For the last twenty years, several assumptions have been expressed in the fields of information processing, neurophysiology and cognitive sciences. First, neural networks and their dynamical behaviors in terms of attractors is the natural way adopted by the brain to encode information. Any information item to be stored in the neural network should be coded in some way or another in one of the dynamical attractors of the brain, and retrieved by stimulating the network to trap its dynamics in the desired item’s basin of attraction. The second view shared by neural network researchers is to base the learning of the synaptic matrix on a local Hebbian mechanism. The third assumption is the presence of chaos and the benefit gained by its presence. Chaos, although very simply produced, inherently possesses an infinite amount of cyclic regimes that can be exploited for coding information. Moreover, the network randomly wanders around these unstable regimes in a spontaneous way, thus rapidly proposing alternative responses to external stimuli, and being easily able to switch from one of these potential attractors to another in response to any incoming stimulus. Finally, since their introduction sixty years ago, cell assemblies have proved to be a powerful paradigm for brain information processing. After their introduction in artificial intelligence, cell assemblies became commonly used in computational neuroscience as a neural substrate for content addressable memories. Based on these assumptions, this thesis provides a computer model of neural network simulation of a brain-like memory. It first shows experimentally that the more information is to be stored in robust cyclic attractors, the more chaos appears as a regime in the background, erratically itinerating among brief appearances of these attractors. Chaos does not appear to be the cause, but the consequence of the learning. However, it appears as an helpful consequence that widens the network’s encoding capacity. To learn the information to be stored, two supervised iterative Hebbian learning algorithm are proposed. One leaves the semantics of the attractors to be associated with the feeding data unprescribed, while the other defines it a priori. Both algorithms show good results, even though the first one is more robust and has a greater storing capacity. Using these promising results, a biologically plausible alternative to these algorithms is proposed using cell assemblies as substrate for information. Even though this is not new, the mechanisms underlying their formation are poorly understood and, so far, there are no biologically plausible algorithms that can explain how external stimuli can be online stored in cell assemblies. This thesis provide such a solution combining a fast Hebbian/anti-Hebbian learning of the network's recurrent connections for the creation of new cell assemblies, and a slower feedback signal which stabilizes the cell assemblies by learning the feed forward input connections. This last mechanism is inspired by the retroaxonal hypothesis.

This work concerns the investigation of the neuronal mechanisms at the basis of language acquisition and processing, and the complex interactions of language and attention processes in the human brain. In particular, this research was motivated by two sets of existing neurophysiological data which cannot be reconciled on the basis of current psycholinguistic accounts: on the one hand, the N400, a robust index of lexico-semantic processing which emerges at around 400ms after stimulus onset in attention demanding tasks and is larger for senseless materials (meaningless pseudowords) than for matched meaningful stimuli (words); on the other, the more recent results on the Mismatch Negativity (MMN, latency 100-250ms), an early automatic brain response elicited under distraction which is larger to words than to pseudowords. We asked what the mechanisms underlying these differential neurophysiological responses may be, and whether attention and language processes could interact so as to produce the observed brain responses, having opposite magnitude and different latencies. We also asked questions about the functional nature and anatomical characteristics of the cortical representation of linguistic elements. These questions were addressed by combining neurocomputational techniques and neuroimaging (magneto-encephalography, MEG) experimental methods. Firstly, a neurobiologically realistic neural-network model composed of neuron-like elements (graded response units) was implemented, which closely replicates the neuroanatomical and connectivity features of the main areas of the left perisylvian cortex involved in spoken language processing (i.e., the areas controlling speech output – left inferior-prefrontal cortex, including Broca’s area – and the main sensory input – auditory – areas, located in the left superior-temporal lobe, including Wernicke’s area). Secondly, the model was used to simulate early word acquisition processes by means of a Hebbian correlation learning rule (which reflects known synaptic plasticity mechanisms of the neocortex). The network was “taught” to associate pairs of auditory and articulatory activation patterns, simulating activity due to perception and production of the same speech sound: as a result, neuronal word representations distributed over the different cortical areas of the model emerged. Thirdly, the network was stimulated, in its “auditory cortex”, with either one of the words it had learned, or new, unfamiliar pseudoword patterns, while the availability of attentional resources was modulated by changing the level of non-specific, global cortical inhibition. In this way, the model was able to replicate both the MMN and N400 brain responses by means of a single set of neuroscientifically grounded principles, providing the first mechanistic account, at the cortical-circuit level, for these data. Finally, in order to verify the neurophysiological validity of the model, its crucial predictions were tested in a novel MEG experiment investigating how attention processes modulate event-related brain responses to speech stimuli. Neurophysiological responses to the same words and pseudowords were recorded while the same subjects were asked to attend to the spoken input or ignore it. The experimental results confirmed the model’s predictions; in particular, profound variability of magnetic brain responses to pseudowords but relative stability of activation to words as a function of attention emerged. While the results of the simulations demonstrated that distributed cortical representations for words can spontaneously emerge in the cortex as a result of neuroanatomical structure and synaptic plasticity, the experimental results confirm the validity of the model and provide evidence in support of the existence of such memory circuits in the brain. This work is a first step towards a mechanistic account of cognition in which the basic atoms of cognitive processing (e.g., words, objects, faces) are represented in the brain as discrete and distributed action-perception networks that behave as closed, independent systems.

