Low frequency oscillations are detrimental to the goals of maximum power transfer and optimal power system security. A contemporary solution to this problem is the addition of power system stabilizers to the automatic voltage regulators on the generators in the power system. The damping provided by this additional stabilizer provides the means to reduce the inhibiting effects of the oscillations. This thesis is an investigation of the use of synchronized phasor measurements as input signals for power system stabilizers installed on the generators of a two-area, 4-machine test power system. A remote measurement feedback controller has been designed and placed in the test power system. Synchronized phasor measurements from optimally sited measurement units were shown to improve the damping of low-frequency inter-area oscillations present in the test system when the proposed controller was included in the generator feedback control loop. The benefit of the damping of these oscillations was evident through the ability to increase the tie-line power flowing in the test system once the proposed control scheme was implemented. Time-domain simulations were used to verify the robustness of the proposed control during severe events, such as a short- circuit or sudden large variations of load. / Master of Science

The wake human brain constantly samples perceptual information from the environment and integrates them into existing neuronal networks. Neuronal oscillations have been ascribed a key role in the formation of novel memories. The theta rhythm (3-8 Hz) reflects a central executive mechanism, which integrates novel information, reflected in theta-coupled gamma oscillations (> 30 Hz). Alpha oscillations (8-14 Hz) reflect an attentional gating mechanism, which inhibit task irrelevant neuronal processes. In my dissertation I further scrutinized the oscillatory dynamics of memory formation. Study 1 demonstrated that theta-gamma coupling reflects a specific mechanism for associative memory formation. In study 2, I experimentally entrained memory encoding by visual evoked theta-gamma coupling processes, to underline its functional relevance. In two developmental studies, I found that the theta rhythm indexes explicit learning processes in adults and young children (study 3), and that visually entrained theta oscillations are sensitive to the encoding of novel, unexpected events, already in the first year of life (study 4). Throughout these studies alpha oscillations were not sensitive to memory formation processes, but indicated perceptual (study 1) and semantic (study 3) processes. I propose an integrative framework, suggesting that the alpha rhythm reflects activated semantic representations in the neocortex, while theta-gamma coupling reflects an explicit mnemonic control mechanism, which selects, elaborates and integrates activated representations. Specifically, by squeezing real time events onto a faster, neuronal time scale, theta-gamma coding facilitates neuronal plasticity in medio-temporal networks and advances neuronal processes ahead of real time to emulate and guide future behavior.

Neural oscillations, including rhythms in the beta1 band (12-20 Hz), are important in various cognitive functions. Often brain networks receive rhythmic input at frequencies different than their natural frequency, so understanding how neural networks process rhythmic input is important for understanding their function in the brain. In the current thesis we study a beta1 rhythm that appears in the parietal cortex, focusing on the way it interacts with other incoming rhythms, and the implications of this interaction for cognition. The main part of the thesis consists of two stand-alone chapters, both using as a basis a biophysical neural network model that has been previously proposed to model the parietal beta1 rhythm and validated with in vitro experiments. In the first chapter we use a reduced version of this model, in order to study its dynamics, applying both analytic and numerical methods from dynamical systems. We show that a cell can respond at the same time to two periodic stimuli of unrelated frequencies, firing in phase with one, but with a mean firing rate equal to the other, a consequence of general properties of the dynamics of the network. We next show numerically that the behavior of a different cell, which is modeled as a high-dimensional dynamical system, can be described in a surprisingly simple way, owing to a reset that occurs in the state space when the cell fires. The interaction of the two cells leads to novel combinations of properties for neural dynamics, such as mode-locking to an input without phase-locking to it. In the second chapter, we study the ability of the beta1 model to support memory functions, in particular working memory. Working memory is a highly distributed component of the brain's memory systems, partially based in the parietal cortex. We show numerically that the parietal beta1 rhythm can provide an anatomical substrate for an episodic buffer of working memory. Specifically, it can support flexible and updatable representations of sensory input which are sensitive to distractors, allow for a read-out mechanism, and can be modulated or terminated by executive input.

