Annotation-based storage and retrieval of models and simulation descriptions in computational biology

This work aimed at enhancing reuse of computational biology models by identifying and formalizing relevant meta-information. One type of meta-information investigated in this thesis is experiment-related meta-information attached to a model, which is necessary to accurately recreate simulations. The main results are: a detailed concept for model annotation, a proposed format for the encoding of simulation experiment setups, a storage solution for standardized model representations and the development of a retrieval concept.Die vorliegende Arbeit widmete sich der besseren Wiederverwendung biologischer Simulationsmodelle. Ziele waren die Identifikation und Formalisierung relevanter Modell-Meta-Informationen, sowie die Entwicklung geeigneter Modellspeicherungs- und Modellretrieval-Konzepte. Wichtigste Ergebnisse der Arbeit sind ein detailliertes Modellannotationskonzept, ein Formatvorschlag für standardisierte Kodierung von Simulationsexperimenten in XML, eine Speicherlösung für Modellrepräsentationen sowie ein Retrieval-Konzept

Biophysics-based modeling and data analysis of local field potential signal

Understanding the neurophysiological mechanisms of information processing within and across brain regions has always been a fundamental and challenging topic in neuroscience. Considerable works in the brain connectome and transcriptome have laid a profound foundation for understanding brain function by its structure. At the same time, the recent advance in recording techniques allows us to probe the nonstationary brain activity from various spatial and temporal scales. However, how to effectively build the dialogue between the anatomical structure and the dynamical brain signal still needs to be solved. To tackle the problem, we explore interpreting electrophysiology signals with mechanistic models. In chapter 2 we first segregate high-coherent brain signals into different pathways and then connect their dynamics to synaptic properties. Based on a state space model of LFP generation, we explore several preprocessing methods to bias the signal to the synaptic inputs and enhance the separatability of pathway-specific contributions. The separated sources are more reliable with the preprocessing methods, especially in highly coherent states, e.g., awake running. With reliably separated pathways, we further studied their synaptic properties and explored the local directional connections in the hippocampus. The estimated synaptic time constant and pathway connection agrees with well-established anatomical studies. In chapter 3 we explore establishing a simple model to capture the impulse response of passive neurons with detailed dendritic morphology. We validate Green’s function methods based on compartmentalized models by comparing them to numerical simulations and analytical solutions on continuous neuron membrane potentials. A parameterized model based on laminar Green’s function is further developed and helps to infer the anatomical properties, like the input current distribution and cell position, from their spatiotemporal response patterns. The effect of cell position and template are examed. Based on the model of chapter 3, we use the biophysical possible impulse response profile to regularize the source separation in the frequency domain in chapter 4. The components from different frequencies are clustered according to the same latent input distributions. The source separation in better-separated frequency bins from the same pathway helps separation in other highly contaminated frequencies. The optimization is formulated as a probabilistic model to maximize the negentropy as well as spatial likelihood. Similar to dipole approximation for EEG signals, Green’s function method provides an effective approximation to capture biologically possible spatiotemporal patterns and helps to guide the separation. We validated the method on real data with optogenetic stimulation. In chapter 5 we further separate the far-field signals from the local pathway activities according to their physiological properties. We propose a pipeline to reliably separate and automatically detect far-field signal components. Based on this, a toolbox is provided to remove the EMG artifacts and assess the cleaning performance. In the free-running