Lack of technical cleanliness and particle contaminations in Lithium-ion battery manufacturing affect the performance of batteries which are a risk for the safety and quality of the product. Therefore, part of the manufacturing process occurs inside the Clean and Dry room area to maintain technical cleanliness. This paper aims to provide a framework to control particle contamination inside the Clean and Dry room and strengthen the product's quality and safety. A literature study was conducted, which was completed by a field study at Northvolt Labs in Västerås to achieve the study's aims. The study contributes to existing theories by providing a framework to find root causes of particle contamination in the manufacturing process based on the existing literature and standards. The Six Sigma problem-solving methodology DMAIC was implemented to conduct the field study. A risk assessment was conducted to find the possible threats toward technical cleanliness in the cell assembly process. The risk sources were identified by implementing measurement methods from relevant standards. The results indicate a high risk for technical cleanliness are coming from the decontamination method, material, machines, and environment. Furthermore, several recommendations were given that are expected to decrease the amount of nonconformity in the process. / Brist på teknisk renhet och partikelföroreningar vid tillverkning av litiumjonbatterier påverkar dess prestanda och utgör en risk för produktens säkerhet och kvalitet. Därför sker en del av tillverkningsprocessen i ett Clean & Dry rum för att upprätthålla teknisk renhet. Denna uppsats syftar till att ge ett ramverk för att kontrollera partikelföroreningar och därmed stärka produktens kvalitet och säkerhet. För att uppnå syftet genomfördes först en litteraturstudie vilket vidare kompletterades med en fältstudie vid Northvolt Labs i Västerås. Studien bidrar till befintliga teorier genom att tillhandahålla ett ramverk för att hitta och åtgärda rotorsaker till partikelkontaminering i tillverkningsprocessen baserat på befintlig litteratur och standarder. Sex Sigma problemlösningsmetoden DMAIC implementerades för att genomföra fältstudien. En riskbedömning genomfördes för att hitta riskfyllda aktiviteter i processen. Vidare implementerades mätmetoder från relevanta standarder för att mäta kontamineringsnivån. Resultaten indikerar stor risk för tekniskrenhet från saneringsmetoder, material, maskiner och miljön. Vidare rekommenderas flera åtgärder för att underhålla tekniskrenhet vilka förväntas minska avvikelser i processen.

