Circuit Network Research Articles

Automatic design of gene regulatory mechanisms for spatial pattern formation

Gene regulatory mechanisms (GRMs) control the formation of spatial and temporal expression patterns that can serve as regulatory signals for the development of complex shapes. Synthetic developmental biology aims to engineer such genetic circuits for understanding and producing desired multicellular spatial patterns. However, designing synthetic GRMs for complex, multi-dimensional spatial patterns is a current challenge due to the nonlinear interactions and feedback loops in genetic circuits. Here we present a methodology to automatically design GRMs that can produce any given two-dimensional spatial pattern. The proposed approach uses two orthogonal morphogen gradients acting as positional information signals in a multicellular tissue area or culture, which constitutes a continuous field of engineered cells implementing the same designed GRM. To efficiently design both the circuit network and the interaction mechanisms—including the number of genes necessary for the formation of the target spatial pattern—we developed an automated algorithm based on high-performance evolutionary computation. The tolerance of the algorithm can be configured to design GRMs that are either simple to produce approximate patterns or complex to produce precise patterns. We demonstrate the approach by automatically designing GRMs that can produce a diverse set of synthetic spatial expression patterns by interpreting just two orthogonal morphogen gradients. The proposed framework offers a versatile approach to systematically design and discover complex genetic circuits producing spatial patterns.

Memristor-Based Conditioned Inhibition Neural Network Circuit With Blocking Generalization and Differentiation

Memristor-Based Conditioned Inhibition Neural Network Circuit With Blocking Generalization and Differentiation

Deterministic observability calculations for zero-dimensional models of lithium–sulfur batteries

Among the various energy storage technologies under development, the lithium‑sulfur (Li–S) battery has considerable promise due to its higher theoretical energy density, small environmental footprint, and low projected costs. One of the main challenges posed by Li–S is the need for a battery management system (BMS) that can accommodate the system's complex multi-step redox behaviours; conventional approaches for lithium-ion batteries do not transfer. Most existing approaches rely on equivalent circuit network models, but there is growing interest in ‘zero-dimensional’ electrochemical models which can potentially give insights into the relative polysulfide species concentrations present at any given time. To be useful for state estimation, a model must be ‘observable’: it must be possible to uniquely determine the internal state through observation of the system's behaviour over time. Previous studies have assessed observability using numerical methods, which is an approximation. This study derives an analytic expression for the observability criterion, which allows greater confidence in the results. The analytic observability criterion is then validated against a numerical comparator. A zero-dimensional model from the literature is translated into an ordinary differential equation (ODE) form to define the state variables matrix A, the output matrix C, and subsequently the observability matrix O. These are compared to simulated numerical equivalents. In addition, the sensitivity of the numerical process has been demonstrated. The results have the potential to offer greater confidence in conclusions around observability, which in turn gives greater confidence in the effects of any algorithms based on them.

Multi-Function Scan Circuit for Assisting the Parallel Computational Map Pattern

Some parallel computing patterns can be accelerated using appropriate networks of simple circuits. We propose a solution, based on the Beneš-Waksman permutation network, which is adapted to efficiently accelerate not only permutation, but some of the most used parallel computational patterns such as: pack, prefix operations and reductions. The structural context considered for deploying our circuit is the map parallel pattern represented by an array of computational elements. The developed network receives a vector from the map array and outputs a vector for functions such as permute, pack, prefix sum (thus closing a first global loop over the map array of cells). For reduction functions (add, min, max) the network returns a scalar (thus closing a second global loop over the map array). With these improvements, this network adds circuit support for frequently used functions, in addition to map-type functions performed in the array of computing elements. While for reduction functions the frame of the permutation network can be easily adapted, for prefix functions and for the pack function new forms of implementation are proposed. The cells of the Bene\v s-Waksman network are redesigned to support the additional functionality. Some applications are then presented to emphasize the utility of our design.

Memristor-based circuit design of episodic memory neural network and its application in hurricane category prediction

Episodic memory, as a type of long-term memory (LTM), is used to learn and store the unique personal experience. Based on the episodic memory biological mechanism, this paper proposes a bionic episodic memory memristive neural network circuit. The proposed memristive neural network circuit includes a neocortical module, a parahippocampal module and a hippocampus module. The neocortical module with the two paths structure is used to receive the sensory signal, and is also used to separate and transmit the spatial information and the non-spatial information involved in the sensory signal. The parahippocampal module is composed of the parahippocampal cortex-MEA and the perirhinal cortex-LEA, which receives the two types of information from the neocortical module respectively. As the last module, the hippocampus module receives and integrates the output information of the parahippocampal module as well as generates the corresponding episodic memory. Meanwhile, the specific scenario information with the certain temporal signal from the generated episodic memory is also extracted by the hippocampus module. The simulation results in PSPICE show that the proposed memristive neural network circuit can generate the various episodic memories and extract the specific scenario information successfully. By configuring the memristor parameters, the proposed bionic episodic memory memristive neural network circuit can be applied to the hurricane category prediction, which verifies the feasibility of this work.

