Frequency synthesizers are essential components for modern wireless and wireline communication systems as they provide the local oscillator signal required to transmit and receive data at very high rates. They are also vital for computing devices and microcontrollers as they generate the clocks required to run all the digital circuitry responsible for the high speed computations. Data rates and clocking speeds are continuously increasing to accommodate for the ever growing demand on data and computational power. This places stringent requirements on the performance metrics of frequency synthesizers. They are required to run at higher speeds, cover a wide range of frequencies, provide a low jitter/phase noise output and consume minimum power and area. In this work, we present new techniques and architectures for implementing high speed frequency synthesizers which fulfill the aforementioned requirements. We propose a new architecture and design approach for the realization of wideband millimeter-wave frequency synthesizers. This architecture uses two-step multi-order harmonic generation of a low frequency phase-locked signal to generate wideband mm-wave frequencies. A prototype of the proposed system is designed and fabricated in 90nm Complementary Metal Oxide Semiconductor (CMOS) technology. Measurement results demonstrated that a very wide tuning range of 5 to 32 GHz can be achieved, which is costly to implement using conventional techniques. Moreover the power consumption per octave resembles that of state-of-the art reports. Next, we propose the N-Push cyclic coupled ring oscillator (CCRO) architecture to implement two high performance oscillators: (1) a wideband N-Push/M-Push CCRO operating from 3.16-12.8GHz implemented by two harmonic generation operations using the availability of different phases from the CCRO, and (2) a 13-25GHz millimeter-wave N-Push CCRO with a low phase noise performance of -118dBc/Hz at 10MHz. The proposed oscillators achieve low phase noise with higher FOM than state of the art work. Finally, we present some improvement techniques applied to the performance of phase locked loops (PLLs). We present an adaptive low pass filtering technique which can reduce the reference spur of integer-N charge-pump based PLLs by around 20dB while maintaining the settling time of the original PLL. Another PLL is presented, which features very low power consumption targeting the Medical Implantable Communication Standard. It operates at 402-405 MHz while consuming 600microW from a 1V supply.

O processamento de sinais feito pelos sistemas neurais biológicos é altamente eficiente e complexo, por isso desperta grande atenção de pesquisa. Basicamente, todo o processamento de sinais funciona com base em redes de neurônios que emitem e recebem pulsos. Portanto, de forma geral, os estímulos recebidos do sistema sensorial por uma rede neural biológica de algum modo são convertidos em trens de pulsos. Aqui, nesta tese, é apresentada uma nova arquitetura composta por duas camadas: a primeira recebe correntes de estímulos de entrada e os mapeia em trens de pulsos; a segunda recebe esses trens de pulsos e os clássica em conjuntos de estímulos. Na primeira camada, a conversão de correntes de estímulos em trens de pulso é feita através de uma rede de neurônios osciladores acoplados por pulso. Esses neurônios possuem uma frequência natural de disparo e quando são agrupados em redes podem se coordenar para apresentar uma dinâmica global a longo prazo. Por sua vez, a dinâmica global também é sensível às correntes de entrada. Na segunda camada, a classificação dos trens de pulsos em conjuntos de estímulos é implementada por um neurônio do tipo integra-e-dispara. O comportamento típico para esse neurônio é de disparar ao menos uma vez para todas as integrações de trens de pulsos de uma determinada classe; caso contrário, ele deve car em silêncio. O processo de aprendizado da segunda camada depende do conhecimento do intervalo de tempo de repetição de um trem de pulsos. Portanto, nesta tese, são apresentadas métricas para definir tal intervalo de tempo, dando, assim, autonomia para a arquitetura. É possível concluir com base nos ensaios realizados que a arquitetura desenvolvida possui uma grande capacidade para mapeamento de correntes de entradas em trens de pulsos sem a necessidade de alterações na estrutura da arquitetura; também que a adição da dimensão tempo pela primeira camada ajuda na classificação realizada pela segunda. Assim, um novo modelo para realizar processos de codificação e decodificação é apresentado, desenvolvido através de séries de ensaios computacionais e caracterizado por medidas de sua dinâmica. / The signal processing done by the neural systems is highly efficient and complex, so that it attracts a large attention for research. Basically, all the signal processing functions are based on networks of neurons that send and receive spikes. Therefore, in general, the stimuli received from the sensory system by a biological neural network somehow are converted into spike trains. Here, in this thesis, we present a new architecture composed of two layers: the first layer receives streams of input stimuli and maps them on spike trains; the second layer receives these spike trains and classifies them in a sets of stimuli. In the first layer, the conversion of currents of stimuli on spike trains is made by a pulse-coupled neural network. Neurons in this context are like oscillators and have a natural frequency to shoot; when they are grouped into networks, they can be coordinated to present a global long-term dynamics. In turn, this global dynamics is also sensible to the input currents. In the second layer, the classification of spike trains in sets of stimuli is implemented by an integrate-and-re neuron. The typical behavior for this neuron is to shoot at least once every time that it receives a known spike train; otherwise, it should be in silence. The learning process of the second layer depends on the knowledge of the time interval of repetition of a spike train. Therefore, in this thesis, metrics are presented to define this time interval, thus giving autonomy to the architecture. It can be concluded on the basis of the tests developed that the architecture has a large capacity for mapping input currents on spike trains without requiring changes in its structure; moreover, the addition of the time dimension done by the first layer helps in the classification performed by the second layer. Thus, a new model to perform the encoding and decoding processes is presented, developed through a series of computational experiments and characterized by measurements of its dynamics.