Cette thèse illustre une recherche sur les assemblées de cellules, groupes de neurones étroitement liés et co-activés, considérés comme les unités de la mémoire. Après une revue des majeures avancées expérimentales et théoriques dans ce domaine, et des techniques de physique statistique et d'inférence pour l'étude de neurones en interaction, on présente une nouvelle méthode pour dévoiler les assemblées decellules à partir des données neuronales et on montre son application à des enregistrements multi-électrodes dans le cortex préfrontal de rats pendant l'exécution d'une tâche et les époques de sommeil précédant et suivant. La méthode est basée sur l'inférence d'un réseau d'Ising d’interactions effectives entre les neurones et sur la simulation du modèle inféré en présence d'une entrée globale uniforme: quand l'entrée augmente, on découvre des configurations d'activité élevée (assemblées de cellules), qui s'activent dans les données à des échelles de temps de dizaines de ms en présence de stimuli transitoires. Les assemblées sont robustes par rapport au bruit. La comparaison des réseaux d'interactions et des résultats des simulations à travers les trois phases expérimentales révèle des règles empiriques pour la modification des assemblées de cellules. Le modèle inféré est également exploité pour estimer la réactivation (replay) des assemblées pendant le sommeil, important pour la consolidation de la mémoire. Inférence et échantillonnage d'un modèle linéaire généralisé montrent qu'il n'y a pas un ordre d'activation spécifique des neurones. On discute enfin une application de statistique descriptive à l'étude de la plasticité synaptique in vitro dans un cadre optogénétique. / This thesis illustrates a research on cell assemblies, groups of closely connected, synchronously activating neurons, which are thought to be the units of memory. After a review of the main experimental and theoretical advances in this field, and of the techniques of statistical physics and inference for the study of interacting neurons, a new method to unveil cell assemblies from neuronal data is illustrated and applied to multi-electrode recordings in the prefrontal cortex of rats during performance of a task and during the preceding and following sleep epochs. The method is based on the inference of an Ising network of effective interactions between the neurons and on the simulation of the inferred model in the presence of a global uniform drive: as the drive increases, configurations of high activity (cell assemblies) are unveiled, which activate in the data on time scales of tens of ms, in the presence of transient stimuli. The assemblies are robust with respect to noise. Comparisonof the interaction networks and of the results of the simulations across the three experimental phases reveals empirical rules for the modification of cell assemblies. The inferred model is also exploited to estimate the reactivation (replay) of the cell assemblies during sleep, important for memory consolidation. Inference and sampling of a generalized linear model show that there is not a specific order of activation of the neurons in the groups. It is finally discussed an application of descriptive statistics to the study of synaptic plasticity of neurons in vitro in an optogenetic framework.

The goal for this research was to reduce the current manufacturing cost of the polycrystalline diamond compact (PDC) inserts utilized in the natural gas and oil drilling industry while not reducing their current performance. Polycrystalline Diamond is added to the tungsten-carbide (WC) substrates commonly utilized in these applications because of its greater wear and thermal resistance. With the current cube cell design for the high-pressure/high-temperature apparatus, it is necessary to bond an extra WC substrate to the polycrystalline diamond insert to achieve the sizes generally ordered by the customers. The problem of bonding the extra WC substrate was solved by increasing the operating volume of the cube cell assembly and changing the heating pattern within the cell while maintaining the temperature and the pressure required for the successful diamond sintering.The new cell design was proposed and tested. The test data were captured and analyzed to prove the hypotheses. The proposed manufacturing methods resulted in reduced cost, processing time, and reduced the need for equipment and operators without diminishing the performance of the PDC insert.