Design of a novel hybrid quantum deep neural network in INEQR images classification

We redesign the parameterized quantum circuit in the quantum deep neural network, construct a three-layer structure as the hidden layer, and then use classical optimization algorithms to train the parameterized quantum circuit, thereby propose a novel hybrid quantum deep neural network (HQDNN) used for image classification. After bilinear interpolation reduces the original image to a suitable size, an improved novel enhanced quantum representation (INEQR) is used to encode it into quantum states as the input of the HQDNN. Multi-layer parameterized quantum circuits are used as the main structure to implement feature extraction and classification. The output results of parameterized quantum circuits are converted into classical data through quantum measurements and then optimized on a classical computer. To verify the performance of the HQDNN, we conduct binary classification and three classification experiments on the MNIST (Modified National Institute of Standards and Technology) data set. In the first binary classification, the accuracy of 0 and 4 exceeds 98%. Then we compare the performance of three classification with other algorithms, the results on two datasets show that the classification accuracy is higher than that of quantum deep neural network and general quantum convolutional neural network.

Multidomain modeling of acoustical transducers and arrays using electrical network theory

Modeling of piezoceramic acoustic transducers involves the transformation of energy in the electrical, mechanical, acoustical radiation domains. By using generalized coordinates and Lagrangian mechanics, in which principle of least action is applied in each domain, the electrical, piezoelectric, mechanical and sound radiation problems can be solved separately and then combined in a multi-contour equivalent electro-mechanical circuit with each mechanical vibrational resonant modes representing separate degrees of freedom in the coupled electrical circuit. This energy approach involves the calculation of the potential and kinetic energies of each practical mode of vibration, which can be determined analytically, experimentally, or by finite-element-analysis. This is an alternative to the use of Newtonian mechanics which requires an accurate description of the real boundary conditions in the device and is common in many FEA modeling approaches. The availability of software (e.g., Matlab, Python, LTSpice, etc.) to model electrical circuits (networks in the case of multi-resonant devices) makes for a powerful and efficient modeling approach. The historical emergence of this approach stems from adapting the Rayleigh-Ritz method for mechanical and electrical domains with the advent of piezoelectric and magnetostrictive bodies. Several example of piezoceramic transducers are presented.

Bisection Neural Network Toward Reconfigurable Hardware Implementation.

A hardware-friendly bisection neural network (BNN) topology is proposed in this work for approximately implementing massive pieces of complex functions in arbitrary on-chip configurations. Instead of the conventional reconfigurable fully connected neural network (FC-NN) circuit topology, the proposed hardware-friendly topology performs NN behaviors in a bisection structure, in which each neuron includes two constant synapse connections for both inputs and outputs. Compared with the FC-NN one, the reconfiguration of the BNN circuit topology eliminates the remarkable amount of dummy synapse connections in hardware. As the main target application, this work aims at building a general-purpose BNN circuit topology that offers a great amount of NN regressions. To achieve this target, we prove that the NN behaviors of the FC-NN circuit topologies can be migrated to the BNN circuit topologies equivalently. We introduce two approaches including the refining training algorithm and the inverted-pyramidal strategy to further reduce the number of neurons and synapses. Finally, we conduct the inaccuracy tolerance analysis to suggest the guideline for ultra-efficient hardware implementations. Compared with the state-of-the-art FC-NN circuit topology-based TrueNorth baseline, the proposed design can achieve 17.8- 22.2× hardware reduction and less than 1% inaccuracy.

Memristive Neural Network Circuit of Operant Conditioning With Reward Delay and Variable Punishment Intensity

Memristive Neural Network Circuit of Operant Conditioning With Reward Delay and Variable Punishment Intensity

PMONN: an optical neural network for photonic integrated circuits based on micro-resonator.

