It is well established that older adults show a deficit in episodic memory. The associative deficit hypothesis (ADH) (Naveh-Benjamin, 2000) suggests that an age-related reduced ability to create links between units of information is a major contributor to the episodic deficit. It has been a robust finding that older adults show a disproportionate decline in associative memory relative to item memory when compared to young adults. Previous researchers have investigated the role of the frontal lobes (FL) by studying the effect of reduced attentional resources in the associative deficit. However, they have not found that divided attention in young adults produces the disproportionate associative decline seen in aging and it is thought that some cognitive process other than the allocation of attentional resources may contribute to the associative deficit. The present study intended to use a divided attention (DA) task that also engages medial temporal brain regions (MTL) in order to tax additional parts of the network involved in creating associations and provide indirect support for the role of the MTL in the associative deficit. However, the associative memory deficit in older adults was not replicated due to unique poor associative memory performance of some young adults in the study. Analyses excluding these participants show support for the role of the MTL in the associative deficit. However, the young poor performers may provide support for the role of FL function in the associative deficit and show that poor associative memory may not be limited to the older adult cohort.

This thesis addresses the problem of how the dendritic structure and other morphological properties of the neuron can determine its pattern recognition performance. The techniques used in this work for generating dendritic trees with different morphologies included the following three methods. Firstly, dendritic trees were produced by exhaustively generating every possible morphology. Where this was not possible due to the size of morphological space, I sampled systematically from the possible morphologies. Lastly, dendritic trees were evolved using an evolutionary algorithm, which varied existing morphologies using selection, mutation and crossover. From these trees, I constructed full compartmental conductance-based models of neurons. I then assessed the performance of the resulting neuronal models by quantifying their ability to discriminate between learned and novel input patterns. The morphologies generated were tested in the presence and absence of active conductances. The results have shown that the morphology does have a considerable effect on pattern recognition performance. In fact, neurons with a small mean depth of their dendritic tree are the best pattern recognizers. Moreover, the performance of neurons is anti-correlated with mean depth. Interestingly, the symmetry of the neuronal morphology does not correlate with performance. This research has also revealed that the evolutionary algorithm could find effective morphologies for both passive models and models with active conductances. In the active model, there was a considerable change in the performance of the original population of neurons, which largely resulted from changes in the morphological parameters such as dendritic compartmental length and tapering. However, no single parameter setting guaranteed good neuronal performance; in three separate runs of the evolutionary algorithm, different sets of well performing parameters were found. In fact, the evolved neurons performed at least five times better than the original hand-tuned neurons. In summary, the combination of morphological parameters plays a key role in determining the performance of neurons in the pattern recognition task and the right combination produces very well performing neurons.

There is growing evidence that integrating classification and association rule mining can produce more efficient and accurate classifiers than traditional techniques. This thesis introduces a new MapReduce based association rule miner for extracting strong rules from large datasets. This miner is used later to develop a new large scale classifier. Also new MapReduce simulator was developed to evaluate the scalability of proposed algorithms on MapReduce clusters. The developed associative rule miner inherits the MapReduce scalability to huge datasets and to thousands of processing nodes. For finding frequent itemsets, it uses hybrid approach between miners that uses counting methods on horizontal datasets, and miners that use set intersections on datasets of vertical formats. The new miner generates same rules that usually generated using apriori-like algorithms because it uses the same confidence and support thresholds definitions. In the last few years, a number of associative classification algorithms have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. This thesis also introduces a new MapReduce classifier that based MapReduce associative rule mining. This algorithm employs different approaches in rule discovery, rule ranking, rule pruning, rule prediction and rule evaluation methods. The new classifier works on multi-class datasets and is able to produce multi-label predications with probabilities for each predicted label. To evaluate the classifier 20 different datasets from the UCI data collection were used. Results show that the proposed approach is an accurate and effective classification technique, highly competitive and scalable if compared with other traditional and associative classification approaches. Also a MapReduce simulator was developed to measure the scalability of MapReduce based applications easily and quickly, and to captures the behaviour of algorithms on cluster environments. This also allows optimizing the configurations of MapReduce clusters to get better execution times and hardware utilization.

