Using Hebb’s (1961) paradigm, it has been shown that older adults (OAs) fail to learn recurrent visuospatial supra-span sequence information (Turcotte, Gagnon, & Poirier, 2005); a deficit which has not been demonstrated on verbal versions of the same task or in younger adults (YAs). Since the Hebb paradigm is thought to rely on working memory and thus attention (Conway & Engle, 1996), one interpretation concerns an OA’s capacity to allocate the necessary attentional resources to carry out the various components of the task. Five studies investigated this proposal. The first three (Article 1) examined attention in a general manner by reducing the amount of attentional resources that a YA could devote to carrying out the visuospatial Hebb supra-span sequence learning task through the implementation of a verbal dual task (DT) procedure. The fourth (Article 2) further investigated the role of attention by using a DT induced at retrieval that overlapped extensively with the requirements (spatial and response features) of the visuospatial Hebb task. The final study (Article 3) aimed to use our previous findings to demonstrate learning among OAs in a visuospatial Hebb learning paradigm in which the motor response was replaced by a verbal response. Our findings confirm that attentional resources employed at the retrieval phase of the task appear to be particularly important for the demonstration of visuospatial sequence learning. The inclusion of a spatial and motor based DT at recall eliminated learning of the repeated sequence in YAs. Interestingly, the learning deficit of OAs was partially eliminated when the motor and spatial requirements at retrieval were reduced. Our findings offer strong support to the contention that supra-span learning of the Hebb type is not altered by the effect of age. However, learning deficits can be observed among OAs when the retrieval component of the task overly taxes attention-related processes. In the case of the visuospatial sequences, the basis of the deficit likely concerns an individual’s capacity to discriminate between responses made to previously presented sequences versus those that need to be made in reaction to the just seen sequence.

Using Hebb’s (1961) paradigm, it has been shown that older adults (OAs) fail to learn recurrent visuospatial supra-span sequence information (Turcotte, Gagnon, & Poirier, 2005); a deficit which has not been demonstrated on verbal versions of the same task or in younger adults (YAs). Since the Hebb paradigm is thought to rely on working memory and thus attention (Conway & Engle, 1996), one interpretation concerns an OA’s capacity to allocate the necessary attentional resources to carry out the various components of the task. Five studies investigated this proposal. The first three (Article 1) examined attention in a general manner by reducing the amount of attentional resources that a YA could devote to carrying out the visuospatial Hebb supra-span sequence learning task through the implementation of a verbal dual task (DT) procedure. The fourth (Article 2) further investigated the role of attention by using a DT induced at retrieval that overlapped extensively with the requirements (spatial and response features) of the visuospatial Hebb task. The final study (Article 3) aimed to use our previous findings to demonstrate learning among OAs in a visuospatial Hebb learning paradigm in which the motor response was replaced by a verbal response. Our findings confirm that attentional resources employed at the retrieval phase of the task appear to be particularly important for the demonstration of visuospatial sequence learning. The inclusion of a spatial and motor based DT at recall eliminated learning of the repeated sequence in YAs. Interestingly, the learning deficit of OAs was partially eliminated when the motor and spatial requirements at retrieval were reduced. Our findings offer strong support to the contention that supra-span learning of the Hebb type is not altered by the effect of age. However, learning deficits can be observed among OAs when the retrieval component of the task overly taxes attention-related processes. In the case of the visuospatial sequences, the basis of the deficit likely concerns an individual’s capacity to discriminate between responses made to previously presented sequences versus those that need to be made in reaction to the just seen sequence.

