Bifurcation and singularity analysis of a molecular network for the induction of long-term memory.

Withdrawal reflexes of the mollusk Aplysia exhibit sensitization, a simple form of long-term memory (LTM). Sensitization is due, in part, to long-term facilitation (LTF) of sensorimotor neuron synapses. LTF is induced by the modulatory actions of serotonin (5-HT). Pettigrew et al. developed a computational model of the nonlinear intracellular signaling and gene network that underlies the induction of 5-HT-induced LTF. The model simulated empirical observations that repeated applications of 5-HT induce persistent activation of protein kinase A (PKA) and that this persistent activation requires a suprathreshold exposure of 5-HT. This study extends the analysis of the Pettigrew model by applying bifurcation analysis, singularity theory, and numerical simulation. Using singularity theory, classification diagrams of parameter space were constructed, identifying regions with qualitatively different steady-state behaviors. The graphical representation of these regions illustrates the robustness of these regions to changes in model parameters. Because persistent protein kinase A (PKA) activity correlates with Aplysia LTM, the analysis focuses on a positive feedback loop in the model that tends to maintain PKA activity. In this loop, PKA phosphorylates a transcription factor (TF-1), thereby increasing the expression of an ubiquitin hydrolase (Ap-Uch). Ap-Uch then acts to increase PKA activity, closing the loop. This positive feedback loop manifests multiple, coexisting steady states, or multiplicity, which provides a mechanism for a bistable switch in PKA activity. After the removal of 5-HT, the PKA activity either returns to its basal level (reversible switch) or remains at a high level (irreversible switch). Such an irreversible switch might be a mechanism that contributes to the persistence of LTM. The classification diagrams also identify parameters and processes that might be manipulated, perhaps pharmacologically, to enhance the induction of memory. Rational drug design, to affect complex processes such as memory formation, can benefit from this type of analysis.

Application of Imagery Theory to Sport Psychology: Some Preliminary Findings

Bioinformational theory has been proposed by Lang (1979a), who suggests that mental images can be understood as products of the brain's information processing capacity. Imagery involves activation of a network of propositionally coded information stored in long-term memory. Propositions concerning physiological and behavioral responses provide a prototype for overt behavior. Processing of response information is associated with somatovisceral arousal. The theory has implications for imagery rehearsal in sport psychology and can account for a variety of findings in the mental practice literature. Hypotheses drawn from bioinformational theory were tested. College athletes imagined four scenes during which their heart rates were recorded. Subjects tended to show increases in heart rate when imagining scenes with which they had personal experience and which would involve cardiovascular activation if experienced in real life. Nonsignificant heart rate changes were found when the scene involved activation but was one with which subjects did not have personal experience.

A Normative Theory of Forgetting: Lessons from the Fruit Fly

Recent experiments revealed that the fruit fly Drosophila melanogaster has a dedicated mechanism for forgetting: blocking the G-protein Rac leads to slower and activating Rac to faster forgetting. This active form of forgetting lacks a satisfactory functional explanation. We investigated optimal decision making for an agent adapting to a stochastic environment where a stimulus may switch between being indicative of reward or punishment. Like Drosophila, an optimal agent shows forgetting with a rate that is linked to the time scale of changes in the environment. Moreover, to reduce the odds of missing future reward, an optimal agent may trade the risk of immediate pain for information gain and thus forget faster after aversive conditioning. A simple neuronal network reproduces these features. Our theory shows that forgetting in Drosophila appears as an optimal adaptive behavior in a changing environment. This is in line with the view that forgetting is adaptive rather than a consequence of limitations of the memory system.

Functional brain network efficiency predicts intelligence

The neuronal causes of individual differences in mental abilities such as intelligence are complex and profoundly important. Understanding these abilities has the potential to facilitate their enhancement. The purpose of this study was to identify the functional brain network characteristics and their relation to psychometric intelligence. In particular, we examined whether the functional network exhibits efficient small-world network attributes (high clustering and short path length) and whether these small-world network parameters are associated with intellectual performance. High-density resting state electroencephalography (EEG) was recorded in 74 healthy subjects to analyze graph-theoretical functional network characteristics at an intracortical level. Ravens advanced progressive matrices were used to assess intelligence. We found that the clustering coefficient and path length of the functional network are strongly related to intelligence. Thus, the more intelligent the subjects are the more the functional brain network resembles a small-world network. We further identified the parietal cortex as a main hub of this resting state network as indicated by increased degree centrality that is associated with higher intelligence. Taken together, this is the first study that substantiates the neural efficiency hypothesis as well as the Parieto-Frontal Integration Theory (P-FIT) of intelligence in the context of functional brain network characteristics. These theories are currently the most established intelligence theories in neuroscience. Our findings revealed robust evidence of an efficiently organized resting state functional brain network for highly productive cognitions.