We propose an improved optical neural network (ONN) circuit architecture based on conventional micro-resonator ONNs, called the Phase-based Micro-resonator Optical Neural Network (PMONN). PMONN's core architecture features a Convolutions and Batch Normalization (CB) unit, comprising a phase-based (PB) convolutional layer, a Depth-Point-Wise (DPW) convolutional layer, and a reconstructed Batch Normalization (RBN) layer. The PB convolution kernel uses modulable phase shifts of Add-drop MRRs as learnable parameters and their optical transfer function as convolution weights. The DPW convolution kernel amplifies PB convolution weights by learning the amplification factors. To address the internal covariate shift during training, the RBN layer normalizes DPW outputs by reconstructing the BN layer of the electronic neural network, which is then merged with the DPW layer in the test stage. We employ the tunable DAs in the architecture to implement the merged layer. PMONN achieves 99.15% and 91.83% accuracy on MNIST and Fashion-MNIST datasets, respectively. This work presents a method for implementing an optical neural network on the improved architecture based on MRRs and increases the flexibility and reusability of the architecture. PMONN has potential applications as the backbone for future optical object detection neural networks.

Functional connectivity and complexity analyses of resting-state fMRI in pre-adolescents demonstrating the behavioral symptoms of ADHD

Attention deficit hyperactivity disorder (ADHD) has been characterized by impairments among distributed functional brain networks, e.g., the frontoparietal network (FPN), default mode network (DMN), reward and motivation-related circuits (RMN), and salience network (SAL). In the current study, we evaluated the complexity and functional connectivity (FC) of resting state fMRI (rsfMRI) in pre-adolescents with the behavioral symptoms of ADHD, for pathology-relevant networks.We leveraged data from the Adolescent Brain and Cognitive Development (ABCD) Study. The final study sample included 63 children demonstrating the behavioral features of ADHD and 92 healthy control children matched on age, sex, and pubertal development status. For selected regions in the relevant networks, ANCOVA compared multiscale entropy (MSE) and FC between the groups. Finally, differences in the association between MSE and FC were evaluated.We found significantly reduced MSE along with increased FC within the FPN of pre-adolescents demonstrating the behavior symptoms of ADHD compared to matched healthy controls. Significant partial correlations between MSE and FC emerged in the FPN and RMN in the healthy controls however the association was absent in the participants demonstrating the behavior symptoms of ADHD.The current findings of complexity and FC in ADHD pathology support hypotheses of altered function of inhibitory control networks in ADHD.

Memristive neural network circuit design based on locally competitive algorithm for sparse coding application

Sparse coding can quickly, accurately, and inexpensively represent the stimulus information received by biological vision neurons. However, there is no entire circuit that can realize real-time sparse coding by efficient analog computation. A memristor neural network circuit that solves the sparse coding problem in real-time and in parallel is proposed to solve such a problem. In our circuit, a novel memristor array structure can realize both reading and writing in parallel. A neural network circuit that can execute the Locally Competitive Algorithm (LCA) is designed based on this structure. Given these designs, the proposed neural networks circuit can utilize the programmability of the memristor array to real-time process various sparse coding problems. Based on the proposed circuit, the module can process binary and grayscale image sparse coding, providing a circuit implementation platform for sparse coding tasks. The simulation results demonstrate that the processing speed of sparse coding is improved compared with the MATLAB simulation and the application robustness is enhanced.

The stochastic ferroelectric field-effect transistors-based probabilistic-bits: from device physics analysis to invertible logic applications

A probabilistic-bit (p-bit) is the fundamental building block in the circuit network of probabilistic computing (PC), producing a continuous random bitstream with tunable probability. Among the various p-bit designs, the recently proposed ferroelectric FET (FeFET)-based p-bit is found to possess the advantages of both ultralow hardware cost and CMOS compatibility. In this work, we perform a comprehensive analysis of the stochasticity in ferroelectric (FE) material, revealing the mechanisms of domain dynamics and temperature on FE stochasticity. The device-to-device variations of several device/material properties are then evaluated, which affect the probabilistic-curves to different extents. Furthermore, the integer factorization is performed based on the invertible logic circuits comprising of FE p-bits to verify its functionality. The accuracy of integer factorization is found to highly depend on FE p-bits’ stochasticity, and various methods have been implemented to optimize its performance, providing unique insights for future large-scale PC applications.

