Hierarchical neural network with high storage capacity

A recent method used to optimize biased neural networks with low levels of activity is applied to a hierarchical model. As a consequence, the performance of the system is strongly enhanced. The steps to achieve optimization are analyzed in detail.

Noise and periodic modulations in neural excitable medium

We have analyzed the interplay between noise and periodic modulations in a mean field model of a neural excitable medium. For this purpose, we have considered two types of modulations, namely, variations of the resistance and oscillations of the threshold. In both cases, stochastic resonance is present, irrespective of whether the system is monostable or bistable.

Data-driven clustering : new methods and applications

Abstract : This work is concerned with the development and application of novel unsupervised learning methods, having in mind two target applications: the analysis of forensic case data and the classification of remote sensing images. First, a method based on a symbolic optimization of the inter-sample distance measure is proposed to improve the flexibility of spectral clustering algorithms, and applied to the problem of forensic case data. This distance is optimized using a loss function related to the preservation of neighborhood structure between the input space and the space of principal components, and solutions are found using genetic programming. Results are compared to a variety of state-of--the-art clustering algorithms. Subsequently, a new large-scale clustering method based on a joint optimization of feature extraction and classification is proposed and applied to various databases, including two hyperspectral remote sensing images. The algorithm makes uses of a functional model (e.g., a neural network) for clustering which is trained by stochastic gradient descent. Results indicate that such a technique can easily scale to huge databases, can avoid the so-called out-of-sample problem, and can compete with or even outperform existing clustering algorithms on both artificial data and real remote sensing images. This is verified on small databases as well as very large problems. Résumé : Ce travail de recherche porte sur le développement et l'application de méthodes d'apprentissage dites non supervisées. Les applications visées par ces méthodes sont l'analyse de données forensiques et la classification d'images hyperspectrales en télédétection. Dans un premier temps, une méthodologie de classification non supervisée fondée sur l'optimisation symbolique d'une mesure de distance inter-échantillons est proposée. Cette mesure est obtenue en optimisant une fonction de coût reliée à la préservation de la structure de voisinage d'un point entre l'espace des variables initiales et l'espace des composantes principales. Cette méthode est appliquée à l'analyse de données forensiques et comparée à un éventail de méthodes déjà existantes. En second lieu, une méthode fondée sur une optimisation conjointe des tâches de sélection de variables et de classification est implémentée dans un réseau de neurones et appliquée à diverses bases de données, dont deux images hyperspectrales. Le réseau de neurones est entraîné à l'aide d'un algorithme de gradient stochastique, ce qui rend cette technique applicable à des images de très haute résolution. Les résultats de l'application de cette dernière montrent que l'utilisation d'une telle technique permet de classifier de très grandes bases de données sans difficulté et donne des résultats avantageusement comparables aux méthodes existantes.

Prevalence of Neural Tube Defects (NTD) in Younger Mothers in Europe 2000-2008: Analysis of the EUROCAT Database - Report to Bayer Schiering Pharma

M.C. Addor is included in the Eurocat Working Group

Podocalyxin is a novel polysialylated neural adhesion protein with multiple roles in neural development and synapse formation

Neural development and plasticity are regulated by neural adhesion proteins, including the polysialylated form of NCAM (PSA-NCAM). Podocalyxin (PC) is a renal PSA-containing protein that has been reported to function as an anti-adhesin in kidney podocytes. Here we show that PC is widely expressed in neurons during neural development. Neural PC interacts with the ERM protein family, and with NHERF1/2 and RhoA/G. Experiments in vitro and phenotypic analyses of podxl-deficient mice indicate that PC is involved in neurite growth, branching and axonal fasciculation, and that PC loss-of-function reduces the number of synapses in the CNS and in the neuromuscular system. We also show that whereas some of the brain PC functions require PSA, others depend on PC per se. Our results show that PC, the second highly sialylated neural adhesion protein, plays multiple roles in neural development.

Neural networks as mechanisms to regulate division of labor.

