Microfluidic platform for bilayer experimatation from a research tooltowards drug screening

The aim of this thesis, which is the development of a microfluidic platform for bilayer experimentation with the potential for drug screening on ion channels, is introduced in this chapter. After a short presentation of the field of drug screening, an outline of this thesis is given, together with a brief summary of the different chapters

Flexibler Birth-Death MCMC Sampler für Changepoint-Modelle

Diese Arbeit beschreibt eine flexible Architektur eines Markov Chain Monte Carlo Samplers, der Bayessche Inferenz für eine Vielzahl von Changepoint-Modellen erlaubt. Die Struktur dieser Klasse von Modellen besteht aus zwei stochastischen Prozessen. Der erste Prozess wird entweder direkt beobachtet oder indirekt durch, möglicherweise verrauschte, Beobachtungen. Der zweite Prozess ist unbeobachtet und bestimmt die Parameter des beobachteten Prozesses. Die Hauptannahme unserer Modellklasse ist, dass der versteckte Prozess stückweise konstant ist, d.h. er springt zwischen diskreten Zuständen. Als beobachteter Prozess diskutieren wir hauptsächlich den Ornstein-Uhlenbeck und Poisson Prozess. Der versteckte Prozess kann eine feste Anzahl von Zuständen haben oder eine unbekannte Anzahl. Im zweiten Fall basiert das Modell auf einem versteckten Chinese Restaurant Prozess und ermöglicht so Bayessche Inferenz über die Anzahl der Zustände des versteckten Parameterprozesses. Der Sampler wendet einen Metropolis-Hastings Random Walk auf den versteckten Prozess an indem Birth-Death Schritte vorgeschlagen werden. Die Arbeit präsentiert unterschiedliche Modifikationen des Pfades des versteckten Prozesses. Die Struktur des Samplers ist sehr flexibel und lässt sich, im Vergleich zu anderen Algorithmen, die für ein spezifisches Modell maßgeschneidert sind, einfach an verschiedene Kombinationen von beobachteten und versteckten Prozessen anpassen. Angewandt auf Genexpressionsdaten ermöglicht der Sampler Bayessche Inferenz für komplexere Modelle als vorherige Methoden. Der berechnete Bayes Faktor deutet an, dass unser Modell, welches es erlaubt die Stärke des intrinsischen Rauschens zu variieren, die Daten besser erklärt als das vorherige Modell. Der Sampler wird für Genexpressionsdaten von Hefezellen benutzt und die Ergebnisse mit denen einer variationellen Näherung verglichen. Der Posterior scheint genauer in der Vorhersage der Aktivierungszeitpunkte der Transkriptionsfaktoren zu sein als es die Näherung zeigt. Die Ergebnisse des Chinese Restaurant Prozess Samplers auf den gleichen Messungen von Hefezellen unterstützt die vorherige Annahme über die Anzahl der Transkriptionsfaktoren, die in die Kontrolle der untersuchten Gene involviert sind. Die Anpassung des Samplers an Markov modulierte Poisson Prozesse beschleunigt die Inferenz und dies wird gezeigt, indem die Zeit zur Berechnung eines unkorrelierten Samples mit einem exakten Gibbs Sampler verglichen wird. Ein Modell, welches einen beobachteten Poisson Prozess mit dem Chinese Restaurant Prozess verbindet wird anschließend benutzt um versteckte Zustände in der Rate von neuronalen Spike-Daten zu finden und sie mit dem Stimulus zu verbinden. Die Vorteile des Modells beim finden und bestimmen von neuronalen Bursts wird diskutiert und mit Modellen verglichen, die eine kontinuierliche Poisson Rate annehmen.This thesis describes a flexible architecture for a Markov chain Monte Carlo sampler which allows Bayesian posterior inference for a variety of changepoint models. The structure of this class of models consists of two stochastic processes. The first process is either observed directly or indirectly through, possibly noisy, observations. The second process is not observed and governs the parameters of the observed process. The main assumption for our class of models is that the hidden process is piecewise constant, i.e. it jumps between discrete states. As the observed process, we discuss mainly the Ornstein-Uhlenbeck and Poisson process. The hidden process can have a fixed number of states, or an unknown number of states. The latter model is based on a hidden Chinese restaurant process and allows Bayesian inference over the number of states of the hidden parameters. The sampler applies a Metropolis-Hastings random walk on the hidden jump process through proposed birth-death moves. Different kinds of proposal moves on the path of the hidden process are presented. The structure of the sampler makes it very flexible and easy to modify to other combinations of observed and hidden processes compared to other inference methods which are tailor-made for a specific model. Applied to gene expression data the sampler allows Bayesian posterior inference on a more complex model than in previous work. We compute the Bayes factor which indicates that our model, which allows the strength of the system noise to switch, is better in explaining the data. The sampler is used on gene expression data from yeast cells and the results are compared to a variational approximation. The posterior is more confident about the times of transcriptional activity than the approximation suggests. The results from the Chinese restaurant process sampler on the same yeast dataset support the initial assumption about the number of transcription factors involved in the control of the examined genes. When the sampler is used on financial data, changepoints are revealed which can be connected to historic events. This is shown both for the Ornstein-Uhlenbeck model as well as a Cox-Ingersoll-Ross model used in a different thesis. Modifying the sampler to work on Markov modulated Poisson processes allows for very fast posterior inference and this is shown when the time to get an uncorrelated sample is compared to an exact Gibbs sampler for the model. A model combining an observed Poisson process with the Chinese restaurant process is then utilized to find hidden states in the rate of neuronal spike trains and linked to the stimulus. The model's advantages in finding and estimating bursting of neurons is discussed and compared to a model which assumes a continuous Poisson rate

