Concept: Electronic oscillator
A detailed study of high-field transient and direct-current (DC) transport in GaN-based Gunn diode oscillators is carried out using the commercial simulator Sentaurus Device. Applicability of drift-diffusion (DD) and hydrodynamic (HD) models to high-speed, high-frequency devices is discussed in depth, and the results of the simulations from these models are compared. It is shown, for a highly homogeneous device based on a short (2 mum) supercritically doped (1017 cm-3) GaN specimen, that the DD model is unable to correctly take into account some essential physical effects which determine the operation mode of the device. At the same time, the HD model is ideally suited to solve such problems due to its ability to incorporate non-local effects. We show that the velocity overshoot near the device contacts and space charge injection and extraction play a crucial role in defining the operation mode of highly homogeneous short diodes in both the transient regime and the voltage-controlled oscillation regime. The transient conduction current responses are fundamentally different in the DD and HD models. The DD current simply repeats the velocity-field (v-F) characteristics, and the sample remains in a completely homogeneous state. In the HD model, the transient current pulse with a full width at half maximum of approximately 0.2 ps is increased about twofold due to the carrier injection (extraction) into (from) the active region and the velocity overshoot. The electron gas is characterized by highly inhomogeneous distributions of the carrier density, the electric field and the electron temperature. The simulation of the DC steady states of the diodes also shows very different results for the two models. The HD model shows the trapped stable anodic domain in the device, while the DD model completely retains all features of the v-F characteristics in a homogeneous gas. Simulation of the voltage-controlled oscillator shows that it operates in the accumulation layer mode generating microwave signals at 0.3 to 0.7 THz. In spite of the fact that the known criterion of a Gunn domain mode n0L > (n0L)0 was satisfied, no Gunn domains were observed. The explanation of this phenomenon is given.
- Journal of the Royal Society, Interface / the Royal Society
- Published over 4 years ago
Bipolar disorder is a chronic, recurrent mental illness characterized by extreme episodes of depressed and manic mood, interspersed with less severe but highly variable mood fluctuations. Here, we develop a novel mathematical approach for exploring the dynamics of bipolar disorder. We investigate how the dynamics of subjective experience of mood in bipolar disorder can be understood using a relaxation oscillator (RO) framework and test the model against mood time-series fluctuations from a set of individuals with bipolar disorder. We show that variable mood fluctuations in individuals diagnosed with bipolar disorder can be driven by the coupled effects of deterministic dynamics (captured by ROs) and noise. Using a statistical likelihood-based approach, we show that, in general, mood dynamics are described by two independent ROs with differing levels of endogenous variability among individuals. We suggest that this sort of nonlinear approach to bipolar disorder has neurobiological, cognitive and clinical implications for understanding this mental illness through a mechacognitive framework.
Strongly correlated phases exhibit collective carrier dynamics that if properly harnessed can enable novel functionalities and applications. In this article, we investigate the phenomenon of electrical oscillations in a prototypical MIT system, vanadium dioxide (VO2). We show that the key to such oscillatory behaviour is the ability to induce and stabilize a non-hysteretic and spontaneously reversible phase transition using a negative feedback mechanism. Further, we investigate the synchronization and coupling dynamics of such VO2 based relaxation oscillators and show, via experiment and simulation, that this coupled oscillator system exhibits rich non-linear dynamics including charge oscillations that are synchronized in both frequency and phase. Our approach of harnessing a non-hysteretic reversible phase transition region is applicable to other correlated systems exhibiting metal-insulator transitions and can be a potential candidate for oscillator based non-Boolean computing.
Neuronal systems have a high propensity to engage in oscillatory activity because both the properties of individual neurons as well as canonical circuit motifs favour rhythmic activity. In addition, coupled oscillators can engage in a large variety of dynamical regimes, ranging from synchronization with different phase offsets to chaotic behaviour. Which regime prevails depends on differences between preferred oscillation frequencies, coupling strength and coupling delays. The ability of delay coupled oscillator networks to generate a rich repertoire of temporally structured activation sequences is exploited by central pattern generator networks for the control of movements. However, it less clear whether temporal patterning of neuronal discharges also plays a role in cognitive processes. Here it will be argued that the temporal patterning of neuronal discharges emerging from delay coupled oscillator networks plays a pivotal role in all instances in which selective relations have to be established between the responses of distributed assemblies of neurons. Examples are the dynamic formation of functional networks, the selective routing of activity in densely interconnected networks, the attention dependent selection of sensory signals, the fast and context dependent binding of responses for further joint processing in pattern recognition and the formation of associations by learning. Special consideration is given to arguments that challenge a functional role of oscillations and synchrony in cognition because of the volatile nature of these phenomena and recent evidence will be reviewed suggesting that this volatility is functionally advantageous. This article is protected by copyright. All rights reserved.
New synthetic oscillator designs provide independent tuning of amplitude and frequency and flexible switching between dynamic regimes.
