternative way to cross the excitation threshold without noise is the variation of the bias value of the sinusoidal signal. The variation of the bias value causes the spikes to appear earlier if the sinusoidal function is moved closer towards the threshold. This so-called time advance coding is shown experimentally for the first time in the BZ reaction by imposing sinusoidal flow rate variations using different bias values. The phenomenon has been proposed by Hopfield11 to be a means for analog pattern recognition.
CHAOS CONTROL BY ELECTRIC CURRENT IN AN ENZYMATIC REACTION
Authors: LEKEBUSCH A. , FÖRSTER A. , SCHNEIDER F.W.
Published: October 2004

We apply the continuous delayed feedback method of Pyragas to control chaos in the enzymatic Peroxidase-Oxidase (PO) reaction, using the electric current as the control parameter. At each data point in the time series, a time delayed feedback function applies a small amplitude perturbation to inert platinum electrodes, which causes redox processes on the surface of the electrodes. These perturbations are calculated as the difference between the previous (time delayed) signal and the actual signal. Unstable periodic P1, 11, and 12 orbits (UPOs) were stabilized in the CSTR (continuous stirred tank reactor) experiments. The stabilization is demonstrated by at least three conditions: A minimum in the experimental dispersion function, the equality of the delay time with the period of the stabilized attractor and the embedment of the stabilized periodic attractor in the chaotic attractor.
NEURONAL OSCILLATIONS AND STOCHASTIC LIMIT CYCLES
Authors: KURRER CHRISTIAN , SCHULTEN KLAUS
Published: October 2004

We investigate a model for synchronous neural activity in networks of coupled neurons. The individual systems are governed by nonlinear dynamics and can continuously vary between excitable and oscillatory behavior. Analytical calculations and computer simulations show that coupled excitable systems can undergo two different phase transitions from synchronous to asynchronous firing behavior. One of the transitions is akin to the synchronization transitions in coupled oscillator systems, while the second transition can only be found in coupled excitable systems. Using the concept of Stochastic Limit Cycles, we present an analytical derivation of the two transitions and discuss implications for synchronization transitions in biological neural networks.
SYNCHRONIZED CHAOS IN LOCAL CORTICAL CIRCUITS
Authors: HANSEL D.
Published: October 2004

Neurons in cortical slices emit spikes or bursts of spikes regularly in response to a suprathreshold current injection. This is in marked contrast to the behavior of cortical neurons in-vivo, whose response to electrical or sensory input displays a strong degree of irregularity. Correlation measurements show significant degree of synchrony in the temporal fluctuations of neuronal activities in cortex. We explore the hypothesis that these phenomena are the result of the synchronized chaos generated by the deterministic dynamics of local cortical networks. A model of a “hypercolumn” in the visual cortex is studied. It consists of two populations of neurons, one inhibitory and one excitatory. The dynamics of the neurons is based on a Hodgkin–Huxley type model with several cellular and synaptic conductances. The pattern of connectivity is correlated with the internal organization of hypercolumns in orientation columns. Numerical simulations of the model show that in an appropriate parameter range, the network settles in a synchronous chaotic state, characterized by a strong temporal variability of the neural activity which is correlated across the hypercolumn. Strong inhibitory feedback is essential for the stabilization of this state. Auto-correlation and cross-correlation functions of neuronal spike trains are computed, and analyzed. The relation between the results of the model and experiments in visual cortex is discussed.
PREDICTION OF ARTERIAL “BURST” ACTIVITY AND TRANSITIONS BETWEEN CHAOTIC ATTRACTORS WITH A MULTILAYER PERCEPTRON OPTIMIZED BY A NEW STOPPING CRITERION
Authors: STAMATIS N. , PARTHIMOS D. , GRIFFITH T.M.
Published: October 2004

We have explored the potential of an artificial neural network to capture the dynamics of chaotic temporal fluctuations in arterial pressure and flow. Model generated signals that simulate this ubiquitous physiological phenomenon in both form and complexity were used to train a Multilayer Perceptron (MLP) after first locating the optimum time delay to unfold the attractor governing the dynamics. Prediction horizons were maximized with a new stopping criterion capable of continuously tracking the trajectories of the model system. Single-step predictions were consistently good throughout the study. Long-term predictions obtained by using the MLP as a signal generator were very successful when the number of hidden nodes was carefully chosen. Moreover, short- and long-term predictions could also be obtained even when the dynamics was nonstationary.
LIGHT ADAPTATION AND RELIABILITY IN BLOWFLY PHOTORECEPTORS
Authors: DE RUYTER VAN STEVENINCK R.R. , LAUGHLIN S.B.
Published: October 2004

We characterize the reliability of response of blowfly photoreceptors at different light levels. These cells convey their information by graded potentials. Their reliability is quantified by the frequency-dependent contrast-normalized signal to noise ratio. Independently we estimate the effective photoconversion rate of the cells by counting individual photoconversion events, or quantum bumps, at calibrated low light levels. Comparing both results we quantify the statistical efficiency of photoconversion at higher light intensities, characterizing the transduction efficiency as a function of frequency. The light intensities used in these experiments ranged from about 300 to about 5×105 photoconversions per second per photoreceptor. Over most of this range, statistical efficiencies are within 50% at frequencies up to about 100 Hz.
LEARNING SYNFIRE CHAINS: TURNING NOISE INTO SIGNAL
Authors: HERTZ JOHN , PRÜGEL-BENNETT ADAM
Published: October 2004

We develop a model of cortical coding of stimuli by the sequences of activation patterns that they ignite in an initially random network. Hebbian learning then stabilizes these sequences, making them attractors of the dynamics. There is a competition between the capacity of the network and the stability of the sequences; for small stability parameter ∊ (the strength of the mean stabilizing PSP in the neurons in a learned sequence) the capacity is proportional to 1/∊2. For ∊ of the order of or less than the PSPs of the untrained network, the capacity exceeds that for sequences learned from tabula rasa.
A NEW TYPE OF SELF-ORGANIZATION ASSOCIATED WITH CHAOTIC DYNAMICS IN NEURAL NETWORKS
Authors: TSUDA I.
Published: October 2004

A new type of self-organized dynamics is presented, in relation with chaos in neural networks. One is chaotic itinerancy and the other is chaos-driven contraction dynamics. The former is addressed as a universal behavior in high-dimensional dynamical systems. In particular, it can be viewed as one possible form of memory dynamics in brain. The latter gives rise to singular-continuous nowhere-differentiable attractors. These dynamics can be related to each other in the context of dimensionality and of chaotic information processings. Possible roles of these complex dynamics in brain are also discussed.