Circuit Architecture Research Articles

Developmental Timing of Associations Among Parenting, Brain Architecture, and Mental Health.

Parenting is associated with brain development and long-term health outcomes, although whether these associations depend on the developmental timing of exposure remains understudied. Identifying these sensitive periods can inform when and how parenting is associated with neurodevelopment and risk for mental illness. To characterize how harsh and warm parenting during early, middle, and late childhood are associated with brain architecture during adolescence and, in turn, psychiatric symptoms in early adulthood during the COVID-19 pandemic. This population-based, 21-year observational, longitudinal birth cohort study of low-income youths and families from Detroit, Michigan; Toledo, Ohio; and Chicago, Illinois, used data from the Future of Families and Child Well-being Study. Data were collected from February 1998 to June 2021. Analyses were conducted from May to October 2023. Parent-reported harsh parenting (psychological aggression or physical aggression) and observer-rated warm parenting (responsiveness) at ages 3, 5, and 9 years. The primary outcomes were brainwide (segregation, integration, and small-worldness), circuit (prefrontal cortex [PFC]-amygdala connectivity), and regional (betweenness centrality of amygdala and PFC) architecture at age 15 years, determined using functional magnetic resonance imaging, and youth-reported anxiety and depression symptoms at age 21 years. The structured life-course modeling approach was used to disentangle timing-dependent from cumulative associations between parenting and brain architecture. A total of 173 youths (mean [SD] age, 15.88 [0.53] years; 95 female [55%]) were included. Parental psychological aggression during early childhood was positively associated with brainwide segregation (β = 0.30; 95% CI, 0.14 to 0.45) and small-worldness (β = 0.17; 95% CI, 0.03 to 0.28), whereas parental psychological aggression during late childhood was negatively associated with PFC-amygdala connectivity (β = -0.37; 95% CI, -0.55 to -0.12). Warm parenting during middle childhood was positively associated with amygdala centrality (β = 0.23; 95% CI, 0.06 to 0.38) and negatively associated with PFC centrality (β = -0.18; 95% CI, -0.31 to -0.03). Warmer parenting during middle childhood was associated with reduced anxiety (β = -0.05; 95% CI -0.10 to -0.01) and depression (β = -0.05; 95% CI -0.10 to -0.003) during early adulthood via greater adolescent amygdala centrality. Neural associations with harsh parenting were widespread across the brain in early childhood but localized in late childhood. Neural associations with warm parenting were localized in middle childhood and, in turn, were associated with mental health during future stress. These developmentally contingent associations can inform the type and timing of interventions.

A Wearable Multichannel Electrical Stimulator for Tremor Suppression

Abstract Pathological wrist and finger tremors can be mitigated by out-of-phase stimulation of the sensory nerves in the flexor and extensor muscles. This study endeavors to develop a portable, multichannel electrical stimulator designed to deliver high-frequency, low-intensity, biphasic charge-balanced current pulses within a practical load range to effectively suppress wrist and finger tremors. The circuit architecture integrates an Arduino Nanomicrocontroller, a four-channel digital potentiometer, and four Howland current pumps. The Howland current pump, employing an operational amplifier, generates bipolar currents by accepting bipolar voltages, whereas the digital potentiometer offers programmable bipolar voltages via its wiper terminals. Experimental results indicate that the pulse frequency can be adjusted between 50 Hz and 200 Hz, and the current level can be modulated within a range of −10 mA to +10 mA, with load resistance spanning 100 Ω to 1.3 kΩ. This portable and multichannel device is effective in delivering high-frequency, low-intensity sensory nerve stimulation through programmable biphasic charge-balanced current pulses. The proposed stimulator has the potential to enable adaptive muscle activation, thereby initiating, promoting, and generating movements, and facilitating interactive in-home neurorehabilitation for disabled patients. The portability and programmability of this device make it a promising tool for personalized tremor management and rehabilitation, enhancing the quality of life for individuals with motor impairments.