O processamento de sinais feito pelos sistemas neurais biológicos é altamente eficiente e complexo, por isso desperta grande atenção de pesquisa. Basicamente, todo o processamento de sinais funciona com base em redes de neurônios que emitem e recebem pulsos. Portanto, de forma geral, os estímulos recebidos do sistema sensorial por uma rede neural biológica de algum modo são convertidos em trens de pulsos. Aqui, nesta tese, é apresentada uma nova arquitetura composta por duas camadas: a primeira recebe correntes de estímulos de entrada e os mapeia em trens de pulsos; a segunda recebe esses trens de pulsos e os clássica em conjuntos de estímulos. Na primeira camada, a conversão de correntes de estímulos em trens de pulso é feita através de uma rede de neurônios osciladores acoplados por pulso. Esses neurônios possuem uma frequência natural de disparo e quando são agrupados em redes podem se coordenar para apresentar uma dinâmica global a longo prazo. Por sua vez, a dinâmica global também é sensível às correntes de entrada. Na segunda camada, a classificação dos trens de pulsos em conjuntos de estímulos é implementada por um neurônio do tipo integra-e-dispara. O comportamento típico para esse neurônio é de disparar ao menos uma vez para todas as integrações de trens de pulsos de uma determinada classe; caso contrário, ele deve car em silêncio. O processo de aprendizado da segunda camada depende do conhecimento do intervalo de tempo de repetição de um trem de pulsos. Portanto, nesta tese, são apresentadas métricas para definir tal intervalo de tempo, dando, assim, autonomia para a arquitetura. É possível concluir com base nos ensaios realizados que a arquitetura desenvolvida possui uma grande capacidade para mapeamento de correntes de entradas em trens de pulsos sem a necessidade de alterações na estrutura da arquitetura; também que a adição da dimensão tempo pela primeira camada ajuda na classificação realizada pela segunda. Assim, um novo modelo para realizar processos de codificação e decodificação é apresentado, desenvolvido através de séries de ensaios computacionais e caracterizado por medidas de sua dinâmica. / The signal processing done by the neural systems is highly efficient and complex, so that it attracts a large attention for research. Basically, all the signal processing functions are based on networks of neurons that send and receive spikes. Therefore, in general, the stimuli received from the sensory system by a biological neural network somehow are converted into spike trains. Here, in this thesis, we present a new architecture composed of two layers: the first layer receives streams of input stimuli and maps them on spike trains; the second layer receives these spike trains and classifies them in a sets of stimuli. In the first layer, the conversion of currents of stimuli on spike trains is made by a pulse-coupled neural network. Neurons in this context are like oscillators and have a natural frequency to shoot; when they are grouped into networks, they can be coordinated to present a global long-term dynamics. In turn, this global dynamics is also sensible to the input currents. In the second layer, the classification of spike trains in sets of stimuli is implemented by an integrate-and-re neuron. The typical behavior for this neuron is to shoot at least once every time that it receives a known spike train; otherwise, it should be in silence. The learning process of the second layer depends on the knowledge of the time interval of repetition of a spike train. Therefore, in this thesis, metrics are presented to define this time interval, thus giving autonomy to the architecture. It can be concluded on the basis of the tests developed that the architecture has a large capacity for mapping input currents on spike trains without requiring changes in its structure; moreover, the addition of the time dimension done by the first layer helps in the classification performed by the second layer. Thus, a new model to perform the encoding and decoding processes is presented, developed through a series of computational experiments and characterized by measurements of its dynamics.

