Circuit Complexity Research Articles

Central mechanisms of muscle tone regulation: implications for pain and performance

Muscle tone represents a foundational property of the motor system with the potential to impact musculoskeletal pain and motor performance. Muscle tone is involuntary, dynamically adaptive, interconnected across the body, sensitive to postural demands, and distinct from voluntary control. Research has historically focused on pathological tone, peripheral regulation, and contributions from passive tissues, without consideration of the neural regulation of active tone and its consequences, particularly for neurologically healthy individuals. Indeed, simplistic models based on the stretch reflex, which neglect the central regulation of tone, are still perpetuated today. Recent advances regarding tone are dispersed across different literatures, including animal physiology, pain science, motor control, neurology, and child development. This paper brings together diverse areas of research to construct a conceptual model of the neuroscience underlying active muscle tone. It highlights how multiple tonic drive networks tune the excitability of complex spinal feedback circuits in concert with various sources of sensory feedback and in relation to postural demands, gravity, and arousal levels. The paper also reveals how tonic muscle activity and excitability are disrupted in people with musculoskeletal pain and how tone disorders can lead to marked pain and motor impairment. The paper presents evidence that integrative somatic methods address the central regulation of tone and discusses potential mechanisms and implications for tone rehabilitation to improve pain and performance.

Metaplasticity-Enabled Graphene Quantum Dot Devices for Mitigating Catastrophic Forgetting in Artificial Neural Networks.

The limitations of deep neural networks in continuous learning stem from oversimplifying the complexities of biological neural circuits, often neglecting the dynamic balance between memory stability and learning plasticity. In this study, artificial synaptic devices enhanced with graphene quantum dots (GQDs) that exhibit metaplasticity is introduced, a higher-order form of synaptic plasticity that facilitates the dynamic regulation of memory and learning processes similar to those observed in biological systems. The GQDs-assisted devices utilize interface-mediated modifications in asymmetric conductive pathways, replicating classical synaptic plasticity mechanisms. This allows for repeatable and linearly programmable adjustments to future weight changes linked to historical weights. Incorporating metaplasticity is essential for achieving generalization within deep neural networks, which enables them to adapt more fluidly to new information while retaining previously acquired knowledge. The GQDs-device-based system achieved a 97% accuracy on the fourth MNIST dataset task, while consistently achieving performance levels above 94% on prior tasks. This performance substantiates the feasibility of directly transferring metaplasticity principles to deep neural networks, thereby addressing the challenges associated with catastrophic forgetting. These findings present a promising hardware solution for developing neuromorphic systems with robust and sustained learning capabilities that can effectively bridge the gap between artificial and biological neural networks.

A thin film lithium niobate near-infrared platform for multiplexing quantum nodes

Practical quantum networks will require multi-qubit quantum nodes. This in turn will increase the complexity of the photonic circuits needed to control each qubit and require strategies to multiplex memories. Integrated photonics operating at visible to near-infrared (VNIR) wavelength range can provide solutions to these needs. In this work, we realize a VNIR thin-film lithium niobate (TFLN) integrated photonics platform with the key components to meet these requirements, including low-loss couplers (<1 dB/facet), switches (>20 dB extinction), and high-bandwidth electro-optic modulators (>50 GHz). With these devices, we demonstrate high-efficiency and CW-compatible frequency shifting (>50% efficiency at 15 GHz), as well as simultaneous laser amplitude and frequency control. Finally, we highlight an architecture for multiplexing quantum memories and outline how this platform can enable a 2-order of magnitude improvement in entanglement rates over single memory nodes. Our results demonstrate that TFLN can meet the necessary performance and scalability benchmarks to enable large-scale quantum nodes.

A Biomolecular Circuit for Automatic Gene Regulation in Mammalian Cells with CRISPR Technology.

