MET receptor tyrosine kinase promotes the generation of functional synapses in adult cortical circuits

Loss of synapse and functional connectivity in brain circuits is associated with aging and neurodegeneration,however,few molecular mechanisms are known to intrinsically promote synaptogenesis or enhance synapse function.We have previously shown that MET receptor tyrosine kinase in the developing cortical circuits promotes dendritic growth and dendritic spine morphogenesis.To investigate whether enhancing MET in adult cortex has synapse regenerating potential,we created a knockin mouse line,in which the human MET gene expression and signaling can be turned on in adult(10–12 months)cortical neurons through doxycycline-containing chow.We found that similar to the developing brain,turning on MET signaling in the adult cortex activates small GTPases and increases spine density in prefrontal projection neurons.These findings are further corroborated by increased synaptic activity and transient generation of immature silent synapses.Prolonged MET signaling resulted in an increasedα-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid/N-methyl-Daspartate(AMPA/NMDA)receptor current ratio,indicative of enhanced synaptic function and connectivity.Our data reveal that enhancing MET signaling could be an interventional approach to promote synaptogenesis and preserve functional connectivity in the adult brain.These findings may have implications for regenerative therapy in aging and neurodegeneration conditions.

Wavelet neural network based fault diagnosis in nonlinear analog circuits

The theories of diagnosing nonlinear analog circuits by means of the transient response testing are studled. Wavelet analysis is made to extract the transient response signature of nonlinear circuits and compress the signature dada. The best wavelet function is selected based on the between-category total scatter of signature. The fault dictionary of nonlinear circuits is constructed based on improved back-propagation（BP） neural network. Experimental results demonstrate that the method proposed has high diagnostic sensitivity and fast fault identification and deducibility.

A Neural-based L1-Norm Optimization Approach for Fault Diagnosis of Nonlinear Resistive Circuits

This paper deals with fault isolation in nonlinear analog circuits with tolerance under an insufficient number of independent voltage measurements.A neural network-based L1-norm optimization approach is proposed and utilized in locating the most likely faulty elements in nonlinear circuits.The validity of the proposed method is verified by both extensive computer simulations and practical examples.One simulation example is presented in the paper.

Imaging changes in neural circuits in patients with depression using 1 H-magnetic resonance spectroscopy and diffusion tensor imaging

1 H-magnetic resonance spectroscopy imaging and diffusion tensor imaging were performed in 19 patients with mild depression and in 13 controls.The mean age of the patients was 31 years.The mean Hamilton depression score of the patients was 22.5±13.2.N-acetylaspartate,choline and creatine concentrations and the average diffusion coefficient and fractional anisotropy values were measured in the bilateral hippocampus,striatum,thalamus and prefrontal deep white matter. Compared with the control group,the mild depressed patients had：（1）a higher choline/creatine ratio and a negative correlation between the choline/creatine ratio and the average diffusion coefficient in the hippocampus;（2）a lower choline/creatine ratio and a higher fractional anisotropy in the striatum;（3）a lower fractional anisotropy and a positive correlation between the fractional anisotropy and the choline/creatine ratio in the prefrontal deep white matter;and（4）a higher average diffusion coefficient and a positive correlation between the choline/creatine ratio and the N-acetylaspartate/creatine ratio in the thalamus,as well as positive correlation between the choline/creatine ratio and Hamilton depression scores.These data suggest evidence of abnormal connectivity in neurofibrotic microstructures and abnormal metabolic alterations in the limbic-cortical-striatal-pallidal-thalamic neural circuit in patients with mild depression.

DECISION OF OPTIMAL HOPFIELD NEURAL NETWORK MODEL OF COMBINATIONAL CIRCUITS

By use of Hopfield model and basis solution of homogeneous linear equations which are established in accordance with consistent state, a practical decision method for the existence of optimal Hopfield model of combinational circuits is provided. Finally, an example is given.

Diffusive field coupling-induced synchronization between neural circuits under energy balance

When charged bodies come up close to each other,the field energy is diffused and their states are regulated under bidirectional field coupling.For biological neurons,the diversity in intrinsic electric and magnetic field energy can create synaptic connection for fast energy balance and synaptic current is passed across the synapse channel;as a result,energy is pumped and exchanged to induce synchronous firing modes.In this paper,a capacitor is used to connect two neural circuits and energy propagation is activated along the coupling channel.The intrinsic field energy in the two neural circuits is exchanged and the coupling intensity is controlled adaptively using the Heaviside function.Some field energy is saved in the coupling channel and is then sent back to the coupled neural circuits to reach energy balance.Therefore the circuits can reach possible energy balance and complete synchronization.It is possible that the diffusive energy of the coupled neurons inspires the synaptic connections to grow stronger for possible energy balance.