The task of finding the beat in music is simple for most people, but surprisingly difficult to replicate in a robot. Progress in this problem has been made using various preprocessing techniques (Hitz 2008; Tomic and Janata 2008). However, a real-time method is not yet available. Methods using a class of oscillators called relay relaxation oscillators are promising. In particular, systems of forced Hopf oscillators (Large 2000; Righetti et al. 2006) have been used with relative success. This work describes current methods of beat tracking and develops a new method that incorporates the best ideas from each existing method and removes the necessity for preprocessing.

The work we present in this thesis is a series of studies of how heterogeneities in coupling affect the synchronization of coupled neural oscillators. We begin by examining how heterogeneity in coupling strength affects the equilibrium phase difference of a pair of coupled, spiking neurons when compared to the case of identical coupling. This study is performed using pairs of Hodgkin-Huxley and Wang-Buzsaki neurons. We find that heterogeneity in coupling strength breaks the symmetry of the bifurcation diagrams of equilibrium phase difference versus the synaptic rate constant for weakly coupled pairs of neurons. We observe important qualitative changes such as the loss of the ubiquitous in-phase and anti-phase solutions found when the coupling is identical and regions of parameter space where no phase locked solution exists. Another type of heterogeneity can be found by having different types of coupling between oscillators. Synaptic coupling between neurons can either be exciting or inhibiting. We examine the synchronization dynamics when a pair of neurons is coupled with one excitatory and one inhibitory synapse. We also use coupled pairs of Hodgkin-Huxley neurons and Wang-Buzsaki neurons for this work. We then explore the existance of 1:n coupled states for a coupled pair of theta neurons. We do this in order to reproduce an observed effect called quantal slowing. Quantal slowing is the phenomena where jumping between different $1:n$ coupled states is observed instead of gradual changes in period as a parameter in the system is varied. All of these topics fall under the general heading of coupled, non-linear oscillators and specifically weakly coupled, neural oscillators. The audience for this thesis is most likely going to be a mixed crowd as the research reported herein is interdisciplinary. Choosing the content for the introduction proved far more challenging than expected. It might be impossible to write a maximally useful introductory portion of a thesis when it could be read by a physicist, mathematician, engineer or biologist. Undoubtedly readers will find some portion of this introduction elementary. At the risk of boring some or all of my readers we decided it was best to proceed so that enough of the mathematical (biological) background is explained in the introduction so that a biologist (mathematician) is able to appreciate the motivations for the research and the results presented. We begin with a introduction in nonlinear dynamics explaining the mathematical tools we use to characterize the excitability of individual neurons, as well as oscillations and synchrony in neural networks. The next part of the introductory material is an overview of the biology of neurons. We then describe the neuron models used in this work and finally describe the techniques we employ to study coupled neurons.

Les propriétés acoustiques des bulles sont reconnues pour leur potentiel dans des applications tant biologiques que médicales. Capables de provoquer la lyse des cellules en générant des écoulements intenses, elles peuvent aussi servir d'agent de contraste en échographie.Ce manuscrit traite de la dynamique de vibration de bulles confinées entre les deux murs d'un canal microfluidique. Ces bulles exhibent une pulsation en volume aux faibles amplitudes d'excitation, à laquelle se superpose un mode de surface paramétrique aux plus fortes amplitudes. Le matériau constituant le canal étant élastique, la pulsation de la bulle confinée a pour effet de générer des ondes de Rayleigh sur les parois du canal. Grâce à ces ondes de surface, les bulles vont pouvoir se coupler les unes aux autres. Ce couplage a un effet sur les écoulements hydrodynamiques autour de ces bulles. En effet, la présence d'une bulle voisine engendre l'apparition d'un mode de translation de la bulle qui, couplé à sa pulsation en volume, conduira à la génération d'écoulements à longue portée. Ce même couplage permet aux bulles de s'auto-organiser en réseau. Afin d'étudier de manière contrôlée les effets collectifs des bulles, leur position a été fixée à l'aide de puits capillaires. Les conditions d'amplification et de synchronisation de la vibration des bulles sont recherchées en vue de créer de nouveaux méta-matériaux. / The pulsation properties of air bubbles under ultrasound have received much attention since the development of sonoporation and contrast agents. Spherical bubbles are well known to induce streaming when excited by ultrasound.We report in this manuscript the acoustic vibration of microbubbles confined between the two walls of a microfluidic channel. These bubbles exhibit a volumetric pulsation at low intensities of ultrasound, superimposed with a parametric surface mode for higher intensities of the pressure field. Because the channel walls are elastic, the bubble pulsation leads to the generation of Rayleigh waves at the channel wall interface. The bubble coupling induced by these surface waves has hydrodynamic consequences. In fact, a neighbouring bubble will create a translation mode of the bubble, in addition to its volumetric pulsation. It gives rise to a long-range mixed-mode streaming. The Rayleigh waves lead also to a self-organization of the bubbles in a network. In order to study the collective effects of these bubble networks in a controlled manner, bubble positions were fixed by capillarity on micropits. Conditions for an amplification or a synchronization of the bubble pulsations are sought in order to develop new bubble metamaterials.