We introduce a biomolecular circuit for precise control of gene expression in mammalian cells. The circuit leverages the stochiometric interaction between the artificial transcription factor VPR-dCas9 and the anti-CRISPR protein AcrIIA4, enhanced with synthetic coiled-coil domains to boost their interaction, to maintain the expression of a reporter protein constant across diverse experimental conditions, including fluctuations in protein degradation rates and plasmid concentrations, by automatically adjusting its mRNA level. This capability, known as robust perfect adaptation (RPA), is crucial for the stable functioning of biological systems and has wide-ranging implications for biotechnological applications. This system belongs to a class of biomolecular circuits named antithetic integral controllers, and it can be easily adapted to regulate any endogenous transcription factor thanks to the versatility of the CRISPR-Cas system. Finally, we show that RPA also holds in cells genomically integrated with the circuit, thus paving the way for practical applications in biotechnology that require stable cell lines.

A modular entanglement-based quantum computer architecture

Abstract We propose a modular quantum computation architecture based on utilizing multipartite entanglement. Each module consists of a small-scale quantum computer comprising data, memory and entangling qubits. Entangling qubits are used to selectively couple different modules by harnessing some non-controllable, distance-dependent interaction, which is effectively controlled and enhanced via a proper adjusting of the internal state of the qubits. In this way, multipartite entangled states with different entanglement topologies can be shared between modules. These states are stored in memory qubits where they can be further processed so they can eventually be used to deterministically perform certain classes of gates or circuits between modules on demand, including parallel controlled-\textit{Z} gates with arbitrary interaction patterns, multi-qubit gates or whole Clifford circuits, depending on their entanglement structure. The usage of different kinds of multipartite entanglement rather than Bell pairs allows for more efficient and flexible coupling between modules, leading to a scalable quantum computation architecture.

The glutamatergic projections from the PVT to mPFC govern methamphetamine-induced conditional place preference behaviors in mice

BackgroundDrug addiction is closely related to the dysregulation of complex neural circuits. However, the neural connections underlying the symptoms of methamphetamine (METH)-induced addiction have yet to be elucidated. MethodsWe conducted ΔFosB (Delta FBJ murine osteosarcoma viral oncogene homolog B) associated immunofluorescence and electrophysiological recording experiments to measure the neural activity of paraventricular thalamus (PVT) neurons in METH treated mice. Then, the METH-mediated conditional place preference (CPP) behaviors were evaluated after chemogenetic manipulation of PVT neurons. Additionally, the neural projection from PVT to medial prefrontal cortex (mPFC) was verified through Adeno-associated virus (AAV) mediating neural tracing method, and its role on METH-mediated CPP behaviors was determined using chemogenetic and neural ablation strategies. ResultsWe found that glutamatergic neurons in PVT were activated by METH. Activating the glutamatergic neurons in PVT promoted the METH-mediated CPP behaviors, while inhibiting these neurons attenuated the CPP behaviors. Moreover, we observed PVT neurons showed robust neuronal projections to mPFC, activation of the mPFC→projecting neurons in PVT or the afferent terminals in mPFC derived from PVT enhanced METH-mediated CPP performance, and ablating the mPFC neurons receipting neural projection from PVT impeded these increased METH-mediated CPP phenotypes. LimitationsThe underlying molecular mechanism of the dysfunctional PVT neurons after METH treatment and the PVT neurons regulating the activity of mPFC target neurons remains unclear. ConclusionsThese results shed light on that PVT is a key METH addiction-controlling nucleus, and PVT → mPFC projection regulated METH-mediated CPP behaviors, which could serve as a vital pathway for morbidity and treatment for METH-mediated addiction.

Optogenetic control of dopamine receptor 2 reveals a novel aspect of dopaminergic neurotransmission in motor function.

