Role of genetic and social factors in modulating the efficiency of division of labour in social insects

Summary : Division of labour is one of the most fascinating aspects of social insects. The efficient allocation of individuals to a multitude of different tasks requires a dynamic adjustment in response to the demands of a changing environment. A considerable number of theoretical models have focussed on identifying the mechanisms allowing colonies to perform efficient task allocation. The large majority of these models are built on the observation that individuals in a colony vary in their propensity (response threshold) to perform different tasks. Since individuals with a low threshold for a given task stimulus are more likely to perform that task than individuals with a high threshold, infra-colony variation in individual thresholds results in colony division of labour. These theoretical models suggest that variation in individual thresholds is affected by the within-colony genetic diversity. However, the models have not considered the genetic architecture underlying the individual response thresholds. This is important because a better understanding of division of labour requires determining how genotypic variation relates to differences in infra-colony response threshold distributions. In this thesis, we investigated the combined influence on task allocation efficiency of both, the within-colony genetic variability (stemming from variation in the number of matings by queens) and the number of genes underlying the response thresholds. We used an agent-based simulator to model a situation where workers in a colony had to perform either a regulatory task (where the amount of a given food item in the colony had to be maintained within predefined bounds) or a foraging task (where the quantity of a second type of food item collected had to be the highest possible). The performance of colonies was a function of workers being able to perform both tasks efficiently. To study the effect of within-colony genetic diversity, we compared the performance of colonies with queens mated with varying number of males. On the other hand, the influence of genetic architecture was investigated by varying the number of loci underlying the response threshold of the foraging and regulatory tasks. Artificial evolution was used to evolve the allelic values underlying the tasks thresholds. The results revealed that multiple matings always translated into higher colony performance, whatever the number of loci encoding the thresholds of the regulatory and foraging tasks. However, the beneficial effect of additional matings was particularly important when the genetic architecture of queens comprised one or few genes for the foraging task's threshold. By contrast, higher number of genes encoding the foraging task reduced colony performance with the detrimental effect being stronger when queens had mated with several males. Finally, the number of genes determining the threshold for the regulatory task only had a minor but incremental effect on colony performance. Overall, our numerical experiments indicate the importance of considering the effects of queen mating frequency, genetic architecture underlying task thresholds and the type of task performed when investigating the factors regulating the efficiency of division of labour in social insects. In this thesis we also investigate the task allocation efficiency of response threshold models and compare them with neural networks. While response threshold models are widely used amongst theoretical biologists interested in division of labour in social insects, our simulation reveals that they perform poorly compared to a neural network model. A major shortcoming of response thresholds is that they fail at one of the most crucial requirement of division of labour, the ability of individuals in a colony to efficiently switch between tasks under varying environmental conditions. Moreover, the intrinsic properties of the threshold models are that they lead to a large proportion of idle workers. Our results highlight these limitations of the response threshold models and provide an adequate substitute. Altogether, the experiments presented in this thesis provide novel contributions to the understanding of how division of labour in social insects is influenced by queen mating frequency and genetic architecture underlying worker task thresholds. Moreover, the thesis also provides a novel model of the mechanisms underlying worker task allocation that maybe more generally applicable than the widely used response threshold models. Resumé : La répartition du travail est l'un des aspects les plus fascinants des insectes vivant en société. Une allocation efficace de la multitude de différentes tâches entre individus demande un ajustement dynamique afin de répondre aux exigences d'un environnement en constant changement. Un nombre considérable de modèles théoriques se sont attachés à identifier les mécanismes permettant aux colonies d'effectuer une allocation efficace des tâches. La grande majorité des ces modèles sont basés sur le