Neural function rebuilding on different bodies using microelectronic neural bridge technique

A microelectronic circuit is used to regenerate the neural signals between the proximal end and the distal end of an injured nerve.An experimental scheme is designed and carried out to verify the feasibility of the so-called microelectronic neural bridge（MNB）.The sciatic signals of the source spinal toad which are evoked by chemical stimuli are used as source signals to stimulate the sciatic of the controlled spinal toad.The sciatic nerve signals of the source spinal toad,the regenerated sciatic signals in the controlled spinal toad,and the resulting electromyography（EMG）signals associated with the gastrocnemius muscle movements of the controlled spinal toad are displayed and recorded by an oscilloscope.By analyzing the coherence between the source sciatic nerve signals and the regenerated sciatic nerve signals and the coherence between the regenerated nerve signals and the EMG signals,it is proved that the regenerated sciatic nerve signals have a relationship with the source sciatic nerve signals and control shrinkage of the leg of the controlled toad.

Mechanism of low molecular weight GTP binding protein RAC1 in injury of neural function of rats with cerebral ischemia reperfusion

Objective: To discuss the mechanism of low molecular weight GTP binding protein RAC1 in the injury of neural function based on building the rat model of cerebral ischemia reperfusion. Methods: Middle cerebral artery of rats was ligated and the ligature was released to restore the perfusion after 2 h, the rat model of cerebral ischemia reperfusion injury was built, while the middle cerebral artery was ligated. The rats were randomly divided into the sham group, cerebral ischemia reperfusion group(I/R group) and the group with the injection of RAC1 activity inhibitor NSC23766(NSC group). The survival and neurological severity score of rats in each group were observed and recorded. Nissl staining was employed to observe the nerve cells, and Western blot to detect expression of RAC1, superoxide dismutase and malondialdehyde. Results: Number of nerve cells for rats in NSC group was significantly more than that in I/R group, but significantly less than that in sham group, with the statistical difference(P<0.05). The brain water content for rats in NSC group was significantly lower than that in I/R group, but significantly higher than that in sham group, with the statistical difference(P<0.05). The expression of RAC1 and malondialdehyde for rats in NSC group was significantly lower than that in I/R group, but higher than that in sham group; while the expression of superoxide dismutase was lower than that in sham group, but higher than that in I/R group, with the statistical difference(P<0.05). Conclusions: The inhibition of RAC1 activity can reduce the oxidative stress, reduce the neurologic impairment because of cerebral ischemia reperfusion and thus protect the neural function.

A Novel Radial Basis Function Neural Network Approach for ECG Signal Classification

ions in the ECG signal.The cardiologist and medical specialistfind numerous difficulties in the process of traditional approaches.The specified restrictions are eliminated in the proposed classifier.The fundamental aim of this work is tofind the R-R interval.To analyze the blockage,different approaches are implemented,which make the computation as facile with high accuracy.The information are recovered from the MIT-BIH dataset.The retrieved data contain normal and pathological ECG signals.To obtain a noiseless signal,Gaborfilter is employed and to compute the amplitude of the signal,DCT-DOST(Discrete cosine based Discrete orthogonal stock well transform)is implemented.The amplitude is computed to detect the cardiac abnormality.The R peak of the underlying ECG signal is noted and the segment length of the ECG cycle is identified.The Genetic algorithm(GA)retrieves the primary highlights and the classifier integrates the data with the chosen attributes to optimize the identification.In addition,the GA helps in performing hereditary calculations to reduce the problem of multi-target enhancement.Finally,the RBFNN(Radial basis function neural network)is applied,which diminishes the local minima present in the signal.It shows enhancement in characterizing the ordinary and anomalous ECG signals.

Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms

Radial Basis Function Neural Network(RBFNN)ensembles have long suffered from non-efficient training,where incorrect parameter settings can be computationally disastrous.This paper examines different evolutionary algorithms for training the Symbolic Radial Basis Function Neural Network(SRBFNN)through the behavior’s integration of satisfiability programming.Inspired by evolutionary algorithms,which can iteratively find the nearoptimal solution,different Evolutionary Algorithms(EAs)were designed to optimize the producer output weight of the SRBFNN that corresponds to the embedded logic programming 2Satisfiability representation(SRBFNN-2SAT).The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms,including Genetic Algorithm(GA),Evolution Strategy Algorithm(ES),Differential Evolution Algorithm(DE),and Evolutionary Programming Algorithm(EP).Each of these methods is presented in the steps in the flowchart form which can be used for its straightforward implementation in any programming language.With the use of SRBFNN-2SAT,a training method based on these algorithms has been presented,then training has been compared among algorithms,which were applied in Microsoft Visual C++software using multiple metrics of performance,including Mean Absolute Relative Error(MARE),Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),Mean Bias Error(MBE),Systematic Error(SD),Schwarz Bayesian Criterion(SBC),and Central Process Unit time(CPU time).Based on the results,the EP algorithm achieved a higher training rate and simple structure compared with the rest of the algorithms.It has been confirmed that the EP algorithm is quite effective in training and obtaining the best output weight,accompanied by the slightest iteration error,which minimizes the objective function of SRBFNN-2SAT.

