Circuit Behavior Research Articles

A novel 0.2–7 GHz microwave hyperchaotic generator based on Hartley oscillator

In this paper, a miniaturized microwave-band hyperchaotic generator prototype has been designed and realized. By improving the topology of Hartley oscillator, the proposed single-stage common-collector structure oscillator enables us to generate a microwave 0.2–7 GHz smooth spectrum signal with a power around −30 dBm. Using BFP650 SiGe transistor as a non-linear component, the proposed circuit has been implemented and simulated then experimentally approved. Introducing the parasitic capacitors C BC and C BE and using the exponential model to describe the active component non-linearity, a simplified electrical model for the developed circuit has been proposed. To exhibit the deterministic chaotic character of the mentioned circuit, mathematical and schematic implementation results using Matlab and Advanced Design System (ADS) simulations have been presented. The concordance between the two simulation results permits us to adopt the simplified state equation model to describe the circuit behavior. The Lyapunov spectrum exponents representation allowed us to verify the hyperchaotic behavior in the presented generator. Finally, an autonomous simple prototype architecture of the generator using the PTFE (Polytetrafluoroethylene) substrate with ε r = 2.2 has been realized and experimentally validated. The achieved performances made the proposed circuit suitable for various fields of telecommunications.

Environmental Enrichment Mitigates the Long-Lasting Sequelae of Perinatal Fentanyl Exposure in Mice.

The opioid epidemic is a rapidly evolving societal issue driven, in part, by a surge in synthetic opioid use. A rise in fentanyl use among pregnant women has led to a 40-fold increase in the number of perinatally-exposed infants in the past decade. These children are more likely to develop mood-related and somatosensory-related conditions later in life, suggesting that fentanyl may permanently alter neural development. Here, we examined the behavioral and synaptic consequences of perinatal fentanyl exposure in adolescent male and female C57BL/6J mice and assessed the therapeutic potential of environmental enrichment to mitigate these effects. Dams were given ad libitum access to fentanyl (10 µg/ml, per os) across pregnancy and until weaning [postnatal day (PD)21]. Perinatally-exposed adolescent mice displayed hyperactivity (PD45), enhanced sensitivity to anxiogenic environments (PD46), and sensory maladaptation (PD47), sustained behavioral effects that were completely normalized by environmental enrichment (PD21-PD45). Additionally, environmental enrichment normalized the fentanyl-induced changes in the frequency of miniature EPSCs (mEPSCs) of layer 2/3 neurons in the primary somatosensory cortex (S1). We also demonstrate that fentanyl impairs short-term potentiation (STP) and long-term potentiation (LTP) in S1 layer 2/3 neurons, which, instead, exhibit a sustained depression of synaptic transmission that is restored by environmental enrichment. On its own, environmental enrichment suppressed long-term depression (LTD) of control S1 neurons from vehicle-treated mice subjected to standard housing conditions. These results demonstrate that the lasting effects of fentanyl can be ameliorated with a noninvasive intervention introduced during early development.SIGNIFICANCE STATEMENT Illicit use of fentanyl accounts for a large proportion of opioid-related overdose deaths. Children exposed to opioids during development have a higher risk of developing neuropsychiatric disorders later in life. Here, we employ a preclinical model of perinatal fentanyl exposure that recapitulates these long-term impairments and show, for the first time, that environmental enrichment can reverse deficits in somatosensory circuit function and behavior. These findings have the potential to directly inform and guide ongoing efforts to mitigate the consequences of perinatal opioid exposure.

Mapping circuit dynamics during function and dysfunction.

Neural circuits can generate many spike patterns, but only some are functional. The study of how circuits generate and maintain functional dynamics is hindered by a poverty of description of circuit dynamics across functional and dysfunctional states. For example, although the regular oscillation of a central pattern generator is well characterized by its frequency and the phase relationships between its neurons, these metrics are ineffective descriptors of the irregular and aperiodic dynamics that circuits can generate under perturbation or in disease states. By recording the circuit dynamics of the well-studied pyloric circuit in Cancer borealis, we used statistical features of spike times from neurons in the circuit to visualize the spike patterns generated by this circuit under a variety of conditions. This approach captures both the variability of functional rhythms and the diversity of atypical dynamics in a single map. Clusters in the map identify qualitatively different spike patterns hinting at different dynamic states in the circuit. State probability and the statistics of the transitions between states varied with environmental perturbations, removal of descending neuromodulatory inputs, and the addition of exogenous neuromodulators. This analysis reveals strong mechanistically interpretable links between complex changes in the collective behavior of a neural circuit and specific experimental manipulations, and can constrain hypotheses of how circuits generate functional dynamics despite variability in circuit architecture and environmental perturbations.