Le but de cette thèse est d'étudier d'une façon expérimentale et théorique le comportement synchrone d'un groupe d'oscillateurs contrôlés par la lumière (LCOs). Ces LCOs sont très simples du point de vue électronique et ont la propriété d'imiter le comportement des lucioles puisqu'ils interagissent par des impulsions de lumière. En même temps, les LCOs sont une bonne approche pour étudier d'autres systèmes qui agissent comme des oscillateurs d'intégration et de tir car un LCO est un oscillateur de relaxation à deux échelles de temps :un long processus de charge alterné avec un très court processus de décharge. Une série d'expériences a été menée pour pouvoir comprendre le processus de synchronisation des LCOs. Nous avons trouvé que l'acquisition de la synchronisation est due aux effets de la perturbation à savoir: le raccourcissement de la charge et l'allongement de la décharge. Les mesures expérimentales ainsi que la physique liée aux LCOs nous ont permis de formuler un modèle qui a été utilisé pour trouver d'une façon analytique la courbe de réponse de phase (PRC) qui caractérise un LCO.<p><p>Le modèle a ensuite été validé en comparant les résultats expérimentaux et théoriques. Le modèle reproduit même, le phénomène de bifurcation qui apparaît lorsque trois LCOs sont couplés et disposés en ligne :deux états stables différents apparaissent selon les conditions initiales. L'accord trouvé entre théorie et expérience nous permet d'utiliser le modèle pour étudier d'autres situations qui ne sont pas facilement abordables du point de vue expérimental.<p><p>Nous avons étudié analytiquement deux LCOs identiques couplés. Même pour ce cas idéal, nous étions obligés de faire des simplifications pour pouvoir trouver des solutions exactes. On a trouvé pour ce système deux états possibles qui dépendent des conditions initiales, la synchronisation (stable) et l'anti-synchronisation (instable). Nous avons également montré que le temps de synchronisation augmente avec la distance entre LCOs. La construction des langues d'Arnold (régions de synchronisation) nous a permis de distinguer des régions de synchronisation pure d'ordre n:m et des régions de superposition synchronisation--modulation.<p><p>Nous avons travaillé numériquement avec des systèmes de LCOs affectés de bruits uniforme et Gaussien. Le comportement synchrone de ce système a été caractérisé en utilisant des paramètres statistiques simples tels que la moyenne de la différence de phase linéaire et la variance de la différence de phase cyclique. Nous avons démontré que le bruit, bien qu'il puisse perturber la synchronisation, peut aussi la favoriser entre deux LCOs qui ne se synchroniseraient pas en conditions normales, surtout quand le bruit est Gaussien et que les variances du bruit ne sont pas égales.<p><p>Nous avons étudié en termes statistiques la synchronisation de LCOs couplés localement et arrangés en ligne, en anneau et en réseau. Nous avons montré que la synchronisation totale se produit plus facilement pour des LCOs disposés en anneau. Concernant le temps de synchronisation, il est imprédictible. Les résultats analytiques et numériques suggèrent que la synchronisation totale est le phénomène le plus probable quand le nombre d'oscillateurs n'est pas très grand.<p><p>Finalement, nous avons étudié des LCOs statiques et mobiles couplés globalement. Dans les deux cas, nous avons trouvé que la synchronisation est moins probable quand le nombre d'oscillateurs augmente. Pour la condition statique, en considérant un couplage du type champ moyen, nous avons observé que le temps de synchronisation diminue avec le nombre de LCOs. Cependant, pour la situation plus réaliste dans laquelle l'interaction entre LCOs dépend de la distance les séparant, le temps de synchronisation devient à nouveau imprédictible. Enfin, nous avons étudié l'influence de la mobilité sur la synchronisation, problème qui est important en biologie et en robotique.<p><p>Notre système, de par ses caractéristiques et sa base expérimentale, est beaucoup plus proche de la réalité que ceux considérés d'habitude dans la littérature. Les résultats obtenus peuvent s'appliquer à des systèmes biologiques (lucioles, cellules cardiaques, neurones, …), mais également à la robotique, où la communication à longue portée par la lumière et l'émergence de patterns de synchronisation pourraient être très utiles dans le but d'effectuer des tâches spécifiques. / Doctorat en sciences, Spécialisation physique / info:eu-repo/semantics/nonPublished