Fully Distributed Learning for Deep Random Vector Functional-Link Networks

In the contemporary era, the proliferation of information technology has led to an unprecedented surge in data generation, with this data being dispersed across a multitude of mobile devices. Facing these situations and the training of deep learning model that needs great computing power support, the distributed algorithm that can carry out multi-party joint modeling has attracted everyone’s attention. The distributed training mode relieves the huge pressure of centralized model on computer computing power and communication. However, most distributed algorithms currently work in a master-slave mode, often including a central server for coordination, which to some extent will cause communication pressure, data leakage, privacy violations and other issues. To solve these problems, a decentralized fully distributed algorithm based on deep random weight neural network is proposed. The algorithm decomposes the original objective function into several sub-problems under consistency constraints, combines the decentralized average consensus (DAC) and alternating direction method of multipliers (ADMM), and achieves the goal of joint modeling and training through local calculation and communication of each node. Finally, we compare the proposed decentralized algorithm with several centralized deep neural networks with random weights, and experimental results demonstrate the effectiveness of the proposed algorithm.

Application of Near Infrared Diffuse Reflectance Spectroscopy with Radial Basis Function Neural Network to Determination of Rifampincin Isoniazid and Pyrazinamide Tablets

Partial least squares（PLS）,back-propagation neural network（BPNN）and radial basis function neural network（RBFNN）were respectively used for estalishing quantative analysis models with near infrared（NIR）diffuse reflectance spectra for determining the contents of rifampincin（RMP）,isoniazid（INH）and pyrazinamide（PZA）in rifampicin isoniazid and pyrazinamide tablets.Savitzky-Golay smoothing,first derivative,second derivative,fast Fourier transform（FFT）and standard normal variate（SNV）transformation methods were applied to pretreating raw NIR diffuse reflectance spectra.The raw and pretreated spectra were divided into several regions,depending on the average spectrum and RSD spectrum.Principal component analysis（PCA）method was used for analyzing the raw and pretreated spectra in different regions in order to reduce the dimensions of input data.The optimum spectral regions and the models＇ parameters were chosen by comparing the root mean square error of cross-validation（RMSECV）values which were obtained by leave-one-out cross-validation method.The RMSECV values of the RBFNN models for determining the contents of RMP,INH and PZA were 0.00288,0.00226 and 0.00341,respectively.Using these models for predicting the contents of INH,RMP and PZA in prediction set,the RMSEP values were 0.00266,0.00227 and 0.00411,respectively.These results are better than those obtained from PLS models and BPNN models.With additional advantages of fast calculation speed and less dependence on the initial conditions,RBFNN is a suitable tool to model complex systems.

Synchronization of chaos using radial basis functions neural networks

The Radial Basis Functions Neural Network （RBFNN） is used to establish the model of a response system through the input and output data of the system. The synchronization between a drive system and the response system can be implemented by employing the RBFNN model and state feedback control. In this case, the exact mathematical model, which is the precondition for the conventional method, is unnecessary for implementing synchronization. The effect of the model error is investigated and a corresponding theorem is developed. The effect of the parameter perturbations and the measurement noise is investigated through simulations. The simulation results under different conditions show the effectiveness of the method.

Application of principal component-radial basis function neural networks (PC-RBFNN) for the detection of water-adulterated bayberry juice by near-infrared spectroscopy

Near-infrared (NIR) spectroscopy combined with chemometrics techniques was used to classify the pure bayberry juice and the one adulterated with 10% (w/w) and 20% (w/w) water. Principal component analysis (PCA) was applied to reduce the dimensions of spectral data, give information regarding a potential capability of separation of objects, and provide principal component (PC) scores for radial basis function neural networks (RBFNN). RBFNN was used to detect bayberry juice adulterant. Multiplicative scatter correction (MSC) and standard normal variate (SNV) transformation were used to preprocess spectra. The results demonstrate that PC-RBFNN with optimum parameters can separate pure bayberry juice samples from water-adulterated bayberry at a recognition rate of 97.62%, but cannot clearly detect water levels in the adulterated bayberry juice. We conclude that NIR technology can be successfully applied to detect water-adulterated bayberry juice.