Energy Analysis of Metal QCA Circuits Behavior Based on Particle-Wave Duality

Quantum cellular automata (QCA) is the emerging technology for designing arithmetic and logic circuits at nanoscale. Because of their tremendous cell density, the analysis of the energy behavior of QCA circuits is of great importance. The present study presents a detailed review of how energy is dissipated in QCA cells. For that, two formalisms of the energy behavior of QCA circuits are examined and used to obtain the energy model of the main building blocks in the QCA paradigm. In the first formalism, using the previously developed coherence vector formalism, we propose a complete model for the energy dynamics of the majority voter and wire straight gates. In the second formalism, metal QCA circuits’ energy behavior based on the particle-wave duality of the electrons is proposed. The energy model of the majority voter and wire straight gates in this formalism is compared to that of the first formalism.

Sex differences in the intrinsic reading neural networks of Chinese children

Sex differences in reading performance have been considered a relatively stable phenomenon. However, there is no general agreement about their neural basis, which might be due to that sex differences are largely influenced by age. This paper focuses on the sex differences in the reading-related neural network of Chinese children and its interaction with age. We also attempt to predict reading abilities based on neural network. Fifty-three boys and 56 girls (8.2–14.6 years of age) were recruited. We collected their resting-state fMRI and behavioural data. Restricted sex differences were found in the resting-state reading neural network compared to extensive age by sex interaction effect. Specifically, the interactions between sex and age indicated that with increasing age, girls showed greater connectivity strength between visual orthographic areas and other brain areas within the reading network, while boys showed an opposite trend. After controlling age, the prediction models of reading performance for the girls mainly included interhemispheric connections, while the intrahemispheric connections (particularly the phonological route) mainly contributed to predicting the reading ability for boys. Taken together, these findings suggest that sex differences in reading neural networks are modulated by age. Partialling out age, boys and girls also show the stable sex differences in relationship between reading neural circuit and reading behaviour.

Adaptation in cone photoreceptors contributes to an unexpected insensitivity of primate On parasol retinal ganglion cells to spatial structure in natural images.

Neural circuits are constructed from nonlinear building blocks, and not surprisingly overall circuit behavior is often strongly nonlinear. But neural circuits can also behave near linearly, and some circuits shift from linear to nonlinear behavior depending on stimulus conditions. Such control of nonlinear circuit behavior is fundamental to neural computation. Here, we study a surprising stimulus dependence of the responses of macaque On (but not Off) parasol retinal ganglion cells: these cells respond nonlinearly to spatial structure in some stimuli but near linearly to spatial structure in others, including natural inputs. We show that these differences in the linearity of the integration of spatial inputs can be explained by a shift in the balance of excitatory and inhibitory synaptic inputs that originates at least partially from adaptation in the cone photoreceptors. More generally, this highlights how subtle asymmetries in signaling - here in the cone signals - can qualitatively alter circuit computation.

Quantum state preparation by adiabatic evolution with custom gates

Quantum state preparation by adiabatic evolution is currently rendered ineffective by the long implementation times of the underlying quantum circuits, comparable to the decoherence time of present and near-term quantum devices. These implementation times can be significantly reduced by realizing these circuits with custom gates. Using classical computing, we model the output of a realistic two-qubit processor implementing the adiabatic evolution of a two-spin system by means of custom gates. This modeled output is then compared with the results of quantum simulations solving the same problem on IBM Quantum (IBMQ) systems. When used to emulate the behavior of the IBMQ quantum circuit, our realistic model yields state fidelities ranging from 65% to 85%, similar to the actual performance of a diverse set of IBMQ devices. When we reduced the implementation time by using custom gates, however, the loss of fidelity was reduced by at least a factor of 4, allowing us to accurately extract the energy of the target state. This improvement is enough to render adiabatic evolution useful for quantum state preparation for small systems or as a preconditioner for other state preparation methods.

Decoupling astrocytes in adult mice impairs synaptic plasticity and spatial learning.