constat que les individus d'une même colonie diffèrent dans leur propension (inclination à répondre) à effectuer différentes tâches. Etant donné que les individus possédant un faible seuil de réponse à un stimulus associé à une tâche donnée sont plus disposés à effectuer cette dernière que les individus possédant un seuil élevé, les différences de seuils parmi les individus vivant au sein d'une même colonie mènent à une certaine répartition du travail. Ces modèles théoriques suggèrent que la variation des seuils des individus est affectée par la diversité génétique propre à la colonie. Cependant, ces modèles ne considèrent pas la structure génétique qui est à la base des seuils de réponse individuels. Ceci est très important car une meilleure compréhension de la répartition du travail requière de déterminer de quelle manière les variations génotypiques sont associées aux différentes distributions de seuils de réponse à l'intérieur d'une même colonie. Dans le cadre de cette thèse, nous étudions l'influence combinée de la variabilité génétique d'une colonie (qui prend son origine dans la variation du nombre d'accouplements des reines) avec le nombre de gènes supportant les seuils de réponse, vis-à-vis de la performance de l'allocation des tâches. Nous avons utilisé un simulateur basé sur des agents pour modéliser une situation où les travailleurs d'une colonie devaient accomplir une tâche de régulation (1a quantité d'une nourriture donnée doit être maintenue à l'intérieur d'un certain intervalle) ou une tâche de recherche de nourriture (la quantité d'une certaine nourriture doit être accumulée autant que possible). Dans ce contexte, 'efficacité des colonies tient en partie des travailleurs qui sont capable d'effectuer les deux tâches de manière efficace. Pour étudier l'effet de la diversité génétique d'une colonie, nous comparons l'efficacité des colonies possédant des reines qui s'accouplent avec un nombre variant de mâles. D'autre part, l'influence de la structure génétique a été étudiée en variant le nombre de loci à la base du seuil de réponse des deux tâches de régulation et de recherche de nourriture. Une évolution artificielle a été réalisée pour évoluer les valeurs alléliques qui sont à l'origine de ces seuils de réponse. Les résultats ont révélé que de nombreux accouplements se traduisaient toujours en une plus grande performance de la colonie, quelque soit le nombre de loci encodant les seuils des tâches de régulation et de recherche de nourriture. Cependant, les effets bénéfiques d'accouplements additionnels ont été particulièrement important lorsque la structure génétique des reines comprenait un ou quelques gènes pour le seuil de réponse pour la tâche de recherche de nourriture. D'autre part, un nombre plus élevé de gènes encodant la tâche de recherche de nourriture a diminué la performance de la colonie avec un effet nuisible d'autant plus fort lorsque les reines s'accouplent avec plusieurs mâles. Finalement, le nombre de gènes déterminant le seuil pour la tâche de régulation eu seulement un effet mineur mais incrémental sur la performance de la colonie. Pour conclure, nos expériences numériques révèlent l'importance de considérer les effets associés à la fréquence d'accouplement des reines, à la structure génétique qui est à l'origine des seuils de réponse pour les tâches ainsi qu'au type de tâche effectué au moment d'étudier les facteurs qui régulent l'efficacité de la répartition du travail chez les insectes vivant en communauté. Dans cette thèse, nous étudions l'efficacité de l'allocation des tâches des modèles prenant en compte des seuils de réponses, et les comparons à des réseaux de neurones. Alors que les modèles basés sur des seuils de réponse sont couramment utilisés parmi les biologistes intéressés par la répartition des tâches chez les insectes vivant en société, notre simulation montre qu'ils se révèlent peu efficace comparé à un modèle faisant usage de réseaux de neurones. Un point faible majeur des seuils de réponse est qu'ils échouent sur un point crucial nécessaire à la répartition des tâches, la capacité des individus d'une colonie à commuter efficacement entre des tâches soumises à des conditions environnementales changeantes. De plus, les propriétés intrinsèques des modèles basés sur l'utilisation de seuils conduisent à de larges populations de travailleurs inactifs. Nos résultats mettent en évidence les limites de ces modèles basés sur l'utilisation de seuils et fournissent un substitut adéquat. Ensemble, les expériences présentées dans cette thèse fournissent de nouvelles contributions pour comprendre comment la répartition du travail chez les insectes vivant en société est influencée par la fréquence d'accouplements des reines ainsi que par la structure génétique qui est à l'origine, pour un travailleur, du seuil de réponse pour une tâche. De plus, cette thèse fournit également un nouveau modèle décrivant les mécanismes qui sont à l'origine de l'allocation des tâches entre travailleurs, mécanismes qui peuvent être appliqué de manière plus générale que ceux couramment utilisés et basés sur des seuils de réponse.

