Intrinsic Noise Sources Research Articles

Range expansions across landscapes with quenched noise

When biological populations expand into new territory, the evolutionary outcomes can be strongly influenced by genetic drift, the random fluctuations in allele frequencies. Meanwhile, spatial variability in the environment can also significantly influence the competition between subpopulations vying for space. Little is known about the interplay of these intrinsic and extrinsic sources of noise in population dynamics: When does environmental heterogeneity dominate over genetic drift or vice versa, and what distinguishes their population genetics signatures? Here, in the context of neutral evolution, we examine the interplay between a population's intrinsic, demographic noise and an extrinsic, quenched random noise provided by a heterogeneous environment. Using a multispecies Eden model, we simulate a population expanding over a landscape with random variations in local growth rates and measure how this variability affects genealogical tree structure, and thus genetic diversity. We find that, for strong heterogeneity, the genetic makeup of the expansion front is to a great extent predetermined by the set of fastest paths through the environment. The landscape-dependent statistics of these optimal paths then supersede those of the population's intrinsic noise as the main determinant of evolutionary dynamics. Remarkably, the statistics for coalescence of genealogical lineages, derived from those deterministic paths, strongly resemble the statistics emerging from demographic noise alone in uniform landscapes. This cautions interpretations of coalescence statistics and raises new challenges for inferring past population dynamics.

Mixtures of strategies underlie rodent behavior during reversal learning.

In reversal learning tasks, the behavior of humans and animals is often assumed to be uniform within single experimental sessions to facilitate data analysis and model fitting. However, behavior of agents can display substantial variability in single experimental sessions, as they execute different blocks of trials with different transition dynamics. Here, we observed that in a deterministic reversal learning task, mice display noisy and sub-optimal choice transitions even at the expert stages of learning. We investigated two sources of the sub-optimality in the behavior. First, we found that mice exhibit a high lapse rate during task execution, as they reverted to unrewarded directions after choice transitions. Second, we unexpectedly found that a majority of mice did not execute a uniform strategy, but rather mixed between several behavioral modes with different transition dynamics. We quantified the use of such mixtures with a state-space model, block Hidden Markov Model (block HMM), to dissociate the mixtures of dynamic choice transitions in individual blocks of trials. Additionally, we found that blockHMM transition modes in rodent behavior can be accounted for by two different types of behavioral algorithms, model-free or inference-based learning, that might be used to solve the task. Combining these approaches, we found that mice used a mixture of both exploratory, model-free strategies and deterministic, inference-based behavior in the task, explaining their overall noisy choice sequences. Together, our combined computational approach highlights intrinsic sources of noise in rodent reversal learning behavior and provides a richer description of behavior than conventional techniques, while uncovering the hidden states that underlie the block-by-block transitions.

A stochastic model of hippocampal synaptic plasticity with geometrical readout of enzyme dynamics.

Discovering the rules of synaptic plasticity is an important step for understanding brain learning. Existing plasticity models are either (1) top-down and interpretable, but not flexible enough to account for experimental data, or (2) bottom-up and biologically realistic, but too intricate to interpret and hard to fit to data. To avoid the shortcomings of these approaches, we present a new plasticity rule based on a geometrical readout mechanism that flexibly maps synaptic enzyme dynamics to predict plasticity outcomes. We apply this readout to a multi-timescale model of hippocampal synaptic plasticity induction that includes electrical dynamics, calcium, CaMKII and calcineurin, and accurate representation of intrinsic noise sources. Using a single set of model parameters, we demonstrate the robustness of this plasticity rule by reproducing nine published ex vivo experiments covering various spike-timing and frequency-dependent plasticity induction protocols, animal ages, and experimental conditions. Our model also predicts that in vivo-like spike timing irregularity strongly shapes plasticity outcome. This geometrical readout modelling approach can be readily applied to other excitatory or inhibitory synapses to discover their synaptic plasticity rules.

Impact of quantum noise on the training of quantum Generative Adversarial Networks

Current noisy intermediate-scale quantum devices suffer from various sources of intrinsic quantum noise. Overcoming the effects of noise is a major challenge, for which different error mitigation and error correction techniques have been proposed. In this paper, we conduct a first study of the performance of quantum Generative Adversarial Networks (qGANs) in the presence of different types of quantum noise, focusing on a simplified use case in high-energy physics. In particular, we explore the effects of readout and two-qubit gate errors on the qGAN training process. Simulating a noisy quantum device classically with IBM’s Qiskit framework, we examine the threshold of error rates up to which a reliable training is possible. In addition, we investigate the importance of various hyperparameters for the training process in the presence of different error rates, and we explore the impact of readout error mitigation on the results.