A Switchable Linear Regulator Implemented in GaAs pHEMT for WiFi Application

ABSTRACTCompared to silicon‐based complementary metal oxide semiconductor (CMOS), second‐generation semiconductor materials, such as gallium arsenide (GaAs) pseudomorphic high electron mobility transistor (pHEMT), offer significant advantages for radio frequency (RF) applications. As communication systems become more functional, designing highly integrated on‐chip transceiver systems presents a substantial challenge, where the voltage regulator as part of the power management module is indispensable. Due to lattice defects and other issues, achieving P‐channel metal oxide semiconductor (PMOS) in GaAs pHEMT is difficult, necessitating new circuit architectures to fulfill traditional linear regulator functions. To realize wireless fidelity (WiFi) transceiver systems on chip (SoC) using compound semiconductors for superior RF performance, this paper proposes a switchable linear regulator based on a 0.25 μm GaAs pHEMT process. The proposed linear regulator consumes a quiescent current of 440 μA in no‐load conditions and regulates a 5 V supply voltage down to 2.6 V output voltage. Measured results indicate that the designed linear regulator features a current load capacity of 20 mA, a load regulation rate of 5.8 mV/mA, and a line regulation rate of 1.8 mV/V. The proposed linear regulator circuit has been applied to WiFi transceiver systems, occupying a chip area of 0.054 mm2.

A Tunable Long Duration Pulse Generation Circuit in Mammalian Cells.

Pulse generator circuits based on incoherent feed-forward logic have been developed in bacterial, yeast, and mammalian systems but are typically limited to production of short pulses lasting less than 1 day. To generate longer-lasting pulses, we introduce a feedback-based topology that induces multiday pulsatile gene expression with tunable duration and amplitude in mammalian cells. We constructed the circuit using the PERSIST platform, which consists of entirely post-transcriptional logic, because our experience suggests that this approach may attenuate long-term epigenetic silencing. To enable external regulation of PERSIST regulatory elements, we engineered inducer-stabilized CRISPR endoRNases that respond to FDA-approved drugs, generating small molecule responses with greater than 20-fold change. These inducer-responsive proteins were connected to a two-state cross-repression positive feedback topology to generate the pulse generator circuit architecture. We then optimized circuit design through chromosomal integration of circuit components at varying stoichiometries, resulting in a small library of circuits displaying tunable pulses lasting between two and 6 days in response to a single 24 h input of inducer. We expect that the small molecule-stabilized PERSIST proteins developed will serve as valuable components in the toolbox for post-transcriptional gene circuit development and that tunable post-transcriptional pulse generator circuits in mammalian cells will enable study of endogenous hysteretic gene networks and support advances in cell therapies and organoid engineering.

Expressibility-induced concentration of quantum neural tangent kernels

Quantum tangent kernel methods provide an efficient approach to analyzing the performance of quantum machine learning models in the infinite-width limit, which is of crucial importance in designing appropriate circuit architectures for certain learning tasks. Recently, they have been adapted to describe the convergence rate of training errors in quantum neural networks in an analytical manner. Here, we study the connections between the expressibility and value concentration of quantum tangent kernel models. In particular, for global loss functions, we rigorously prove that high expressibility of both the global and local quantum encodings can lead to exponential concentration of quantum tangent kernel values to zero. Whereas for local loss functions, such issue of exponential concentration persists owing to the high expressibility, but can be partially mitigated. We further carry out extensive numerical simulations to support our analytical theories. Our discoveries unveil a fundamental feature of quantum neural tangent kernels, indicating that the issue of their concentration cannot be bypassed merely by transitioning to a local encoding scheme while maintaining high expressibility. This offers valuable insights for the design of wide quantum variational circuit models in practical applications.

Synaptic weight dynamics underlying memory consolidation: Implications for learning rules, circuit organization, and circuit function

Systems consolidation is a common feature of learning and memory systems, in which a long-term memory initially stored in one brain region becomes persistently stored in another region. We studied the dynamics of systems consolidation in simple circuit architectures with two sites of plasticity, one in an early-learning and one in a late-learning brain area. We show that the synaptic dynamics of the circuit during consolidation of an analog memory can be understood as a temporal integration process, by which transient changes in activity driven by plasticity in the early-learning area are accumulated into persistent synaptic changes at the late-learning site. This simple principle naturally leads to a speed-accuracy tradeoff in systems consolidation and provides insight into how the circuit mitigates the stability-plasticity dilemma of storing new memories while preserving core features of older ones. Furthermore, it imposes two constraints on the circuit. First, the plasticity rule at the late-learning site must stably support a continuum of possible outputs for a given input. We show that this is readily achieved by heterosynaptic but not standard Hebbian rules. Second, to turn off the consolidation process and prevent erroneous changes at the late-learning site, neural activity in the early-learning area must be reset to its baseline activity. We provide two biologically plausible implementations for this reset that propose functional roles in stabilizing consolidation for core elements of the cerebellar circuit.