Adaptive integral dynamic surface control based on fully tuned radial basis function neural network

An adaptive integral dynamic surface control approach based on fully tuned radial basis function neural network （FTRBFNN） is presented for a general class of strict-feedback nonlinear systems,which may possess a wide class of uncertainties that are not linearly parameterized and do not have any prior knowledge of the bounding functions.FTRBFNN is employed to approximate the uncertainty online,and a systematic framework for adaptive controller design is given by dynamic surface control. The control algorithm has two outstanding features,namely,the neural network regulates the weights,width and center of Gaussian function simultaneously,which ensures the control system has perfect ability of restraining different unknown uncertainties and the integral term of tracking error introduced in the control law can eliminate the static error of the closed loop system effectively. As a result,high control precision can be achieved.All signals in the closed loop system can be guaranteed bounded by Lyapunov approach.Finally,simulation results demonstrate the validity of the control approach.

Optimized functional linked neural network for predicting diaphragm wall deflection induced by braced excavations in clays

Deep excavation during the construction of underground systems can cause movement on the ground,especially in soft clay layers.At high levels,excessive ground movements can lead to severe damage to adjacent structures.In this study,finite element analyses(FEM)and the hardening small strain(HSS)model were performed to investigate the deflection of the diaphragm wall in the soft clay layer induced by braced excavations.Different geometric and mechanical properties of the wall were investigated to study the deflection behavior of the wall in soft clays.Accordingly,1090 hypothetical cases were surveyed and simulated based on the HSS model and FEM to evaluate the wall deflection behavior.The results were then used to develop an intelligent model for predicting wall deflection using the functional linked neural network(FLNN)with different functional expansions and activation functions.Although the FLNN is a novel approach to predict wall deflection;however,in order to improve the accuracy of the FLNN model in predicting wall deflection,three swarm-based optimization algorithms,such as artificial bee colony(ABC),Harris’s hawk’s optimization(HHO),and hunger games search(HGS),were hybridized to the FLNN model to generate three novel intelligent models,namely ABC-FLNN,HHO-FLNN,HGS-FLNN.The results of the hybrid models were then compared with the basic FLNN and MLP models.They revealed that FLNN is a good solution for predicting wall deflection,and the application of different functional expansions and activation functions has a significant effect on the outcome predictions of the wall deflection.It is remarkably interesting that the performance of the FLNN model was better than the MLP model with a mean absolute error(MAE)of 19.971,root-mean-squared error(RMSE)of 24.574,and determination coefficient(R^(2))of 0.878.Meanwhile,the performance of the MLP model only obtained an MAE of 20.321,RMSE of 27.091,and R^(2)of 0.851.Furthermore,the results also indicated that the proposed hybrid models,i.e.,ABC-FLNN,HHO-FLNN,HGS-FLNN,yielded more superior performances than those of the FLNN and MLP models in terms of the prediction of deflection behavior of diaphragm walls with an MAE in the range of 11.877 to 12.239,RMSE in the range of 15.821 to 16.045,and R^(2)in the range of 0.949 to 0.951.They can be used as an alternative tool to simulate diaphragm wall deflections under different conditions with a high degree of accuracy.

Wear State Recognition of Drills Based on K-means Cluster and Radial Basis Function Neural Network

Drill wear not only affects the surface smoothness of the hole, but also influences the life of the drill. Drill wear state recognition is important in the manufacturing process, which consists of two steps： first, decomposing cutting torque components from the original signals by wavelet packet decomposition （WPD）; second, extracting wavelet coefficients of different wear states （i.e., slight, normal, or severe wear） with signal features adapting to Welch spectrum. Finally, monitoring and recognition of the feature vectors of cutting torque signal are performed by using the K-means cluster and radial basis function neural network （RBFNN）. The experiments on different tool wears of the multivariable features reveal that the results of monitoring and recognition are significant and effective.

Neural prostheses for restoring functions lost after spinal cord injury

Spinal cord injury（SCI）is a debilitating condition that affects more than 2.5 million individuals worldwide（Thuret et al.,2006）.In addition to its devastating effects on the individual,this disease is a heavy burden to the society in terms of health care costs, which are estimated in billions of dollars annually in most developed countries （Cadotte and Fehlings, 2011）.