Contexte La connectomique, ou la cartographie des connexions neuronales, est un champ de recherche des neurosciences évoluant rapidement, promettant des avancées majeures en ce qui concerne la compréhension du fonctionnement cérébral. La formation de circuits neuronaux en réponse à des stimuli environnementaux est une propriété émergente du cerveau. Cependant, la connaissance que nous avons de la nature précise de ces réseaux est encore limitée. Au niveau du cortex visuel, qui est l’aire cérébrale la plus étudiée, la manière dont les informations se transmettent de neurone en neurone est une question qui reste encore inexplorée. Cela nous invite à étudier l’émergence des microcircuits en réponse aux stimuli visuels. Autrement dit, comment l’interaction entre un stimulus et une assemblée cellulaire est-elle mise en place et modulée? Méthodes En réponse à la présentation de grilles sinusoïdales en mouvement, des ensembles neuronaux ont été enregistrés dans la couche II/III (aire 17) du cortex visuel primaire de chats anesthésiés, à l’aide de multi-électrodes en tungstène. Des corrélations croisées ont été effectuées entre l’activité de chacun des neurones enregistrés simultanément pour mettre en évidence les liens fonctionnels de quasi-synchronie (fenêtre de ± 5 ms sur les corrélogrammes croisés corrigés). Ces liens fonctionnels dévoilés indiquent des connexions synaptiques putatives entre les neurones. Par la suite, les histogrammes peri-stimulus (PSTH) des neurones ont été comparés afin de mettre en évidence la collaboration synergique temporelle dans les réseaux fonctionnels révélés. Enfin, des spectrogrammes dépendants du taux de décharges entre neurones ou stimulus-dépendants ont été calculés pour observer les oscillations gamma dans les microcircuits émergents. Un indice de corrélation (Rsc) a également été calculé pour les neurones connectés et non connectés. Résultats Les neurones liés fonctionnellement ont une activité accrue durant une période de 50 ms contrairement aux neurones fonctionnellement non connectés. Cela suggère que les connexions entre neurones mènent à une synergie de leur inter-excitabilité. En outre, l’analyse du spectrogramme dépendant du taux de décharge entre neurones révèle que les neurones connectés ont une plus forte activité gamma que les neurones non connectés durant une fenêtre d’opportunité de 50ms. L’activité gamma de basse-fréquence (20-40 Hz) a été associée aux neurones à décharge régulière (RS) et l’activité de haute fréquence (60-80 Hz) aux neurones à décharge rapide (FS). Aussi, les neurones fonctionnellement connectés ont systématiquement un Rsc plus élevé que les neurones non connectés. Finalement, l’analyse des corrélogrammes croisés révèle que dans une assemblée neuronale, le réseau fonctionnel change selon l’orientation de la grille. Nous démontrons ainsi que l’intensité des relations fonctionnelles dépend de l’orientation de la grille sinusoïdale. Cette relation nous a amené à proposer l’hypothèse suivante : outre la sélectivité des neurones aux caractères spécifiques du stimulus, il y a aussi une sélectivité du connectome. En bref, les réseaux fonctionnels «signature » sont activés dans une assemblée qui est strictement associée à l’orientation présentée et plus généralement aux propriétés des stimuli. Conclusion Cette étude souligne le fait que l’assemblée cellulaire, plutôt que le neurone, est l'unité fonctionnelle fondamentale du cerveau. Cela dilue l'importance du travail isolé de chaque neurone, c’est à dire le paradigme classique du taux de décharge qui a été traditionnellement utilisé pour étudier l'encodage des stimuli. Cette étude contribue aussi à faire avancer le débat sur les oscillations gamma, en ce qu'elles surviennent systématiquement entre neurones connectés dans les assemblées, en conséquence d’un ajout de cohérence. Bien que la taille des assemblées enregistrées soit relativement faible, cette étude suggère néanmoins une intrigante spécificité fonctionnelle entre neurones interagissant dans une assemblée en réponse à une stimulation visuelle. Cette étude peut être considérée comme une prémisse à la modélisation informatique à grande échelle de connectomes fonctionnels. / Background ‘Connectomics’— the mapping of neural connections, is a rapidly advancing field in neurosciences and it promises significant insights into the brain functioning. The formation of neuronal circuits in response to the sensory environment is an emergent property of the brain; however, the knowledge about the precise nature of these sub-networks is still limited. Even at the level of the visual cortex, which is the most studied area in the brain, how sensory inputs are processed between its neurons, is a question yet to be completely explored. Heuristically, this invites an investigation into the emergence of micro-circuits in response to a visual input — that is, how the intriguing interplay between a stimulus and a cell assembly is engineered and modulated? Methods Neuronal assemblies were recorded in response to randomly presented drifting sine-wave gratings in the layer II/III (area 17) of the primary visual cortex (V1) in anaesthetized cats using tungsten multi-electrodes. Cross-correlograms (CCGs) between simultaneously recorded neural activities were computed to reveal the functional links between neurons that were indicative of putative synaptic connections between them. Further, the peristimulus time histograms (PSTH) of neurons were compared to divulge the epochal synergistic collaboration in the revealed functional networks. Thereafter, perievent spectrograms were computed to observe the gamma oscillations in emergent microcircuits. Noise correlation (Rsc) was calculated for the connected and unconnected neurons within these microcircuits. Results The functionally linked neurons collaborate synergistically with augmented activity in a 50-ms window of opportunity compared with the functionally unconnected neurons suggesting that the connectivity