Using self organizing maps on compositional data

Self-organizing maps (Kohonen 1997) is a type of artificial neural network developedto explore patterns in high-dimensional multivariate data. The conventional versionof the algorithm involves the use of Euclidean metric in the process of adaptation ofthe model vectors, thus rendering in theory a whole methodology incompatible withnon-Euclidean geometries.In this contribution we explore the two main aspects of the problem:1. Whether the conventional approach using Euclidean metric can shed valid resultswith compositional data.2. If a modification of the conventional approach replacing vectorial sum and scalarmultiplication by the canonical operators in the simplex (i.e. perturbation andpowering) can converge to an adequate solution.Preliminary tests showed that both methodologies can be used on compositional data.However, the modified version of the algorithm performs poorer than the conventionalversion, in particular, when the data is pathological. Moreover, the conventional ap-proach converges faster to a solution, when data is \well-behaved".Key words: Self Organizing Map; Artificial Neural networks; Compositional data

Semi-online neural-Q_leaming for real-time robot learning

Reinforcement learning (RL) is a very suitable technique for robot learning, as it can learn in unknown environments and in real-time computation. The main difficulties in adapting classic RL algorithms to robotic systems are the generalization problem and the correct observation of the Markovian state. This paper attempts to solve the generalization problem by proposing the semi-online neural-Q_learning algorithm (SONQL). The algorithm uses the classic Q_learning technique with two modifications. First, a neural network (NN) approximates the Q_function allowing the use of continuous states and actions. Second, a database of the most representative learning samples accelerates and stabilizes the convergence. The term semi-online is referred to the fact that the algorithm uses the current but also past learning samples. However, the algorithm is able to learn in real-time while the robot is interacting with the environment. The paper shows simulated results with the "mountain-car" benchmark and, also, real results with an underwater robot in a target following behavior

A behavior-based scheme using reinforcement learning for autonomous underwater vehicles

This paper presents a hybrid behavior-based scheme using reinforcement learning for high-level control of autonomous underwater vehicles (AUVs). Two main features of the presented approach are hybrid behavior coordination and semi on-line neural-Q_learning (SONQL). Hybrid behavior coordination takes advantages of robustness and modularity in the competitive approach as well as efficient trajectories in the cooperative approach. SONQL, a new continuous approach of the Q_learning algorithm with a multilayer neural network is used to learn behavior state/action mapping online. Experimental results show the feasibility of the presented approach for AUVs

Neural correlates of alerting and orienting impairment in multiple sclerosis patients.

BACKGROUND A considerable percentage of multiple sclerosis patients have attentional impairment, but understanding its neurophysiological basis remains a challenge. The Attention Network Test allows 3 attentional networks to be studied. Previous behavioural studies using this test have shown that the alerting network is impaired in multiple sclerosis. The aim of this study was to identify neurophysiological indexes of the attention impairment in relapsing-remitting multiple sclerosis patients using this test. RESULTS After general slowing had been removed in patients group to isolate the effects of each condition, some behavioral differences between them were obtained. About Contingent Negative Variation, a statistically significant decrement were found in the amplitude for Central and Spatial Cue Conditions for patient group (p<0.05). ANOVAs showed for the patient group a significant latency delay for P1 and N1 components (p<0.05) and a decrease of P3 amplitude for congruent and incongruent stimuli (p<0.01). With regard to correlation analysis, PASAT-3s and SDMT showed significant correlations with behavioral measures of the Attention Network Test (p<0.01) and an ERP parameter (CNV amplitude). CONCLUSIONS Behavioral data are highly correlated with the neuropsychological scores and show that the alerting and orienting mechanisms in the patient group were impaired. Reduced amplitude for the Contingent Negative Variation in the patient group suggests that this component could be a physiological marker related to the alerting and orienting impairment in relapsing-remitting multiple sclerosis. P1 and N1 delayed latencies are evidence of the demyelination process that causes impairment in the first steps of the visual sensory processing. Lastly, P3 amplitude shows a general decrease for the pathological group probably indexing a more central impairment. These results suggest that the Attention Network Test give evidence of multiple levels of attention impairment, which could help in the assessment and treatment of relapsing-remitting multiple sclerosis patients.

