Neural networks in fault detection: a case study

We study the applications of neural nets in the area of fault detection in real vibrational data. The study is one of the first to include a large set of real vibrational data and to illustrate the potential as well as the limitations of neural networks for fault detection

Toward a further understanding of object feature binding: a cognitive neuroscience perspective.

The aim of this thesis is to lead to a further understanding of the neural mechanisms underlying object feature binding in the human brain. The focus is on information processing and integration in the visual system and visual shortterm memory. From a review of the literature it is clear that there are three major competing binding theories, however, none of these individually solves the binding problem satisfactorily. Thus the aim of this research is to conduct behavioural experimentation into object feature binding, paying particular attention to visual short-term memory. The behavioural experiment was designed and conducted using a within-subjects delayed responset ask comprising a battery of sixty-four composite objects each with three features and four dimensions in each of three conditions (spatial, temporal and spatio-temporal).Findings from the experiment,which focus on spatial and temporal aspects of object feature binding and feature proximity on binding errors, support the spatial theories on object feature binding, in addition we propose that temporal theories and convergence, through hierarchical feature analysis, are also involved. Because spatial properties have a dedicated processing neural stream, and temporal properties rely on limited capacity memory systems, memories for sequential information would likely be more difficult to accuratelyr ecall. Our study supports other studies which suggest that both spatial and temporal coherence to differing degrees,may be involved in object feature binding. Traditionally, these theories have purported to provide individual solutions, but this thesis proposes a novel unified theory of object feature binding in which hierarchical feature analysis, spatial attention and temporal synchrony each plays a role. It is further proposed that binding takes place in visual short-term memory through concerted and integrated information processing in distributed cortical areas. A cognitive model detailing this integrated proposal is given. Next, the cognitive model is used to inform the design and suggested implementation of a computational model which would be able to test the theory put forward in this thesis. In order to verify the model, future work is needed to implement the computational model.Thus it is argued that this doctoral thesis provides valuable experimental evidence concerning spatio-temporal aspects of the binding problem and as such is an additional building block in the quest for a solution to the object feature binding problem

Integrating incremental learning and episodic memory models of the hippocampal region.

By integrating previous computational models of corticohippocampal function, the authors develop and test a unified theory of the neural substrates of familiarity, recollection, and classical conditioning. This approach integrates models from 2 traditions of hippocampal modeling, those of episodic memory and incremental learning, by drawing on an earlier mathematical model of conditioning, SOP (A. Wagner, 1981). The model describes how a familiarity signal may arise from parahippocampal cortices, giving a novel explanation for the finding that the neural response to a stimulus in these regions decreases with increasing stimulus familiarity. Recollection is ascribed to the hippocampus proper. It is shown how the properties of episodic representations in the neocortex, parahippocampal gyrus, and hippocampus proper may explain phenomena in classical conditioning. The model reproduces the effects of hippocampal, septal, and broad hippocampal region lesions on contextual modulation of classical conditioning, blocking, learned irrelevance, and latent inhibition

Modelling etiopathogenesis of the FOXG1-duplication-linked variant of West syndrome

ABSTRACT Here we describe the characterization of a novel Foxg1-GOF model we created to dissect the role of Foxg1 in postmitotic neuronal differentiation and reconstruct pathogenetic mechanisms which underlie the FOXG1 duplication-linked West syndrome. This is a devastating neurological disorder, triggered by a complex variety of pathogenic conditions. It is characterized by infantile spasms, abnormal EEG with hypsarrhytmia and seizures and dramatic cognitive impairment. For it only symptomatic treatments are presently available. As expected, these Foxg1-GOF mice showed increased neuronal activity in baseline conditions and were more prone to limbic motor seizures upon kainic acid administration. A preliminary developmental profiling of their cerebral cortex unveiled four major histogenetic anomalies, likely contributing to their hyperexcitability. These anomalies were: (1) an altered neocortical laminar blueprint with impaired layer VI/layer V segregation and defective activation of layer IV-II programs; (2) a substantial reduction of PV+ interneurons; (3) a patterned, area- and lamina-specific astrocyte deprivation; (4) a defective expression of the Gabra1 receptor subunit. Similar phenomena might concur to neurological anomalies of West syndrome patients harboring FOXG1 duplications. A parallel in vitro study, run on dissociated cortico-cerebral cultures, revealed that a substantial Foxg1 upregulation occurred upon delivery of depolarizing stimuli. Neuronal, activity-linked Foxg1 elevation required the presence of astrocytes. Activity-linked Foxg1 fluctuations were inter-twinned with immediate-early genes fluctuations and depended on them, according to distinct, neuron- and astrocyte-specific patterns. In West syndrome patients with augmented FOXG1 dosage, a FOXG1-mRNA increase evoked by depolarizing stimuli might ignite a vicious circle, exacerbating neuronal hyperactivity and contributing to interictal EEG anomalies and seizures

Toward a further understanding of object feature binding : a cognitive neuroscience perspective