Assessing the function of the fronto-parietal attention network: Insights from resting-state fMRI and the attentional network test

In the recent past, various intrinsic connectivity networks (ICN) have been identified in the resting brain. It has been hypothesized that the fronto-parietal ICN is involved in attentional processes. Evidence for this claim stems from task-related activation studies that show a joint activation of the implicated brain regions during tasks that require sustained attention. In this study, we used functional magnetic resonance imaging (fMRI) to demonstrate that functional connectivity within the fronto-parietal network at rest directly relates to attention. We applied graph theory to functional connectivity data from multiple regions of interest and tested for associations with behavioral measures of attention as provided by the attentional network test (ANT), which we acquired in a separate session outside the MRI environment. We found robust statistical associations with centrality measures of global and local connectivity of nodes within the network with the alerting and executive control subfunctions of attention. The results provide further evidence for the functional significance of ICN and the hypothesized role of the fronto-parietal attention network. Hum Brain Mapp , 2013. © 2013 Wiley Periodicals, Inc.

Structural and Institutional Determinants of Influence Reputation: A Comparison of Collaborative and Adversarial Policy Networks in Decision Making and Implementation

The central assumption in the literature on collaborative networks and policy networks is that political outcomes are affected by a variety of state and nonstate actors. Some of these actors are more powerful than others and can therefore have a considerable effect on decision making. In this article, we seek to provide a structural and institutional explanation for these power differentials in policy networks and support the explanation with empirical evidence. We use a dyadic measure of influence reputation as a proxy for power, and posit that influence reputation over the political outcome is related to vertical integration into the political system by means of formal decision-making authority, and to horizontal integration by means of being well embedded into the policy network. Hence, we argue that actors are perceived as influential because of two complementary factors: (a) their institutional roles and (b) their structural positions in the policy network. Based on temporal and cross-sectional exponential random graph models, we compare five cases about climate, telecommunications, flood prevention, and toxic chemicals politics in Switzerland and Germany. The five networks cover national and local networks at different stages of the policy cycle. The results confirm that institutional and structural drivers seem to have a crucial impact on how an actor is perceived in decision making and implementation and, therefore, their ability to significantly shape outputs and service delivery.

Sustainability principles put into practice: case studies of network analysis in Swiss climate change adaptation

Climate adaptation policies increasingly incorporate sustainability principles into their design and implementation. Since successful adaptation by means of adaptive capacity is recognized as being dependent upon progress toward sustainable development, policy design is increasingly characterized by the inclusion of state and non-state actors (horizontal actor integration), cross-sectoral collaboration, and inter-generational planning perspectives. Comparing four case studies in Swiss mountain regions, three located in the Upper Rhone region and one case from western Switzerland, we investigate how sustainability is put into practice. We argue that collaboration networks and sustainability perceptions matter when assessing the implementation of sustainability in local climate change adaptation. In other words, we suggest that adaptation is successful where sustainability perceptions translate into cross-sectoral integration and collaboration on the ground. Data about perceptions and network relations are assessed through surveys and treated via cluster and social network analysis.

En primera persona : Un caso cooperativo ante la crisis algodonera de la década del '60

Este artículo busca describir y analizar las acciones y reacciones que generó sobre el discurso y práctica del actor social cooperativo, la crisis del cultivo algodonero durante los años '60 en la Argentina. Se toma el caso de la Cooperativa Agrícola Algodonera La Banda Limitada (CAALBA) de Santiago del Estero. Asociación de pequeños productores inserta en un entramado de relaciones con instituciones de la sociedad civil y el Estado que -en términos gramscianos- fueron compartimentos con interacción en el marco de las disputas hegemónicas sobre el proyecto de desarrollo social. Se presentan también a lo largo del trabajo a la CAALBA ante estos nuevos escenarios, así como fue la Corporación del Río Dulce

