Multi-channel neural signal stimulating module and in-vivo experiments

The module for function electrical stimulation （FES） of neurons is designed for the research of the neural function regeneration microelectronic system, which is an in-body embedded micro module. It is implemented by using discrete devices at first and characterized in vitro. The module is used to stimulate sciatic nerve and spinal cord of rats and rabbits for in-vivo real-time experiments of the neural function regeneration system. Based on the module, a four channel module for the FES of neurons is designed for 12 sites cuff electrode or 10 sites shaft electrode. Three animal experiments with total five rats and two rabbits were made. In the in-vivo experiment, the neural signals including spontaneous and imitated were regenerated by the module. The stimulating signal was used to drive sciatic nerve and spinal cord of rats and rabbits, successfully caused them twitch in different parts of their bodies, such as legs, tails, and fingers. This testifies that the neural function regeneration system can regenerate the neural signals.

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.

Function electrical stimulation circuit for neural signal regeneration system

A low-power, high-gain circuit for function electrical stimulation （FES） is designed for the microelectronic neural signal regeneration system based on CSMC （CSMC Technologies Corporation） 0. 6μm CMOS （complementary metal-oxide-semiconductor transistor） technology. It can be used to stimulate microelectrodes connected with the nerve bundles to regenerate neural signals. This circuit consists of two stages： a full differential folded-cascode amplifier input stage and a complementary class-AB output stage with an overload protection circuit. The rail-to-rail input and output stages are used to ensure a wide range of input and output voltages. The simulation results show that the gain of the circuit is 81 dB; the 3 dB-bandwidth is 295 kHz. The chip occupies a die area of 1.06 mm × 0. 52 mm. The on-wafer measurement results show that under a single supply voltage of ＋ 5 V, the DC power consumption is about 7. 5 mW and the output voltage amplitude is 4. 8 V. The chip can also mn well under single supply voltage of ＋ 3.3 V.

A Miniaturized System for Neural Signal Acquiring and Processing

To collect neural activity data from awake, behaving freely animals, we develop miniaturized implantable recording system by the modem chip：Programmable System on Chip （PSoC） and through chronic electrodes in the cortex. With PSoC family member CY8C29466,the system completed operational and instrument amplifiers, filters, timers, AD convertors, and serial communication, etc. The signal processing was dealt with virtual instrument technology. All of these factors can significantly affect the price and development cycle of the project. The result showed that the system was able to record and analyze neural extrocellular discharge generated by neurons continuously for a week or more. This is very useful for the interdisciplinary research of neuroscience and information engineering technique. The circuits and architecture of the devices can be adapted for neurobiology and research with other small animals.

An electromagnetic view of relay time in propagation of neural signals

We review the experimental and computational data about the propagation of neural signals in myelinated axons in mice,cats,rabbits,and frogs published in the past five decades.In contrast to the natural assumption that neural signals occur one by one in time and in space,we figure out that neural signals are highly overlapped in time between neighboring nodes.This phenomenon was occasionally illustrated in some early reports,but seemed to have been overlooked for some time.The shift in time between two successive neural signals from neighboring nodes,defined as relay timeτ,was calculated to be only 16.3μs-87.0μs,i.e.,0.8%-4.4%of the average duration of an action potential peak(roughly 2 ms).We present a clearer picture of the exact physical process about how the information transmits along a myelinated axon,rather than a whole action potential peak,what is transmitted is only a rising electric field caused by transmembrane ion flows.Here in the paper,τrepresents the waiting time until the neighboring node senses an attenuated electric field reaching the threshold to trigger the open state.The mechanisms addressed in this work have the potential to be universal,and may hold clues to revealing the exact triggering processes of voltage-gated ion channels and various brain functions.

Simulation of the physical process of neural electromagnetic signal generation based on a simple but functional bionic Na^(+)channel

The physical processes occurring at open Na^(+) channels in neural fibers are essential for the understanding of the nature of neural signals and the mechanism by which the signals are generated and transmitted along nerves.However,there is a less generally accepted description of these physical processes.We studied changes in the transmembrane ionic flux and the resulting two types of electromagnetic signals by simulating the Na^(+) transport across a bionic nanochannel model simplified from voltage-gated Na^(+) channels.The results show that the Na^(+) flux can reach a steady state in approximately 10 ns due to the dynamic equilibrium of the Na^(+) ion concentration difference between both sides of the membrane.After characterizing the spectrum and transmission of these two electromagnetic signals,the low-frequency transmembrane electric field is regarded as the physical quantity transmitting in the waveguide-like lipid dielectric layer and triggering the neighboring voltage-gated channels.Factors influencing the Na^(+) flux transport are also studied.The impact of the Na^(+) concentration gradient is found to be higher than that of the initial transmembrane potential on the Na^(+) transport rate,and introducing the surface-negative charge in the upper third channel could increase the transmembrane Na^(+) current.This work can be further studied by improving the simulation model;however,the current work helps to better understand the electrical functions of voltage-gated ion channels in neural systems.

Olfactory Decoding Method Using Neural Spike Signals

This paper presents a novel method for inferring the odor based on neural activities observed from rats' main olfactory bulbs.Multi-channel extra-cellular single unit recordings are done by micro-wire electrodes(Tungsten,50 μm,32 channels)implanted in the mitral/tufted cell layers of the main olfactory bulb of the anesthetized rats to obtain neural responses to various odors.Neural responses as a key feature are measured by subtraction firing rates before stimulus from after.For odor inference,a decoding method is developed based on the ML estimation.The results show that the average decoding accuracy is about 100.0%,96.0%,and 80.0% with three rats,respectively.This work has profound implications for a novel brain-machine interface system for odor inference.