animals, we show that EMG artifacts shadow the high-frequency oscillatory events detection, and EMG cleaning rescues this effect. Overall, this thesis explored multiple possibilities to incorporate neurophysiology knowledge to understand and model the electrical field potential signals.Das Verständnis der neurophysiologischen Mechanismen der Informationsverarbeitung innerhalb und zwischen Gehirnregionen war schon immer ein grundlegendes und herausforderndes Thema in den Neurowissenschaften. Weitreichende Arbeiten zum Konnektom und Transkriptom des Gehirns haben eine Grundlage für das Verständnis der Gehirnfunktion gelegt. Des Weiteren ermöglicht uns der derzeitige Fortschritt in der Aufnahmetechnik, die nicht stationäre Gehirnaktivität auf verschiedenen räumlichen und zeitlichen Skalen zu untersuchen. Wie jedoch die anatomischen Strukturen und die dynamischen Gehirnsignal effektiv zusammen wirken können, muss jedoch noch gelöst werden. Um dieses Problem anzugehen, untersuchen wir die Interpretation elektrophysiologischer Signale mit mechanistischen Modellen. In Kapitel 2 trennen wir zunächst die hochkohärenten Gehirnsignale in verschiedene Leitungsbahnen und verbinden dann die Dynamik mit synaptischen Eigenschaften. Basierend auf einem Zustandsraummodell zur Erzeugung lokaler Feldpotentiale (LFP) untersuchen wir verschiedene Vorverarbeitungsmethoden, die die Signale bestmöglich auf die synaptischen Eingangsströme ausrichten und die Trennbarkeit von leitungsbahnspezifischen Beiträgen verbessert. Die Trennung der Signalquellen ist durch das Vorverarbeitungsverfahren insbesondere während hochkohärenter Verhaltenszustände (z. B. laufen im Wachzustand) zuverlässiger. Mit zuverlässig getrennten Leitungsbahnen konnten wir die entsprechenden synaptischen Eigenschaften weiter untersuchen und die lokalen gerichteten Verbindungen im Hippocampus untersuchen. Die geschätzte synaptische Zeitkonstante und die Verbindungen der Leitungsbahnen stimmen mit etablierten anatomischen Studien überein. In Kapitel 3 untersuchen wir die Erstellung eines einfachen Modells zur Beschreibung der Impulsantwort passiver Neuronen mit detaillierter dendritischer Morphologie. Wir validieren Greensche Funktionsmethoden basierend auf kompartimentierten Modellen, indem wir sie mit numerischen Simulationen und analytischen Lösungen des kontinuierlichen Membranpotentials von Neuronen vergleichen. Ein parametrisiertes Modell, das auf der laminaren Greenschen Funktion basiert, wird weiterentwickelt. Es hilft dabei, die anatomischen Eigenschaften - die Verteilung des Eingangsstroms und die Zellposition - aus ihren raumzeitlichen Reaktionsmustern abzuleiten. Die Auswirkung der Zellposition und des Templates werden untersucht. Basierend auf dem Modell aus Kapitel 3 verwenden wir in Kapitel 4 das biophysikalisch mögliche Profil der Impulsantwort, um die Quellentrennung im Frequenzbereich festzulegen. Die Komponenten verschiedener Frequenzen werden nach derselben latenten Eingangsverteilungen geclustert. Die Quellentrennung in besser getrennten Frequenzbereichen derselben Leitungsbahn hilft bei der Quelltrennung in anderen stark kontaminierten Frequenzbereichen. Die Optimierung wird als probabilistisches Modell formuliert, um sowohl die Negentropie als auch die räumliche Wahrscheinlichkeit zu maximieren. Ähnlich wie die Dipolnäherungen für EEG-Signale bietet die Greensche Funktionsmethode eine effektive Annäherung, um biologisch mögliche raumzeitliche Muster zu erfassen, und hilft, die Quellen zu trennen. Wir haben die Methode an realen Daten mit optogenetischer Stimulation validiert. Im Kapitel 5 trennen wir weiter die Fernfeldsignale von den Signalen der lokalen Leitungsbahnen nach ihren physiologischen Eigenschaften. Wir schlagen eine Methode vor, die es erlaubt, Fernfeld-Signalkomponenten zuverlässig von lokaler Aktivitaet zu trennen und automatisch zu erkennen. Es wird eine Toolbox bereitgestellt, die EMG-Artefakte entfernt und die bereinigten Signale bewertet. In Ableitungen von freilaufenden Tieren zeigen wir, dass EMG-Artefakte die Erkennung von hochfrequenten Oszillationen beeintraechtigt, aber nach der Bereinigung des EMG-Signals erkannt werden kann. Insgesamt untersucht diese Dissertation mehrere Möglichkeiten die elektrischen Feldpotentiale neuronaler Aktivität unter Einbeziehung neurophysiologischen Wissens zu modellieren und zu verstehen