En primera persona : Un caso cooperativo ante la crisis algodonera de la década del '60

Este artículo busca describir y analizar las acciones y reacciones que generó sobre el discurso y práctica del actor social cooperativo, la crisis del cultivo algodonero durante los años '60 en la Argentina. Se toma el caso de la Cooperativa Agrícola Algodonera La Banda Limitada (CAALBA) de Santiago del Estero. Asociación de pequeños productores inserta en un entramado de relaciones con instituciones de la sociedad civil y el Estado que -en términos gramscianos- fueron compartimentos con interacción en el marco de las disputas hegemónicas sobre el proyecto de desarrollo social. Se presentan también a lo largo del trabajo a la CAALBA ante estos nuevos escenarios, así como fue la Corporación del Río Dulce

En primera persona : Un caso cooperativo ante la crisis algodonera de la década del '60

Este artículo busca describir y analizar las acciones y reacciones que generó sobre el discurso y práctica del actor social cooperativo, la crisis del cultivo algodonero durante los años '60 en la Argentina. Se toma el caso de la Cooperativa Agrícola Algodonera La Banda Limitada (CAALBA) de Santiago del Estero. Asociación de pequeños productores inserta en un entramado de relaciones con instituciones de la sociedad civil y el Estado que -en términos gramscianos- fueron compartimentos con interacción en el marco de las disputas hegemónicas sobre el proyecto de desarrollo social. Se presentan también a lo largo del trabajo a la CAALBA ante estos nuevos escenarios, así como fue la Corporación del Río Dulce

En primera persona : Un caso cooperativo ante la crisis algodonera de la década del '60

Este artículo busca describir y analizar las acciones y reacciones que generó sobre el discurso y práctica del actor social cooperativo, la crisis del cultivo algodonero durante los años '60 en la Argentina. Se toma el caso de la Cooperativa Agrícola Algodonera La Banda Limitada (CAALBA) de Santiago del Estero. Asociación de pequeños productores inserta en un entramado de relaciones con instituciones de la sociedad civil y el Estado que -en términos gramscianos- fueron compartimentos con interacción en el marco de las disputas hegemónicas sobre el proyecto de desarrollo social. Se presentan también a lo largo del trabajo a la CAALBA ante estos nuevos escenarios, así como fue la Corporación del Río Dulce

Feature selection in the reconstruction of complex network representations of spectral data

Complex networks have been extensively used in the last decade to characterize and analyze complex systems, and they have been recently proposed as a novel instrument for the analysis of spectra extracted from biological samples. Yet, the high number of measurements composing spectra, and the consequent high computational cost, make a direct network analysis unfeasible. We here present a comparative analysis of three customary feature selection algorithms, including the binning of spectral data and the use of information theory metrics. Such algorithms are compared by assessing the score obtained in a classification task, where healthy subjects and people suffering from different types of cancers should be discriminated. Results indicate that a feature selection strategy based on Mutual Information outperforms the more classical data binning, while allowing a reduction of the dimensionality of the data set in two orders of magnitude