Deep Neural Network Based Behavioral Model of Nonlinear Circuits

With the rapid growth of complexity and functionality of modern electronic systems, creating precise behavioral models of nonlinear circuits has become an attractive topic. Deep neural networks (DNNs) have been recognized as a powerful tool for nonlinear system modeling. To characterize the behavior of nonlinear circuits, a DNN based modeling approach is proposed in this paper. The procedure is illustrated by modeling a power amplifier (PA), which is a typical nonlinear circuit in electronic systems. The PA model is constructed based on a feedforward neural network with three hidden layers, and then Multisim circuit simulator is applied to generating the raw training data. Training and validation are carried out in Tensorflow deep learning framework. Compared with the commonly used polynomial model, the proposed DNN model exhibits a faster convergence rate and improves the mean squared error by 13 dB. The results demonstrate that the proposed DNN model can accurately depict the input-output characteristics of nonlinear circuits in both training and validation data sets.

Integrated circuit for single channel neural signal regeneration

Based on the 4-channel neural signal regeneration system which is realized by using discrete devices and successfully used for in-vivo experiments on rats and rabbits, a single channel neural signal regeneration integrated circuit （IC）is designed and realized in CSMC ＇ s 0. 6 μm CMOS （ complementary metal-oxide-semiconductor transistor ） technology. The IC consists of a neural signal detection circuit with an adjustable gain, a buffer, and a function electrical stimulation （FES） circuit. The neural signal regenerating IC occupies a die area of 1.42 mm × 1.34 mm. Under a dual supply voltage of ±2. 5 V, the DC power consumption is less than 10 mW. The on-wafer measurement results are as follows： the output resistor is 118 ml）, the 3 dB bandwidth is greater than 30 kHz, and the gain can be variable from 50 to 90 dB. The circuit is used for in-vivo experiments on the rat＇ s sciatic nerve as well as on the spinal cord with the cuff type electrode array and the twin-needle electrode. The neural signal is successfully regenerated both on a rat＇ s sciatic nerve bundle and on the spinal cord.

Feature evaluation and extraction based on neural network in analog circuit fault diagnosis

Choosing the right characteristic parameter is the key to fault diagnosis in analog circuit. The feature evaluation and extraction methods based on neural network are presented. Parameter evaluation of circuit features is realized by training results from neural network; the superior nonlinear mapping capability is competent for extracting fault features which are normalized and compressed subsequently. The complex classification problem on fault pattern recognition in analog circuit is transferred into feature processing stage by feature extraction based on neural network effectively, which improves the diagnosis efficiency. A fault diagnosis illustration validated this method.

Combinatorial Optimization Based Analog Circuit Fault Diagnosis with Back Propagation Neural Network

Electronic components' reliability has become the key of the complex system mission execution. Analog circuit is an important part of electronic components. Its fault diagnosis is far more challenging than that of digital circuit. Simulations and applications have shown that the methods based on BP neural network are effective in analog circuit fault diagnosis. Aiming at the tolerance of analog circuit,a combinatorial optimization diagnosis scheme was proposed with back propagation( BP) neural network( BPNN).The main contributions of this scheme included two parts:( 1) the random tolerance samples were added into the nominal training samples to establish new training samples,which were used to train the BP neural network based diagnosis model;( 2) the initial weights of the BP neural network were optimized by genetic algorithm( GA) to avoid local minima,and the BP neural network was tuned with Levenberg-Marquardt algorithm( LMA) in the local solution space to look for the optimum solution or approximate optimal solutions. The experimental results show preliminarily that the scheme substantially improves the whole learning process approximation and generalization ability,and effectively promotes analog circuit fault diagnosis performance based on BPNN.