Mosaic evolution of a learning and memory circuit in Heliconiini butterflies.

How do neural circuits accommodate changes that produce cognitive variation? We explore this question by analyzing the evolutionary dynamics of an insect learning and memory circuit centered within the mushroom body. Mushroom bodies are composed of a conserved wiring logic, mainly consisting of Kenyon cells, dopaminergic neurons, and mushroom body output neurons. Despite this conserved makeup, there is huge diversity in mushroom body size and shape across insects. However, empirical data on how evolution modifies the function and architecture of this circuit are largely lacking. To address this, we leverage the recent radiation of a Neotropical tribe of butterflies, the Heliconiini (Nymphalidae), which show extensive variation in mushroom body size over comparatively short phylogenetic timescales, linked to specific changes in foraging ecology, life history, and cognition. To understand how such an extensive increase in size is accommodated through changes in lobe circuit architecture, we combined immunostainings of structural markers, neurotransmitters, and neural injections to generate new, quantitative anatomies of the Nymphalid mushroom body lobe. Our comparative analyses across Heliconiini demonstrate that some Kenyon cell sub-populations expanded at higher rates than others in Heliconius and identify an additional increase in GABA-ergic feedback neurons, which are essential for non-elemental learning and sparse coding. Taken together, our results demonstrate mosaic evolution of functionally related neural systems and cell types and identify that evolutionary malleability in an architecturally conserved parallel circuit guides adaptation in cognitive ability.

Fast machine-learning-enabled size reduction of microwave components using response features

Achieving compact size has emerged as a key consideration in modern microwave design. While structural miniaturization can be accomplished through judicious circuit architecture selection, precise parameter tuning is equally vital to minimize physical dimensions while meeting stringent performance requirements for electrical characteristics. Due to the intricate nature of compact structures, global optimization is recommended, yet hindered by the excessive expenses associated with system evaluation, typically conducted through electromagnetic (EM) simulation. This challenge is further compounded by the fact that size reduction is a constrained problem entailing expensive constraints. This paper introduces an innovative method for cost-effective explicit miniaturization of microwave components on a global scale. Our approach leverages response feature technology, formulating the optimization problem based on a set of characteristic points derived from EM-analyzed responses, combined with an implicit constraint handling approach. Both elements facilitate handling size reduction by transforming it into an unconstrained problem and regularizing the objective function. The core search engine employs a machine-learning framework with kriging-based surrogates refined using the predicted improvement in the objective function as the infill criterion. Our algorithm is demonstrated using two miniaturized couplers and is shown superior over several benchmark routines, encompassing both conventional (gradient-based) and population-based procedures, alongside a machine learning technique. The primary strengths of the proposed framework lie in its reliability, computational efficiency (with a typical optimization cost ranging from 100 to 150 EM circuit analyses), and straightforward setup.

Exploiting Spatial Ionic Dynamics in Solid-State Organic Electrochemical Transistors for Multi-Tactile Sensing and Processing.

The human nervous system inspires the next generation of sensory and communication systems for robotics, human-machine interfaces (HMIs), biomedical applications, and artificial intelligence. Neuromorphic approaches address processing challenges; however, the vast number of sensors and their large-scale distribution complicate analog data manipulation. Conventional digital multiplexers are limited by complex circuit architecture and high supply voltage. Large sensory arrays further complicate wiring. An 'in-electrolyte computing' platform is presented by integrating organic electrochemical transistors (OECTs) with a solid-state polymer electrolyte. These devices use synapse-like signal transport and spatially dependent bulk ionic doping, achieving over 400 times modulation in channel conductance, allowing discrimination of locally random-access events without peripheral circuitry or address assignment. It demonstrates information processing from 12 tactile sensors with a single OECT output, showing clear advantages in circuit simplicity over existing all-electronic, all-digital implementations. This self-multiplexer platform offers exciting prospects for circuit-free integration with sensory arrays for high-quality, large-volume analog signal processing.