Work Toward a Theory of Brain Function

This dissertation reports research from 1971 to the present, performed in three parts. The first part arose from unilateral electrical stimulation of motivational/reward pathways in the lateral hypothalamus and brain stem of “split-brain” cats, in which the great cerebral commissures were surgically divided. This showed that motivation systems in split-brain animals exert joint influence upon learning in both of the divided cerebral hemispheres, in contrast to the separation of cognitive functions produced by commissurotomy. However, attempts to identify separate signatures of electrocortical activity associated with the diffuse motivational/alerting effects and those of the cortically lateralised processes failed to achieve this goal, and showed that an adequate model of cerebral information processing was lacking. The second part describes how this recognition of inadequacy led into computer simulations of large populations of cortical neurons – work which slowly led my colleagues and me to successful explanations of mechanisms for cortical synchrony and oscillation, and of evoked potentials and the global EEG. These results complemented the work of overseas groups led by Nunez, by Freeman, by Lopes da Silva and others, but also differed from the directions taken by these workers in certain important respects. It became possible to conceive of information transfer in the active cortex as a series of punctuated synchronous equilibria of signal exchange among cortical neurons – equilibria reached repeatedly, with sequential perturbations of the neural activity away from equilibrium caused by exogenous inputs and endogenous pulse-bursting, thus forming a basis for cognitive sequences. The third part reports how the explanation of synchrony gave rise to a new theory of the regulation of embryonic cortical growth and the emergence of mature functional connections. This work was based upon very different assumptions, and reaches very different conclusions, to that of pioneers of the field such as Hubel and Wiesel, whose ideas have dominated cortical physiology for more than fifty years. In conclusion, findings from all the stages of this research are linked together, to show they provide a sketch of the working brain, fitting within and helping to unify wider contemporary concepts of brain function

Neutral coding - A report based on an NRP work session

Neural coding by impulses and trains on single and multiple channels, and representation of information in nonimpulse carrier

Bone remodeling simulations: challenges, problems and applications

La remodelación ósea es el mecanismo que regula la relación entre la morfología del hueso y sus cargas mecánicas externas. Se basa en el hecho de que el hueso se adapta a las condiciones mecánicas a las que está expuesto. Varios factores mecánicos y bioquímicos pueden regular la respuesta final de la remodelación ósea. De hecho, se considera que la remodelación ósea pretende alcanzar varios objetivos mecánicos: reparar el daño para reducir el riesgo de fractura y optimizar la rigidez y resistencia con el mínimo peso. Durante las últimas décadas, se han propuesto un gran número de leyes matemáticas implementadas numéricamente, pero la mayoría de ellas presentan diferentes problemas como la estabilidad, la convergencia o la dependencia de las condiciones iniciales. Por tanto, el objetivo principal de esta tesis es estudiar los modelos de remodelación ósea, mostrando sus retos, su problemática y su aplicación en el ámbito clínico. En primer lugar, se han revisado dos teorías clásicas de la remodelación ósea (conocidas como modelo de Stanford y modelo de Doblaré y García). En ambos casos, se propone un aspecto novedoso planteando que el estímulo homeostático de referencia no es constante, sino que depende localmente de la historia de carga que cada punto local está soportando. Como consecuencia directa de esta hipótesis, se demuestra que las inestabilidades numéricas que normalmente presentan estos algoritmos, pueden quedar resueltas, mejorando claramente los resultados finales. Esta metodología se aplicó a un modelo de elementos finitos 2D/3D mejorando la convergencia de la solución y asegurando su estabilidad numérica a largo plazo. Por otra parte, en un intento de dilucidar las características de adaptación mecánica del hueso en diferentes escalas, se plantea una relación a nivel órgano y a nivel de tejido que depende de un cambio en el estímulo homeostático de referencia acorde con la densidad aparente, mientras que se considera que la densidad de energía de deformación a nivel de tejido permanece invariante. Esta hipótesis mejora la unicidad de la solución y la hace independiente de las condiciones iniciales, ayudando también a su estabilidad numérica. Además, en esta tesis se aborda el modelado de paciente específico que es un tema que está adquiriendo cada vez más importancia. Una de las principales dificultades en la creación de modelos de paciente específico, es la determinación de las cargas que el hueso está realmente soportando. Los datos relativos a pacientes específicos, como la geometría ósea y la distribución de la densidad ósea, puede ser utilizados para determinar estas cargas. Por lo tanto, se ha estudiado la estimación de la cargas con tres diferentes técnicas matemáticas: regresión lineal, redes neuronales artificiales y máquinas de soporte vector. Estas técnicas se han aplicado a un ejemplo teórico para obtener las cargas a través de la densidad aparente que se predice con los modelos de remodelación ósea. Para concluir, la metodología desarrollada que combina modelos de remodelación ósea con redes neuronales se ha aplicado a la predicción de las cargas de cinco tibias de pacientes. Para ello, se han determinado la geometría y la distribución de la densidad a partir de un TAC y se han introducido los valores de densidad en el modelo previamente desarrollado, obteniendo así, las cargas específicas de las tibias de los pacientes. Con el fin de validar la capacidad de esta novedosa técnica, se han comparado las cargas obtenidas de la técnica propuesta con las cargas obtenidas en un análisis de marcha de dichos pacientes. Los errores obtenidos en las predicciones han sido menores de un 6 %. Por lo tanto, se puede concluir que la metodología aquí propuesta, permite determinar de forma aproximada las cargas que un hueso específico soporta.Bone remodeling is the mechanism that regulates the relationship between bone morphology and its external mechanical loads. It is based on the fact that bone adapts itself to the mechanical conditions to which it is exposed. Several mechanical and biochemical factors may regulate the final bone remodeling response. In fact, bone remodeling is hypothesized to achieve several mechanical objectives: repair damage to reduce the risk of fracture and optimize stiffness and strength with minimum weight. During recent decades, a great number of numerically implemented mathematical laws have been proposed, but most of them present different problems as stability, convergence or dependence of the initial conditions. Thus, the main scope of this Thesis is to study bone remodeling models, showing their challenges, their problematic and their applicability in the clinical setting. Firstly, we revisit two classical bone remodeling theories (Stanford model and Doblaré and García model). In both of them, the reference homeostatic stimulus is hypothesized that is not constant, but it is locally dependent on the loading history that each local point is effectively supporting. As a direct consequence of this assumption, we demonstrate that the numerical instabilities that all these algorithms normally present can be solved, clearly improving the final results. For this reason, we applied this methodology to 2D/3D finite element models. This contribution improves the convergence of the solution, leading to its numerical stability in the long-term. In an attempt to elucidate the features of bone adaptation at the di erent scales, we hypothesize that the relationship between the organ level and tissue level depends on the reference homeostatic stimulus changes according to the density and the tissue effective energy remains unchanged. This assumption improves the uniqueness of the solution, independently of the initial conditions selected and clearly helps in its numerical stability. In addition, patient-specific modeling is becoming increasingly important. One of the most challenging diffculties in creating patient-specific models is the determination of the specific load that the bone is really supporting. Real information related to specific patients, such as bone geometry and bone density distribution, can be used to determine patient loads. Therefore, we studied three different mathematical techniques: linear regression, artificial neural networks (ANN) and support vector machines (SVM). These techniques have been applied to a theoretical femur to obtain the load through the density that came from many bone remodeling simulations. Finally, the application of this novel methodology has been applied for the loading prediction of five real tibias. We are able to determine the subject-specific forces from CT data, from which we obtain bone geometry and density distribuviition of the five tibias. Then, the density values at certain bone regions have been introduced in the methodology developed that combines bone remodeling models and artificial neuronal networks (ANN) for obtaining the predicted subject-specific loads. Finally, in order to validate this novel technique for tibia loading predictions, we compare predicted loads with the loads obtained from the patientspecific musculoskeletal model. The errors between both loads were lower tan 6%. Therefore, the methodology proposed has been validate