between neurons leads to the added excitability between them. Further, the perievent spectrogram analysis revealed that the connected neurons had an augmented power of gamma activity compared with the unconnected neurons in the emergent 50-ms window of opportunity. The low-band (20-40 Hz) gamma activity was linked to the regular-spiking (RS) neurons, whereas the high-band (60-80 Hz) activity was related to the fast-spiking (FS) neurons. The functionally connected neurons systematically displayed higher Rsc compared with the unconnected neurons in emergent microcircuits. Finally, the CCG analysis revealed that there is an activation of a salient functional network in an assembly in relation to the presented orientation. Closely tuned neurons exhibited more connections than the distantly tuned neurons. Untuned assemblies did not display functional linkage. In short, a ‘signature’ functional network was formed between neurons comprising an assembly that was strictly related to the presented orientation. Conclusion Indeed, this study points to the fact that a cell-assembly is the fundamental functional unit of information processing in the brain, rather than the individual neurons. This dilutes the importance of a neuron working in isolation, that is, the classical firing rate paradigm that has been traditionally used to study the encoding of a stimulus. This study also helps to reconcile the debate on gamma oscillations in that they systematically originate between the connected neurons in assemblies. Though the size of the recorded assemblies in the current investigation was relatively small, nevertheless, this study shows the intriguing functional specificity of interacting neurons in an assembly in response to a visual input. One may form this study as a premise to computationally infer the functional connectomes on a larger scale.

Dans le cadre du développement des véhicules électriques et hybrides, la connaissance et la gestion de l'énergie du pack de batteries est une problématique majeure. Pour cela, les constructeurs automobiles ont besoin de modèles numériques pour représenter le comportement dynamique des batteries. L'objectif de cette thèse est de développer, d'une part une méthodologie de caractérisation du comportement dynamique des cellules de batterie et de leurs assemblages et d'autre part des modèles numériques associés qui soient simples, rapides, robustes, présentant le meilleur compromis précision/simplicité. La première partie du travail de la thèse a consisté à développer une nouvelle méthode de caractérisation expérimentale avec un modèle de circuit électrique équivalent, qui permet de s'appliquer facilement à différentes batteries et de calibrer la complexité du modèle (nombre de circuits utilisés) en fonction de la durée des mesures de la phase de repos après une sollicitation. Le modèle généré est capable de suivre les évolutions rapides et lentes de la tension de la batterie, ce qui peut améliorer l'estimation de la tension dans les applications BMS (Battery Management System). Des essais de validations sur différentes batteries ont montré que les modèles générés permettent une prédiction précise du comportement dynamique de la batterie. Ensuite, le manuscrit aborde les assemblages des cellules en série avec la méthode de caractérisation élaborée. Elle commence par une définition énergétique de l'assemblage. Puis, la modélisation de l'assemblage avec la méthode de caractérisation est discutée. Les essais de validation ont été menés sur différents assemblages et ont montré que le comportement dynamique de l'assemblage peut aussi être bien représenté avec les modèles identifiés / As part of the development of electric and hybrid vehicles, energy management in the battery pack is a major issue. Car manufacturers need a numerical model to represent the dynamic behavior of batteries. The objective of this work is to develop, on the one hand, a characterization method of the dynamic behavior of battery cells and their assemblies, and on the other hand the combined numerical models which are simple, fast, robust and with the best accuracy/simplicity compromise. The first part of the work is dedicated to develop a new experimental characterization method with an equivalent circuit model, which can be applied easily to different battery cells and allows calibrating the complexity of the model (number of the RC circuits) according to the measurement duration of the resting phase after a solicitation. Therefore, the generated model is able to follow the rapid and slow voltage change of the battery cell, which improves voltage and state of charge estimation for the BMS (Battery Management System) applications. The validation tests on different battery cells show that the generated model allows accurate prediction of the battery cell’s dynamic behavior. The second part of the work studies the cell assemblies with cells connected in series. It begins with an energy definition of the cell assembly. Then modelling of the assembly with the developed characterization method is discussed. The validation tests were carried out on different assemblies and show that the dynamic behavior of the assembly can be also well represented with the identified models