Towards robust network based complex systems : from evolutionary cellular automata to biological models

Abstract Sitting between your past and your future doesn't mean you are in the present. Dakota Skye Complex systems science is an interdisciplinary field grouping under the same umbrella dynamical phenomena from social, natural or mathematical sciences. The emergence of a higher order organization or behavior, transcending that expected of the linear addition of the parts, is a key factor shared by all these systems. Most complex systems can be modeled as networks that represent the interactions amongst the system's components. In addition to the actual nature of the part's interactions, the intrinsic topological structure of underlying network is believed to play a crucial role in the remarkable emergent behaviors exhibited by the systems. Moreover, the topology is also a key a factor to explain the extraordinary flexibility and resilience to perturbations when applied to transmission and diffusion phenomena. In this work, we study the effect of different network structures on the performance and on the fault tolerance of systems in two different contexts. In the first part, we study cellular automata, which are a simple paradigm for distributed computation. Cellular automata are made of basic Boolean computational units, the cells; relying on simple rules and information from- the surrounding cells to perform a global task. The limited visibility of the cells can be modeled as a network, where interactions amongst cells are governed by an underlying structure, usually a regular one. In order to increase the performance of cellular automata, we chose to change its topology. We applied computational principles inspired by Darwinian evolution, called evolutionary algorithms, to alter the system's topological structure starting from either a regular or a random one. The outcome is remarkable, as the resulting topologies find themselves sharing properties of both regular and random network, and display similitudes Watts-Strogtz's small-world network found in social systems. Moreover, the performance and tolerance to probabilistic faults of our small-world like cellular automata surpasses that of regular ones. In the second part, we use the context of biological genetic regulatory networks and, in particular, Kauffman's random Boolean networks model. In some ways, this model is close to cellular automata, although is not expected to perform any task. Instead, it simulates the time-evolution of genetic regulation within living organisms under strict conditions. The original model, though very attractive by it's simplicity, suffered from important shortcomings unveiled by the recent advances in genetics and biology. We propose to use these new discoveries to improve the original model. Firstly, we have used artificial topologies believed to be closer to that of gene regulatory networks. We have also studied actual biological organisms, and used parts of their genetic regulatory networks in our models. Secondly, we have addressed the improbable full synchronicity of the event taking place on. Boolean networks and proposed a more biologically plausible cascading scheme. Finally, we tackled the actual Boolean functions of the model, i.e. the specifics of how genes activate according to the activity of upstream genes, and presented a new update function that takes into account the actual promoting and repressing effects of one gene on another. Our improved models demonstrate the expected, biologically sound, behavior of previous GRN model, yet with superior resistance to perturbations. We believe they are one step closer to the biological reality.

Time Series Input Selection using Multiple Kernel Learning

In this paper we study the relevance of multiple kernel learning (MKL) for the automatic selection of time series inputs. Recently, MKL has gained great attention in the machine learning community due to its flexibility in modelling complex patterns and performing feature selection. In general, MKL constructs the kernel as a weighted linear combination of basis kernels, exploiting different sources of information. An efficient algorithm wrapping a Support Vector Regression model for optimizing the MKL weights, named SimpleMKL, is used for the analysis. In this sense, MKL performs feature selection by discarding inputs/kernels with low or null weights. The approach proposed is tested with simulated linear and nonlinear time series (AutoRegressive, Henon and Lorenz series).

Neural control of vascular reactions: impact of emotion and attention.

This study investigated the neural regions involved in blood pressure reactions to negative stimuli and their possible modulation by attention. Twenty-four healthy human subjects (11 females; age = 24.75 ± 2.49 years) participated in an affective perceptual load task that manipulated attention to negative/neutral distractor pictures. fMRI data were collected simultaneously with continuous recording of peripheral arterial blood pressure. A parametric modulation analysis examined the impact of attention and emotion on the relation between neural activation and blood pressure reactivity during the task. When attention was available for processing the distractor pictures, negative pictures resulted in behavioral interference, neural activation in brain regions previously related to emotion, a transient decrease of blood pressure, and a positive correlation between blood pressure response and activation in a network including prefrontal and parietal regions, the amygdala, caudate, and mid-brain. These effects were modulated by attention; behavioral and neural responses to highly negative distractor pictures (compared with neutral pictures) were smaller or diminished, as was the negative blood pressure response when the central task involved high perceptual load. Furthermore, comparing high and low load revealed enhanced activation in frontoparietal regions implicated in attention control. Our results fit theories emphasizing the role of attention in the control of behavioral and neural reactions to irrelevant emotional distracting information. Our findings furthermore extend the function of attention to the control of autonomous reactions associated with negative emotions by showing altered blood pressure reactions to emotional stimuli, the latter being of potential clinical relevance.