The mechanisms by which astrocytes modulate neural homeostasis, synaptic plasticity, and memory are still poorly explored. Astrocytes form large intercellular networks by gap junction coupling, mainly composed of two gap junction channel proteins, connexin 30 (Cx30) and connexin 43 (Cx43). To circumvent developmental perturbations and to test whether astrocytic gap junction coupling is required for hippocampal neural circuit function and behavior, we generate and study inducible, astrocyte-specific Cx30 and Cx43 double knockouts. Surprisingly, disrupting astrocytic coupling in adult mice results in broad activation of astrocytes and microglia, without obvious signs of pathology. We show that hippocampal CA1 neuron excitability, excitatory synaptic transmission, and long-term potentiation are significantly affected. Moreover, behavioral inspection reveals deficits in sensorimotor performance and a complete lack of spatial learning and memory. Together, our findings establish that astrocytic connexins and an intact astroglial network in the adult brain are vital for neural homeostasis, plasticity, and spatial cognition.

Analytical Study of Piezoelectric Harvesters With SECE and SSHI Under Variable Excitation

A theoretical analysis aimed at predicting the power extracted from resonant piezoelectric vibration energy harvesters feeding synchronous switching-type electronic interfaces under variable mechanical excitation is proposed. In particular, for the widely used synchronous electric charge extraction (SECE) and synchronized switching harvesting on an inductor (SSHI) switching interfaces, the nonlinear equations describing the circuit behavior are solved under reasonable assumptions, and closed-form expressions of the extracted power are derived as a function of the circuit parameters and of the vibration characteristics, in the case of modulated mechanical vibrations. Numerical and experimental results validate the proposed closed-form expressions and show their usefulness for design purposes.

An 85-GHz Power Amplifier Utilizing a Transformer-Based Power Combiner Operating Beyond the Self-Resonance Frequency

In this study, we demonstrate an 85-GHz eight-way four-stage power amplifier (PA) with transformer-based power combiners (PCs) and power splitters (PSs) operating beyond their self-resonance frequencies in 65-nm CMOS. An equivalent two-port network of a multiport PC/PS was developed to investigate the circuit behavior for estimating the possible operating frequency ranges and performing optimal impedance matching of the PA with PCs/PSs. From the results, the PA achieved a power gain of 29.3 dB with a 3-dB gain bandwidth recorded from 82.7 to 86.7 GHz, a saturated output power of 19.1 dBm with an output 1-dB gain compression point of 16.2 dBm, and a peak power-added efficiency of 8.6%. The total chip size is 0.72 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , and the core size excluding pads is only 0.172 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .

Mid-infrared silicon-on-insulator waveguides with single-mode propagation over an octave of frequency.

Increasing the working optical bandwidth of a photonic circuit is important for many applications, in particular chemical sensing at mid-infrared wavelengths. This useful bandwidth is not only limited by the transparency range of waveguide materials, but also the range over which a waveguide is single or multimoded for predictable circuit behaviour. In this work, we show the first experimental demonstration of "endlessly single-mode" waveguiding in silicon photonics. Silicon-on-insulator waveguides were designed, fabricated and characterised at 1.95 µm and 3.80 µm. The waveguides were shown to support low-loss propagation (1.46 ± 0.13 dB/cm loss at 1.95 µm and 1.55 ± 0.35 dB/cm at 3.80 µm) and single-mode propagation was confirmed at 1.95 µm, meaning that only the fundamental mode was present over the wavelength range 1.95 - 3.80 µm. We also present the prospects for the use of these waveguides in sensing applications.

EIS Investigation of the Corrosion Behavior of Steel Bars Embedded into Modified Concretes with Eggshell Contents

This investigation is focused on evaluation of the corrosion behavior of embedded steel bars (SB) into concretes. Conventional and modified concretes with eggshell are prepared. Although the effect of calcium carbonate on mechanical behavior is recognized and reported, their effects as eggshell (ES) particles replacing portions of sand and cement contents are reasonably scarce. Corrosion behavior is evaluated by electrochemical impedance spectroscopy (EIS) and the potentiodynamic polarization technique. Equivalent circuit and porous electrode behavior are also considered. The novelty concerns a promising use of concrete with ES content to maintain corrosion resistance concatenated with reasonable structural properties. For this purpose, three distinct concrete mixtures are proposed, i.e., a reference and two modified concretes. One replaces 10 wt.% with cement and another 10 wt.% with sand content. It is found that porous electrode behavior helps to predict the corrosion mechanism. Finer ES particles in concrete mixture provides a rapidly passivation on rebar. This reflects positively in corrosion current density after long-term immersion. Additionally, an environmentally friendly aspect associated with economical factor constitutes a promise use of the concrete.