Wavenet based low rate speech coding

Traditional parametric coding of speech facilitates low rate but provides poor reconstruction quality because of the inadequacy of the model used. We describe how a WaveNet generative speech model can be used to generate high quality speech from the bit stream of a standard parametric coder operating at 2.4 kb/s. We compare this parametric coder with a waveform coder based on the same generative model and show that approximating the signal waveform incurs a large rate penalty. Our experiments confirm the high performance of the WaveNet based coder and show that the speech produced by the system is able to additionally perform implicit bandwidth extension and does not significantly impair recognition of the original speaker for the human listener, even when that speaker has not been used during the training of the generative model.Comment: 5 pages, 2 figure

Brian2GeNN: accelerating spiking neural network simulations with graphics hardware

“Brian” is a popular Python-based simulator for spiking neural networks, commonly used in computational neuroscience. GeNN is a C++-based meta-compiler for accelerating spiking neural network simulations using consumer or high performance grade graphics processing units (GPUs). Here we introduce a new software package, Brian2GeNN, that connects the two systems so that users can make use of GeNN GPU acceleration when developing their models in Brian, without requiring any technical knowledge about GPUs, C++ or GeNN. The new Brian2GeNN software uses a pipeline of code generation to translate Brian scripts into C++ code that can be used as input to GeNN, and subsequently can be run on suitable NVIDIA GPU accelerators. From the user’s perspective, the entire pipeline is invoked by adding two simple lines to their Brian scripts. We have shown that using Brian2GeNN, two non-trivial models from the literature can run tens to hundreds of times faster than on CPU

Modes of innovation and responsibility within regional innovation systems:Reflections from the Twente region

Increasing public investments in distributed platform infrastructures have created new opportunities for economic growth and social welfare but at the same time have been associated with growing societal distrust in the power of science to solve societal problems. The concept of Responsible Research & Innovation has been advanced as providing mechanisms to recouple science and society to ensure that research and innovation continues to uphold its duties to society. In this paper, we explore the extent to which it is possible to identify repertoires of responsible innovation behaviour within extant research and innovation networks. We distinguish between two kinds of regional innovation network, those based on science and technology innovation, and those based on doing, using, inventing innovation in the eHealth sector where there are substantive societal concerns regarding responsibility and innovation. We contend that it appears that the coupling of patients to innovation networks through their prior association with innovators (e.g. as patients) affects the scope for responsibility. We therefore contend that more attention is required for understanding the dynamics of citizen-innovator coupling in regional innovation networks if responsibility is to become a more common property of these systems

Equation-oriented specification of neural models for simulations

Simulating biological neuronal networks is a core method of research in computational neuroscience. A full specification of such a network model includes a description of the dynamics and state changes of neurons and synapses, as well as the synaptic connectivity patterns and the initial values of all parameters. A standard approach in neuronal modeling software is to build network models based on a library of pre-defined components and mechanisms; if a model component does not yet exist, it has to be defined in a special-purpose or general low-level language and potentially be compiled and linked with the simulator. Here we propose an alternative approach that allows flexible definition of models by writing textual descriptions based on mathematical notation. We demonstrate that this approach allows the definition of a wide range of models with minimal syntax. Furthermore, such explicit model descriptions allow the generation of executable code for various target languages and devices, since the description is not tied to an implementation. Finally, this approach also has advantages for readability and reproducibility, because the model description is fully explicit, and because it can be automatically parsed and transformed into formatted descriptions. The presented approach has been implemented in the Brian2 simulator

SpineML and Brian 2.0 interfaces for using GPU enhanced Neuronal Networks (GeNN)

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