Abstract In social insects, workers perform a multitude of tasks, such as foraging, nest construction, and brood rearing, without central control of how work is allocated among individuals. It has been suggested that workers choose a task by responding to stimuli gathered from the environment. Response-threshold models assume that individuals in a colony vary in the stimulus intensity (response threshold) at which they begin to perform the corresponding task. Here we highlight the limitations of these models with respect to colony performance in task allocation. First, we show with analysis and quantitative simulations that the deterministic response-threshold model constrains the workers' behavioral flexibility under some stimulus conditions. Next, we show that the probabilistic response-threshold model fails to explain precise colony responses to varying stimuli. Both of these limitations would be detrimental to colony performance when dynamic and precise task allocation is needed. To address these problems, we propose extensions of the response-threshold model by adding variables that weigh stimuli. We test the extended response-threshold model in a foraging scenario and show in simulations that it results in an efficient task allocation. Finally, we show that response-threshold models can be formulated as artificial neural networks, which consequently provide a comprehensive framework for modeling task allocation in social insects.

Neural differentiation and therapeutic potential of adipose tissue derived stem cells.

Neural tissue has historically been regarded as having poor regenerative capacity but recent advances in the growing fields of tissue engineering and regenerative medicine have opened new hopes for the treatment of nerve injuries and neurodegenerative disorders. Adipose tissue has been shown to contain a large quantity of adult stem cells (ASC). These cells can be easily harvested with low associated morbidity and because of their potential to differentiate into multiple cell types, their use has been suggested for a wide variety of therapeutic applications. In this review we examine the evidence indicating that ASC can stimulate nerve regeneration by both undergoing neural differentiation and through the release of a range of growth factors. We also discuss some of the issues that need to be addressed before ASC can be developed as an effective cellular therapy for the treatment of neural tissue disorders.

Development and Demonstration of a Freezing Drizzle Algorithm for Roadway Environmental Sensing Systems, Final Report, October 2012

The primary goal of this project is to demonstrate the accuracy and utility of a freezing drizzle algorithm that can be implemented on roadway environmental sensing systems (ESSs). The types of problems related to the occurrence of freezing precipitation range from simple traffic delays to major accidents that involve fatalities. Freezing drizzle can also lead to economic impacts in communities with lost work hours, vehicular damage, and downed power lines. There are means for transportation agencies to perform preventive and reactive treatments to roadways, but freezing drizzle can be difficult to forecast accurately or even detect as weather radar and surface observation networks poorly observe these conditions. The detection of freezing precipitation is problematic and requires special instrumentation and analysis. The Federal Aviation Administration (FAA) development of aircraft anti-icing and deicing technologies has led to the development of a freezing drizzle algorithm that utilizes air temperature data and a specialized sensor capable of detecting ice accretion. However, at present, roadway ESSs are not capable of reporting freezing drizzle. This study investigates the use of the methods developed for the FAA and the National Weather Service (NWS) within a roadway environment to detect the occurrence of freezing drizzle using a combination of icing detection equipment and available ESS sensors. The work performed in this study incorporated the algorithm developed initially and further modified for work with the FAA for aircraft icing. The freezing drizzle algorithm developed for the FAA was applied using data from standard roadway ESSs. The work performed in this study lays the foundation for addressing the central question of interest to winter maintenance professionals as to whether it is possible to use roadside freezing precipitation detection (e.g., icing detection) sensors to determine the occurrence of pavement icing during freezing precipitation events and the rates at which this occurs.

Etude de la variation interlaboratoire du test de dépistage prénatal des défauts de fermeture du tube neural [Interlaboratory variation in the prenatal screening test for neural tube closing defects].

Early detection of neural-tude defects is possible by determining Alpha-fetoprotein (AFP) in maternal serum. 16'685 pregnant women were observed. Three methods for the determination of the "normal" range are compared. The first one, already used in similar studies, makes use of a constant multiple of the median. The other two ones make use of robust estimates of location and scale. Their comparison shows the interest of the robust methods to reduce the interlaboratory variability.

A circuit-based gatekeeper for adult neural stem cell proliferation: Parvalbumin-expressing interneurons of the dentate gyrus control the activation and proliferation of quiescent adult neural stem cells.

