Made available in DSpace on 2014-06-12T18:08:23Z (GMT). No. of bitstreams: 2 arquivo9473_1.pdf: 920450 bytes, checksum: 8de41a22d93f1f66a4f3481b45626f98 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2012 / Devido a maior oferta de petróleos pesados e alto grau de contaminantes que os derivados deste possuem, os processos de hidrorrefino têm recebido atenção especial ao longo dos últimos por possibilitar a remoção de contaminantes e melhorar a margem de lucro das refinarias por tonar possível a obtenção de derivados de maior valor agregado. Entre esses o processo de hidrotratamento (HDT), no qual ocorre uma série de reações que utilizam o gás hidrogênio como reagente, foi o foco de estudo deste trabalho. Ao ser aplicado em correntes de Diesel o HDT realiza a remoção de contaminantes como enxofre e nitrogênio, aumentando a qualidade do mesmo. A unidade de HDT tem como principal equipamento o reator, que consiste em um leito com partículas sólidas, onde gás e líquido escoam em fluxo co-corrente ou em contracorrente. Apesar deste processo já ser maduro, o crescente aumento nas exigências de mercado demandam por melhorias no mesmo, a fim de atingir uma rentabilidade cada vez maior. Desta forma o uso de inferenciadores na estimação das variáveis tornaria possível o melhor acompanhamento do processo como também a implementação de novas estratégias de controle. Visto a relevância desse tema o presente trabalho abordou o desenvolvimento de observadores de estado para o reator do processo de HDT, para isto foi necessário a aquisição de dados do processo, o que foi conseguido através de um modelo matemático do reator, o qual foi denominado como planta virtual. Esta forneceu os dados para treinamento e validação dos inferenciadores aqui estudados: as redes neuronais e a neuro-fuzzy. No decorrer do trabalho foi definido o tempo de amostragem e o período de excitação do sinal através da menor constante de tempo. Para treinamento dos inferenciadores foi utilizado dois bancos de dados distintos, um com tempo de amostragem de 50s, onde este foi obtido pelo método da constante de tempo, e outro com amostragem de 10 minutos, em que as seguintes variáveis foram inferenciadas: concentração de compostos sulfurados, nitrogenados e olefinas na saída do reator. Dessas o melhor resultado foi obtido na inferência da concentração de compostos sulfurados realizada através da Rede Neuronal. Foi escolhida esta rede neuronal na implementação de um controlador PID e como modelo interno de um controlador NNMPC. O controlador PID cuja variável de controle foi à concentração de sulfurados foi chamado de controlador PID inferencial e os resultados deste se mostraram melhores do que os resultados obtidos pelo controlador NNMPC

Robots should offer a human-like level of dexterity when handling objects if humans are to be replaced in dangerous and uncertain working environments. This level of dexterity for human-like manipulation must come from both the hardware, and the control. Exact replication of human-like degrees of freedom in mobility for anthropomorphic robotic hands are seen in bulky, costly, fully actuated solutions, while machine learning to apply some level of human-like dexterity in underacted solutions is unable to be applied to a various array of objects. This thesis presents experimental and theoretical contributions of a novel neuro-fuzzy control method for dextrous human grasping based on grasp synergies using a Human Computer Interface glove and upgraded haptic-enabled anthropomorphic Ring Ada dexterous robotic hand. Experimental results proved the efficiency of the proposed Adaptive Neuro-Fuzzy Inference Systems to grasp objects with high levels of accuracy.

The focus of this thesis was to measure the intracellular voltage of a living neural cell using a silicon transistor. The coupling of neurological tissues with silicon devices is of interest to the fields of neurology, neuroscience, electrophysiology and cellular biology. In previous work by Peter Fromherz, single neurons were successfully coupled to transistors [1]. This thesis aims to show proof of concept of the fabrication of a simple neuro-silicon interface using wafer processing methods currently available at Cal Poly. The types of transistors and cells used, the methods for dissecting and preparing the cells, the electrophysiology methods for validating the experiments, and portions of the design of the junction were based on Fromherz’s 1991 work. Other aspects were revised to be compatible with technologies available at Cal Poly. Leech Retzius cells were isolated and cultured from Hirudo Medicinalis and joined to the gate oxide of a P-channel field effect transistor using SU-8 photoresist wells treated with poly-l-lysine. Transistors were operated in strong inversion and source-drain currentfluctuations were observed that correlated with action potentials of the current clamped Retzius cell. Further work is needed to develop better junctions that can reliably couple action potentials. This work lays a foundation for neuro-silicon interface fabrication at Cal Poly.