The aim of this thesis is to lead to a further understanding of the neural mechanisms underlying object feature binding in the human brain. The focus is on information processing and integration in the visual system and visual shortterm memory. From a review of the literature it is clear that there are three major competing binding theories, however, none of these individually solves the binding problem satisfactorily. Thus the aim of this research is to conduct behavioural experimentation into object feature binding, paying particular attention to visual short-term memory. The behavioural experiment was designed and conducted using a within-subjects delayed responset ask comprising a battery of sixty-four composite objects each with three features and four dimensions in each of three conditions (spatial, temporal and spatio-temporal).Findings from the experiment,which focus on spatial and temporal aspects of object feature binding and feature proximity on binding errors, support the spatial theories on object feature binding, in addition we propose that temporal theories and convergence, through hierarchical feature analysis, are also involved. Because spatial properties have a dedicated processing neural stream, and temporal properties rely on limited capacity memory systems, memories for sequential information would likely be more difficult to accuratelyr ecall. Our study supports other studies which suggest that both spatial and temporal coherence to differing degrees,may be involved in object feature binding. Traditionally, these theories have purported to provide individual solutions, but this thesis proposes a novel unified theory of object feature binding in which hierarchical feature analysis, spatial attention and temporal synchrony each plays a role. It is further proposed that binding takes place in visual short-term memory through concerted and integrated information processing in distributed cortical areas. A cognitive model detailing this integrated proposal is given. Next, the cognitive model is used to inform the design and suggested implementation of a computational model which would be able to test the theory put forward in this thesis. In order to verify the model, future work is needed to implement the computational model.Thus it is argued that this doctoral thesis provides valuable experimental evidence concerning spatio-temporal aspects of the binding problem and as such is an additional building block in the quest for a solution to the object feature binding problem.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

The use of emotions in the implementation of various types of learning in a cognitive agent

Les tuteurs professionnels humains sont capables de prendre en considération des événements du passé et du présent et ont une capacité d'adaptation en fonction d'événements sociaux. Afin d'être considéré comme une technologie valable pour l'amélioration de l'apprentissage humain, un agent cognitif artificiel devrait pouvoir faire de même. Puisque les environnements dynamiques sont en constante évolution, un agent cognitif doit pareillement évoluer et s'adapter aux modifications structurales et aux phénomènes nouveaux. Par conséquent, l'agent cognitif idéal devrait posséder des capacités d'apprentissage similaires à celles que l'on retrouve chez l'être humain ; l'apprentissage émotif, l'apprentissage épisodique, l'apprentissage procédural, et l'apprentissage causal. Cette thèse contribue à l'amélioration des architectures d'agents cognitifs. Elle propose 1) une méthode d'intégration des émotions inspirée du fonctionnement du cerveau; et 2) un ensemble de méthodes d'apprentissage (épisodique, causale, etc.) qui tiennent compte de la dimension émotionnelle. Le modèle proposé que nous avons appelé CELTS (Conscious Emotional Learning Tutoring System) est une extension d'un agent cognitif conscient dans le rôle d'un tutoriel intelligent. Il comporte un module de gestion des émotions qui permet d'attribuer des valences émotionnelles positives ou négatives à chaque événement perçu par l'agent. Deux voies de traitement sont prévues : 1) une voie courte qui permet au système de répondre immédiatement à certains événements sans un traitement approfondis, et 2) une voie longue qui intervient lors de tout événement qui exige la volition. Dans cette perspective, la dimension émotionnelle est considérée dans les processus cognitifs de l'agent pour la prise de décision et l'apprentissage. L'apprentissage épisodique dans CELTS est basé sur la théorie du Multiple Trace Memory consolidation qui postule que lorsque l'on perçoit un événement, l'hippocampe fait une première interprétation et un premier apprentissage. Ensuite, l'information acquise est distribuée aux différents cortex. Selon cette théorie, la reconsolidation de la mémoire dépend toujours de l'hippocampe. Pour simuler de tel processus, nous avons utilisé des techniques de fouille de données qui permettent la recherche de motifs séquentiels fréquents dans les données générées durant chaque cycle cognitif. L'apprentissage causal dans CELTS se produit à l'aide de la mémoire épisodique. Il permet de trouver les causes et les effets possibles entre différents événements. Il est mise en œuvre grâce à des algorithmes de recherche de règles d'associations. Les associations établies sont utilisées pour piloter les interventions tutorielles de CELTS et, par le biais des réponses de l'apprenant, pour évaluer les règles causales découvertes. \ud ______________________________________________________________________________ \ud MOTS-CLÉS DE L’AUTEUR : agents cognitifs, émotions, apprentissage épisodique, apprentissage causal

A Posture Sequence Learning System for an Anthropomorphic Robotic Hand

The paper presents a cognitive architecture for posture learning of an anthropomorphic robotic hand. Our approach is aimed to allow the robotic system to perform complex perceptual operations, to interact with a human user and to integrate the perceptions by a cognitive representation of the scene and the observed actions. The anthropomorphic robotic hand imitates the gestures acquired by the vision system in order to learn meaningful movements, to build its knowledge by different conceptual spaces and to perform complex interaction with the human operator

Products and Services

Today’s global economy offers more opportunities, but is also more complex and competitive than ever before. This fact leads to a wide range of research activity in different fields of interest, especially in the so-called high-tech sectors. This book is a result of widespread research and development activity from many researchers worldwide, covering the aspects of development activities in general, as well as various aspects of the practical application of knowledge