Neural differentiation of human Wharton＇s jelly-derived mesenchymal stem cells improves the recovery of neurological function after transplantation in ischemic stroke rats

Human Wharton＇s jelly-derived mesenchymal stem cells（h WJ-MSCs）have excellent proliferative ability,differentiation ability,low immunogenicity,and can be easily obtained.However,there are few studies on their application in the treatment of ischemic stroke,therefore their therapeutic effect requires further verification.In this study,h WJ-MSCs were transplanted into an ischemic stroke rat model via the tail vein 48 hours after transient middle cerebral artery occlusion.After 4 weeks,neurological functions of the rats implanted with h WJ-MSCs were significantly recovered.Furthermore,many h WJ-MSCs homed to the ischemic frontal cortex whereby they differentiated into neuron-like cells at this region.These results confirm that h WJ-MSCs transplanted into the ischemic stroke rat can differentiate into neuron-like cells to improve rat neurological function and behavior.

Functional Link Neural Network for Predicting Crystallization Temperature of Ammonium Chloride in Air Cooler System

The air cooler is an important equipment in the petroleum refining industry.Ammonium chloride(NH4 Cl)deposition-induced corrosion is one of its main failure forms.In this study,the ammonium salt crystallization temperature is chosen as the key decision variable of NH4 Cl deposition-induced corrosion through in-depth mechanism research and experimental analysis.The functional link neural network(FLNN)is adopted as the basic algorithm for modeling because of its advantages in dealing with non-linear problems and its fast-computational ability.A hybrid FLNN attached to a small norm is built to improve the generalization performance of the model.Then,the trained model is used to predict the NH4 Cl salt crystallization temperature in the air cooler of a sour water stripper plant.Experimental results show the proposed improved FLNN algorithm can achieve better generalization performance than the PLS,the back propagation neural network,and the conventional FLNN models.

Prediction of Parkinson’s Disease Using Improved Radial Basis Function Neural Network

Parkinson’s disease is a neurogenerative disorder and it is difficult to diagnose as no therapies may slow down its progression.This paper contributes a novel analytic system for Parkinson’s Disease Prediction mechanism using Improved Radial Basis Function Neural Network(IRBFNN).Particle swarm optimization(PSO)with K-means is used to find the hidden neuron’s centers to improve the accuracy of IRBFNN.The performance of RBFNN is seriously affected by the centers of hidden neurons.Conventionally K-means was used to find the centers of hidden neurons.The problem of sensitiveness to the random initial centroid in K-means degrades the performance of RBFNN.Thus,a metaheuristic algorithm called PSO integrated with K-means alleviates initial random centroid and computes optimal centers for hidden neurons in IRBFNN.The IRBFNN uses Particle swarm optimization K-means to find the centers of hidden neurons and the PSO K-means was designed to evaluate the fitness measures such as Intracluster distance and Intercluster distance.Experimentation have been performed on three Parkinson’s datasets obtained from the UCI repository.The proposed IRBFNN is compared with other variations of RBFNN,conventional machine learning algorithms and other Parkinson’s Disease prediction algorithms.The proposed IRBFNN achieves an accuracy of 98.73%,98.47%and 99.03%for three Parkinson’s datasets taken for experimentation.The experimental results show that IRBFNN maximizes the accuracy in predicting Parkinson’s disease with minimum root mean square error.

Reorganization of spinal neural circuitry and functional recovery after spinal cord injury

The ability of the adult central nervous system to reorganize its circuits over time is the key to understand the functional improvement in subjects with spinal cord injury （SCI）. Adaptive changes within spared neuronal circuits may occur at cortical, brainstem, or spinal cord level, both above and below a spinal lesion （Bareyre et al., 2004）. At each level the reorganization is a very dynamic process, and its degree is highly variable, depending on several factors, including the age of the subject when SCI has occurred and the rehabilitative therapy. The use of electrophysiological techniques to assess these functional changes in neural networks is of great interest, because invasive methodologies as employed in preclinical models can obviously not be used in clinical studies.

State of charge estimation of Li-ion batteries in an electric vehicle based on a radial-basis-function neural network

The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial-basis-function neural network （RBF NN） has good characteristics to solve the nonlinear problem, a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed. Firstly, in this paper, the model of on-line SOC estimation with the RBF NN is set. Secondly, four important factors for estimating the SOC are confirmed based on the contribution analysis method, which simplifies the input variables of the RBF NN and enhttnces the real-time performance of estimation. FiItally, the pure electric buses with LiFePO4 Li-ion batteries running during the period of the 2010 Shanghai World Expo are considered as the experimental object. The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.