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This dissertation investigates the turn of the century fascination with synesthesia and efforts by Modernist artists and writers to produce cross-modal works that attempt to defy sensory boundaries. Works of impossible art are artistic and literary experiments with style and form that develop out of the realism and naturalism of the nineteenth century, to be sure; they are also conceived of by their creators as scientific experiments that test what is possible at the limits of perception. Accordingly, while my work is situated within the field of aesthetics, I take a neuroscientific approach to aid in understanding the modes of perception these works are attempting to explore. My project applies the findings of recent neuroscientific studies into clinical synesthesia as a guide for thinking about these Modernist works. The methodology of neuro-aesthetics allows me to develop a theory of sensory mimesis. Sensory mimesis is a holistic approach to explaining phenomenological experience that depends on a sensory semantics, more fundamental and more comprehensive than a linguistic semantics, that I propose filters our access to the world. What we ultimately learn from impossible art is that the range of neurodiversity in humans is broader than we tend acknowledge or appreciate. The notoriously indefinable and uncategorizable character of queer theory is an applicable framework to match the innumerable neurocognitive possibilities that are actually available. To this end, my dissertation suggests that a shift to a neuro-queer-aesthetic paradigm would not only expand human perceptive possibilities, but also enable compassionate engagement within and among our diverse communities. / English

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In the realm of natural language programming, translating free-form sentences in natural language into a functional, machine-executable program remains difficult due to the following 4 challenges. First, the inherent ambiguity of natural languages. Second, the high-level verbose nature in user descriptions. Third, the complexity in the sentences and Fourth, the invalid or semantically unclear sentences. Our first solution is a Large Language Model (LLM) based Artificial Intelligence driven assistant to process free-form sentences and decompose them into sequences of simplified, unambiguous sentences that abide by a set of rules, thereby stripping away the complexities embedded within the original sentences. These resulting sentences are then used to generate the code. We applied the proposed approach to a set of free-form sentences written by middle-school students for describing the logic behind video games. More than 60% of the free-form sentences containing these problems were sufficiently converted to sequences of simple unambiguous object-oriented sentences by our approach. Next, the thesis also presents "IntentGuide," a neuro-symbolic integration framework to enhance the clarity and executability of human intentions expressed in freeform sentences. IntentGuide effectively integrates the rule-based error detection capabilities of symbolic AI with the powerful adaptive learning abilities of Large Language Model to convert ambiguous or complex sentences into clear, machine-understandable instructions. The empirical evaluation of IntentGuide performed on natural language sentences written by middle school students for designing video games, reveals a significant improvement in error correction and code generation abilities compared to previous approach, attaining an accuracy rate of 90%. / Master of Science / Imagine if you could talk to machines in everyday language and they could understand exactly what you meant, turning your words into programs that do exactly what you describe. That's the goal of the thesis. We've developed a system that helps machines make sense of the kind of free-form language that people, especially students, use when they describe what they want a computer to do. Understanding and converting everyday language into computer code is a complex challenge, primarily because the way we naturally speak can be vague, overly detailed, or just complex. This thesis presents a new tool using artificial intelligence that helps break down and simplify these sentences. By transforming them into clearer, rulefollowing instructions, this tool makes it easier for machines to understand and execute the tasks we describe. The technology was tested using descriptions from middle-school students on how video games should work. Over 60% of these complex or unclear descriptions were sufficiently converted into straightforward instructions that a machine could use. Additionally, a new system called "IntentGuide" was introduced, combining traditional AI methods with advanced language models to improve how effectively machines can interpret and act on human instructions. This improved system showed a 90% accuracy in understanding and correcting errors in the students' game descriptions, marking a significant step forward in helping computers better understand us.

In the past decade, reinforcement learning (RL) has achieved breakthroughs across various domains, from surpassing human performance in strategy games to enhancing the training of large language models (LLMs) with human feedback. However, RL has yet to gain widespread adoption in mission-critical fields such as healthcare and autonomous vehicles. This is primarily attributed to the inherent lack of trust, explainability, and generalizability of neural networks in deep reinforcement learning (DRL) agents. While neural DRL agents leverage the power of neural networks to solve specific tasks robustly and efficiently, this often comes at the cost of explainability and generalizability. In contrast, pure symbolic agents maintain explainability and trust but often underperform in high-dimensional data. In this work, we developed a method to distill explainable and trustworthy agents using neuro-symbolic AI. Neuro-symbolic distillation combines the strengths of symbolic reasoning and neural networks, creating a hybrid framework that leverages the structured knowledge representation of symbolic systems alongside the learning capabilities of neural networks. The key steps of neuro-symbolic distillation involve training traditional DRL agents, followed by extracting, selecting, and distilling their learned policies into symbolic forms using symbolic regression and tree-based models. These symbolic representations are then employed instead of the neural agents to make interpretable decisions with comparable accuracy. The approach is validated through experiments on Lunar Lander and Pong, demonstrating that symbolic representations can effectively replace neural agents while enhancing transparency and trustworthiness. Our findings suggest that this approach mitigates the black-box nature of neural networks, providing a pathway toward more transparent and trustworthy AI systems. The implications of this research are significant for fields requiring both high performance and explainability, such as autonomous systems, healthcare, and financial modeling.