Dopaminergic neurotransmission plays a crucial role in motor function through the coordination of dopamine receptor (DRD) subtypes, such as DRD1 and DRD2, thus the functional imbalance of these receptors can lead to Parkinson's disease. However, due to the complexity of dopaminergic circuits in the brain, it is limited to investigating the individual functions of each DRD subtype in specific brain regions. Here, we developed a light-responsive chimeric DRD2, OptoDRD2, which can selectively activate DRD2-like signaling pathways with spatiotemporal resolution. OptoDRD2 was designed to include the light-sensitive component of rhodopsin and the intracellular signaling domain of DRD2. Upon illumination with blue light, OptoDRD2 triggered DRD2-like signaling pathways, such as Gαi/o subtype recruitment, a decrease in cAMP levels, and ERK phosphorylation. To explore unknown roles of DRD2 in glutamatergic cell populations of basal ganglia circuitry, OptoDRD2 was genetically expressed in excitatory neurons in lateral globus pallidus (LGP) of the male mouse brain. The optogenetic stimulation of OptoDRD2 in the LGP region affected a wide range of locomotion-related parameters, such as increased frequency of movement and decreased immobility time, resulting in the facilitation of motor function of living male mice. Therefore, our findings indicate a potential novel role for DRD2 in the excitatory neurons of the LGP region, suggesting that OptoDRD2 can be a valuable tool enabling the investigation of unknown roles of DRD2 at specific cell types or brain regions.Significance Statement We developed a light-responsive chimeric dopamine receptor type 2, OptoDRD2, by combining the blue-light sensing part of rhodopsin and intracellular functional regions of DRD2. OptoDRD2 can selectively trigger DRD2-like downstream signaling pathways upon illumination of blue light. To explore unknown roles of DRD2 in glutamatergic cell populations of basal ganglia circuitry, OptoDRD2 was genetically expressed in excitatory neurons at lateral globus pallidus (LGP) in the mouse brain. Optogenetic stimulation of OptoDRD2 in living mice suggested a potential novel function of DRD2 in the LGP that enhances motor outputs. Therefore, OptoDRD2 enabled the precise control of DRD2-like signaling in specific cell types and brain regions, allowing the exploration of potential novel DRD2 functions in living mice.

Accurate modeling and parameters estimation of photovoltaic models: Analytical and artificial intelligence solutions

The single-diode model is a widely used framework for simulating solar cell behavior. It consists of a diode, a current source, and two resistors to replicate real-world performance. This work introduces three alternative single-diode equivalent circuits with five parameters each. The current-voltage relationships of these equivalent circuits are derived using the Lambert W function, and a closed-form analytical solution is presented using Special Trans Function Theory (STFT). Additionally, the paper introduces a chaotic variant of the Gorilla Troops Optimizer (GTO), referred to as CGTO, for parameter estimation. The effectiveness of the proposed alternative single-diode models and the CGTO algorithm was validated by comparing their accuracy, using root mean square error (RMSE) metrics, against traditional models across various solar cells and conditions (different values of irradiance and temperature). The analysis confirmed that the alternative configurations could achieve comparable precision in modeling solar cell characteristics under different conditions, challenging the exclusivity of the standard single-diode model's configuration. This indicates that various single-diode solar cell models can effectively represent the current-voltage behavior of solar cells. Furthermore, the configuration of elements in the classic SDM equivalent circuit is not definitive or the most precise. This opens the door for the creation of more sophisticated models of solar cells with diverse element configurations.

PW-FBPNN: A Hybrid Fault Diagnosis Method for Power Circuit Systems Combining Principal Component Analysis, Wavelet Packet Transform, and Fuzzy Neural Networks

Due to the complexity of fault states and the non-linear relationship between input and output responses, fault diagnosis in complex power circuit systems faces significant challenges. This study proposes a novel hybrid method, PW-FBPNN, which integrates principal component analysis (PCA), wavelet packet transform (WPT), and fuzzy back propagation neural network (FBPNN) to enhance fault diagnosis. The effectiveness of this method was demonstrated through experiments on the voltage divider basic operational amplifier and the second-order filter circuit of the four operational amplifiers. PW-FBPNN achieved 100% accuracy in diagnosing most types of faults, with a minimum accuracy of 91.67% for challenging faults. This method was significantly superior to existing methods such as FCM-HMM-SVM and KICA-DNN in terms of accuracy and computational efficiency and could complete the diagnosis in just 0.01 seconds. These results indicate that PW-FBPNN has the potential to improve fault diagnosis in power circuit systems, providing a promising solution for enhancing system reliability and maintenance efficiency.