Periodic self-triggered strategy for highly non-linear hybrid stochastic delayed networks based on impulsive control

This article investigates the stabilization issue of highly non-linear hybrid stochastic delayed networks (HSDNs) via periodic self-triggered control under impulse (PS-TCI). Firstly, the existence of a unique global solution for highly non-linear HSDNs under PS-TCI is studied. Then, a stabilization criterion for highly non-linear HSDNs is established, by combining a graph-theoretic approach with a novel Lyapunov-based analysis, based on a ‘genuine’ Lyapunov function defined by introducing an auxiliary timer. Therein, the less conservative polynomial growth condition and local Lipschitz condition for the drift and diffusion coefficients are used than the linear growth condition and global Lipschitz condition. Meanwhile, the design idea of PS-TCI is based on the evolution of an upper bound of the mathematical expectation for Lyapunov function (not directly Lyapunov function or system state), which implies that the triggered instant of PS-TCI is not a random variable. Finally the theoretical results are employed to study the stability of a class of FitzHugh–Nagumo circuits networks and the central pattern generators networks of a hexapod robot, and correlative numerical simulations are provided for demonstration.

RESnet: 3D direct-current resistivity simulation using the equivalent resistor network circuit

Thin and highly conductive objects are challenging to model in 3D direct-current (DC) problems because they often require excessive mesh refinement that leads to a significant increase in computational costs. RESistor network (RESnet) is a novel algorithm that converts any 3D geo-electric simulation to solving an equivalent 3D resistor network circuit. Two features of RESnet make it an attractive choice in the DC modeling of thin and conductive objects. First, in addition to the conductivity with units of Siemens per meter (S/m) defined at the cell centers (cell conductivity), RESnet allows conductive properties defined on mesh faces and edges as face conductivity with units of S and edge conductivity with units of S·m, respectively. Face conductivity is the thickness-integrated conductivity, which preserves the electric effect of sheet-like conductors without an explicit statement in the mesh. Similarly, edge conductivity is the product of the cross-sectional area and the intrinsic conductivity of a line-like conductive object. Modeling thin objects using face and edge conductivity can avoid extremely small mesh grids if the DC problem concerns electric field responses at a much larger scale. Second, once the original simulation is transformed into an equivalent resistor network, certain types of infrastructure, similar to above-ground metallic pipes, can be conveniently modeled by directly connecting the circuit nodes, which cannot interact with each other in conventional modeling programs. Bilingually implemented in MATLAB and Python, the algorithm has been made open source to promote wide use in academia and industry. Three examples are provided to validate its numerical accuracy, demonstrate its capability in modeling steel well casings, and indicate how it can be used to simulate the effect of complex metallic infrastructure on DC resistivity data.

Application of Under-Impedance Criterion to Protect against Effects of Phase-to-Phase Short Circuits in Medium-Voltage Networks

Common overcurrent criteria used to detect phase-to-phase short circuits in medium-voltage (MV) networks in some cases do not bring the expected results. In particular, this applies to instantaneous overcurrent protections in lines with variable ranges and lines leading to local power plants (LPP), which generate a short-circuit current slightly greater than the rated current. When using overcurrent protections, there are some problems with ensuring the appropriate sensitivity and selectivity. This article proposes a protection based on impedance measurement against the effects of phase-to-phase short circuits in MV lines. The protection can be used at switchgears and also at protection points (PP) located deep in the network. The under-impedance criterion has a range independent of the value of the short-circuit current, and it can be alternative or complement to classic overcurrent protection. The introduction of these criteria allows the protection range to be independent of the type of short circuit and the short-circuit power on the busbar, and to determine this range more precisely. The under-impedance protection is particularly useful in conditions of a growing number of energy sources deep inside the networks and its automation. The main idea was to develop a uncomplicated characteristic that would ensure the possibility of setting the under-impedance protection by people who are not specialists in the field of distance protections. The characteristics have been optimally developed both in terms of the area of detected impedances and easy implementation, operation, and configuration.

The design of self-healing memristive network circuit based on VTA DA neurons and its application

This paper proposes a self-healing memristive network circuit, which simulates the resilience mechanism of VTA DA neurons. The proposed memristive network circuit mainly consists of four modules: input (synapse) module, damage detection module, threshold trigger module and negative feedback module. The input module is composed of a memristive synaptic circuit, which can react to a harmful initial input rather than a normal initial input. Apart from reacting to initial input, the input module also can receive a feedback signal released by the negative feedback module, and the process of receiving feedback signal is the key of realizing the self-healing function. The damage detection module can output a low level pulse that acts as an input signal of the threshold trigger module when the harmful initial input exists. The threshold trigger module will activate the negative feedback module when the trigger point which corresponds to the positive threshold voltage of memristor is reached. As the last module of the self-healing function, the negative feedback module can transmit the feedback signal, also be called a repairing signal, to the damaged memristor of the input module. The PSPICE simulation results indicate that the proposed memristive network circuit can realize the self-healing function which corresponds to the resilience mechanism of the VTA DA neurons. Meanwhile, when the proposed memristive network circuit has been applied to the damaged electronic machines or robots, the self-healing function of it can make the machines be reusable.