Fast neural oscillations known as beta (12-30Hz) and gamma (30-100Hz) rhythms are recorded across several brain areas of various species. They have been linked to diverse functions like perception, attention, cognition, or interareal brain communication. The majority of the tasks performed by the brain involves communication between brain areas. To efficiently perform communication, mathematical models of brain activity require representing neural oscillations as sustained and coherent rhythms. However, some recordings show that fast oscillations are not sustained or coherent. Rather they are noisy and appear as short and random epochs of sustained activity called bursts. Therefore, modeling such noisy oscillations and investigating their ability to show interareal coherence and phase synchronization are important questions that need to be addressed. In this thesis, we propose theoretical models of noisy oscillations in the gamma and beta bands with the same properties as those observed in in \textit{vivo}. Such models should exhibit dynamic and statistical features of the data and support dynamic phase synchronization. We consider networks composed of excitatory and inhibitory populations. Noise is the result of the finite size effect of the system or the synaptic inputs. The associated dynamics of the Local Field Potentials (LFPs) are modeled as linear equations, sustained by additive and/or multiplicative noises. Such oscillatory LFPs are also known as noise-induced or quasi-cycles oscillations. The LFPs are better described using the envelope-phase representation. In this framework, a burst is defined as an epoch during which the envelope magnitude exceeds a given threshold. Fortunately, to the lowest order, the envelope dynamics are uncoupled from the phase dynamics for both additive and multiplicative noises. For additive noise, we derive the mean burst duration via a mean first passage time approach and uncover an optimal range of parameters for healthy rhythms. Multiplicative noise is shown theoretically to further synchronize neural activities and better explain pathologies with an excess of neural synchronization. We used the stochastic averaging method (SAM) as a theoretical tool to derive the envelope-phase equations. The SAM is extended to extract the envelope-phase equations of two coupled brain areas. The goal is to tackle the question of phase synchronization of noise-induced oscillations with application to interareal brain communication. The results show that noise and propagation delay are essential ingredients for dynamic phase synchronization of quasi-cycles. This suggests that the noisy oscillations recorded in \textit{vivo} and modeled here as quasi-cycles are good candidates for such neural communication. We further extend the use of the SAM to describe several coupled networks subject to white and colored noises across the Hopf bifurcation ie in both quasi-cycle and limit cycle regimes. This allows the description of multiple brain areas in the envelope-phase framework. The SAM constitutes an appropriate and flexible theoretical tool to describe a large class of stochastic oscillatory phenomena through the envelope-phase framework.

No description available.

Rhythmic oscillations in neuronal activity display variations in amplitude (power) over a range of frequencies. Attention and cognitive performance correlate with increases in cortical gamma oscillations (40-70Hz) that are generated by the coordinated firing of glutamatergic pyramidal neurons and GABAergic interneurons, and are modulated by dopamine. In the medial prefrontal cortex (mPFC) of rats, gamma power increases during treadmill walking, or after administration of an acute subanesthetic dose of the NMDA receptor antagonist ketamine. Ketamine is also used to mimic symptoms of schizophrenia, including cognitive deficits, in healthy humans and rodents. Additionally, the ability of a drug to modify ketamine-induced gamma power has been proposed to predict its pro-cognitive therapeutic efficacy. However, the mechanism underlying ketamine-induced gamma oscillations is poorly understood. We hypothesized that gamma oscillations induced by walking and ketamine would be generated by a shared mechanism in the mPFC and one of its major sources of innervation, the mediodorsal thalamus (MD). Recordings from chronically implanted electrodes in rats showed that both treadmill walking and ketamine increased gamma power, firing rates, and spike-gamma LFP correlations in the mPFC. By contrast, in the MD, treadmill walking increased all three measures, but ketamine decreased firing rates and spike-gamma LFP correlations while increasing gamma power. Therefore, walking- and ketamine-induced gamma oscillations may arise from a shared circuit in the mPFC, but different circuits in the MD. Recent work in normal animals suggests that dopamine D4 receptors (D4Rs) synergize with the neuregulin/ErbB4 signaling pathway to modulate gamma oscillations and cognitive performance. Consequently, we hypothesized that drugs targeting the D4Rs and ErbB receptors would show pro-cognitive potential by reducing ketamine-induced gamma oscillations in mPFC. However, when injected before ketamine, neither the D4R agonist nor antagonist altered ketamine’s effects on gamma power or firing rates in the mPFC, but the pan-ErbB antagonist potentiated ketamine’s increase in gamma power, and prevented ketamine from increasing firing rates. This indicates that D4Rs and ErbB receptors influence gamma power via distinct mechanisms that interact with NMDA receptor antagonism differently. Our results highlight the value of using ketamine-induced changes in gamma power as a means of testing novel pharmaceutical agents.