NOTICE EN COURS DE TRAITEMENT / L'imagerie cérébrale englobe un éventail de techniques qui permettent la collecte de données neurobiologiques abondantes présentant de l'hétérogénéité dans leur composition chimique. Pour analyser et décrire leur complexité, de nombreuses mesures morphométriques sont extraites afin de caractériser les événements observés. Cependant, sur la base de ces caractéristiques morphométriques, les données semblent souvent homogènes lors de l'analyse. Pour saisir et comprendre la diversité de ces phénomènes biologiques, nous avons choisi d'utiliser des méthodes computationnelles, notamment la réduction de dimension des données et le regroupement. Dans cette thèse, nous présenterons deux exemples d'application. La première partie est consacrée à l'étude de l'hétérogénéité des cellules en migration dans le cerveau en fonction de leur dynamique migratoire. La migration cellulaire est un phénomène important dans le développement du cerveau, notamment dans le cadre des troubles neurodéveloppementaux. Les précurseurs neuronaux, appelés neuroblastes, changent de formes lors de leur migration. Il existe deux phases pour ce processus, une phase stationnaire et une phase migratoire. L'objectif de cette étude est de déterminer si ces populations de neuroblastes peuvent être séparées sur la base de leurs propriétés migratoires mais également d'utiliser des méthodes d'analyses statistiques pour trouver les différentes sous-populations afin de déterminer lesquelles sont communes. Enfin, nous avons étudié les propriétés migratoires de ces différentes populations des neuroblastes en venant perturber la migration à l'aide de modifications génétique ou environnementale. La seconde partie porte sur l'étude de la plasticité structurelle, qui fait référence à la capacité qu'ont deux neurones à former une connexion, appelée synapse, qui peut se renforcer ou s'affaiblir. Ces changements synaptiques sont essentiels pour les processus d'apprentissage et de mémoire. En examinant des images de dendrites du bulbe olfactif prises avec un microscope confocal, on observe des protrusions sur la surface de la dendrite qui servent à recevoir les entrées synaptiques. Pour analyser ces images, nous avons développé un pipeline computationnel destiné à prétraiter les images et extraire les épines dendritiques. À la suite de la reconstruction 3D de la dendrite, nous avons extrait les épines et calculé plusieurs métriques, telles que la longueur et la surface de l'épine, des indicateurs couramment utilisés dans l'analyse des épines dendritiques. En procédant à une réduction de la dimensionnalité du jeu de données et à son partitionnement, nous avons relié la morphologie de chacune de ces sous-populations à leurs propriétés structurelles. Enfin, nous avons comparé le groupe contrôle et le groupe expérimental dans le cas de trois expériences olfactives, deux tâches de renforcement, et une de déprivation, qui ont conduit à des changements de plasticité. Les résultats montrent que la morphologie des épines ou leurs densités sont affectées par ces différentes conditions. En résumé, nous avons développé des outils computationnels permettant de révéler l'hétérogénéité des neurones en développement en fonction de leur dynamique migratoire et de leurs propriétés structurelles. / Brain imaging encompasses a range of techniques that enable the collection of abundant neurobiological data that presents heterogeneity in their chemical composition. To analyse and describe their complexity, numerous morphometric metrics are extracted to characterise the observed events. However, based on these morphometric features, the data often appear homogeneous during analysis. To grasp and understand the diversity of these biological phenomena, we have chosen to use computational methods including dimension reduction of data and clustering. In this thesis, we will present two application examples. The first part is devoted to the study of the heterogeneity of migrating neuronal cells based on their migratory dynamics. Cell migration is an important phenomenon in brain development, particularly in the context of neurodevelopmental disorders. Neuronal precursors, called neuroblasts, change shape during their migration. There are two phases for this process, so-called stationary phase and a migratory phase. The aim of this study is to determine whether neuroblasts can be separated to different sub-populations based on their migratory properties and to use statistical analysis methods to find the different subpopulations and determine which ones are common. Finally, we have studied the migratory properties of these different neuroblast populations by disrupting migration using genetic or environmental modifications. The second part focuses on the study of synaptic plasticity, which refers to the capacity of two neurons to form a connection, called a synapse, which can strengthen or weaken. These changes are central to the synaptic remodelling that occurs during the learning and memory phase. From images of dendrites, taken with a confocal microscope in the olfactory bulb, we have set up a computational pipeline to perform image pre-processing and then extract dendritic spines, which are protrusions on the surface of the dendrite that serve to receive synaptic inputs. After 3D reconstruction of the dendrite, these spines are extracted, and several metrics are calculated, including the length and surface area of the spine, which are standard metrics in spine analysis. After dimension reduction of the dataset and clustering, we have linked the morphology of each of these subpopulations to their structural properties. Finally, we have compared the control group and the experimental group in the case of three experiments that led to plasticity changes. The results show that the morphology of spines or their densities are affected by these different conditions. In summary, we have developed computational tools that reveal the heterogeneity of developing neurons based on their migratory dynamics and structural properties.