Identification of optimal structural connectivity using functional connectivity and neural modeling.

The complex network dynamics that arise from the interaction of the brain's structural and functional architectures give rise to mental function. Theoretical models demonstrate that the structure-function relation is maximal when the global network dynamics operate at a critical point of state transition. In the present work, we used a dynamic mean-field neural model to fit empirical structural connectivity (SC) and functional connectivity (FC) data acquired in humans and macaques and developed a new iterative-fitting algorithm to optimize the SC matrix based on the FC matrix. A dramatic improvement of the fitting of the matrices was obtained with the addition of a small number of anatomical links, particularly cross-hemispheric connections, and reweighting of existing connections. We suggest that the notion of a critical working point, where the structure-function interplay is maximal, may provide a new way to link behavior and cognition, and a new perspective to understand recovery of function in clinical conditions.

Identificación de puntos de pesca en los viajes pesqueros de anchoveta monitoreados por satélites mediante redes neuronales artificiales

La sostenibilidad de los recursos marinos y de su ecosistema hace necesario un manejo responsable de las pesquerías. Conocer la distribución espacial del esfuerzo pesquero y en particular de las operaciones de pesca es indispensable para mejorar el monitoreo pesquero y el análisis de la vulnerabilidad de las especies frente a la pesca. Actualmente en la pesquería de anchoveta peruana, se recoge información del esfuerzo y capturas mediante un programa de observadores a bordo, pero esta solo representa una muestra de 2% del total de viajes pesqueros. Por otro lado, se dispone de información por cada hora (en promedio) de la posición de cada barco de la flota gracias al sistema de seguimiento satelital de las embarcaciones (VMS), aunque en estos no se señala cuándo ni dónde ocurrieron las calas. Las redes neuronales artificiales (ANN) podrían ser un método estadístico capaz de inferir esa información, entrenándose en una muestra para la cual sí conocemos las posiciones de calas (el 2% anteriormente referido), estableciendo relaciones analíticas entre las calas y ciertas características geométricas de las trayectorias observadas por el VMS y así, a partir de las últimas, identificar la posición de las operaciones de pesca. La aplicación de la red neuronal requiere un análisis previo que examine la sensibilidad de la red a variaciones en sus parámetros y bases de datos de entrenamiento, y que nos permita desarrollar criterios para definir la estructura de la red e interpretar sus resultados de manera adecuada. La problemática descrita en el párrafo anterior, aplicada específicamente a la anchoveta (Engraulis ringens) es detalllada en el primer capítulo, mientras que en el segundo se hace una revisión teórica de las redes neuronales. Luego se describe el proceso de construcción y pre-tratamiento de la base de datos, y definición de la estructura de la red previa al análisis de sensibilidad. A continuación se presentan los resultados para el análisis en los que obtenemos una estimación del 100% de calas, de las cuales aproximadamente 80% están correctamente ubicadas y 20% poseen un error de ubicación. Finalmente se discuten las fortalezas y debilidades de la técnica empleada, de métodos alternativos potenciales y de las perspectivas abiertas por este trabajo.

Incremental neural networks for function approximation

A new strategy for incremental building of multilayer feedforward neural networks is proposed in the context of approximation of functions from R-p to R-q using noisy data. A stopping criterion based on the properties of the noise is also proposed. Experimental results for both artificial and real data are performed and two alternatives of the proposed construction strategy are compared.

Recovery of biological motion perception and network plasticity after cerebellar tumor removal.