An Enhanced Detection Method of Hardware Trojan Based on CNN- Attention-LSTM

The security issues caused by the insertion of hardware Trojans seriously threaten the security and reliability of the entire hardware device. This article constructs a detection model that combines convolutional neural networks (CNN) and long short-term memory networks (LSTM), and introduces attention mechanism to enhance the model's ability to recognize complex circuits. This method can automatically learn and optimize feature extraction and classification models, reduce reliance on manual experience through training on large amounts of data, and improve the intelligence level of detection. Especially, by combining attention mechanisms and LSTM models, it is possible to more effectively capture small anomalies in circuit design and improve the accuracy and efficiency of hardware Trojan detection. The experimental results show that the proposed CNN-Attention-LSTM model exhibits superior Trojan detection performance and good generalization ability on different datasets, with an precision of 96.3%, a recall rate of 94.7%, and an F1 score of 95.5%.

Unified linear response theory of quantum electronic circuits

Modeling the electrical response of multi-level quantum systems at finite frequency has been typically performed in the context of two incomplete paradigms: (i) input-output theory, which is valid at any frequency but neglects dynamic losses, and (ii) semiclassical theory, which captures dynamic dissipation effects well but is only accurate at low frequencies. Here, we develop a unifying theory, valid for arbitrary frequencies, that captures both the small-signal quantum behavior and the non-unitary effects introduced by relaxation and dephasing. The theory allows a multi-level system to be described by a universal small-signal equivalent-circuit model, a resonant RLC circuit, whose topology only depends on the number of energy levels. We apply our model to a double-quantum-dot charge qubit and a Majorana qubit, showing the capability to continuously describe the systems from adiabatic to resonant and from coherent to incoherent, suggesting new and realistic experiments for improved quantum state readout. Our model will facilitate the design of hybrid quantum–classical circuits and the simulation of qubit control and quantum state readout.

A Study of High Transient Response Low Dropout Linear Regulator Chips

Abstract. With the rapid development of the microelectronics industry in the world, electronic products are being updated more and more quickly. This has created a growing large market demand for power management chips. Low drop-out linear regulators have become one of the most widely used types of power management chips due to their advantages of high stability, low drop-out voltage, and fast response. This paper is a data collection study and comparison of improvement methods based on high transient response low drop-out linear regulators. It describes the basic principles of LDOs as well as the principles, advantages and disadvantages of different improvement methods. It was found that the method of shortening the overshoot and undershoot durations by constructing transient enhancement circuits was the best in terms of performance among the following research methods. The stability of capacitor-less LDO is harder to control. Using compensation circuits to improve stability will increase circuit complexity. The capacitor-coupled current mirror method has a longer startup time, because it takes some time for it to reach homeostasis. The high complexity and energy consumption of the dual feedback loop structure makes the circuit costly.

Shallower CNOT circuits on realistic quantum hardware

We focus on the depth optimization of CNOT circuits on hardwares with limited connectivity. We adapt the algorithm from Kutin et al. [17] that implements any n -qubit CNOT circuit in depth at most 5 n on a Linear Nearest Neighbour (LNN) architecture. Our proposal is a block version of Kutin et al. ’s algorithm that is scalable with the number of interactions available in the hardware: the more interactions we have the less the depth. We derive better theoretical upper bounds and we provide a simple implementation of the algorithm. Overall, we achieve better depth complexity for CNOT circuits on some realistic quantum hardware like a grid or a ladder. For instance the execution of a n -qubit CNOT circuit on a grid can be done in depth 4 n + 8.