Clock transitions guard against spin decoherence in singlet fission.

Short coherence times present a primary obstacle in quantum computing and sensing applications. In atomic systems, clock transitions (CTs), formed from avoided crossings in an applied Zeeman field, can substantially increase coherence times. We show how CTs can dampen intrinsic and extrinsic sources of quantum noise in molecules. Conical intersections between two periodic potentials form CTs in electron paramagnetic resonance experiments of the spin-polarized singlet fission photoproduct. We report on a pair of CTs for a two-chromophore molecule in terms of the Zeeman field strength, molecular orientation relative to the field, and molecular geometry.

Approaching optimum phase measurement in the presence of amplifier noise

In fundamental papers from 1962, Heffener and Haus showed that it is not possible to construct a linear noiseless amplifier [Proc. IRE 50, 1604 (1962)PIREAE0096-839010.1109/JRPROC.1962.288130; Phys. Rev. Lett. 128, 2407 (1962)PHRVAO0031-899X10.1103/PhysRev.128.2407]. This implies that amplifier intrinsic noise sources induce random perturbations on the phase of the incoming optical signal, which translates into spectral broadening. Achieving the minimum induced phase fluctuation requires a phase measurement method that introduces minimum uncertainty, i.e., optimum phase measurement. We demonstrate that a measurement method based on heterodyne detection and extended Kalman filtering approaches optimum phase measurement in the presence of amplifier noise. A penalty of 5 dB (numerical) and 15 dB (experimental) compared to quantum limited spectral broadening is achieved. Spectral broadening reduction of 44 dB is achieved, compared to when using the widely employed phase measurement method, based purely on the argument of the signal field. Our results reveal new scientific insights by demonstrating a phase measurement method that enables to approach minimum phase fluctuation, induced by amplifier noise. An impact is envisioned for phase-based optical sensing systems, as optical amplification could increase sensing distance with minimum impact on the phase.

Mechanobiological conceptual framework for assessing stem cell bioprocess effectiveness.

Fully realizing the enormous potential of stem cells requires developing efficient bioprocesses and optimizations founded in mechanobiological considerations. Here, we emphasize the importance of mechanotransduction as one of the governing principles of stem cell bioprocesses, underscoring the need to further explore the behavioral mechanisms involved in sensing mechanical cues and coordinating transcriptional responses. We identify the sources of intrinsic, extrinsic, and external noise in bioprocesses requiring further study, and discuss the criteria and indicators that may be used to assess and predict cell-to-cell variability resulting from environmental fluctuations. Specifically, we propose a conceptual framework to explain the impact of mechanical forces within the cellular environment, identify key cell state determinants in bioprocesses, and discuss downstream implementation challenges.

The impact of thermal-noise on bifurcation MEMS sensors

In this work, we investigate and quantify intrinsic noise sources in electrostatic MEMS and study their impact on the mass sensitivity of bifurcation-based inertial sensors. Experiments are carried out to measure the power spectral densities (PSD) of the two leading intrinsic noise sources, mechanical-thermal noise and electric-thermal noise. We also present a stochastic model of the sensor that encompasses those noise sources. The model is deployed to obtain the probability distribution of the cyclic-fold bifurcation point in the presence of noise. It shows that thermal noise leads to an uncertainty of 2Hz in the bifurcation frequency. Within that range, stochastic switching occurs between the two co-existing stable orbits. Therefore, the closest operating frequency of the sensor should exclude this region to protect against false positives. This represents the lower bound on the sensor's minimum detectable mass. The predictions of this model were found to be in close agreement with previous experimental results.

Stochastic Differential Equations for Practical Simulation of Gene Circuits.

The Chemical Langevin Equation approach allows simple stochastic simulation of gene circuits under many practical situations where the number of molecules of the species involved is not extremely low. Here, we describe methods and a computational framework to simulate a population of cells containing gene circuits of interest. These methods account for both intrinsic and extrinsic noise sources, and allow us to have both individual cell-related species and population-related ones. The protocol covers aspects related to proper description of the system and setting the software tools. It also helps to deal with the optimization of data storage and the simulation precision versus computational time issue. Finally, it also gives practical tests to assess the validity of the underlying technical assumptions.