This thesis explores the behaviour of participants taking an associative approach to a task-cueing paradigm. Task-cueing is usually intended to explore controlled processing of task-sets. But small stimulus sets plausibly afford associative learning via simple and conditional discriminations. In six experiments participants were presented with typical task-cueing trials: a cue (coloured shape) followed by a digit (or in Experiment 5 a symbol) requiring one of two responses. In the standard Tasks condition (Monsell Experiment and Experiments 1-3), the participant was instructed to perform either an odd/even or a high/low task dependent on the cue. The second condition was intended to induce associative learning of cue + stimulus-response mappings. In general, the Tasks condition showed a large switch cost that reduced with preparation time, a small, constant congruency effect and a small perturbation when new stimuli were introduced. By contrast the CSR condition showed a small, reliable switch cost that did not reduce with preparation time, a large congruency effect that changed over time and a large perturbation when new stimuli were introduced. These differences may indicate automatic associative processing in the CSR condition and rule-based classification in the Tasks condition. Furthermore, an associative model based on the APECS learning algorithm (McLaren, 1993) provided an account of the CSR data. Experiment 3 showed that participants were able to deliberately change their approach to the experiment from using CSR instructions to using Tasks instructions, and to some extent vice versa. Experiments 4 & 5 explored the cause of the small switch cost in the CSR condition. Consideration of the aspects of the paradigm that produced the switch cost in the APECS model produced predictions, which were tested against behavioural data. Experiment 4 found that the resulting manipulation made participants more likely to induce task-sets. Experiment 5 used random symbols instead of numbers, removing the underlying task-sets. The results of this experiment broadly agreed with the predictions made using APECS. Chapter 6 considers an initial attempt to create a real-time version of APECS. It also finds that an associative model of a different class (AMAN, Harris & Livesey, 2010) can provide an account of some, but not all, of the phenomena found in the CSR condition. This thesis concludes that performance in the Tasks condition is suggestive of the use of cognitive control processes, whilst associatively based responding is available as a basis for performance in the CSR condition.

Calculating a sparse code for signals with high dimensionality, such as high-resolution images, takes substantial time to compute on a traditional computer architecture. Memristors present the opportunity to combine storage and computing elements into a single, compact device, drastically reducing the area required to perform these calculations. This work focused on the analysis of two existing sparse coding architectures, one of which utilizes memristors, as well as the design of a new, third architecture that employs a memristive crossbar. These architectures implement either a non-spiking or spiking variety of sparse coding based on the Locally Competitive Algorithm (LCA) introduced by Rozell et al. in 2008. Each architecture receives an arbitrary number of input lines and drives an arbitrary number of output lines. Training of the dictionary used for the sparse code was implemented through external control signals that approximate Oja's rule. The resulting designs were capable of representing input in real-time: no resets would be needed between frames of a video, for instance, though some settle time would be needed. The spiking architecture proposed is novel, emphasizing simplicity to achieve lower power than existing designs. The architectures presented were tested for their ability to encode and reconstruct 8 x 8 patches of natural images. The proposed network reconstructed patches with a normalized, root-mean-square error of 0.13, while a more complicated CMOS-only approach yielded 0.095, and a non-spiking approach yielded 0.074. Several outputs competing for representation of the input was shown to improve reconstruction quality and preserve more subtle components in the final encoding; the proposed algorithm lacks this feature. Steps to address this were proposed for future work by scaling input spikes according to the current expected residual, without adding much complexity. The architectures were also tested with the MNIST digit database, passing a sparse code onto a basic classifier. The proposed architecture scored 81% on this test, a CMOS-only spiking variant scored 76%, and the non-spiking algorithm scored 85%. Power calculations were made for each design and compared against other publications. The overall findings showed great promise for spiking memristor-based ASICs, consuming only 28% of the power used by non-spiking architectures and 6.6% as much power as a CMOS-only spiking architecture on this task. The spike-based nature of the novel design was also parameterized into several intuitive parameters that could be adjusted to prefer either performance or power efficiency. The design and analysis of architectures for sparse coding should greatly reduce the amount of future work needed to implement an end-to-end classification pipeline for images or other signal data. When lower power is a primary concern, the proposed architecture should be considered as it surpassed other published algorithms. These pipelines could be used to provide low-power visual assistance, highlighting objects within high-definition video frames in real-time. The technology could also be used to help self-driving cars identify hazards more quickly and efficiently.