Construction of multilayered gene circuits using de-novo-designed synthetic transcriptional regulators in cell-free systems

BackgroundDe-novo-designed synthetic transcriptional regulators have great potential as the genetic parts for constructing complex multilayered gene circuits. The design flexibility afforded by advanced nucleic acid sequence design tools vastly expands the repertoire of regulatory elements for circuit design. In principle, the design space of synthetic regulators should allow for the construction of regulatory circuits of arbitrary complexity; still, the orthogonality and robustness of such components have not been fully elucidated, thereby limiting the depth and width of synthetic circuits.ResultsIn this work, we systematically explored the design strategy of synthetic transcriptional regulators, termed switchable transcription terminators. Specifically, by redesigning key sequence domains, we created a high-performance switchable transcription terminator with a maximum fold change of 283.11 upon activation by its cognate input RNA. Further, an automated design algorithm was developed for these elements to improve orthogonality for a complex multi-layered circuit construction. The resulting orthogonal switchable transcription terminators could be used to construct a three-layer cascade circuit and a two-input three-layer OR gate.ConclusionsWe demonstrated a practical strategy for designing standardized regulatory elements and assembling modular gene circuits, ultimately laying the foundation for the streamlined construction of complex synthetic gene circuits.

Chemi-Impeditive Sensing Platform Based on Single-Walled Carbon Nanotubes.

Chemical sensing methodology based on electrochemical impedance spectroscopy (EIS) targeting analytes in aqueous samples on functionalized single-walled carbon nanotube (SWCNT) is reported. The SWCNT in contact with electrolyte shows unique impedance spectra that cannot be analyzed with classical equivalent circuit models. Inspired by the charge transport property of mixed ionic-electronic conductors, we propose an equivalent circuit based on transmission line model (TLM), by which the impedance of the CNT-electrolyte system can be analyzed to track down all the equivalent circuit parameters. By combining multiple pieces of information, which are technically immeasurable with conventional chemiresistive or chemicapacitive techniques, several analyte species responding to the sensor can be differentiated from each other. We demonstrate the "chemi-impeditive" concept on chemically modified SWCNTs for detecting perfluoroalkyl substances (PFAS) in aqueous solutions. The EIS coupled with a fluorination chemistry on SWCNT surface provides unique changes in equivalent circuit components for each PFAS, i.e., changes in CNT and solution resistances, as well as interfacial CNT-solution capacitance, through which perfluorooctanesulfonic acid, perfluorooctanoic acid, hexafluoropropylene oxide dimer acid, and perfluorobutanesulfonic acid are detected in a discriminative manner. The new impedimetric method opens up new vistas in chemical sensing in that the EIS analysis provides an additional dimension of information beyond the single resistance or capacitance typically measured by many conventional types of sensors.

A NOVEL FAULT-TOLERANT GENERALIZED SYMMETRICAL TOPOLOGY FOR RENEWABLE ENERGY AND ELECTRIC VEHICLE APPLICATIONS

Multilevel inverters are a versatile and powerful technology for applications such as motor drives, high voltage direct current transmission (HVDC), renewable energy systems, and grid-connected applications. As the demand for high-quality, clean energy and low distortion continues to grow, multilevel inverters are preferred over traditional square wave inverters. This paper proposes a generalized 13-level symmetrical topology to meet the power quality requirements. It consists of six DC sources and twelve semiconductor devices. Further, the topology has cascaded to increase the number of levels, but the cost and circuit complexity increase with the number of units. The gate pulses for switches are produced by the nearest-level modulation technique. The performance comparison of the topology with recent literature provides comprehensive information about the novelty of the configuration. The MLI performance parameters are number of switches, number of gates driving circuits, total harmonic distortion (THD), cost function per level (CCPL), total standing voltage (TSV), and efficiency (η). MATLAB/Simulink investigates the proposed topology, and further, to validate its simulation output, the hardware prototype model has been implemented in the laboratory.

A non‐isolated modified ultra high step‐up quadratic boost converter with classical voltage doubler circuit

AbstractThis paper proposes a modified ultra‐high gain DC–DC boost converter. The proposed converter has improved the performance of the conventional converter by reducing the total number of components (1 Capacitor and 1 Inductor) in the design structure. In addition, the continuous input current and high voltage gain features are retained in the proposed structure. The steady‐state modelling including the operating principles and the stress calculation along with the cost analysis of the components have been illustrated. The comparative performance analysis has been carried out to clarify the competitiveness of the proposed converter against the recent models. The proposed model has been simulated in Matlab/Simulink software and further, a 100 W laboratory prototype has been built to evaluate the performance of the proposed converter. The peak efficiency received was 90.5% while delivering 100 W power from the source. The results from the experiments have aligned well with the outcomes of the simulation and verify the correctness of the proposed converter.