The Translocal Event and the Polyrhythmic Diagram

This thesis identifies and analyses the key creative protocols in translocal performance practice, and ends with suggestions for new forms of transversal live and mediated performance practice, informed by theory. It argues that ontologies of emergence in dynamic systems nourish contemporary practice in the digital arts. Feedback in self-organised, recursive systems and organisms elicit change, and change transforms. The arguments trace concepts from chaos and complexity theory to virtual multiplicity, relationality, intuition and individuation (in the work of Bergson, Deleuze, Guattari, Simondon, Massumi, and other process theorists). It then examines the intersection of methodologies in philosophy, science and art and the radical contingencies implicit in the technicity of real-time, collaborative composition. Simultaneous forces or tendencies such as perception/memory, content/ expression and instinct/intellect produce composites (experience, meaning, and intuition- respectively) that affect the sensation of interplay. The translocal event is itself a diagram - an interstice between the forces of the local and the global, between the tendencies of the individual and the collective. The translocal is a point of reference for exploring the distribution of affect, parameters of control and emergent aesthetics. Translocal interplay, enabled by digital technologies and network protocols, is ontogenetic and autopoietic; diagrammatic and synaesthetic; intuitive and transductive. KeyWorx is a software application developed for realtime, distributed, multimodal media processing. As a technological tool created by artists, KeyWorx supports this intuitive type of creative experience: a real-time, translocal “jamming” that transduces the lived experience of a “biogram,” a synaesthetic hinge-dimension. The emerging aesthetics are processual – intuitive, diagrammatic and transversal

Economic indicators used for EU projects, in other criteria of aggregation than national / regional