Titre de l'écran-titre (visionné le 29 novembre 2023) / L'apprentissage automatique a considérablement progressé dans le domaine de la recherche en neurosciences, mais son application pose des défis en raison des différences entre les principes biologiques du cerveau et les méthodes traditionnelles d'apprentissage automatique. Dans ce contexte, le projet présenté propose NeuroTorch, un pipeline convivial d'apprentissage automatique spécialement conçu pour les neuroscientifiques, afin de relever ces défis. Les objectifs clés de ce projet sont de fournir une librairie d'apprentissage profond adaptée aux neurosciences computationnelles, d'implémenter l'algorithme eligibility trace forward propagation (e-prop) pour sa plausibilité biologique, de comparer les réseaux de neurones continus et à impulsions en termes de résilience, et d'intégrer un pipeline d'apprentissage par renforcement. Le projet se divise en plusieurs parties. Tout d'abord, la théorie des dynamiques neuronales, des algorithmes d'optimisation et des fonctions de transformation d'espaces sera développée. Ensuite, l'attention sera portée sur la conception du pipeline NeuroTorch, incluant l'implémentation de l'algorithme e-prop. Les résultats de la prédiction de séries temporelles d'activité neuronale chez le poisson-zèbre seront présentés, ainsi que des observations sur la résilience à l'ablation des réseaux obtenus. Enfin, une section sera consacrée à l'exploration du pipeline d'apprentissage par renforcement de NeuroTorch et à la validation de son architecture dans l'environnement LunarLander de Gym. En résumé, les modèles à impulsions de NeuroTorch ont atteint des précisions de 96,37%, 85,58% et 74,16% respectivement sur les ensembles de validation MNIST, Fashion-MNIST et Heidelberg. De plus, les dynamiques leaky-integrate-and-fire with explicit synaptic current - low pass filter (SpyLIF-LPF) et Wilson-Cowan ont été entraînées avec succès à l'aide de l'algorithme e-prop sur des données neuronales expérimentales du ventral habenula du poisson-zèbre, obtenant respectivement des valeurs de pVar de 0,97 et 0,96. Les résultats concernant la résilience indiquent que l'application de la loi de Dale améliore la robustesse des modèles en termes d'ablation hiérarchique. Enfin, grâce au pipeline d'apprentissage par renforcement de NeuroTorch, différents types d'agents inspirés des neurosciences ont atteint le critère de réussite dans l'environnement LunarLander de Gym. Ces résultats soulignent la pertinence et l'efficacité de NeuroTorch pour les applications en neurosciences computationnelles. / Machine learning has made significant advancements in neuroscience research, but its application presents challenges due to the differences between the biological principles of the brain and traditional machine learning methods. In this context, the presented project proposes NeuroTorch, a comprehensive machine learning pipeline specifically designed for neuroscientists to address these challenges. The key objectives of this project are to provide a deep learning library tailored to computational neuroscience, implement the eligibility trace forward propagation (e-prop) algorithm for biological plausibility, compare continuous and spiking neural networks in terms of resilience, and integrate a reinforcement learning pipeline. The project is divided into several parts. Firstly, the theory of neural dynamics, optimization algorithms, and space transformation functions will be developed. Next focus will be on the design of the NeuroTorch pipeline, including the implementation of the e-prop algorithm. Results of predicting a time series of neuronal activity in zebrafish will be presented, along with observations on the resilience to network ablations obtained. Finally, a section will be dedicated to exploring the NeuroTorch reinforcement learning pipeline and validating its architecture in the LunarLander environment of Gym. In summary, NeuroTorch spiking models achieved accuracies of 96.37%, 85.58%, and 74.16% on the MNIST, Fashion-MNIST, and Heidelberg validation sets, respectively. Furthermore, the leaky-integrate-and-fire with explicit synaptic current - low pass filter (SpyLIF-LPF) and Wilson-Cowan dynamics were successfully trained using the e-prop algorithm on experimental neuronal data from the ventral habenula of zebrafish, achieving pVar values of 0.97 and 0.96, respectively. Results regarding resilience indicate that the application of the Dale law improves the robustness of models in terms of hierarchical ablation. Lastly, through the NeuroTorch reinforcement learning pipeline, different types of neuroscience-inspired agents successfully met the success criterion in the Gym's LunarLander environment. These results highlight the relevance and effectiveness of NeuroTorch for applications in computational neuroscience.