This study proposes a two-dimensional (2D) oscillator model of p53 network, which is derived via reducing the multidimensional two-phase dynamics model into a model of ataxia telangiectasia mutated (ATM) and Wip1 variables, and studies the impact of p53-regulators on cell fate decision. First, the authors identify a 6D core oscillator module, then reduce this module into a 2D oscillator model while preserving the qualitative behaviours. The introduced 2D model is shown to be an excitable relaxation oscillator. This oscillator provides a mechanism that leads diverse modes underpinning cell fate, each corresponding to a cell state. To investigate the effects of p53 inhibitors and the intrinsic time delay of Wip1 on the characteristics of oscillations, they introduce also a delay differential equation version of the 2D oscillator. They observe that the suppression of p53 inhibitors decreases the amplitudes of p53 oscillation, though the suppression increases the sustained level of p53. They identify Wip1 and P53DINP1 as possible targets for cancer therapies considering their impact on the oscillator, supported by biological findings. They model some mutations as critical changes of the phase space characteristics. Possible cancer therapeutic strategies are then proposed for preventing these mutations' effects using the phase space approach.
Even identically-designed autonomous microfluidic oscillators have device-to-device oscillation variability that arises due to inconsistencies in fabrication, materials, and operation conditions. This work demonstrates, experimentally and theoretically, that with appropriate capacitive coupling these microfluidic oscillators can be synchronized. The size and characteristics of the capacitive coupling needed and the range of input flow rate differences that can be synchronized are also characterized. In addition to device-to-device variability, there is also within-device oscillation noise that arises. An additional advantage of coupling multiple fluidic oscillators together is that the oscillation noise decreases. The ability to synchronize multiple autonomous oscillators is also a first step towards enhancing their usefulness as tools for biochemical research applications where multiplicate experiments with identical temporal-stimulation conditions are required. This article is protected by copyright. All rights reserved.
This paper analyzes the influence of phase noise and distortion on the performance of oscillator-based sensor data acquisition systems. Circuit noise inherent to the oscillator circuit manifests as phase noise and limits the SNR. Moreover, oscillator nonlinearity generates distortion for large input signals. Phase noise analysis of oscillators is well known in the literature, but the relationship between phase noise and the SNR of an oscillator-based sensor is not straightforward. This paper proposes a model to estimate the influence of phase noise in the performance of an oscillator-based system by reflecting the phase noise to the oscillator input. The proposed model is based on periodic steady-state analysis tools to predict the SNR of the oscillator. The accuracy of this model has been validated by both simulation and experiment in a 130 nm CMOS prototype. We also propose a method to estimate the SNDR and the dynamic range of an oscillator-based readout circuit that improves by more than one order of magnitude the simulation time compared to standard time domain simulations. This speed up enables the optimization and verification of this kind of systems with iterative algorithms.
Cortical slow oscillations (SO) of neural activity spontaneously emerge and propagate during deep sleep and anesthesia and are also expressed in isolated brain slices and cortical slabs. We lack full understanding of how SO integrate the different structural levels underlying local excitability of cell assemblies and their mutual interaction. Here, we focus on ongoing slow waves (SWs) in cortical slices reconstructed from a 16-electrode array designed to probe the neuronal activity at multiple spatial scales. In spite of the variable propagation patterns observed, we reproducibly found a smooth strip of loci leading the SW fronts, overlapping cortical layers 4 and 5, along which Up states were the longest and displayed the highest firing rate. Propagation modes were uncorrelated in time, signaling a memoryless generation of SWs. All these features could be modeled by a multimodular large-scale network of spiking neurons with a specific balance between local and intermodular connectivity. Modules work as relaxation oscillators with a weakly stable Down state and a peak of local excitability to model layers 4 and 5. These conditions allow for both optimal sensitivity to the network structure and richness of propagation modes, both of which are potential substrates for dynamic flexibility in more general contexts.
The MinDE protein system from E. coli has recently been identified as a minimal biological oscillator, based on two proteins only: The ATPase MinD and the ATPase activating protein MinE. In E. coli the system works as the molecular ruler to place the divisome at midcell for cell division. In spite of its compositional simplicity, the molecular mechanism leading to protein patterns and oscillations is still insufficiently understood. Here we used high-speed atomic force microscopy to analyze the mechanism of MinDE membrane association/dissociation dynamics on isolated membrane patches, down to the level of individual point oscillators. This nanoscale analysis shows that MinD association to and dissociation from the membrane are both highly cooperative but mechanistically different processes. We propose that they represent the two directions of a single allosteric switch leading to MinD filament formation and depolymerization. Association/dissociation are separated by rather long apparently silent periods. The membrane-associated period is characterized by MinD filament multivalent binding, avidity, while the dissociated period is defined by seeding of individual MinD. Analyzing association/dissociation kinetics with varying MinD and MinE concentrations and dependent of membrane patch size allowed us to disentangle the essential dynamic variables of the MinDE oscillation cycle.