Made available in DSpace on 2014-12-17T14:55:49Z (GMT). No. of bitstreams: 1 VitorLS_DISSERT.pdf: 1833534 bytes, checksum: 72ebc7d9d8be6ba8ae53eaad106afa8d (MD5) Previous issue date: 2011-03-03 / Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico / Following the new tendency of interdisciplinarity of modern science, a new field called neuroengineering has come to light in the last decades. After 2000, scientific journals and conferences all around the world have been created on this theme. The present work comprises three different subareas related to neuroengineering and electrical engineering: neural stimulation; theoretical and computational neuroscience; and neuronal signal processing; as well as biomedical engineering. The research can be divided in three parts: (i) A new method of neuronal photostimulation was developed based on the use of caged compounds. Using the inhibitory neurotransmitter GABA caged by a ruthenium complex it was possible to block neuronal population activity using a laser pulse. The obtained results were evaluated by Wavelet analysis and tested by non-parametric statistics. (ii) A mathematical method was created to identify neuronal assemblies. Neuronal assemblies were proposed as the basis of learning by Donald Hebb remain the most accepted theory for neuronal representation of external stimuli. Using the Marcenko-Pastur law of eigenvalue distribution it was possible to detect neuronal assemblies and to compute their activity with high temporal resolution. The application of the method in real electrophysiological data revealed that neurons from the neocortex and hippocampus can be part of the same assembly, and that neurons can participate in multiple assemblies. (iii) A new method of automatic classification of heart beats was developed, which does not rely on a data base for training and is not specialized in specific pathologies. The method is based on Wavelet decomposition and normality measures of random variables. Throughout, the results presented in the three fields of knowledge represent qualification in neural and biomedical engineering / Following the new tendency of interdisciplinarity of modern science, a new field called neuroengineering has come to light in the last decades. After 2000, scientific journals and conferences all around the world have been created on this theme. The present work comprises three different subareas related to neuroengineering and electrical engineering: neural stimulation; theoretical and computational neuroscience; and neuronal signal processing; as well as biomedical engineering. The research can be divided in three parts: (i) A new method of neuronal photostimulation was developed based on the use of caged compounds. Using the inhibitory neurotransmitter GABA caged by a ruthenium complex it was possible to block neuronal population activity using a laser pulse. The obtained results were evaluated by Wavelet analysis and tested by non-parametric statistics. (ii) A mathematical method was created to identify neuronal assemblies. Neuronal assemblies were proposed as the basis of learning by Donald Hebb remain the most accepted theory for neuronal representation of external stimuli. Using the Marcenko-Pastur law of eigenvalue distribution it was possible to detect neuronal assemblies and to compute their activity with high temporal resolution. The application of the method in real electrophysiological data revealed that neurons from the neocortex and hippocampus can be part of the same assembly, and that neurons can participate in multiple assemblies. (iii) A new method of automatic classification of heart beats was developed, which does not rely on a data base for training and is not specialized in specific pathologies. The method is based on Wavelet decomposition and normality measures of random variables. Throughout, the results presented in the three fields of knowledge represent qualification in neural and biomedical engineering

Using Hebb’s (1961) paradigm, it has been shown that older adults (OAs) fail to learn recurrent visuospatial supra-span sequence information (Turcotte, Gagnon, & Poirier, 2005); a deficit which has not been demonstrated on verbal versions of the same task or in younger adults (YAs). Since the Hebb paradigm is thought to rely on working memory and thus attention (Conway & Engle, 1996), one interpretation concerns an OA’s capacity to allocate the necessary attentional resources to carry out the various components of the task. Five studies investigated this proposal. The first three (Article 1) examined attention in a general manner by reducing the amount of attentional resources that a YA could devote to carrying out the visuospatial Hebb supra-span sequence learning task through the implementation of a verbal dual task (DT) procedure. The fourth (Article 2) further investigated the role of attention by using a DT induced at retrieval that overlapped extensively with the requirements (spatial and response features) of the visuospatial Hebb task. The final study (Article 3) aimed to use our previous findings to demonstrate learning among OAs in a visuospatial Hebb learning paradigm in which the motor response was replaced by a verbal response. Our findings confirm that attentional resources employed at the retrieval phase of the task appear to be particularly important for the demonstration of visuospatial sequence learning. The inclusion of a spatial and motor based DT at recall eliminated learning of the repeated sequence in YAs. Interestingly, the learning deficit of OAs was partially eliminated when the motor and spatial requirements at retrieval were reduced. Our findings offer strong support to the contention that supra-span learning of the Hebb type is not altered by the effect of age. However, learning deficits can be observed among OAs when the retrieval component of the task overly taxes attention-related processes. In the case of the visuospatial sequences, the basis of the deficit likely concerns an individual’s capacity to discriminate between responses made to previously presented sequences versus those that need to be made in reaction to the just seen sequence.