Altered protein markers related to neural plasticity and motor function following electro-acupuncture treatment in a rat model of ischemic-hypoxic brain injury

BACKGROUND：Previous studies have demonstrated that acupuncture treatment could ameliorate impaired motor function,and these positive effects might be due to neural plasticity.OBJECTIVE：Myelin basic protein（MBP）,microtubule-associated protein 2（MAP2）,growth-associated protein-43（GAP-43）,and synaptophysin（SYN） were selected as markers of neural remodeling,and expression of these markers was evaluated with regard to altered motor function following brain injury and acupuncture treatment.DESIGN,TIME AND SETTING：A completely randomized experiment was performed at the Central Laboratory of Peking University First Hospital from November 2006 to May 2007.MATERIALS：Twenty-four Sprague Dawley rat pups,aged 7 days,were selected for the present experiment.The left common carotid artery was ligated to establish a rat model of ischemic-hypoxic brain injury.METHODS：All animals were randomly divided into three groups：sham operation,model,and electro-acupuncture treatment,with 8 rats in each group.Rats in the model and electro-acupuncture treatment group underwent establishment of ischemic-hypoxic brain injury.Upon model established,rats underwent hypobaric oxygen intervention for 24 hours.Only the left common carotid artery was exposed in rats of the sham operation group,without model establishment or oxygen intervention.The rats in the electro-acupuncture treatment group were treated with electro-acupuncture.One acupuncture needle electrode was inserted into the subcutaneous layer at the Baihui and Dazhui acupoint.The stimulation condition of the electro-acupuncture simulator was set to an amplitude-modulated wave of 0-100% and alternative frequency of 100 cycles/second,as well as frequency-modulated wave of 2-100 Hz and an alternative frequency of 3 cycles/second.Maximal current through the two electrodes was limited to 3-5 mA.The stimulation lasted for 30 minutes per day for 2 weeks.Rats in the sham operation and model groups were not treated with electro-acupuncture,but only fixed to the table for the same time period.MAIN OUTCOME MEASURES：After 2 weeks stimulation,expression of MBP,MAP2,GAP-43,and SYN were detected in the brain by immunohistochemistry.Motor function was evaluated in the three groups.RESULTS：In the sham operation group,MBP was abundant in the myelinated nerve fibers.In the electro-acupuncture treatment group,however,the corpus callosum exhibited more MBP staining than the model group.MAP2 expression was increased in the model group,and increased further in the electro-acupuncture treatment group compared with the sham operation group.GAP-43 expression in the cerebral cortex was less in model group than in sham operation,but present in abundance in the electro-acupuncture treatment group.SYN expression in the cerebral cortex was less in the model and electro-acupuncture treatment group compared with the sham operation group.There was no significant difference in SYN expression and distribution between the model and electro-acupuncture treatment groups.Motor function of rats in the electro-acupuncture treatment group was significantly better than the model group（P 〈 0.05）,although function remained lower than the sham operation group（P 〈 0.05）.CONCLUSION：Two weeks of electro-acupuncture treatment improved motor function in rats,and protein markers related to neural plasticity also changed,which may be a mechanism for improved motor function in rats with ischemic-hypoxic brain injury.

The construction and approximation of feedforward neural network with hyperbolic tangent function

In this paper, we discuss some analytic properties of hyperbolic tangent function and estimate some approximation errors of neural network operators with the hyperbolic tan- gent activation function. Firstly, an equation of partitions of unity for the hyperbolic tangent function is given. Then, two kinds of quasi-interpolation type neural network operators are con- structed to approximate univariate and bivariate functions, respectively. Also, the errors of the approximation are estimated by means of the modulus of continuity of function. Moreover, for approximated functions with high order derivatives, the approximation errors of the constructed operators are estimated.

Neural network as a function approximator and its application in solving differential equations

A neural network(NN) is a powerful tool for approximating bounded continuous functions in machine learning. The NN provides a framework for numerically solving ordinary differential equations(ODEs) and partial differential equations(PDEs)combined with the automatic differentiation(AD) technique. In this work, we explore the use of NN for the function approximation and propose a universal solver for ODEs and PDEs. The solver is tested for initial value problems and boundary value problems of ODEs, and the results exhibit high accuracy for not only the unknown functions but also their derivatives. The same strategy can be used to construct a PDE solver based on collocation points instead of a mesh, which is tested with the Burgers equation and the heat equation(i.e., the Laplace equation).