Equivalent Magnetic Noise Analysis for a Tunneling Magnetoresistive Magnetometer

Tunneling magnetoresistive (TMR) magnetic sensors are broadly applied in various fields. However, to further improve the limit of detection (LOD), it is highly desirable to realize and calculate the noise limits of such sensor systems. Thus, techniques for noise elimination can be developed. In this paper, we propose an equivalent magnetic noise model to describe the LOD of a TMR magnetometer by considering intrinsic noise sources in the TMR sensing element and readout electronics. According to the model, the contribution of each noise source can be directly obtained. Therefore, the LOD of the magnetometer can be established theoretically. Moreover, the predicted detectivity of 390 pT/ $\surd $ Hz is also demonstrated by the experimental results. The model is applicable to all kinds of TMR sensors with a “Wheatstone bridge” configuration.

Long-term variations of X-ray pulse profiles for the Crab pulsar: data analysis and modeling

The pulse profiles of the Crab pulsar (as well as some other pulsars) vary with time. They can lead to a major source of intrinsic timing noise, which lacks a detailed physical model. The phase separation $\\Delta$ between the first left peak (P1) and the second right peak (P2) is a key parameter that shows the variations of pulse profiles for the Crab pulsar.It was found that the evolution of $\\Delta$ has a tendency with increasing rates of $0.82^{\\circ}\\pm0.25^{\\circ}$, $0.80^{\\circ}\\pm0.54^{\\circ}$, and $0.77^{\\circ}\\pm0.28^{\\circ}$ per century for the 2-6, 6-15, and 15-60 keV bands, respectively. Furthermore, the flux ratios (P2/P1) of X-ray pulse profiles in the three bands were calculated, and the derived flux ratios were consistent with the radio and X-ray measurements of the sl Insight-HXMT.In addition to discovering the physical origin of the pulse changes, the high-SNR X-ray pulse profiles were simulated in the annular gap model, and two model parameters (e.g., the maximum emission heights of the two peaks) were observed to slightly affect the variations of peak separation. We fitted the long-term variations of emission heights of the two peaks and discovered that the emission heights showed increasing tendencies with time. Variations of these emission heights induced a characteristic period derivative, and a complete formula for both the magnetic dipole radiation and wind-particle-induced variations of the moment of inertia was used for the pulsars spin-down to obtain the variation rate $\\dot{\\alpha}$ of the magnetic inclination angle, which was $-1.60^{\\circ}$ per century.Intrinsic timing noise is observed to be mainly induced by the variations of pulse profiles, which might correlate with a characteristic spin period derivative arising from the fluctuations of the emission regions. This work will lay a foundation for understanding the origin of intrinsic timing noise and making high-precision timing models in the future.

Linewidth characteristics of period-one dynamics induced by optically injected semiconductor lasers.

Period-one (P1) nonlinear dynamics of optically injected semiconductor lasers exhibit periodic intensity fluctuations with operating frequencies that are tunable in the micro/millimeter-wave range. Through numerical experiments, the characteristics of the microwave linewidth of the P1 oscillation and the linewidths of its optical frequency components due to intrinsic laser noise sources are investigated. The field noise and carrier noise of the master and slave lasers are systematically considered. The contribution of the field noise of the master laser is significant; it considerably broadens the microwave linewidth of the P1 oscillation. When the operating conditions in the regions of high microwave power are properly adjusted, the microwave linewidth of the P1 oscillation becomes narrower than the free-running optical linewidths of the master and slave lasers. Regions with low sensitivity to time-dependent fluctuations in an operating parameter show reduced microwave linewidth of the P1 oscillation.

Impact of Fundamental Temperature Fluctuations on the Frequency Stability of Metallo- Dielectric Nanolasers

The capability of nanolasers to generate coherent light in small volume resonators has made them attractive to be implemented in future ultra-compact photonic integrated circuits. However, compared to conventional lasers, nanolasers are also known for their broader spectral linewidths, that are usually on the order of 1 nm. While it is well known that the broad linewidths in light emitters originate from various noise sources, there has been no rigorous study on evaluating the origins of the linewidth broadening for nanolasers to date to the best of our knowledge. In this manuscript, we investigate the impact of fundamental thermal fluctuations on the nanolaser linewidth. We show that such thermal fluctuations are one of the intrinsic noise sources in a sub-wavelength metal-clad nanolaser inducing significant linewidth broadening. We further show that with the reduction of the nanolaser’s dimensions, i.e., mode volume, and the increase of the ambient temperature, such linewidth broadening is enhanced, due to the effect of more pronounced fundamental thermal fluctuation. Specifically, we show that the finite linewidths induced by the thermal fluctuations at room temperature are 1.14nm and 0.16nm, for nanolasers with core radii of 250nm and 750nm, respectively. Although our study was performed on a metallo-dielectric nanolaser, it is reasonable to assume that, in general, other nanolaser architectures are also more prone to thermal fluctuations, and hence exhibit larger finite linewidths than conventional large mode volume lasers.