The principal objective of this investigation is to develop a constitutive model to simulate the hysteresis behaviour of unbonded flexible risers. A new constitutive model for flexible risers is proposed and a procedure for the identification of the related input parameters is developed using a multi-scale approach. The constitutive model is formulated in the framework of an Euler-Bernoulli beam model, with the addition of suitable pressure terms to the generalised stresses to account for the internal and external pressures, and therefore can be efficiently used for large-scale analyses. The developed non-linear relationship between generalised stresses and strains in the beam is based on the analogy between frictional slipping between different layers of a flexible riser and frictional slipping between micro-planes of a continuum medium in nonassociative elasto-plasticity. Hence, a linear elastic relationship is used for the initial response in which no-slip occurs; an onset-slip function is introduced to define the ‘noslip’ domain, i.e. the set of generalised stresses for which no slip occurs; a nonassociative rule with linear kinematic hardening is used to model the full-slip phase. The results of several numerical simulations for a riser of small-length, obtained with a very detailed (small-scale) non-linear finite-element model, are used to identify the parameters of the constitutive law, bridging in this way the small scale of the detailed finite-element simulations with the large scale of the beam model. The effectiveness of the proposed method is validated by the satisfactory agreement between the results of various detailed finite-element simulations for a short riser, subject to internal and external uniform pressures and cyclic bending and tensile loadings, with those given by the proposed constitutive law. The merit of the present constitutive law lies in the capturing of many important aspects of risers structural response, including the energy dissipation due to frictional slip between layers and the hysteretic response. This privilege allows one to accurately study the cyclic behavior of unbonded flexible risers subject to axial tension, bending moment, internal and external pressures.

A Cohomologia de Hochschild é um invariante associado a álgebras o qual pode nos fornecer propiedades homologicas das álgebras e suas categorias de módulos. Além disso tem aplicações em Geometria Algébrica e Teoria de Representações, entre outras áreas. Para álgebras A sobre um corpo, o i-ésimo grupo de cohomologia de Hochschild HH^i(A,M) de A, com coeficientes no bimódulo M, coincide com Ext^i_{A^e}(A,M). Logo, este pode ser calculado usando uma resolução projetiva da álgebra como A-bimódulo. Diferentes autores como Dieter Happel, Claude Cibils, Edward Green, David Anick, Michael Bardzell e Andrea Solotar desenvolveram ferramentas para a construção destas resoluções em casos específicos. Um resultado recente e muito importante é apresentado por Andrea Solotar e Sergio Chohuy, onde se mostra a construção de uma resolução projetiva de bimódulos para álgebras associativas generalizando o resultado para álgebras monomiais feito por Bardzell. Nesta dissertação pretendemos introduzir ao leitor no conceito de Cohomologia de Hochschild mostrando a importância da mesma mediante resultados conhecidos para álgebras de dimensão finita. Além disso, apresentamos os conceitos e resultados do trabalho de Chohuy e Solotar mencionado acima. No decorrer deste trabalho complementamos algumas demonstrações dos resultados enunciados com o fim de propiciar uma ferramenta para o melhor entendimento dos tópicos trabalhados aqui. / The Hochschild Cohomology is an invariant attached to associative algebras which may provide us some homological aspects of the algebras and its category of modules. Moreover, it has applications to Algebraic Geometry and Representation Theory, among others areas. For algebras A over a field the Hochschild cohomology group HH^i(A,M) of A with coeficients in a bimodule M coincides with Ext^i_{A^e}(A,M). So it can be computed using a projective resolution of the algebra, as a bimodule over itself. Therefore different authors like Dieter Happel, Claude Cibils, Edward Green, David Anick, Michael Bardzell, Sergio Chohuy and Andrea Solotar developed tools for the construction of these resolutions in particular cases. A recent and very important result was introduced by Andrea Solotar and Sergio Chohuy, where they show a construction of a projective bimodule resolution for associative algebras generalizing the result for monomial algebras made by Bardzell. In this dissertation we intend to introduce the reader in the cohomology Hochschild concept, showing its importance through known results for finite dimensional algebras. Besides, we exhibit the concepts and results of Chohuy and Solotar mentioned before. During this text, we complement some demonstrations with the purpose of giving a tool for the a better understanding.