Patients with Alzheimer’s disease (AD) experience sleep disturbances, including disruption in slow-wave sleep (SWS). Slow oscillations (≤1 Hz), a brain rhythm prevalent during SWS, play a role in memory consolidation. Interestingly, patients with AD exhibit slow oscillations of low amplitude, which might contribute to their memory impairments. The mechanisms underlying slow-wave disruptions in AD remain unknown. Slow oscillations originate in the neocortex. Cortical neurons from all layers oscillate between UP and DOWN states during slow oscillations. Astrocytes are known to support neuronal circuit functions, and disruptions in astrocyte activity might contribute to slow-wave aberrations. Here, we investigated astrocytic contributions to slow-wave disruptions in an animal model of beta-amyloidosis (APP mice). First, we monitored astrocytic calcium transients to determine whether astrocytic calcium dynamics were disrupted in APP mice. Fourier transform analysis revealed that the power, but not the frequency of astrocytic calcium transients, was disrupted in young APP mice. This suggested calcium dynamic of astrocytic network was altered and might contribute to the disruption of slow waves in APP mice. Second, we used optogenetics to synchronize cortical astrocyte activity at 0.6 Hz to drive slow oscillations in APP mice. Our results showed that optogenetic activation of ChR2-expressing astrocytes at the endogenous frequency of slow waves restored slow-wave power. Furthermore, chronic optogenetic stimulation of astrocytes at 0.6Hz for 14 or 28 days reduced amyloid plaque deposition, prevented calcium overload in neurites, and improved memory performance in APP mice. These results revealed a malfunction of the astrocytic network driving slow-wave disruptions, and suggested a novel target to restore slow-wave power in APP mice, with translational potential to treat AD.