La thèse porte sur les propriétés d'un phénomène capital dans le domaine de la mémoire : l'effet de répétition de Hebb (ERH). Cet effet, originellement observé par Donald O. Hebb (1961), renvoie à l'amélioration involontaire de la performance pour une séquence d'items ordonnés répétée à plusieurs reprises au cours d'une tâche de rappel sériel immédiat. L'étude des propriétés de l'ERH est déterminante puisqu'elle favorise la compréhension de différents concepts liés à la mémoire à court terme, à la mémoire de l'ordre et à la mémoire implicite (voir Page, Cumming, Norris, Hitch & McNeil, 2006). Dans le premier chapitre empirique de la thèse (Chapitre II), l'ERH est étudié avec du matériel spatial dans le but d'établir la correspondance entre les apprentissages verbal et spatial. De plus, certaines propriétés importantes sont examinées, dont l'impact de la similitude entre la série répétée et les séquences non-répétées et l'influence de la conscience sur l'amplitude de l'apprentissage. Il est noté que les ERH verbal et spatial présentent sensiblement les mêmes caractéristiques : leurs rythmes d'apprentissage sont similaires, les deux semblent indépendants de la conscience des participants et les deux sont présents lorsque les séquences diffèrent en termes d'ordre et d'item ou en termes d'ordre seulement. Le deuxième chapitre empirique (Chapitre III) tente d'élucider à quelle étape du traitement de l'information l'ERH se concrétise - au niveau de la perception, du maintien ou de la production d'une réponse - en évaluant le rôle de chacune de ces étapes de façon isolée. Les résultats de ce chapitre appuient l'importance de la phase de rappel en démontrant que lorsque l'ERH ne se manifeste pas, les participants apprennent plutôt les erreurs qu'ils produisent. En plus de permettre de redéfinir l'ERH, qui représente un apprentissage de la bonne réponse ou des erreurs commises, et de clarifier certaines incohérences, les résultats de la thèse permettent d'inférer des propriétés de la mémoire implicite. Par surcroît, ils reconnaissent l'importance de l'équivalence fonctionnelle lorsque l'information d'ordre est traitée et soulignent plusieurs points importants devant être considérés par les modèles computationnels pour rendre compte plus parcimonieusement des phénomènes liés au traitement de l'ordre.

Αντικείµενο της διατριβής είναι η ανάπτυξη νέων µεθοδολογιών εκµάθησης και σύγκλισης των Ασαφών Γνωστικών ∆ικτύων που προτείνονται για τη βελτίωση και προσαρµογή της συµπεριφοράς τους, καθώς και για την αύξηση της απόδοσής τους, αναδεικνύοντάς τα σε αποτελεσµατικά δυναµικά συστήµατα µοντελοποίησης. Τα νέα βελτιωµένα Ασαφή Γνωστικά ∆ίκτυα, µέσω της εκµάθησης και προσαρµογής των βαρών τους, έχουν χρησιµοποιηθεί στην ιατρική σε θέµατα διάγνωσης και υποστήριξης στη λήψη απόφασης, καθώς και σε µοντέλα βιοµηχανικών συστηµάτων που αφορούν τον έλεγχο διαδικασιών, µε πολύ ικανοποιητικά αποτελέσµατα. Στη διατριβή αυτή παρουσιάζονται, αξιολογούνται και εφαρµόζονται δύο νέοι αλγόριθµοι εκµάθησης χωρίς επίβλεψη των Ασαφών Γνωστικών ∆ικτύων, οι αλγόριθµοι Active Hebbian Learning (AHL) και Nonlinear Hebbian Learning (NHL), βασισµένοι στον κλασσικό αλγόριθµό εκµάθησης χωρίς επίβλεψη τύπου Hebb των νευρωνικών δικτύων, καθώς και µια νέα προσέγγιση εκµάθησης των Ασαφών Γνωστικών ∆ικτύων βασισµένη στους εξελικτικούς αλγορίθµους και πιο συγκεκριµένα στον αλγόριθµο Βελτιστοποίησης µε Σµήνος Σωµατιδίων και στον ∆ιαφοροεξελικτικό αλγόριθµο. Οι προτεινόµενοι αλγόριθµοι AHL και NHL στηρίζουν νέες µεθοδολογίες εκµάθησης για τα ΑΓ∆ που βελτιώνουν τη λειτουργία, και την αξιοπιστία τους, και που παρέχουν στους εµπειρογνώµονες του εκάστοτε προβλήµατος