Contributions to Bayesian network learning with applications to neuroscience

Neuronal morphology is a key feature in the study of brain circuits, as it is highly related to information processing and functional identification. Neuronal morphology affects the process of integration of inputs from other neurons and determines the neurons which receive the output of the neurons. Different parts of the neurons can operate semi-independently according to the spatial location of the synaptic connections. As a result, there is considerable interest in the analysis of the microanatomy of nervous cells since it constitutes an excellent tool for better understanding cortical function. However, the morphologies, molecular features and electrophysiological properties of neuronal cells are extremely variable. Except for some special cases, this variability makes it hard to find a set of features that unambiguously define a neuronal type. In addition, there are distinct types of neurons in particular regions of the brain. This morphological variability makes the analysis and modeling of neuronal morphology a challenge. Uncertainty is a key feature in many complex real-world problems. Probability theory provides a framework for modeling and reasoning with uncertainty. Probabilistic graphical models combine statistical theory and graph theory to provide a tool for managing domains with uncertainty. In particular, we focus on Bayesian networks, the most commonly used probabilistic graphical model. In this dissertation, we design new methods for learning Bayesian networks and apply them to the problem of modeling and analyzing morphological data from neurons. The morphology of a neuron can be quantified using a number of measurements, e.g., the length of the dendrites and the axon, the number of bifurcations, the direction of the dendrites and the axon, etc. These measurements can be modeled as discrete or continuous data. The continuous data can be linear (e.g., the length or the width of a dendrite) or directional (e.g., the direction of the axon). These data may follow complex probability distributions and may not fit any known parametric distribution. Modeling this kind of problems using hybrid Bayesian networks with discrete, linear and directional variables poses a number of challenges regarding learning from data, inference, etc. In this dissertation, we propose a method for modeling and simulating basal dendritic trees from pyramidal neurons using Bayesian networks to capture the interactions between the variables in the problem domain. A complete set of variables is measured from the dendrites, and a learning algorithm is applied to find the structure and estimate the parameters of the probability distributions included in the Bayesian networks. Then, a simulation algorithm is used to build the virtual dendrites by sampling values from the Bayesian networks, and a thorough evaluation is performed to show the model’s ability to generate realistic dendrites. In this first approach, the variables are discretized so that discrete Bayesian networks can be learned and simulated. Then, we address the problem of learning hybrid Bayesian networks with different kinds of variables. Mixtures of polynomials have been proposed as a way of representing probability densities in hybrid Bayesian networks. We present a method for learning mixtures of polynomials approximations of one-dimensional, multidimensional and conditional probability densities from data. The method is based on basis spline interpolation, where a density is approximated as a linear combination of basis splines. The proposed algorithms are evaluated using artificial datasets. We also use the proposed methods as a non-parametric density estimation technique in Bayesian network classifiers. Next, we address the problem of including directional data in Bayesian networks. These data have some special properties that rule out the use of classical statistics. Therefore, different distributions and statistics, such as the univariate von Mises and the multivariate von Mises–Fisher distributions, should be used to deal with this kind of information. In particular, we extend the naive Bayes classifier to the case where the conditional probability distributions of the predictive variables given the class follow either of these distributions. We consider the simple scenario, where only directional predictive variables are used, and the hybrid case, where discrete, Gaussian and directional distributions are mixed. The classifier decision functions and their decision surfaces are studied at length. Artificial examples are used to illustrate the behavior of the classifiers. The proposed classifiers are empirically evaluated over real datasets. We also study the problem of interneuron