A 0.5 V Nanowatt Biquadratic Low-Pass Filter with Tunable Quality Factor for Electronic Cochlea Applications

A novel implementation of an analogue low-power, second-order, low-pass filter with tunable quality factor (Q) is presented and discussed. The filter feature is a relatively simple, buffer-based, circuit network consisting of eleven transistors operating in a subthreshold region. Q tuning is accomplished by injecting direct current into a network node, which changes the output resistance of the transistors and, as a result, modifies the filter network’s loss, and thus its Q. Q tuning is independent of a filter cut-off frequency (ω0). The filter, with a nominal ω0 of 1 kHz, was fabricated using a 0.18 µm CMOS technology, and features a Q range of 2–11, power consumption of up to 52 nW, and a 59 dB dynamic range when using a 0.5 V supply. The ω0 can be tuned from 0.5 to 2.5 kHz using a traditional method by changing the transistor transconductances, but this process partially affects the quality factor.

Analysis and design of a passive regenerative inductor‐capacitor‐diode snubber for DC link over‐voltage mitigation in Y‐source inverter

AbstractRenewable energy‐based distributed generation presents new challenges for power inverters, requiring greater flexibility, reliability, cost‐effectiveness, and efficiency. Impedance source network‐based inverters (INIs) are an attractive option for addressing some of these issues since they provide a single‐stage power conversion structure with buck‐boost capability, allowing them to manage shoot‐through conduction on the inverter arms. However, some INI topologies based on magnetic coupling, such as those of the Y‐source impedance network family, have problems related to overvoltages at the power switches because of the presence of unwanted leakage inductances in the coupled‐inductor windings. The insertion of the regenerative and passive LCD snubber in the impedance network circuit is a solution to mitigate this problem, but there is currently a lack of a clear and concise methodology to design its components, which hinders a wider adoption of this solution in different types of INI. This paper presents a comprehensive and analytical design for determining LCD snubber components. This study was carried out with the quasi‐Y‐source inverter, but the same methodology can be extended to other types of INI based on the Y‐source network.

Treatment effects of N-acetyl cysteine on resting-state functional MRI and cognitive performance in patients with chronic mild traumatic brain injury: a longitudinal study.

Mild traumatic brain injury (mTBI) is a significant public health concern, specially characterized by a complex pattern of abnormal neural activity and functional connectivity. It is often associated with a broad spectrum of short-term and long-term cognitive and behavioral symptoms including memory dysfunction, headache, and balance difficulties. Furthermore, there is evidence that oxidative stress significantly contributes to these symptoms and neurophysiological changes. The purpose of this study was to assess the effect of N-acetylcysteine (NAC) on brain function and chronic symptoms in mTBI patients. Fifty patients diagnosed with chronic mTBI participated in this study. They were categorized into two groups including controls (CN, n = 25), and patients receiving treatment with N-acetyl cysteine (NAC, n = 25). NAC group received 50 mg/kg intravenous (IV) medication once a day per week. In the rest of the week, they took one 500 mg NAC tablet twice per day. Each patient underwent rs-fMRI scanning at two timepoints including the baseline and 3 months later at follow-up, while the NAC group received a combination of oral and IV NAC over that time. Three rs-fMRI metrics were measured including fractional amplitude of low frequency fluctuations (fALFF), degree centrality (DC), and functional connectivity strength (FCS). Neuropsychological tests were also assessed at the same day of scanning for each patient. The alteration of rs-fMRI metrics and cognitive scores were measured over 3 months treatment with NAC. Then, the correlation analysis was executed to estimate the association of rs-fMRI measurements and cognitive performance over 3 months (p < 0.05). Two significant group-by-time effects demonstrated the changes of rs-fMRI metrics particularly in the regions located in the default mode network (DMN), sensorimotor network, and emotional circuits that were significantly correlated with cognitive function recovery over 3 months treatment with NAC (p < 0.05). NAC appears to modulate neural activity and functional connectivity in specific brain networks, and these changes could account for clinical improvement. This study confirmed the short-term therapeutic efficacy of NAC in chronic mTBI patients that may contribute to understanding of neurophysiological effects of NAC in mTBI. These findings encourage further research on long-term neurobehavioral assessment of NAC assisting development of therapeutic plans in mTBI.