Modulation of stress-related behaviour by preproglucagon neurons and hypothalamic projections to the nucleus of the solitary tract

Stress-induced behaviours are driven by complex neural circuits and some neuronal populations concurrently modulate diverse behavioural and physiological responses to stress. Glucagon-like peptide-1 (GLP-1)-producing preproglucagon (PPG) neurons within the lower brainstem caudal nucleus of the solitary tract (cNTS) are particularly sensitive to stressful stimuli and are implicated in multiple physiological and behavioural responses to interoceptive and psychogenic threats. However, the afferent inputs driving stress-induced activation of PPG neurons are largely unknown, and the role of PPG neurons in anxiety-like behaviour is controversial. Through chemogenetic manipulations we reveal that cNTS PPG neurons have the ability to moderately increase anxiety-like behaviours in mice in a sex-dependent manner. Using an intersectional approach, we show that input from the paraventricular nucleus of the hypothalamus (PVN) drives activation of both the cNTS as a whole and PPG neurons in particular in response to acute restraint stress, but that while this input is rich in corticotropin-releasing hormone (CRH), PPG neurons do not express significant levels of receptors for CRH and are not activated following lateral ventricle delivery of CRH. Finally, we demonstrate that cNTS-projecting PVN neurons are necessary for the ability of restraint stress to suppress food intake in male mice. Our findings reveal sex differences in behavioural responses to PPG neural activation and highlight a hypothalamic-brainstem pathway in stress-induced hypophagia.

Cryogenic optical-to-microwave conversion using Si photonic integrated circuit Ge photodiodes

Integrated circuit technology enables the scaling of circuit complexity and functionality while maintaining manufacturability and reliability. Integration is expected to play an important role in quantum information technologies, including in the highly demanding task of producing the classical signals to control and measure quantum circuits at scales needed for fault-tolerant quantum computation. Here, we experimentally characterize the cryogenic performance of a miniaturized photonic integrated circuit fabricated by a commercial foundry that downconverts classical optical signals into microwave signals. The circuit consists of waveguide-integrated germanium PIN photodiodes packaged using a scalable photonic wire bonding approach to a multi-channel optical fiber array that provides the optical excitation. We find the peak optical-to-microwave conversion response to be ∼150 ± 13 mA/W in the O-band at 4.2 K, well below the temperature the circuit was designed for and tested at in the past, for two different diode designs. The second diode design operates to over 6 GHz of 3 dB bandwidth, making it suitable for controlling quantum circuits, with improvements in bandwidth and response expected from improved packaging. The demonstrated miniaturization and integration offers new perspectives for wavelength-division multiplexed control of microwave quantum circuits and scalable processors using light delivered by optical fiber arrays.

Approximate single precision floating point adder for low power applications

With an increasing demand for power-hungry data-intensive computing, design methodologies with low power consumption are increasingly gaining prominence in the industry. Most of the systems operate on critical and noncritical data both. An attempt to generate a precision result results in excessive power consumption and results in a slower system. For noncritical data, approximate computing circuits significantly reduce the circuit complexity and hence power consumption. In this paper, a novel approximate single precision floating point adder is proposed with an approximate mantissa adder. The mantissa adder is designed with three 8-bit full adder blocks. In this paper, a detailed mathematical background, and proposed design approach in terms of the circuit configuration and truth tables are discussed. Additionally, a concept of switching between exact computing and approximate computing is analysed considering an approximate carry look-ahead adder. The delay and power consumption for the exact operating mode and approximate operation mode considering varied window sizes is observed. Performance of the approximate computation is compared against exact computation and varied approximate computing approaches.