που αναπτύσσουν το ΑΓ∆, την εκµάθηση των παραµέτρων για τη ρύθµιση των αιτιατών διασυνδέσεων µεταξύ των κόµβων. Αυτοί οι τύποι εκµάθησης που συνοδεύονται από την σωστή γνώση του εκάστοτε προβλήµατος-συστήµατος, συµβάλλουν στην αύξηση της απόδοσης των ΑΓ∆ και διευρύνουν τη χρήση τους. Επιπρόσθετα µε τους αλγορίθµους εκµάθησης χωρίς επίβλεψη τύπου Hebb για τα ΑΓ∆, αναπτύσσονται και προτείνονται νέες τεχνικές εκµάθησης των ΑΓ∆ βασισµένες στους εξελικτικούς αλγορίθµους. Πιο συγκεκριµένα, προτείνεται µια νέα µεθοδολογία για την εφαρµογή του εξελικτικού αλγορίθµου Βελτιστοποίησης µε Σµήνος Σωµατιδίων στην εκµάθηση των Ασαφών Γνωστικών ∆ικτύων και πιο συγκεκριµένα στον καθορισµό των βέλτιστων περιοχών τιµών των βαρών των Ασαφών Γνωστικών ∆ικτύων. Με τη µεθοδο αυτή λαµβάνεται υπόψη η γνώση των εµπειρογνωµόνων για τον σχεδιασµό του µοντέλου µε τη µορφή περιορισµών στους κόµβους που µας ενδιαφέρουν οι τιµές των καταστάσεών τους, που έχουν οριστοί ως κόµβοι έξοδοι του συστήµατος, και για τα βάρη λαµβάνονται υπόψη οι περιοχές των ασαφών συνόλων που έχουν συµφωνήσει όλοι οι εµπειρογνώµονες. Έτσι θέτoντας περιορισµούς σε όλα τα βάρη και στους κόµβους εξόδου και καθορίζοντας µια κατάλληλη αντικειµενική συνάρτηση για το εκάστοτε πρόβληµα, προκύπτουν κατάλληλοι πίνακες βαρών (appropriate weight matrices) που µπορούν να οδηγήσουν το σύστηµα σε επιθυµητές περιοχές λειτουργίας και ταυτόχρονα να ικανοποιούν τις ειδικές συνθήκες- περιορισµούς του προβλήµατος. Οι δύο νέες µέθοδοι εκµάθησης χωρίς επίβλεψη που έχουν προταθεί για τα ΑΓ∆ χρησιµοποιούνται και εφαρµόζονται µε επιτυχία σε δυο πολύπλοκα προβλήµατα από το χώρο της ιατρικής, στο πρόβληµα λήψης απόφασης στην ακτινοθεραπεία και στο πρόβληµα κατηγοριοποίησης των καρκινικών όγκων της ουροδόχου κύστης σε πραγµατικές κλινικές περιπτώσεις. Επίσης όλοι οι προτεινόµενοι αλγόριθµοι εφαρµόζονται σε µοντέλα βιοµηχανικών συστηµάτων που αφορούν τον έλεγχο διαδικασιών µε πολύ ικανοποιητικά αποτελέσµατα. Οι αλγόριθµοι αυτοί, όπως προκύπτει από την εφαρµογή τους σε συγκεκριµένα προβλήµατα, βελτιώνουν το µοντέλο του ΑΓ∆, συµβάλλουν σε ευφυέστερα συστήµατα και διευρύνουν τη δυνατότητα εφαρµογής τους σε πραγµατικά και πολύπλοκα προβλήµατα. Η κύρια συνεισφορά αυτής της διατριβής είναι η ανάπτυξη νέων µεθοδολογιών εκµάθησης και σύγκλισης των Ασαφών Γνωστικών ∆ικτύων προτείνοντας δυο νέους αλγορίθµους µη επιβλεπόµενης µάθησης τύπου Hebb, τον αλγόριθµο Active Hebbian Learning και τον αλγόριθµο Nonlinear Hebbian Learning για την προσαρµογή των βαρών των διασυνδέσεων µεταξύ των κόµβων των Ασαφών Γνωστικών ∆ικτύων, καθώς και εξελικτικούς αλγορίθµους βελτιστοποιώντας συγκεκριµένες αντικειµενικές συναρτήσεις για κάθε εξεταζόµενο πρόβληµα. Τα νέα βελτιωµένα Ασαφή Γνωστικά ∆ίκτυα µέσω των αλγορίθµων προσαρµογής των βαρών τους έχουν χρησιµοποιηθεί για την ανάπτυξη ενός ∆ιεπίπεδου Ιεραρχικού Συστήµατος για την υποστήριξη λήψης απόφασης στην ακτινοθεραπεία, για την ανάπτυξη ενός διαγνωστικού εργαλείου για την κατηγοριοποίηση του βαθµού κακοήθειας των καρκινικών όγκων της ουροδόχου κύστης, καθώς και για την επίλυση βιοµηχανικών προβληµάτων για τον έλεγχο διαδικασιών. / The main contribution of this Dissertation is the development of new learning and convergence methodologies for Fuzzy Cognitive Maps that are proposed for the improvement and adaptation of their behaviour, as well as for the increase of their performance, electing them in effective dynamic systems of modelling. The new improved Fuzzy Cognitive Maps, via the learning and adaptation of their weights, have been used in medicine for diagnosis and decision-making, as well as to alleviate the problem of the potential uncontrollable convergence to