classification. An extensive group of experts is asked to classify a set of neurons according to their most prominent anatomical features. A web application is developed to retrieve the experts’ classifications. We compute agreement measures to analyze the consensus between the experts when classifying the neurons. Using Bayesian networks and clustering algorithms on the resulting data, we investigate the suitability of the anatomical terms and neuron types commonly used in the literature. Additionally, we apply supervised learning approaches to automatically classify interneurons using the values of their morphological measurements. Then, a methodology for building a model which captures the opinions of all the experts is presented. First, one Bayesian network is learned for each expert, and we propose an algorithm for clustering Bayesian networks corresponding to experts with similar behaviors. Then, a Bayesian network which represents the opinions of each group of experts is induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts is built. A thorough analysis of the consensus model identifies different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types can be defined by performing inference in the Bayesian multinet. These findings are used to validate the model and to gain some insights into neuron morphology. Finally, we study a classification problem where the true class label of the training instances is not known. Instead, a set of class labels is available for each instance. This is inspired by the neuron classification problem, where a group of experts is asked to individually provide a class label for each instance. We propose a novel approach for learning Bayesian networks using count vectors which represent the number of experts who selected each class label for each instance. These Bayesian networks are evaluated using artificial datasets from supervised learning problems. Resumen La morfología neuronal es una característica clave en el estudio de los circuitos cerebrales, ya que está altamente relacionada con el procesado de información y con los roles funcionales. La morfología neuronal afecta al proceso de integración de las señales de entrada y determina las neuronas que reciben las salidas de otras neuronas. Las diferentes partes de la neurona pueden operar de forma semi-independiente de acuerdo a la localización espacial de las conexiones sinápticas. Por tanto, existe un interés considerable en el análisis de la microanatomía de las células nerviosas, ya que constituye una excelente herramienta para comprender mejor el funcionamiento de la corteza cerebral. Sin embargo, las propiedades morfológicas, moleculares y electrofisiológicas de las células neuronales son extremadamente variables. Excepto en algunos casos especiales, esta variabilidad morfológica dificulta la definición de un conjunto de características que distingan claramente un tipo neuronal. Además, existen diferentes tipos de neuronas en regiones particulares del cerebro. La variabilidad neuronal hace que el análisis y el modelado de la morfología neuronal sean un importante reto científico. La incertidumbre es una propiedad clave en muchos problemas reales. La teoría de la probabilidad proporciona un marco para modelar y razonar bajo incertidumbre. Los modelos gráficos probabilísticos combinan la teoría estadística y la teoría de grafos con el objetivo de proporcionar una herramienta con la que trabajar bajo incertidumbre. En particular, nos centraremos en las redes bayesianas, el modelo más utilizado dentro de los modelos gráficos probabilísticos. En esta tesis hemos diseñado nuevos métodos para aprender redes bayesianas, inspirados por y aplicados al problema del modelado y análisis de datos morfológicos de neuronas. La morfología de una neurona puede ser cuantificada usando una serie de medidas, por ejemplo, la longitud de las dendritas y el axón, el número de bifurcaciones, la dirección de las dendritas y el axón, etc. Estas medidas pueden ser modeladas como datos continuos o discretos. A su vez, los datos continuos pueden ser lineales (por ejemplo, la longitud o la anchura de una dendrita) o direccionales (por ejemplo, la dirección del axón). Estos datos pueden llegar a seguir distribuciones de probabilidad muy complejas y pueden no ajustarse a ninguna distribución paramétrica conocida. El modelado de este tipo de problemas con redes bayesianas híbridas incluyendo variables discretas, lineales y direccionales presenta una serie de retos en relación al aprendizaje a partir de datos, la inferencia, etc. En esta tesis se propone un método para modelar y simular árboles dendríticos basales de neuronas piramidales usando redes bayesianas para capturar las interacciones entre las variables del problema. Para ello, se mide un amplio conjunto