Visual perception of body motion is vital for everyday activities such as social interaction, motor learning or car driving. Tumors to the left lateral cerebellum impair visual perception of body motion. However, compensatory potential after cerebellar damage and underlying neural mechanisms remain unknown. In the present study, visual sensitivity to point-light body motion was psychophysically assessed in patient SL with dysplastic gangliocytoma (Lhermitte-Duclos disease) to the left cerebellum before and after neurosurgery, and in a group of healthy matched controls. Brain activity during processing of body motion was assessed by functional magnetic resonance imaging (MRI). Alterations in underlying cerebro-cerebellar circuitry were studied by psychophysiological interaction (PPI) analysis. Visual sensitivity to body motion in patient SL before neurosurgery was substantially lower than in controls, with significant improvement after neurosurgery. Functional MRI in patient SL revealed a similar pattern of cerebellar activation during biological motion processing as in healthy participants, but located more medially, in the left cerebellar lobules III and IX. As in normalcy, PPI analysis showed cerebellar communication with a region in the superior temporal sulcus, but located more anteriorly. The findings demonstrate a potential for recovery of visual body motion processing after cerebellar damage, likely mediated by topographic shifts within the corresponding cerebro-cerebellar circuitry induced by cerebellar reorganization. The outcome is of importance for further understanding of cerebellar plasticity and neural circuits underpinning visual social cognition.

Integração de dados do quickbird e atributos do terreno no mapeamento digital de solos por redes neurais artificiais

No presente estudo, foi realizada uma avaliação de diferentes variáveis ambientais no mapeamento digital de solos em uma região no norte do Estado de Minas Gerais, utilizando redes neurais artificiais (RNA). Os atributos do terreno declividade e índice topográfico combinado (CTI), derivados de um modelo digital de elevação, três bandas do sensor Quickbird e um mapa de litologia foram combinados, e a importância de cada variável para discriminação das unidades de mapeamento foi avaliada. O simulador de redes neurais utilizado foi o "Java Neural Network Simulator", e o algoritmo de aprendizado, o "backpropagation". Para cada conjunto testado, foi selecionada uma RNA para a predição das unidades de mapeamento; os mapas gerados por esses conjuntos foram comparados com um mapa de solos produzido com o método convencional, para determinação da concordância entre as classificações. Essa comparação mostrou que o mapa produzido com o uso de todas as variáveis ambientais (declividade, índice CTI, bandas 1, 2 e 3 do Quickbird e litologia) obteve desempenho superior (67,4 % de concordância) ao dos mapas produzidos pelos demais conjuntos de variáveis. Das variáveis utilizadas, a declividade foi a que contribuiu com maior peso, pois, quando suprimida da análise, os resultados da concordância foram os mais baixos (33,7 %). Os resultados demonstraram que a abordagem utilizada pode contribuir para superar alguns dos problemas do mapeamento de solos no Brasil, especialmente em escalas maiores que 1:25.000, tornando sua execução mais rápida e mais barata, sobretudo se houver disponibilidade de dados de sensores remotos de alta resolução espacial a custos mais baixos e facilidade de obtenção dos atributos do terreno nos sistemas de informação geográfica (SIG).