undesired states in models of industrial process control systems, with very satisfactory results. In this Dissertation are presented, validated and implemented two new learning algorithms without supervision for Fuzzy Cognitive Maps, the algorithms Active Hebbian Learning (AHL) and Nonlinear Hebbian Learning (NHL), based on the classic unsupervised Hebb-type learning algorithm of neural networks, as well as a new approach of learning for Fuzzy Cognitive Maps based on the evolutionary algorithms and more specifically on the algorithm of Particles Swarm Optimization and on the Differential Evolution algorithm. The proposed algorithms AHL and NHL support new learning methodologies for FCMs that improve their operation, efficiency and reliability, and that provide in the experts of each problem that develop the FCM, the learning of parameters for the regulation (fine-tuning) of cause-effect relationships (weights) between the concepts. These types of learning that are accompanied with the right knowledge of each problem-system, contribute in the increase of performance of FCMs and extend their use. Additionally to the unsupervised learning algorithms of Hebb-type for the FCMs, are developed and proposed new learning techniques of FCMs based on the evolutionary algorithms. More specifically, it is proposed a new learning methodology for the application of evolutionary algorithm of Particle Swarm Optimisation in the adaptation of FCMs and more concretely in the determination of the optimal regions of weight values of FCMs. With this method it is taken into consideration the experts’ knowledge for the modelling with the form of restrictions in the concepts that interest us their values, and are defined as output concepts, and for weights are received the arithmetic values of the fuzzy regions that have agreed all the experts. Thus considering restrictions in all weights and in the output concepts and determining a suitable objective function for each problem, result appropriate weight matrices that can lead the system to desirable regions of operation and simultaneously satisfy specific conditions of problem. The first two proposed methods of unsupervised learning that have been suggested for the FCMs are used and applied with success in two complicated problems in medicine, in the problem of decision-making in the radiotherapy process and in the problem of tumor characterization for urinary bladder in real clinical cases. Also all the proposed algorithms are applied in models of industrial systems that concern the control of processes with very satisfactory results. These algorithms, as it results from their application in concrete problems, improve the model of FCMs, they contribute in more intelligent systems and they extend their possibility of application in real and complex problems. The main contribution of the present Dissertation is to develop new learning and convergence methodologies for Fuzzy Cognitive Maps proposing two new unsupervised learning algorithms, the algorithm Active Hebbian Learning and the algorithm Nonlinear Hebbian Learning for the adaptation of weights of the interconnections between the concepts of Fuzzy Cognitive Maps, as well as Evolutionary Algorithms optimizing concrete objective functions for each examined problem. New improved Fuzzy Cognitive Maps via the algorithms of weight adaptation have been used for the development of an Integrated Two-level hierarchical System for the support of decision-making in the radiotherapy, for the development of a new diagnostic tool for tumour characterization of urinary bladder, as well as for the solution of industrial process control problems.