Fault Diagnosis of Power Electronic Circuits Based on Adaptive Simulated Annealing Particle Swarm Optimization

In the field of energy conversion,the increasing attention on power electronic equipment is fault detection and diagnosis.A power electronic circuit is an essential part of a power electronic system.The state of its internal components affects the performance of the system.The stability and reliability of an energy system can be improved by studying the fault diagnosis of power electronic circuits.Therefore,an algorithm based on adaptive simulated annealing particle swarm optimization(ASAPSO)was used in the present study to optimize a backpropagation(BP)neural network employed for the online fault diagnosis of a power electronic circuit.We built a circuit simulation model in MATLAB to obtain its DC output voltage.Using Fourier analysis,we extracted fault features.These were normalized as training samples and input to an unoptimized BP neural network and BP neural networks optimized by particle swarm optimization(PSO)and the ASAPSO algorithm.The accuracy of fault diagnosis was compared for the three networks.The simulation results demonstrate that a BP neural network optimized with the ASAPSO algorithm has higher fault diagnosis accuracy,better reliability,and adaptability and can more effectively diagnose and locate faults in power electronic circuits.

Optimising repetitive transcranial magnetic stimulation for neural circuit repair following traumatic brain injury

While it is well-known that neuronal activity promotes plasticity and connectivity, the success of activity-based neural rehabilitation programs remains extremely limited in human clinical experience because they cannot adequately control neuronal excitability and activity within the injured brain in order to induce repair. However, it is possible to non-invasively modulate brain plasticity using brain stimu- lation techniques such as repetitive transcranial （rTMS） and transcranial direct current stimulation （tDCS） techniques, which show promise for repairing injured neural circuits （Henrich-Noack et al., 2013; Lefaucher et al., 2014）. Yet we are far from having full control of these techniques to repair the brain following neurotrauma and need more fundamen- tal research （Ellaway et al., 2014; Lefaucher et al., 2014）. In this perspective we discuss the mechanisms by which rTMS may facilitate neurorehabilitation and propose experimental techniques with which magnetic stimulation may be investi- gated in order to optimise its treatment potential.

Robust Fault Diagnosis of Analog Circuits with Tolerances

A method for robust analog fault diagnosis using hybrid neural networks is proposed. The primary focus of the paper is to provide robust diagnosis using a mechanism to deal with the problem of element tolerances and reduce testing time. The proposed approach is based on the fault dictionary diagnosis method and backward propagation neural network (BPNN) and the adaptive resonance theory (ART) neural network. Simulation results show that the method is robust and fast for fault diagnosis of analog circuits with tolerances.

ANALOG CIRCUIT IMPLEMENTATION OF NEURAL NETWORK WITH HIGH PRECISION WEIGHTS

A current-mode MOS neuron circuit with 4-bit programmable weights is presented by using CMOS technology. The weights of the neurcn have high resolution and also can easily be digitally stored. The resolution can be extended into high levels such as 8-bit, etc. by the design methodology presented in this paper. The operational principle of the neuron is discussed. Circuit simulation has been made by use of SPICE II. The results give a good agreement for the design requirements.

Memristor-based multi-synaptic spiking neuron circuit for spiking neural network

Spiking neural networks(SNNs) are widely used in many fields because they work closer to biological neurons.However,due to its computational complexity,many SNNs implementations are limited to computer programs.First,this paper proposes a multi-synaptic circuit(MSC) based on memristor,which realizes the multi-synapse connection between neurons and the multi-delay transmission of pulse signals.The synapse circuit participates in the calculation of the network while transmitting the pulse signal,and completes the complex calculations on the software with hardware.Secondly,a new spiking neuron circuit based on the leaky integrate-and-fire(LIF) model is designed in this paper.The amplitude and width of the pulse emitted by the spiking neuron circuit can be adjusted as required.The combination of spiking neuron circuit and MSC forms the multi-synaptic spiking neuron(MSSN).The MSSN was simulated in PSPICE and the expected result was obtained,which verified the feasibility of the circuit.Finally,a small SNN was designed based on the mathematical model of MSSN.After the SNN is trained and optimized,it obtains a good accuracy in the classification of the IRIS-dataset,which verifies the practicability of the design in the network.