de variables de las dendritas y se aplica un algoritmo de aprendizaje con el que se aprende la estructura y se estiman los parámetros de las distribuciones de probabilidad que constituyen las redes bayesianas. Después, se usa un algoritmo de simulación para construir dendritas virtuales mediante el muestreo de valores de las redes bayesianas. Finalmente, se lleva a cabo una profunda evaluaci ón para verificar la capacidad del modelo a la hora de generar dendritas realistas. En esta primera aproximación, las variables fueron discretizadas para poder aprender y muestrear las redes bayesianas. A continuación, se aborda el problema del aprendizaje de redes bayesianas con diferentes tipos de variables. Las mixturas de polinomios constituyen un método para representar densidades de probabilidad en redes bayesianas híbridas. Presentamos un método para aprender aproximaciones de densidades unidimensionales, multidimensionales y condicionales a partir de datos utilizando mixturas de polinomios. El método se basa en interpolación con splines, que aproxima una densidad como una combinación lineal de splines. Los algoritmos propuestos se evalúan utilizando bases de datos artificiales. Además, las mixturas de polinomios son utilizadas como un método no paramétrico de estimación de densidades para clasificadores basados en redes bayesianas. Después, se estudia el problema de incluir información direccional en redes bayesianas. Este tipo de datos presenta una serie de características especiales que impiden el uso de las técnicas estadísticas clásicas. Por ello, para manejar este tipo de información se deben usar estadísticos y distribuciones de probabilidad específicos, como la distribución univariante von Mises y la distribución multivariante von Mises–Fisher. En concreto, en esta tesis extendemos el clasificador naive Bayes al caso en el que las distribuciones de probabilidad condicionada de las variables predictoras dada la clase siguen alguna de estas distribuciones. Se estudia el caso base, en el que sólo se utilizan variables direccionales, y el caso híbrido, en el que variables discretas, lineales y direccionales aparecen mezcladas. También se estudian los clasificadores desde un punto de vista teórico, derivando sus funciones de decisión y las superficies de decisión asociadas. El comportamiento de los clasificadores se ilustra utilizando bases de datos artificiales. Además, los clasificadores son evaluados empíricamente utilizando bases de datos reales. También se estudia el problema de la clasificación de interneuronas. Desarrollamos una aplicación web que permite a un grupo de expertos clasificar un conjunto de neuronas de acuerdo a sus características morfológicas más destacadas. Se utilizan medidas de concordancia para analizar el consenso entre los expertos a la hora de clasificar las neuronas. Se investiga la idoneidad de los términos anatómicos y de los tipos neuronales utilizados frecuentemente en la literatura a través del análisis de redes bayesianas y la aplicación de algoritmos de clustering. Además, se aplican técnicas de aprendizaje supervisado con el objetivo de clasificar de forma automática las interneuronas a partir de sus valores morfológicos. A continuación, se presenta una metodología para construir un modelo que captura las opiniones de todos los expertos. Primero, se genera una red bayesiana para cada experto y se propone un algoritmo para agrupar las redes bayesianas que se corresponden con expertos con comportamientos similares. Después, se induce una red bayesiana que modela la opinión de cada grupo de expertos. Por último, se construye una multired bayesiana que modela las opiniones del conjunto completo de expertos. El análisis del modelo consensuado permite identificar diferentes comportamientos entre los expertos a la hora de clasificar las neuronas. Además, permite extraer un conjunto de características morfológicas relevantes para cada uno de los tipos neuronales mediante inferencia con la multired bayesiana. Estos descubrimientos se utilizan para validar el modelo y constituyen información relevante acerca de la morfología neuronal. Por último, se estudia un problema de clasificación en el que la etiqueta de clase de los datos de entrenamiento es incierta. En cambio, disponemos de un conjunto de etiquetas para cada instancia. Este problema está inspirado en el problema de la clasificación de neuronas, en el que un grupo de expertos proporciona una etiqueta de clase para cada instancia de manera individual. Se propone un método para aprender redes bayesianas utilizando vectores de cuentas, que representan el número de expertos que seleccionan cada etiqueta de clase para cada instancia. Estas redes bayesianas se evalúan utilizando bases de datos artificiales de problemas de aprendizaje supervisado.