Analysis, modelling and visualisation of spatiotemporal data for urban studies

The proportion of population living in or around cites is more important than ever. Urban sprawl and car dependence have taken over the pedestrian-friendly compact city. Environmental problems like air pollution, land waste or noise, and health problems are the result of this still continuing process. The urban planners have to find solutions to these complex problems, and at the same time insure the economic performance of the city and its surroundings. At the same time, an increasing quantity of socio-economic and environmental data is acquired. In order to get a better understanding of the processes and phenomena taking place in the complex urban environment, these data should be analysed. Numerous methods for modelling and simulating such a system exist and are still under development and can be exploited by the urban geographers for improving our understanding of the urban metabolism. Modern and innovative visualisation techniques help in communicating the results of such models and simulations. This thesis covers several methods for analysis, modelling, simulation and visualisation of problems related to urban geography. The analysis of high dimensional socio-economic data using artificial neural network techniques, especially self-organising maps, is showed using two examples at different scales. The problem of spatiotemporal modelling and data representation is treated and some possible solutions are shown. The simulation of urban dynamics and more specifically the traffic due to commuting to work is illustrated using multi-agent micro-simulation techniques. A section on visualisation methods presents cartograms for transforming the geographic space into a feature space, and the distance circle map, a centre-based map representation particularly useful for urban agglomerations. Some issues on the importance of scale in urban analysis and clustering of urban phenomena are exposed. A new approach on how to define urban areas at different scales is developed, and the link with percolation theory established. Fractal statistics, especially the lacunarity measure, and scale laws are used for characterising urban clusters. In a last section, the population evolution is modelled using a model close to the well-established gravity model. The work covers quite a wide range of methods useful in urban geography. Methods should still be developed further and at the same time find their way into the daily work and decision process of urban planners. La part de personnes vivant dans une région urbaine est plus élevé que jamais et continue à croître. L'étalement urbain et la dépendance automobile ont supplanté la ville compacte adaptée aux piétons. La pollution de l'air, le gaspillage du sol, le bruit, et des problèmes de santé pour les habitants en sont la conséquence. Les urbanistes doivent trouver, ensemble avec toute la société, des solutions à ces problèmes complexes. En même temps, il faut assurer la performance économique de la ville et de sa région. Actuellement, une quantité grandissante de données socio-économiques et environnementales est récoltée. Pour mieux comprendre les processus et phénomènes du système complexe "ville", ces données doivent être traitées et analysées. Des nombreuses méthodes pour modéliser et simuler un tel système existent et sont continuellement en développement. Elles peuvent être exploitées par le géographe urbain pour améliorer sa connaissance du métabolisme urbain. Des techniques modernes et innovatrices de visualisation aident dans la communication des résultats de tels modèles et simulations. Cette thèse décrit plusieurs méthodes permettant d'analyser, de modéliser, de simuler et de visualiser des phénomènes urbains. L'analyse de données socio-économiques à très haute dimension à l'aide de réseaux de neurones artificiels, notamment des cartes auto-organisatrices, est montré à travers deux exemples aux échelles différentes. Le problème de modélisation spatio-temporelle et de représentation des données est discuté et quelques ébauches de solutions esquissées. La simulation de la dynamique urbaine, et plus spécifiquement du trafic automobile engendré par les pendulaires est illustrée à l'aide d'une simulation multi-agents. Une section sur les méthodes de visualisation montre des cartes en anamorphoses permettant de transformer l'espace géographique en espace fonctionnel. Un autre type de carte, les cartes circulaires, est présenté. Ce type de carte est particulièrement utile pour les agglomérations urbaines. Quelques questions liées à l'importance de l'échelle dans l'analyse urbaine sont également discutées. Une nouvelle approche pour définir des clusters urbains à des échelles différentes est développée, et le lien avec la théorie de la percolation est établi. Des statistiques fractales, notamment la lacunarité, sont utilisées pour caractériser ces clusters urbains. L'évolution de la population est modélisée à l'aide d'un modèle proche du modèle gravitaire bien connu. Le travail couvre une large panoplie de méthodes utiles en géographie urbaine. Toutefois, il est toujours nécessaire de développer plus loin ces méthodes et en même temps, elles doivent trouver leur chemin dans la vie quotidienne des urbanistes et planificateurs.

Resting-brain functional connectivity predicted by analytic measures of network communication.

The complex relationship between structural and functional connectivity, as measured by noninvasive imaging of the human brain, poses many unresolved challenges and open questions. Here, we apply analytic measures of network communication to the structural connectivity of the human brain and explore the capacity of these measures to predict resting-state functional connectivity across three independently acquired datasets. We focus on the layout of shortest paths across the network and on two communication measures-search information and path transitivity-which account for how these paths are embedded in the rest of the network. Search information is an existing measure of information needed to access or trace shortest paths; we introduce path transitivity to measure the density of local detours along the shortest path. We find that both search information and path transitivity predict the strength of functional connectivity among both connected and unconnected node pairs. They do so at levels that match or significantly exceed path length measures, Euclidean distance, as well as computational models of neural dynamics. This capacity suggests that dynamic couplings due to interactions among neural elements in brain networks are substantially influenced by the broader network context adjacent to the shortest communication pathways.