Newborn neurons are generated in the adult hippocampus from a pool of self-renewing stem cells located in the subgranular zone (SGZ) of the dentate gyrus. Their activation, proliferation, and maturation depend on a host of environmental and cellular factors but, until recently, the contribution of local neuronal circuitry to this process was relatively unknown. In their recent publication, Song and colleagues have uncovered a novel circuit-based mechanism by which release of the neurotransmitter, γ-aminobutyric acid (GABA), from parvalbumin-expressing (PV) interneurons, can hold radial glia-like (RGL) stem cells of the adult SGZ in a quiescent state. This tonic GABAergic signal, dependent upon the activation of γ(2) subunit-containing GABA(A) receptors of RGL stem cells, can thus prevent their proliferation and subsequent maturation or return them to quiescence if previously activated. PV interneurons are thus capable of suppressing neurogenesis during periods of high network activity and facilitating neurogenesis when network activity is low.

Coagulation Factor Xa Activates Thrombin and Signals Ischemic Neuronal Death Via Par-1 and JNK in Ischemic Neural Tissue

Background: The coagulation factor thrombin mediates ischemic neuronal deathand, at a low concentration, induces tolerance to ischemia.We investigated its modeof activation in ischemic neural tissue using an in vitro approach to distinguish therole of circulating coagulation factors from endogenous cerebral mechanisms. Wealso studied the signalling pathway downstream of thrombin in ischemia and afterthrombin preconditioning.Methods: Rat organotypic hippocampal slice cultures to 30 minute oxygen (5%)and glucose (1 mmol/L) deprivation (OGD).Results: Selective factor Xa (FXa) inhibition by fondaparinux during and afterOGD significantly reduced neuronal death in the CA1 after 48 hours. Thrombinactivity was increased in the medium 24 hours after OGD and this increasewas prevented by fondaparinux suggesting that FXa catalyzes the conversion ofprothrombin to thrombin in neural tissue after ischemia in vitro. Treatment withSCH79797, a selective antagonist of the thrombin receptor protease activatedreceptor-1 (PAR-1), significantly decreased neuronal cell death indicating thatthrombin signals ischemic damage via PAR-1. The JNK pathway plays an importantrole in cerebral ischemia and we observed activation of the JNK substrate,c-Jun in our model. Both the FXa inhibitor, fondaparinux and the PAR-1 antagonistSCH79797, decreased the level of phospho-c-Jun Ser73. After thrombin preconditioningc-Jun was activated by phosphorylation in the nuclei of neurons of the CA1.Treatment with a synthetic thrombin receptor agonist resulted in the same c-Junactivation profile and protection against subsequent OGD indicating that thrombinalso signals via PAR-1 and c-Jun in cell protection.Conclusion: These results indicate that FXa activates thrombin in cerebral ischemia,leading via PAR-1 to the activation of the JNK pathway resulting in neuronal death.Thrombin induced tolerance also involves PAR-1 and JNK, revealing commonfeatures in cell death and survival signalling.

A program of nurse algorithm-guided care for adult patients with acute minor illnesses in primary care

Background: Attention to patients with acute minor-illnesses requesting same-day consultation represents a major burden in primary care. The workload is assumed by general practitioners in many countries. A number of reports suggest that care to these patients may be provided, at in least in part, by nurses. However, there is scarce information with respect to the applicability of a program of nurse management for adult patients with acute minor-illnesses in large areas. The aim of this study is to assess the effectiveness of a program of nurse algorithm-guided care for adult patients with acute minor illnesses requesting same-day consultation in primary care in a largely populated area. Methods: A cross-sectional study of all adult patients seeking same day consultation for 16 common acute minor illnesses in a large geographical area with 284 primary care practices. Patients were included in a program of nurse case management using management algorithms. The main outcome measure was case resolution, defined as completion of the algorithm by the nurse without need of referral of the patient to the general practitioner. The secondary outcome measure was return to consultation, defined as requirement of new consultation for the same reason as the first one, in primary care within a 7-day period. Results: During a two year period (April 2009-April 2011), a total of 1,209,669 consultations were performed in the program. Case resolution was achieved by nurses in 62.5% of consultations. The remaining cases were referred to a general practitioner. Resolution rates ranged from 94.2% in patients with burns to 42% in patients with upper respiratory symptoms. None of the 16 minor illnesses had a resolution rate below 40%. Return to consultation during a 7-day period was low, only 4.6%. Conclusions: A program of algorithms-guided care is effective for nurse case management of patients requesting same day consultation for minor illnesses in primary care.