A Tree-Based Architecture for High-Performance Ultra-Low-Voltage Amplifiers

In this paper, we introduce a novel tree-based architecture which allows the implementation of Ultra-Low-Voltage (ULV) amplifiers. The architecture exploits a body-driven input stage to guarantee a rail-to-rail input common mode range and body-diode loading to avoid Miller compensation, thanks to the absence of high-impedance internal nodes. The tree-based structure improves the CMRR of the proposed amplifier with respect to the conventional OTA architectures and allows achievement of a reasonable CMRR even at supply voltages as low as 0.3 V and without tail current generators which cannot be used in ULV circuits. The bias currents and the static output voltages of all the stages implementing the architecture are accurately set through the gate terminals of biasing transistors in order to guarantee good robustness against PVT variations. The proposed architecture and the implementing stages are investigated from an analytical point of view and design equations for the main performance metrics are presented to provide insight into circuit behavior. A 0.3 V supply voltage, subthreshold, ultra-low-power (ULP) OTA, based on the proposed tree-based architecture, was designed in a commercial 130 nm CMOS process. Simulation results show a dc gain higher than 52 dB with a gain-bandwidth product of about 35 kHz and reasonable values of CMRR and PSRR, even at such low supply voltages and considering mismatches. The power consumption is as low as 21.89 nW and state-of-the-art small-signal and large-signal FoMs are achieved. Extensive parametric and Monte Carlo simulations show the robustness of the proposed circuit to PVT variations and mismatch. These results confirm that the proposed OTA is a good candidate to implement ULV, ULP, high performance analog building blocks for directly harvested IoT nodes.

Modeling pandemic surges using an electric circuit analogue

The spread of COVID-19 has much in common with the behavior of an electric circuit, which can be used to predict pandemic trends.

Double-edged role of resource competition in gene expression noise and control.

Despite extensive investigation demonstrating that resource competition can significantly alter the deterministic behaviors of synthetic gene circuits, it remains unclear how resource competition contributes to the gene expression noise and how this noise can be controlled. Utilizing a two-gene circuit as a prototypical system, we uncover a surprising double-edged role of resource competition in gene expression noise: competition decreases noise through introducing a resource constraint but generates its own type of noise which we name as "resource competitive noise." Utilization of orthogonal resources enables retainment of the noise reduction conferred by resource constraint while removing the added resource competitive noise. The noise reduction effects are studied using three negative feedback types: negatively competitive regulation (NCR), local, and global controllers, each having four placement architectures in the protein biosynthesis pathway (mRNA or protein inhibition on transcription or translation). Our results show that both local and NCR controllers with mRNA-mediated inhibition are efficacious at reducing noise, with NCR controllers demonstrating a superior noise-reduction capability. We also find that combining feedback controllers with orthogonal resources can improve the local controllers. This work provides deep insights into the origin of stochasticity in gene circuits with resource competition and guidance for developing effective noise control strategies.

Modulation of inspiratory burst duration and frequency by bombesin in vitro.

Mammalian respiratory rhythm-generating circuits in the brainstem are subject to neuromodulation by multiple peptidergic afferent inputs controlling circuit behavior and outputs. Although functionally important, actions of neuropeptide modulators have not been fully characterized. We analyzed at cellular and circuit levels two inspiratory patterns intrinsically generated by the preBötzinger complex (preBötC) and their modulation by the neuropeptides bombesin and substance P (SP) in neonatal rat medullary slices in vitro. We found that, in recordings of hypoglossal nerve and preBötC neuron inspiratory activity, some inspiratory bursts occurring spontaneously under basal conditions have a biphasic shape with longer duration than normal inspiratory bursts and occur at a lower frequency. This biphasic burst pattern has been proposed to represent inspiratory activity underling periodic sighs. Bath-applied bombesin or SP decreased the period and increased the duration of both normal inspiratory and biphasic bursts and their underlying synaptic drives. The ratio of the biphasic long-duration burst period to the normal inspiratory burst period and the ratio of their burst durations remained the same before and after peptidergic modulation. Bombesin increased the frequency of the inspiratory rhythm in a Ca2+-independent manner and the frequency of long-duration bursts in a Ca2+-dependent manner. This finding suggests that period and burst duration coupling are due to intrinsic mechanisms controlling simultaneously timing and burst termination within the inspiratory rhythm-generating network. We propose a model in which signaling cascades activated by bombesin and SP modulate mechanisms controlling inspiratory burst frequency and duration to coordinate preBötC circuit behavioral outputs.