Active colloids are small particles that can convert external energy supply into self-propulsion. Because of the existence of the energy current inside and across the system, active colloids exhibit behaviors that are far away from thermodynamic equilibrium. During the past decades, active colloids have been used to provide models for many different non-equilibrium system studies and have been designed to complete tasks on small scale. By tuning the particle size, shape, etc, or changing the actuation methods of the active colloid systems, people have developed a large number of different active colloid systems. Among all active colloid systems, the Quincke rotation system can effectively propel particles with rapid speed. This phenomenon refers to the spontaneous rolling of a dielectric sphere in a weakly conducting liquid under a DC electric field. Although the basic mechanism of a single Quincke roller has been well explained, some behaviors that occur in complex environments or with multiple Quincke particles are still mysteries. For example, one particle will move back and forth on the bottom electrode under a high electric DC field. This so-called Quincke Oscillation motion cannot be explained by the previous models well. So a new model is required. In this dissertation, we will focus on explaining this newly-discovered dynamic in the Quincke system. Then we will study the collective dynamics of multiple Quincke oscillators with designed experiments and models. In Chapter 1, the background and different actuation methods of active colloid systems are first introduced. Then the Quincke rotation system and its field-dependent dynamics are explained with a classic leaky dielectric model. The recent research results with Quincke systems are shortly reviewed afterward. In Chapter 2, we introduce the experimentally discovered Quincke Oscillation phenomenon. Then we reveal its dependency on liquid conductivity and particle size. This dynamic is finally explained by the asymmetric charging of the particle surface in the field-induced boundary layer near the electrode. This work opens the door to the study of the collective dynamics of Quincke oscillators. In Chapter 3, we first introduce a dynamical model considering the charge, dipole, and quadrupole moments of the sphere and predict its oscillatory motion under a non-uniform liquid conductivity environment. Then we study the behavior of two coupled Quincke oscillators with far-field hydrodynamic and electrostatic interactions. The numerical simulations predict the synchronization and alignment of two oscillators with fixed positions. We further develop a model based on weakly coupled oscillator assumptions by considering the relative phase and oscillating orientations of two oscillators. The model successfully explains the numerical simulation results and can be applied to other active colloid systems with multiple mobile oscillators. In Chapter 4, we show that the Quincke oscillators can assemble into a cluster and oscillate with high synchronization and alignment. This formation of the cluster can also increase the oscillation frequency of the oscillators. By considering the perfect contact rolling of the oscillators on the electrode, we develop a weakly coupled oscillator theory model. This model explains the tendency of particles to synchronize and align in a cluster and predicts the increase of the oscillation frequency when particles are in synchronized phases. The cluster is stabilized due to the existing phase waves observed in experiments and simulations. In Chapter 5, we introduce two other studies on Quincke rollers with different experimental designs. Particles of helical shape exhibit self-propulsion in the liquid bulk and highlight the role of shape in controlling particle dynamics. For multiple spheres in a height-confined system, the particles display a transition from a fluctuating state to an absorbing stable state depending on their density and the applied field strength. This work provides an experimental model for studying absorbing state. In Chapter 6, the development of the Quincke system study is reviewed and some future directions are suggested.

No description available.

Complex nonlinear aspects of aeroelastic phenomena include unsteady nonlinear aerodynamic loads, structural nonlinearities, as well as nonlinear couplings between the flow and the structural response. Nonlinearities in aerodynamic loads originate from unsteady shocks and/or flow separation. Structural nonlinearities are geometric, or a result of free play. Nonlinear fluid structure couplings result from nonlinear resonance between the aerodynamic load and structural modes. Under different conditions, one or a combination of these aspects could yield flutter or Limit Cycle Oscillations (LCO). The overall goal of this work is to develop the capabilities to quantify the role that these different nonlinear mechanisms could play in observed flutter and LCO. The realization of such a goal would help in providing a benchmark for the detection of nonlinear aeroelastic instabilities and possibly effective means for obtaining improved performance and reduced uncertainties through operation beyond conventional boundaries that are based on linear analysis. Additionally, this effort will provide a benchmark for the validation of computational methodologies. In this thesis, wavelet-based higher order spectra are applied to identify different nonlinear aeroelastic phenomena as encountered in two experiments. First, the analysis is applied to a set of experiments involving a flexible semispan model (FSM) of a High Speed Civil Transport (HSCT) wing configuration conducted by Silva et al. (Experimental Steady and Unsteady Aerodynamic and Flutter Results for HSCT Semispan Models; AIAA/ASME/ASCE/AHS/ASC 41st Structures, Structural Dynamics, and Materials Conference, 2000). The interest is in the identification of nonlinear aeroelastic phenomena associated with a high dynamic response region which was measured over a large range of dynamic pressures around Mach number 0.98. At the top of this region is a ``hard'' flutter point that resulted in the loss of the model. The results show that ``hard'' flutter is related to intermittent nonlinear coupling between the shock motion and large amplitude structural motions. Second, the analysis is applied to identify nonlinear aspects of LCO encountered during test flights of an F-16 aircraft. The results show quadratic and cubic couplings in the acceleration signals of the under-wing launchers and high quadratic coupling levels between flaperon motions and wing oscillations. The implications of applying these techniques in the capacity of a ``flutterometer'' are also discussed. / Ph. D.