Comprehensive Design Considerations and Noise Modeling of Preamplifier for MEMS Electrometry

The purpose of the work is to evaluate various intrinsic noise sources that limit the charge resolution of microelectromechanical systems (MEMS) vibrating-reed (VR) electrometers. We demonstrate a method of reducing total equivalent referred-to-input (RTI) noise voltage per square root hertz for an operational amplifier-based preamplifier (preamp) circuit topology. The proposed circuit’s noise model accurately describes the various noise sources and total RTI noise voltage is experimentally verified. The noise analysis clarifies the path toward a preamp with a noise floor limited by thermal noise due to low-valued shunt resistance at its input. Furthermore, during the charge measurement, a variation of sensitivity occurs by changing the isolation (or dc blocking) capacitor of the preamp. This phenomenon also affects the noise performance of the circuit and the overall charge resolution for the electrometer. RTI noise voltage of 290 nV/ $\surd $ Hz at 5.2 kHz was measured for 50 $\text{M}\Omega $ of shunt resistor and 2 pF of isolation capacitor to implement a prototype of the complete preamp, along with the MEMS device fabricated in silicon-on-glass (SOG) process. Finally, we present a charge resolution model of the proposed SOG-MEMS electrometry system. The model’s underlying design principles reveal low noise preamps accessible to the circuit designers without tangible circuitries and after-the-fact noise measurement.

Suppression of Intensity and Frequency Noise at Low-Sensitivity Operating Points of Period-One Dynamics of Optically Injected Semiconductor Lasers

Period-one (P1) dynamics induced by an optically injected semiconductor laser oscillate at microwave frequencies. The oscillation frequency of a P1 dynamic can be rendered nearly insensitive to small-signal parameter fluctuations and intrinsic laser noise at appropriate operating conditions of the master and slave lasers. In this paper, the noise-canceling properties of the various low-sensitivity (LS) operating points is demonstrated through their dependency on the noise fluctuation frequency and power. The three different types of LS operating points show their effectiveness in suppressing not only P1 intensity and frequency fluctuations caused by narrowband laser perturbations but also intensity and frequency noise caused by broadband intrinsic and extrinsic noise sources.

Intrinsic stochastic resonance via set-point variation.

In the present paper, the possibility of invoking stochastic resonance (SR, periodic and aperiodic) by regulating the operating value of an appropriate parameter is explored. The operating values of these parameters are defined as the set point of the system throughout the present paper. Brusselator, a mathematical model [I. Prigogine and R. Lefever, J. Chem. Phys. 48, 1695 (1968)JCPSA60021-960610.1063/1.1668896] of nonlinear chemical reactions, is used for this purpose. We consider the effect of intrinsic noise in the Brusselator due to the Markovian nature of the chemical reactions. The stochastic time evolution is studied using the Gillespie algorithm [D. T. Gillespie, J. Comput. Phys. 22, 403 (1976)JCTPAH0021-999110.1016/0021-9991(76)90041-3], which is an exact stochastic simulation algorithm. We analyze the dependence of the resonance point on both the strength of the intrinsic noise as well as the distance from the bifurcation point. Subsequently, the phenomena of SR is explored using both periodic and aperiodic stimulus. It was found that, for a given system size, in both cases, SR is achieved by variation of the set point. Set-point variation can be achieved by regulating either the source concentration or the rate constants. Resonance is observed in both cases. However, this resonance occurs at different values of the set point, even with a fixed system size. This is clearly seen in the set-point versus system-size plane, where the resonance line has different slopes for the two scenarios. Our semianalytic treatment points to the fact that for a given system size intrinsic noise is affected differently for different methods involving the variation of the set point. This is explained by writing the corresponding chemical Langevin equation and comparing the various intrinsic noise sources.

Measuring Frequency Fluctuations in Nonlinear Nanomechanical Resonators.