For the last twenty years, several assumptions have been expressed in the fields of information processing, neurophysiology and cognitive sciences. First, neural networks and their dynamical behaviors in terms of attractors is the natural way adopted by the brain to encode information. Any information item to be stored in the neural network should be coded in some way or another in one of the dynamical attractors of the brain, and retrieved by stimulating the network to trap its dynamics in the desired item’s basin of attraction. The second view shared by neural network researchers is to base the learning of the synaptic matrix on a local Hebbian mechanism. The third assumption is the presence of chaos and the benefit gained by its presence. Chaos, although very simply produced, inherently possesses an infinite amount of cyclic regimes that can be exploited for coding information. Moreover, the network randomly wanders around these unstable regimes in a spontaneous way, thus rapidly proposing alternative responses to external stimuli, and being easily able to switch from one of these potential attractors to another in response to any incoming stimulus. Finally, since their introduction sixty years ago, cell assemblies have proved to be a powerful paradigm for brain information processing. After their introduction in artificial intelligence, cell assemblies became commonly used in computational neuroscience as a neural substrate for content addressable memories. <p> <p>Based on these assumptions, this thesis provides a computer model of neural network simulation of a brain-like memory. It first shows experimentally that the more information is to be stored in robust cyclic attractors, the more chaos appears as a regime in the background, erratically itinerating among brief appearances of these attractors. Chaos does not appear to be the cause, but the consequence of the learning. However, it appears as an helpful consequence that widens the network’s encoding capacity. To learn the information to be stored, two supervised iterative Hebbian learning algorithm are proposed. One leaves the semantics of the attractors to be associated with the feeding data unprescribed, while the other defines it a priori. Both algorithms show good results, even though the first one is more robust and has a greater storing capacity. Using these promising results, a biologically plausible alternative to these algorithms is proposed using cell assemblies as substrate for information. Even though this is not new, the mechanisms underlying their formation are poorly understood and, so far, there are no biologically plausible algorithms that can explain how external stimuli can be online stored in cell assemblies. This thesis provide such a solution combining a fast Hebbian/anti-Hebbian learning of the network's recurrent connections for the creation of new cell assemblies, and a slower feedback signal which stabilizes the cell assemblies by learning the feed forward input connections. This last mechanism is inspired by the retroaxonal hypothesis. <p> / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

Nowadays, shorter product life cycles and fluctuating demand quantities require flexible and adaptable production planning techniques. Fuel cell technology offers an innovative product, for which future demands in terms of quantities and variety are difficult to predict making it hardly possible to plan demand-adequate production capacities. One feasible solution is the application of Discrete Event Simulations (DES) with a high degree of adaptability and scalability. In this paper, a concept for the modular simulation of assembly lines with scalable automation is introduced and applied to an assembly line for fuel cell stacks. The model presents a modular and hierarchical system structure, which allows for adaptability and reusability. The model can be easily integrated on a factory level to study the behavior of parallel assembly lines. For an industrial use case different experiments offer valuable insights for the optimization, the automation and the upscaling of the assembly. / Kürzere Produktlebenszyklen und schwankende Bedarfsmengen erfordern heute flexible und anpassungsfähige Produktionsplanungstechniken. Die Brennstoffzellentechnologie bietet ein innovatives Produkt, dessen zukünftige Nachfrage hinsichtlich Stückzahl und Vielfalt nur schwer vorhersehbar ist und somit kaum bedarfsgerechte Produktionskapazitäten planbar sind. Eine mögliche Lösung ist die Anwendung von Discrete Event Simulations (DES) mit einem hohen Maß an Anpassungsfähigkeit und Skalierbarkeit. In diesem Beitrag wird ein Konzept zur modularen Simulation von Montagelinien mit skalierbarem Automatisierung vorgestellt und auf eine Montagelinie für Brennstoffzellenstacks angewendet. Das Modell nutzt eine modulare und hierarchische Systemstruktur, die Anpassungsfähigkeit und Wiederverwendbarkeit ermöglicht. Das Modell kann leicht auf Fabrikebene integriert werden, um das Verhalten paralleler Montagelinien zu untersuchen. Für einen industriellen Anwendungsfall bieten verschiedene Experimente wertvolle Erkenntnisse zur Optimierung, Automatisierung und Hochskalierung der Montagelinie.