A dual-pathway architecture enables chronic stress to disrupt agency and promote habit formation.

Chronic stress can change how we learn and, thus, how we make decisions. Here we investigated the neuronal circuit mechanisms that enable this. Using a multifaceted systems neuroscience approach in male and female mice, we reveal a dual pathway, amygdala-striatal neuronal circuit architecture by which a recent history of chronic stress disrupts the action-outcome learning underlying adaptive agency and promotes the formation of inflexible habits. We found that the basolateral amygdala projection to the dorsomedial striatum is activated by rewarding events to support the action-outcome learning needed for flexible, goal-directed decision making. Chronic stress attenuates this to disrupt action-outcome learning and, therefore, agency. Conversely, the central amygdala projection to the dorsomedial striatum mediates habit formation. Following stress this pathway is progressively recruited to learning to promote the premature formation of inflexible habits. Thus, stress exerts opposing effects on two amygdala-striatal pathways to disrupt agency and promote habit. These data provide neuronal circuit insights into how chronic stress shapes learning and decision making, and help understand how stress can lead to the disrupted decision making and pathological habits that characterize substance use disorders and mental health conditions.

Serotonergic modulation of swallowing in a complete fly vagus nerve connectome

How the body interacts with the brain to perform vital life functions, such as feeding, is a fundamental issue in physiology and neuroscience. Here, we use a whole-animal scanning transmission electron microscopy volume of Drosophila to map the neuronal circuits that connect the entire enteric nervous system to the brain via the insect vagus nerve at synaptic resolution. We identify a gut-brain feedback loop in which Piezo-expressing mechanosensory neurons in the esophagus convey food passage information to a cluster of six serotonergic neurons in the brain. Together with information on food value, these central serotonergic neurons enhance the activity of serotonin receptor 7-expressing motor neurons that drive swallowing. This elemental circuit architecture includes an axo-axonic synaptic connection from the glutamatergic motor neurons innervating the esophageal muscles onto the mechanosensory neurons that signal to the serotonergic neurons. Our analysis elucidates a neuromodulatory sensory-motor system in which ongoing motor activity is strengthened through serotonin upon completion of a biologically meaningful action, and it may represent an ancient form of motor learning.

A high gain quasi Z‐source based full‐bridge isolated DC‐DC converter with extendable structure for grid‐tied/standalone PV system

AbstractA multi‐input single‐output converter that is based on the impedance network and the standard isolated converters is presented. The topology is named quasi Z‐source full‐bridge isolated converter (qZSFBIC). The proposed topology helps to integrate various renewable power generation systems with a common three‐phase grid‐connected inverter. It was also capable of providing isolation between the input and output circuits in addition to the boost function. The integration of multiple energy sources is achieved using fewer components than conventional converters. As a result, it achieves greater conversion efficiency and has improved circuit characteristics. It offers a larger voltage control range as compared to the conventional ZSC and enhances the input‐output voltage transformation ratio. To determine whether or not the suggested converter is technically feasible, the circuit architecture, operating principle, control mechanism, and simulation results are presented. An improved proportional resonant‐second order general integrator (IPR‐SOGI) has been utilized to provide a gating signal for the voltage source inverter. The 1.5 kW, 400 V, 50 Hz model is designed to authenticate the proposed scheme with qZSFBIC. The results showed that the proposed converter offered double the time of boosting than the conventional ZSC converter and the conversion efficiency is around 89%.