Advances in nanomechanics within recent years have demonstrated an always expanding range of devices, from top-down structures to appealing bottom-up MoS2 and graphene membranes, used for both sensing and component-oriented applications. One of the main concerns in all of these devices is frequency noise, which ultimately limits their applicability. This issue has attracted a lot of attention recently, and the origin of this noise remains elusive to date. In this article we present a very simple technique to measure frequency noise in nonlinear mechanical devices, based on the presence of bistability. It is illustrated on silicon-nitride high-stress doubly clamped beams, in a cryogenic environment. We report on the same T/ f dependence of the frequency noise power spectra as reported in the literature. But we also find unexpected damping fluctuations, amplified in the vicinity of the bifurcation points; this effect is clearly distinct from already reported nonlinear dephasing and poses a fundamental limit on the measurement of bifurcation frequencies. The technique is further applied to the measurement of frequency noise as a function of mode number, within the same device. The relative frequency noise for the fundamental flexure δ f/ f0 lies in the range 0.5-0.01 ppm (consistent with the literature for cryogenic MHz devices) and decreases with mode number in the range studied. The technique can be applied to any type of nanomechanical structure, enabling progress toward the understanding of intrinsic sources of noise in these devices.

A Sub-Threshold Noise Transient Simulator Based on Integrated Random Telegraph and Thermal Noise Modeling

Near-threshold and sub-threshold voltage designs have been identified as possible solutions to overcome the limitations introduced by energy consumption in modern very large scale integration circuits. However, as we approach sub-10 nm transistor technology, aggressive voltage, and gate length scaling will reduce the reliability of logic circuits due to the increasing impact of noise and variability effects. Therefore, designers need new tools to simulate logic circuits in the presence of noise. Time-domain analysis helps understand how transient faults affect a circuit and can guide designers in producing noise-resistant circuitry. However, standard approaches to modeling intrinsic noise sources in the time domain are computationally expensive. Moreover, small noise-driven fluctuations in electron occupation of circuit nodes introduce time-varying biasing point fluctuations, increasing the modeling complexity. To address these challenges, this paper introduces a new approach to modeling thermal noise and random telegraph signal noise directly in the time domain by developing and solving a series of stochastic differential equations. In comparisons to traditional SPICE-based simulations, our approach can provide three orders of magnitude speedup in simulation time without sacrificing accuracy. Moreover, we introduce a novel, iterative threshold-crossing algorithm, aimed at the efficient sampling of rare noise transients. We show that Monte-Carlo simulations based on this approach can detect rare high-amplitude single event transients that would be impossible to uncover with standard transient simulators.

Light activation of noise at microwave frequencies: a study on scaled gallium arsenide HEMT's

This study is focused on the experimental investigation of noise at microwave frequencies for scaled gallium arsenide high-electron-mobility transistor's (HEMT's). The light activation of noise has been achieved by laser exposure in the visible range. The devices have 0.25 μm gate length and 100-200-300 μm gate widths. Their DC characteristics, linear scattering and noise parameters were measured both in dark condition and under continuous wave light exposure in the 2-18 GHz frequency range. Previous results had shown a remarkable influence on all the above-measured parameters under illumination, with a special concern for the noise performance. Therefore, the authors investigated the origin of this light-activated noise in terms of the intrinsic noise sources, by extracting a noise temperature circuit model for each HEMT.In addition, the noise model formulation based on the P, R and C as well as the K g , K r and K c coefficients is used to enlighten the key aspects of the optically activated noise on the device performance. It is observed that the degradation of the minimum noise figure can be attributed to the noise coefficient R, related to the gate noise source that is strongly affected by the charge generation related to light exposure.

Robust Low-Dose CT Sinogram Preprocessing via Exploiting Noise-Generating Mechanism.

Computed tomography (CT) image recovery from low-mAs acquisitions without adequate treatment is always severely degraded due to a number of physical factors. In this paper, we formulate the low-dose CT sinogram preprocessing as a standard maximum a posteriori (MAP) estimation, which takes full consideration of the statistical properties of the two intrinsic noise sources in low-dose CT, i.e., the X-ray photon statistics and the electronic noise background. In addition, instead of using a general image prior as found in the traditional sinogram recovery models, we design a new prior formulation to more rationally encode the piecewise-linear configurations underlying a sinogram than previously used ones, like the TV prior term. As compared with the previous methods, especially the MAP-based ones, both the likelihood/loss and prior/regularization terms in the proposed model are ameliorated in a more accurate manner and better comply with the statistical essence of the generation mechanism of a practical sinogram. We further construct an efficient alternating direction method of multipliers algorithm to solve the proposed MAP framework. Experiments on simulated and real low-dose CT data demonstrate the superiority of the proposed method according to both visual inspection and comprehensive quantitative performance evaluation.