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An innovative mathematical architecture for modelling neuronal electrical activity is presented, called the cognitive rhythm generator (CRG), wherein the proposed architecture is a hybrid model comprised of three interconnected stages, namely: (1) a bank of neuronal modes; (2) a ring device (limit-cycle oscillator); and (3) a static output nonlinearity (mapper). Coupled CRG networks are employed to emulate and elucidate the dynamics of biological neural networks, including the recurrent networks in the hippocampus. Several species of ring devices are described and investigated, including the clock, labile clock, hourglass and multistable ring systems, and their applications to neuronal modelling explored. Complexity measures such as the maximum Lyapunov exponent, correlation dimension and detrended fluctuation analysis are applied to compare model and biological records and validate the CRG methodology. The basis of neural coding is also examined in mathematical detail, with particular regard to its description by Volterra-Wiener kernel formalism, from which the neuronal modes are derived. Applications to theta-gamma coding are discussed. Further on in the thesis, a CRG epileptiform network model of spontaneous seizure-like events (SLEs) is developed and used as a platform to test neuromodulation approaches for seizure abatement. (Neuromodulation mentioned here refers to methods involving electrical stimulation of neural tissue for therapeutic benefit). Spontaneous SLE transitions in the epileptiform network are shown to be related to the mechanism of intermittency, as determined by examining the state space dynamics of the model. The onset of SLEs is associated with increased network excitability and decreased stability, consistent with experimental results from the low-magnesium/high-potassium in vitro model of epilepsy. Lastly, a novel strategy for therapeutic neuromodulation is presented wherein a coupled CRG network (called the “therapeutic network”) is interfaced with the epileptiform network model, forming a closed loop for responsive, biomimetic neuromodulation of the epileptiform network. Relevance to clinical applications and future work is discussed.

An innovative mathematical architecture for modelling neuronal electrical activity is presented, called the cognitive rhythm generator (CRG), wherein the proposed architecture is a hybrid model comprised of three interconnected stages, namely: (1) a bank of neuronal modes; (2) a ring device (limit-cycle oscillator); and (3) a static output nonlinearity (mapper). Coupled CRG networks are employed to emulate and elucidate the dynamics of biological neural networks, including the recurrent networks in the hippocampus. Several species of ring devices are described and investigated, including the clock, labile clock, hourglass and multistable ring systems, and their applications to neuronal modelling explored. Complexity measures such as the maximum Lyapunov exponent, correlation dimension and detrended fluctuation analysis are applied to compare model and biological records and validate the CRG methodology. The basis of neural coding is also examined in mathematical detail, with particular regard to its description by Volterra-Wiener kernel formalism, from which the neuronal modes are derived. Applications to theta-gamma coding are discussed. Further on in the thesis, a CRG epileptiform network model of spontaneous seizure-like events (SLEs) is developed and used as a platform to test neuromodulation approaches for seizure abatement. (Neuromodulation mentioned here refers to methods involving electrical stimulation of neural tissue for therapeutic benefit). Spontaneous SLE transitions in the epileptiform network are shown to be related to the mechanism of intermittency, as determined by examining the state space dynamics of the model. The onset of SLEs is associated with increased network excitability and decreased stability, consistent with experimental results from the low-magnesium/high-potassium in vitro model of epilepsy. Lastly, a novel strategy for therapeutic neuromodulation is presented wherein a coupled CRG network (called the “therapeutic network”) is interfaced with the epileptiform network model, forming a closed loop for responsive, biomimetic neuromodulation of the epileptiform network. Relevance to clinical applications and future work is discussed.