Continuous evolution for efficient quantum architecture search

Variational quantum algorithms (VQAs) have been successfully applied to quantum approximate optimization algorithms, variational quantum compiling, and quantum machine learning models. The performance of VQAs is significantly influenced by the architecture of parameterized quantum circuits (PQCs). Quantum architecture search aims to automatically discover high-performance quantum circuits for specific VQA tasks. Quantum architecture search algorithms that utilize both SuperCircuit training and a parameter-sharing approach can save computational resources. If we directly follow the parameter-sharing approach, the SuperCircuit has to be trained to compensate for the worse search space. To address the challenges posed by the worse search space, we introduce an optimization strategy known as the efficient continuous evolutionary approach using Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Then, we leverage prior information (symmetric property) designing Structure Symmetric Pruning for removing redundant gates of the searched ansatz. Experiments show that the efficient continuous evolutionary approach can search for more quantum architectures with better performance; the number of high-performance ansatzes obtained by our method is 10% higher than that in the literature (Du et al. in npj Quantum Inf. 8:62, 2022). The application of Structure Symmetric Pruning effectively reduces the number of parameters in quantum circuits without compromising their performance significantly. In binary classification tasks, the pruned quantum circuits exhibit an average accuracy reduction of 0.044 compared to their unpruned counterparts.

Field-free multistate spin–orbit torque devices for programmable image edge recognition circuit

The application of spin–orbit torque (SOT) devices to neuromorphic computing platforms is focused on the development of hardware circuit architectures. However, the inter-device variability, the integration modes of devices and peripheral circuits, and appropriate application scenarios are still unclear, limiting the development of SOT devices in neuromorphic computing. To solve this problem, this paper first proposes a circuit compensation scheme for the difference in resistance values of SOT devices, which solves this variability problem at the circuit level. Moreover, a synergistic scheme with the circuit is developed based on the correspondence between the multistate resistance characteristics of the SOT devices and a convolutional algorithm. To achieve this, a multichannel SOT convolutional kernel circuit architecture is built, which implements an image edge recognition application. Finally, based on a simulation model, an image edge recognition hardware circuit based on our CoPt-SOT devices is implemented, which is capable of performing image edge recognition with an accuracy of 96.33%. This scheme provides technical support and development prospects for SOT devices in neural network hardware applications.

Design of Coplanar Stripline Bandpass Filter With Reconfigurable Filter Switch

ABSTRACTThis article has designed a bandpass filter using a coplanar stripline stub (CPS) resonator consisting of open and short‐ended strip lines connected to the PIN diode switches. The use of spurline stub resonators inside CPS results in bandpass and bandstop filters, depending on the PIN diode switch configurations. The work presents a novel circuit architecture aimed at reducing the parasitic resonance of the spurline resonators and acquiring the necessary series stub characteristics. The proposed filter resonates at 6.9–9, 1.7–4.7, and 8.4 GHz when the PIN diodes are forward and reverse‐biased, respectively. It also resonates at 1–2.1, 4.8–5.3, and 6.9–9 GHz when one diode is reverse‐biased, and the other is connected in forward bias. An insertion loss below −0.55 dB and a return loss less than 10 dB have been obtained during simulation and measurement. The designed filter can find different applications for the 1.8 GHz GSM band, 2.4/5.8 GHz (WLAN), 3.6 GHz (WiMAX), long‐term evolution (LTE), and WIFI. The filter can be used in various multi‐frequency systems owing to its compact size. The measured and simulated findings of the proposed CPS spurline stub resonator wideband bandpass filters are substantially consistent.

Making profit from energy transition. Political struggles and accumulation strategies in Guadeloupe

ABSTRACT The electricity industry in Guadeloupe has undergone a rapid transition. In this paper we analyse how this energy transition concretely occurred and what are the implications for industrial players and their accumulation strategies? Energy transition policies are configured by political struggles involving power producers (both incumbents and challengers) to ensure their profitability. We focus specifically on financial relationships and circuits underlying the Guadeloupean electricity industry, arguing that the political (de)construction of these circuits by (public and private) actors is key to better understand the transition process and/or its path-dependence. We identify three different and successive sequences of energy transition, from 2000 to 2023, characterized by different financial circuit architectures and distributive outcomes.