Economical and social indicators are created and published for national and regional dimensions. Nowadays, both local and territorial indicators are really able to define more adequate the stage of social and economical development and to illustrate the impact of European programs and projects in fields like: long lasting development, entrepreneurial development, scientific research development and strategies, education and learning resources, IT resources, dissemination of European culture etc. If in the first part, there is only quantitative information, offered by our National Institute of Statistics (NIS), in the following few examples of some useful economical and social indicators provide a dynamic vision in defining objectives, methods and implementation Thus the need for a quantitative framework of local and territorial indicators demands for an original statistical methodology.gross domestic product, indicators in macro, mezo and micro economics, weight of selected, factors, representative methodology

Considerations in designing a cybernetic simple 'learning' model; and an overview of the problem of modelling learning

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Learning is viewed as a central feature of living systems and must be manifested in any artifact that claims to exhibit general intelligence. The central aims of the thesis are twofold: (1) - To review and critically assess the empirical and theoretical aspects of learning as have been addressed in a multitude of disciplines, with the aim of extracting fundamental features and elements. (2) - To develop a more systematic approach to the cybernetic modelling of learning than has been achieved hitherto. In pursuit of aim (1) above the following discussions are included: Historical and Philosophical backgrounds; Natural learning, both physiological and psychological aspects; Hierarchies of learning identified in the evolutionary, functional and developmental senses; An extensive section on the general problem of modelling of learning and the formal tools, is included as a link between aims (1) and (2). Following this a systematic and historically oriented study of cybernetic and other related approaches to the problem of modelling of learning is presented. This then leads to the development of a state-of-the-art general purpose experimental cybernetic learning model. The programming and use of this model is also fully described, including an elaborate scheme for the manifestation of simple learning

Memory retrieval in balanced neural networks with dynamical synapses

Neuronal recordings from animals performing memory tasks have revealed a phenomenon known as selective persistent activity: the presentation of stimuli to be remembered increase the level of activity of selective (i.e., depending on the specific stimulus) neuronal populations which then persist long after stimulus offset. Such persistent activity is considered to be a major neuronal correlate of short-term memory. A time-honored theoretical account of persistent activity is the attrac- tor hypothesis, originated from the first studies of spin-glass inspired neural network models [1]. According to this hypothesis, the neurons within the selective populations have strong recurrent excitatory couplings. The resulting positive feedback, together with the the nonlinearity of the single-cell response function, allows such populations to have two stable states of activity: one corresponding to the spontaneous activity, the other (at higher rate) corresponding to the mnemonic retention of the stimulus. Since the neuron response function is typically S-shaped, and the recurrent input is a linear function of the activity, the stable states generically occur outside the dynamic range of the neuron function, that is near extremely low or high activity levels. This constitutes a major inconsistency of the model, as experimental data show that the activity level at which cortical neurons operate is much lower than saturation. It is well established that synapses display activity-dependent modulations of their efficacy, like short-term depression(STD): whenever a neuron stays active, the intensity of the signals transmitted through its synapses is gradually reduced[2]. In the thesis we examined the possibility to obtain bistability far from saturation by making the recurrent excitatory inputs a non-linear function of the activity through STD. The study have been carried out within the framework of balanced networks [3] as it captures essential features of cortical networks activity while remaining analytically tractable. The first chapter, after a short summary of the relevant neurophysiological background, is dedicated to review the standard balanced network model of binary neurons and its general properties. In the second chapter, it is illustrated a possible extension of balanced networks to the attractor framework: reinforcing suitably the excitatory couplings between the neurons of certain populations, the system can have multiple stable attractors corresponding to memory states. This allows us to illustrate the issue of unrealistically high level of activity in memory states. The remaining chapters contain the original part of the work. In the third chapter, the phenomenological model which mimics STD is introduced. As a first step, it is considered a balanced network endowed with STD in absence of memory-supporting reinforcement of the couplings. A mean-field description have been derived to characterize the stationary states of the network; this has been done by neglecting the time delayed autocorrelations of the neuron’s activities, which have been approximated as Markov process to obtain a system of equations in closed form. Numerical simulations of the network show that, despite the approximation, the meanfield theory gives an excellent quantitative prediction for the system’s order parameters. The content of the fourth chapter is the implementation of the STD synaptic dynamics into the memory model introduced in chapter two and the extension of the mean-field theory to the scenario with multiple memory states. The theoretical analysis of the model shows that it’s indeed possible to produce stable states with biologically plausible levels of activity, far from saturation, and network simulations confirm the result. Moreover, the network operates in a regime such that temporal fluctuations and spatial inhomogeneities of the neuron’s activity are generated by the dynamics (without the addition of any source of external noise), reproducing the experimentally observed statistics of neural activity