Atomistic simulation toward real-scale microprocessor circuits

A highly efficient and novel atomistic simulation framework is first established for the thermal and mechanical behaviors of a whole microprocessor chip or its constituent functional modules, important for the performance and reliability of high-end microprocessor circuits. The largest simulated module contains about 55.3 thousand nano-transistors with around 107 billion atoms. Traditionally, the macroscopic continuous methods are difficult to treat nanoscale factors such as doping, thin dielectric layer, surface and interface in the nano-transistor devices, while the microscopic quantum mechanics method can only calculate one or several nano-transistors. This proposed simulation method realizes the integrated treatment of the above nanoscale factors and complex gate layout by coupling multiple interatomic potential models for different materials and designing efficient parallel algorithms, and bridges the mesoscale simulation gap between the aforementioned macroscopic and microscopic methods. The development provides the first atomic-scale simulation framework for predicting and modulating the thermal behavior of a microprocessor circuit or its functional module, which paves an exciting way to the atomic-resolution design of novel high-performance microprocessor chips in the post-Moore era.

Variational quantum classifiers through the lens of the Hessian.

In quantum computing, the variational quantum algorithms (VQAs) are well suited for finding optimal combinations of things in specific applications ranging from chemistry all the way to finance. The training of VQAs with gradient descent optimization algorithm has shown a good convergence. At an early stage, the simulation of variational quantum circuits on noisy intermediate-scale quantum (NISQ) devices suffers from noisy outputs. Just like classical deep learning, it also suffers from vanishing gradient problems. It is a realistic goal to study the topology of loss landscape, to visualize the curvature information and trainability of these circuits in the existence of vanishing gradients. In this paper, we calculate the Hessian and visualize the loss landscape of variational quantum classifiers at different points in parameter space. The curvature information of variational quantum classifiers (VQC) is interpreted and the loss function’s convergence is shown. It helps us better understand the behavior of variational quantum circuits to tackle optimization problems efficiently. We investigated the variational quantum classifiers via Hessian on quantum computers, starting with a simple 4-bit parity problem to gain insight into the practical behavior of Hessian, then thoroughly analyzed the behavior of Hessian’s eigenvalues on training the variational quantum classifier for the Diabetes dataset. Finally, we show how the adaptive Hessian learning rate can influence the convergence while training the variational circuits.

A novel cache based on dynamic mapping against speculative execution attacks

The Spectre attacks exploit the speculative execution vulnerabilities to exfiltrate private information by building a leakage channel. Creation of a leakage channel is the basic element for spectre attacks, among which the cache-tag side channel is considered to be the most serious one. To block the leakage channels, a novel cache applies Dynamic Mapping technology, named DmCache, is presented in this paper. DmCache applies a dynamic mapping mechanism to temporarily store all the cache lines polluted by speculative execution and keep invisible when accessing. Then it monitors the head of the reorder buffer to determine which polluted cache line can become visible. In this paper, we demonstrated that Spectre attacks exerted no impact on a processor system equipped with DmCache based on the analysis of the processor’s circuit behaviour, which equipped with the DmCache and under the Spectre attack.

Reconstruction of unintended interactions in synthetic gene circuits from time-series perturbation experiments

Synthetic gene circuit behaviour can be qualitatively different from the intended one due to effects from unintended interactions with the host cell. Such unintended interactions are essentially inevitable, since the circuits rely on the cellular resources in the host cell. The paper considers the feasibility of reconstructing such unintended interactions from time-series perturbation experiments. Identifying the specific interactions with the host cell is a prerequisite to mitigate their effects on the circuit behaviour. Previously proposed methods either cannot distinguish such unintended interactions from the intended direct interactions between circuit genes or require perturbing all the circuit genes by unique perturbations in separate experiments, which typically imply very costly experiments. We show herein that it in principle is feasible to identify all unintended interactions with the host cell from one single perturbation experiment. We also discuss fundamental limitations of steady-state data. The results are illustrated using a recently implemented three-gene cascade synthetic circuit in E. coli.