QUSL: Quantum unsupervised image similarity learning with enhanced performance

Leveraging quantum properties to enhance complex learning tasks has been proven feasible, with excellent recent achievements in the field of unsupervised learning. However, current quantum schemes neglect adaptive adjustments for unsupervised task scenarios. This work proposes a novel quantum unsupervised similarity learning method — QUSL. Firstly, QUSL uses similarity triplets for unsupervised learning, generating positive samples by perturbing anchor images, achieving a learning process independent of classical algorithms. Subsequently, combining the feature interweaving of triplets, QUSL employs metaheuristic algorithms to systematically explore high-performance mapping processes, obtaining quantum circuit architectures more suitable for unsupervised image similarity tasks. Ultimately, QUSL realizes feature learning with lower quantum resource costs. Comprehensive numerical simulations and experiments on quantum computers demonstrate that QUSL outperforms state-of-the-art quantum methods. QUSL achieves over 50% reduction in critical quantum resource utilization. QUSL improves similarity detection correlation by up to 19.5% across multiple datasets, exhibiting robustness in NISQ environments. While using fewer quantum resources, QUSL shows potential for large-scale unsupervised tasks.

Quantum Architecture Search with Neural Predictor Based on Graph Measures

AbstractQuantum architecture search (QAS) has attracted increasing attention owing to its remarkable ability to automate the design of quantum circuits for variational quantum algorithms (VQAs). However, evaluating the performance of numerous quantum circuits is essential to provide feedback for the search strategy, which inevitably renders QAS computationally expensive. Performance predictors have emerged as highly efficient evaluation methods to mitigate this challenge. However, the performance predictor faces a critical challenge in reducing the required number of circuit‐performance pairs for training. This study encodes circuit architecture by representing a quantum circuit as a relational graph that emphasizes message exchange. Subsequently, valuable information about circuit architecture is extracted through three types of graph measures, including distance‐based, degree‐based, and cluster‐based measures. The graph measures define a smooth space related to circuit performance, facilitating the training of the performance predictor. The effectiveness of the proposed method is assessed across three tasks within variational quantum eigensolvers (VQE): identifying the ground states of the Transverse Field Ising Model (TFIM), the Heisenberg model, and the molecule. The simulation results demonstrate notable enhancements in predictive accuracy achieved by our method, coupled with a substantial reduction in the required number of training samples for the predictor.

Power-Law Entanglement and Hilbert Space Fragmentation in Nonreciprocal Quantum Circuits.

Quantum circuits utilizing measurement to evolve a quantum wave function offer a new and rich playground to engineer unconventional entanglement dynamics. Here, we introduce a hybrid, nonreciprocal setup featuring a quantum circuit, whose updates are conditioned on the state of a classical dynamical agent. In our example the circuit is represented by a Majorana quantum chain controlled by a classical N-state Potts chain undergoing pair flips. The local orientation of the classical spins controls whether randomly drawn local measurements on the quantum chain are allowed or not. This imposes a dynamical kinetic constraint on the entanglement growth, described by the transfer matrix of an N-colored loop model. It yields an equivalent description of the circuit by an SU(N)-symmetric Temperley-Lieb Hamiltonian or by a kinetically constrained surface growth model for an N-component height field. For N=2, we find a diffusive growth of the half-chain entanglement toward a stationary profile S(L)∼L^{1/2} for L sites. For N≥3, the kinetic constraints impose Hilbert space fragmentation, yielding subdiffusive growth toward S(L)∼L^{0.57}. This showcases how the control by a classical dynamical agent can enrich the entanglement dynamics in quantum circuits, paving a route toward novel entanglement dynamics in nonreciprocal hybrid circuit architectures.

Neural Sequences Underlying Directed Turning in C. elegans.

Complex behaviors like navigation rely on sequenced motor outputs that combine to generate effective movement. The brain-wide organization of the circuits that integrate sensory signals to select and execute appropriate motor sequences is not well understood. Here, we characterize the architecture of neural circuits that control C. elegans olfactory navigation. We identify error-correcting turns during navigation and use whole-brain calcium imaging and cell-specific perturbations to determine their neural underpinnings. These turns occur as motor sequences accompanied by neural sequences, in which defined neurons activate in a stereotyped order during each turn. Distinct neurons in this sequence respond to sensory cues, anticipate upcoming turn directions, and drive movement, linking key features of this sensorimotor behavior across time. The neuromodulator tyramine coordinates these sequential brain dynamics. Our results illustrate how neuromodulation can act on a defined neural architecture to generate sequential patterns of activity that link sensory cues to motor actions.