A game theory based strategy for reducing energy consumption in cognitive WSN

Wireless sensor networks (WSNs) are one of the most important users of wireless communication technologies in the coming years and some challenges in this area must be addressed for their complete development. Energy consumption and spectrum availability are two of the most severe constraints of WSNs due to their intrinsic nature. The introduction of cognitive capabilities into these networks has arisen to face the issue of spectrum scarcity but could be used to face energy challenges too due to their new range of communication possibilities. In this paper a new strategy based on game theory for cognitive WSNs is discussed. The presented strategy improves energy consumption by taking advantage of the new change-communication-channel capability. Based on game theory, the strategy decides when to change the transmission channel depending on the behavior of the rest of the network nodes. The strategy presented is lightweight but still has higher energy saving rates as compared to noncognitive networks and even to other strategies based on scheduled spectrum sensing. Simulations are presented for several scenarios that demonstrate energy saving rates of around 65% as compared to WSNs without cognitive techniques.

Ecología política y economía de la visibilidad de los dispositivos tecnológicos de escala urbana durante el siglo XX Abriendo la caja negra

En paralelo a la superficie cotidiana de la ciudad moderna, discurre otra "ciudad invisible" o "caja negra" tecnológica, que opera, de manera silenciosa e imperceptible, al servicio de los ciudadanos. Paradójicamente, en este espacio invisible, se toman decisiones de la máxima relevancia para la ciudad: en la "caja negra", las ciudades pactan sus relaciones con la naturaleza; a través de ella, se produce la administración y distribución de los recursos que componen su metabolismo urbano. En definitiva, la "caja negra" es el conjunto de las arquitecturas donde se urbaniza la naturaleza, donde ésta pasa a ser ciudad. Y, sin embargo, ha tendido a permanecer invisible. No obstante, en el último tercio del siglo XX, se ha "abierto la caja negra" urbana y lo que era invisible ha dejado de serlo o, al menos, ha alterado su estatuto de visibilidad. El objetivo de esta tesis doctoral es evaluar las repercusiones arquitectónicas, urbanísticas y ecológicas, que conlleva este reciente fenómeno urbano que, desde hace unas décadas, está teniendo lugar en muchas de las ciudades herederas de las grandes reformas urbanas acometidas en las metrópolis europeas durante el siglo XIX, bajo el paradigma simbólico de un proyecto moderno "prometeico" y emancipador. Para abordar dicho análisis, se pondrán en relación dos parámetros urbanos elementales que han tendido a ser estudiados por separado. Por un lado, la "ecología política urbana", es decir, la red de relaciones socio-ecológicas que acontecen en la ciudad. Por otro lado, la "economía de la visibilidad", es decir, las formas de articular lo visible y lo invisible, en relación a los marcos de gobernanza. La intersección entre la "ecología política urbana" y la "economía de la visibilidad" proporciona un marco de análisis efectivo para comprender el fenómeno de la "apertura de la caja negra" y conlleva un cuestionamiento de algunas nociones dominantes en la teoría urbana y arquitectónica clásicas, como la segregación de la naturaleza, las infraestructuras y la sociedad o como las formas tradicionales de explotación ecológica del medio. Además, ofrece un criterio de análisis privilegiado para la comprensión del proceso de deslegitimación (filosófica, arquitectónica, económica, así como desde perspectivas ecológicas, sociales, de género o queer) de los modelos urbanísticos integrales modernos, herederos de los marcos antropocéntricos del siglo XIX. Por último, proporciona algunas herramientas arquitectónicas para afrontar los desafíos ecosistémicos del siglo XXI. A través del estudio de autores relevantes que han analizado esta problemática para la arquitectura, así como del estudio de casos arquitectónicos que han marcado hitos fundamentales en la consolidación urbana de los procesos asociados a la "caja negra", se concluirá que, en términos ecológicos, la ciudad moderna ha movilizado una "ecología política urbana" basada en fórmulas de sometimiento del entorno, a partir de operaciones arquitectónicas y tecnológicas invisibles. En esta tesis doctoral se estudiará la organización arquitectónica de las arquitecturas de la "caja negra" y se evaluará si el fenómeno de la "apertura de la caja negra" puede ser considerado como un síntoma de la alteración en la "ecología política urbana". 'Abriremos la caja negra" para estudiar cómo se integran en el espacio urbano los dispositivos tecnológicos de escala urbana, toda vez éstos han dejado de ser invisibles. Cómo participan, como un actor más, en la configuración de otros marcos de cohabitación, dentro de la ciudad contemporánea. ABSTRACT An 'invisible city' or technological 'black box' runs parallel to the day-to-day surface of modern cities, remaining silent, unnoticed, at the service of the citizenry. Paradoxically, this invisible space is where some of the most relevant decisions concerning the city are made: the 'black box' is where cities agree on their relationships with nature; it is used to manage and distribute the resources that form its urban metabolism. In short, the 'black box' is the collection of architectures where nature is urbanised, where it becomes a city. And in spite of all this, it has mostly remained invisible. Nevertheless, this urban 'black box' was opened during the last third of the 20th century, so what used to be invisible is invisible no more, or at least the laws governing its visibility have been altered. The purpose of this doctoral thesis is to evaluate the architectural, urban planning and ecological repercussions of this recent urban phenomenon that has been taking place for several decades in many of the cities that followed in the footsteps of the large European metropolises of the 19th century, under the symbolic paradigm of a modern 'prometheic' and emancipating project. This analysis shall be done by juxtaposing two basic urban parameters that in general have been studied separately: frstly the ‘urban political ecology', that is, the network of socio-ecological relationships within the city. Secondly, the 'economy of visibility', that is, the way the visible and invisible spheres are structured in relation with the governance frameworks. The intersection between the 'urban political ecology' and the 'economy of visibility' provides an effective analysis framework to understand the phenomenon of the 'opening of the black box'. It entails calling into question some of the predominant notions in classical urban and architectural theory, such as the segregation of nature, infrastructures and society, or the traditional forms of ecological usage of the environment. It also offers an exceptional analysis criterion to understand the discrediting process (from a philosophical, architectural, economic perspective, but also from the point of view of ecology, society, gender or queerness) of modern all-encompassing urban models that draw from the anthropocentric frameworks of the 19th century. Finally, it provides some architectural tools to tackle 21st-century ecosystemic challenges. The study of relevant authors that have analysed these problems for architecture, as well as the study of milestone architectural cases for the urban consolidation of processes associated to the 'black box', shall serve to reach the conclusion that, in ecological terms, modern cities have mobilised an 'urban political ecology' based on formulas of subjugation of the environment, through invisible architectural and technological operations. This thesis shall present an evaluation of whether the phenomenon of the 'opening of the black box' can be considered a symptom of the alteration of the 'urban political ecology'. We shall 'open the black box' to study the integration of the various urbanscale technological devices within the urban landscape, now that they have ceased to be invisible. We shall see how they participate, like any other actor, in the confguration of other cohabitation frameworks within today's cities.