Uma variedade M de álgebras associativas é dita ser não matricial se F² não está em M, em que F² é o anel das matrizes quadradas de ordem 2 sobre F. Latyshev introduziu estas variedades em 1977. A respeito desta definição, outras caracterizações equivalentes para uma variedade não matricial foram obtidas, por exemplo, ao considerar elementos algébricos (Cekanu, 1979) e nilpotentes (Mishckenko et al, 2011). Variedades não matriciais são estudadas principalmente no caso sobre os corpos de característica zero para álgebras associativas. A teoria geral de variedades de álgebras, entretanto, não está restrita à classe das álgebras associativas. Além das álgebras de Lie, entre as muitas classes de álgebras não associativas, nós destacamos as álgebras alternativas, as de Jordan e as de Jordan não comutativas. Estas classes de álgebras têm muitas conexões e aplicações a diversas áreas da Matemática e da Física e têm uma teoria estrutural bem desenvolvida, assim como a classe das álgebras associativas. O conceito de variedade não matricial pode ser reformulado para as classes de álgebras supracitadas e nosso trabalho consiste em adaptar, estender ou generalizar alguns resultados, conforme mencionado, para variedades não matriciais nestas classes de álgebras. / A variety M of associative algebras (over a field F) is called ``nonmatrix\'\' if F² is not in M, where F² is the usual matrix algebra of second order over F. Latyshev introduced these varieties in 1977. Concerning this definition, other equivalent characterizations for a nonmatrix variety were obtained, for instance, by considering algebraic (Cekanu, 79) and nilpotent (Mishchenko et all, 2011) elements. Non-matrix varieties are studied mainly in the case of characteristic zero for associative algebras. However, the general theory of varieties of algebras is not restricted to the class of associative algebras. In addition to the Lie algebras, among many classes of non associative algebras, we highlight the alternative, the Jordan and the non commutative Jordan algebras. These classes of algebras have many connexions and applications to several areas of Mathematics and Physics and have a well-developed structural theory, as in the class of associative algebras. The concept of ``nonmatrix variety\'\' can be reformulated in the classes of algebras above and our work is to adapt, extend or generalize some results, as mentioned, for non-matrix varieties in these classes of algebras.

Vidros de spins são sistemas extremamente complexos caracterizados por um número enorme de estados estáveis e meta estáveis. Se identificarmos cada um desses estados com uma informação memorizada, esses sistemas podem ser utilizados como memórias associativas ou endereçáveis por conteúdo. O modelo de vidro de spins passa então a ser chamado de rede de neurônios. Neste trabalho estudamos a termodinâmica e alguns aspectos dinâmicos de uma rede de neurônios com processamento paralelo ou síncrono - o Modelo de Little de memória associativa - no regime em que o número de informações memorizadas p cresce como p = αN, onde N é o número de neurônios. Usando a teoria simétrica em relação às réplicas obtemos o diagrama de fases no espaço de parâmetros do modelo no qual incluímos um termo de autointeração dos neurônios.A riqueza do diagrama de fases que possui uma superfície de pontos tricríticos é devida à competição entre os dois regimes assintóticos da dinâmica síncrona: pontos fixos e ciclos de período dois. / Spin glasses are very complex systems characterized by a huge number of stable and metastable states. If we identify each state with a memorized information then spin glasses may be used as associative or content addressable memories. This spin glass model is then called a neural network. In this work we study the thermodynamics and some dynamical aspects of a neural network with parallel or synchronous processing - Little\'s model of associative memory -in the regime where the number of memorized informations p grows as p = αN, where N is the number of neurons. Using the replica symmetric theory we determine the phase diagram in the space of the model\'s parameters, in which we include a neural self interaction term. The richness of the phase diagram which possesses a surface of tricritical points is due to the competition between the two asymptotic dynamical behaviours of the synchronous dynamics: fixed points and cycles of lenght two.

Social learning plays a valuable role in the lives of many animal taxa, sometimes allowing individuals to bypass the costs of personal exploration. The ubiquity of this behaviour may arise from the fact that learning from others is often underpinned by simple learning processes that also enable individuals to learn asocially. Insects have proven to be particularly valuable models for investigating parsimonious hypotheses with regards to social learning processes, due to their small brain sizes and the prevalence of social information use in their life histories. In this thesis, I use social insects to further investigate the mechanisms underlying more complex social learning behaviours and explore the circumstances under which social information use manifests. In the first chapter, I investigate the proximate mechanisms underlying social learning and demonstrate that even seemingly complex social learning behaviours can arise through simple associative learning processes. In Chapter two, I investigate whether bees are more predisposed to learning from conspecific cues and discover that social information is learnt to a greater extent than information originating from non-social sources. In Chapter four, I demonstrate that classical conditioning also underpins learning from evolved social signals in honeybees. Finally, I investigate whether social information is used adaptively by bumblebees: Chapter three demonstrates that joining behaviour in free-flying bees is contingent upon whether flowers are familiar or not, and in Chapter six, I show that when social information is costly to acquire, bees are more likely to rely on social information to make foraging decisions. Taken as a whole, my findings suggest that bees may be specially adapted for receiving social information, but the ability to learn from others arises through general associative learning mechanisms.