Underlying mechanisms of mindfulness meditation:Genomics,circuits,and networks

Understanding neuropsychological mechanisms of mindfulness meditation(MM)has been a hot topic in recent years.This review was conducted with the goal of synthesizing empirical relationships via the genomics,circuits and networks between MM and mental disorders.We describe progress made in assessing the effects of MM on gene expression in immune cells,with particular focus on stressrelated inflammatory markers and associated biological pathways.We then focus on key brain circuits associated with mindfulness practices and effects on symptoms of mental disorders,and expand our discussion to identify three key brain networks associated with mindfulness practices including default mode network,central executive network,and salience network.More research efforts need to be devoted into identifying underlying neuropsychological mechanisms of MM on how it alleviates the symptoms of mental disorders.

Defect Detection in Printed Circuit Boards with Pre-Trained Feature Extraction Methodology with Convolution Neural Networks

Printed Circuit Boards(PCBs)are very important for proper functioning of any electronic device.PCBs are installed in almost all the electronic device and their functionality is dependent on the perfection of PCBs.If PCBs do not function properly then the whole electric machine might fail.So,keeping this in mind researchers are working in this field to develop error free PCBs.Initially these PCBs were examined by the human beings manually,but the human error did not give good results as sometime defected PCBs were categorized as non-defective.So,researchers and experts transformed this manual traditional examination to automated systems.Further to this research image processing and computer vision came into actions where the computer vision experts applied image processing techniques to extract the defects.But,this also did not yield good results.So,to further explore this area Machine Learning and Artificial Intelligence Techniques were applied.In this studywe have appliedDeep Neural Networks to detect the defects in the PCBS.PretrainedVGG16and Inception networkswere applied to extract the relevant features.DeepPCB dataset was used in this study,it has 1500 pairs of both defected and non-defected images.Image pre-processing and data augmentation techniques were applied to increase the training set.Convolution neural networks were applied to classify the test data.The results were compared with state-of-the art technique and it proved that the proposed methodology outperformed it.Performance evaluation metrics were applied to evaluate the proposed methodology.Precision 94.11%,Recall 89.23%,F-Measure 91.91%,and Accuracy 92.67%.

Fault Diagnosis of Analog Circuit Based on PSO and BP Neural Network

In order to improve the speed and accuracy of analog circuit fault diagnosis,using Back Propagation Neural Network(BPNN),a new method is proposed based on Particle Swarm Optimization(PSO)to adjust weights of BP neural network.The model can not only overcome the limitations of the slow convergence and the local extreme values by basic BP algorithm,but also improve the learning ability and generalization ability with a higher precision.The response signals of analog circuit is preprocessed by Wavelet Packet Transform(WPT)as the fault feature.The simulation result shows that the proposed method has higher diagnostic accuracy and faster convergence speed,which is effective for fault location.

Analog-Circuit Model of FGH96 Superalloy Hot Deformation Behaviors Based on Artificial Neural Network

At the present time, numerical models （such as, numerical simulation based on FEM） adopted broadly in technological design and process control in forging field can not implement the realtime control of material forming process. It is thus necessary to establish a dynamic model fitting for the real-time control of material deformation processing in order to increase production efficiency, improve forging qualities and increase yields. In this paper, hot deformation behaviors of FGH96 superalloy are characterized by using hot compressive simulation experiments. The artificial neural network （ANN） model of FGH96 superalloy during hot deformation is established by using back propagation （BP） network. Then according to electrical analogy theory, its analog-circuit （AC） model is obtained through mapping the ANN model into analog circuit. Testing results show that the ANN model and the AC model of FGH96 superalloy hot deformation behaviors possess high predictive precisions and can well describe the superalloy＇s dynamic flow behaviors. The ideas proposed in this paper can be applied in the real-time control of material deformation processing.

Dynamics analysis and cryptographic implementation of a fractional-order memristive cellular neural network model

Due to the fact that a memristor with memory properties is an ideal electronic component for implementation of the artificial neural synaptic function,a brand-new tristable locally active memristor model is first proposed in this paper.Here,a novel four-dimensional fractional-order memristive cellular neural network(FO-MCNN)model with hidden attractors is constructed to enhance the engineering feasibility of the original CNN model and its performance.Then,its hardware circuit implementation and complicated dynamic properties are investigated on multi-simulation platforms.Subsequently,it is used toward secure communication application scenarios.Taking it as the pseudo-random number generator(PRNG),a new privacy image security scheme is designed based on the adaptive sampling rate compressive sensing(ASR-CS)model.Eventually,the simulation analysis and comparative experiments manifest that the proposed data encryption scheme possesses strong immunity against various security attack models and satisfactory compression performance.