Recovery of saturated signal waveform acquired from high-energy particles with artificial neural networks

Artificial neural networks(ANNs)are a core component of artificial intelligence and are frequently used in machine learning.In this report,we investigate the use of ANNs to recover the saturated signals acquired in highenergy particle and nuclear physics experiments.The inherent properties of the detector and hardware imply that particles with relatively high energies probably often generate saturated signals.Usually,these saturated signals are discarded during data processing,and therefore,some useful information is lost.Thus,it is worth restoring the saturated signals to their normal form.The mapping from a saturated signal waveform to a normal signal waveform constitutes a regression problem.Given that the scintillator and collection usually do not form a linear system,typical regression methods such as multi-parameter fitting are not immediately applicable.One important advantage of ANNs is their capability to process nonlinear regression problems.To recover the saturated signal,three typical ANNs were tested including backpropagation(BP),simple recurrent(Elman),and generalized radial basis function(GRBF)neural networks(NNs).They represent a basic network structure,a network structure with feedback,and a network structure with a kernel function,respectively.The saturated waveforms were produced mainly by the environmental gamma in a liquid scintillation detector for the China Dark Matter Detection Experiment(CDEX).The training and test data sets consisted of 6000 and 3000 recordings of background radiation,respectively,in which saturation was simulated by truncating each waveform at 40%of the maximum signal.The results show that the GBRF-NN performed best as measured using a Chi-squared test to compare the original and reconstructed signals in the region in which saturation was simulated.A comparison of the original and reconstructed signals in this region shows that the GBRF neural network produced the best performance.This ANN demonstrates a powerful efficacy in terms of solving the saturation recovery problem.The proposed method outlines new ideas and possibilities for the recovery of saturated signals in high-energy particle and nuclear physics experiments.This study also illustrates an innovative application of machine learning in the analysis of experimental data in particle physics.

Study on the Classification of Pulse Signal Based on the BP Neural Network

The objectification of the pulse signal analysis is a practical problem. The classification of the pulse signal is studied based on the BP neural network. It is first analyzed how to select the characteristic factors of the pulse signal. Then the method of nondimensionalization/normalization on the pulse signal is presented to preprocess the characteristic factors. The classification of the pulse signal and the effects of the selection of characteristic factors are studied by using the normalized data and BP neural network. It is shown that nondimensionalization/normalization of the data is in favor of the training and forecasting of the network. The selection of characteristic factors affects the accuracy of forecasting obviously. The results of forecasting by selection of 8, 6 and 4 factors respectively show that the less the factors are, the worse the effects are.

Neural Blind Beamformer for Cyclostationary Signals

A blind beamforming algorithm based on a neural network is presented according to the characteristic of cyclostationary signals. This method transforms the question of estimating beamformer weight vectors into the one of computing the SVD of the cross correlation matrix of array input signals and their frequency shift signals. A cross correlation neural network is introduced to compute the SVD of the cross correlation matrix so as to reduce the computational complexity and carry out the blind beanfforming more efficiently. The improved cross-coupled Hebbian learning rule presented can make the weights of the neural network converge much fast. Therefore, it is more promising in the practical use. This method can restrain noise and interference, simulation proves its correctness.

Preliminary Biometrics of ECG Signal Based on Temporal Organization through the Implementation of a Multilayer Perceptron Neural Network

The attributes of the ECG signal signifying the unique electrical properties of the heart offer the opportunity to expand the realm of biometrics, which pertains the identification of an individual based on physical characteristics. The temporal organization of the ECG signal offers a basis for composing a machine learning feature set. The four attributes of the feature set are derived through software automation enabled by Python. These four attributes are the temporal differential of the P wave maximum and T wave maximum relative to the R wave maximum and the Q wave minimum and S wave minimum relative to the R wave maximum. The multilayer perceptron neural network was applied and evaluated in terms of classification accuracy and time to develop the model. Superior performance was achieved with respect to a reduced feature set considering only the temporal differential of the P wave maximum and T wave maximum relative to the R wave maximum by comparison to all four attributes applied to the feature set and the temporal differential of the Q wave minimum and S wave minimum relative to the R wave maximum. With these preliminary findings and the advent of portable and wearable devices for the acquisition of the ECG signal, the temporal organization of the ECG signal offers robust potential for the field of biometrics.

A Sensor Failure Detection Method Based on Artificial Neural Network and Signal Processing

This paper proposes a sensor failure detection method based on artificial neural network and signal processing,in comparison with other methods,which does not need any redundancy information among sensor outputs and divides the output of a sensor into'Signal dominant component'and'Noise dominant component'because the pattern of sensor failure often appears in the'Noise dominant component'.With an ARMA model built for'Noise dominant component'using artificial neural network,such sensor failures as bias failure,hard failure,drift failure,spike failure and cyclic failure may be detected through residual analysis,and the type of sensor failure can be indicated by an appropriate indicator.The failure detection procedure for a temperature sensor in a hovercraft engine is simulated to prove the applicability of the method proposed in this paper.

Regenerative potential of targeting glycogen synthase kinase-3 signaling in neural tissues

Multiple roles of glycogen synthase kinase-3（GSK-3）in neural tissues：GSK-3 is a serine/threonine kinase that has two isoforms encoded by two different genes,GSK-3αand GSK-3β,in mammals.GSK-3 has several sites of serine and tyrosine phosphorylation.

Use of Chinese herbal medicine to treat AD: advantageous efficacy and stimulation of PKA signaling at crossroad between neural plasticity enhancement and pTau clearance

OBJECTIVE Currently, almost all chemical compounds or biological reagents to reverse or slow down the AD process have failed in clinical trials. An integrative and multi-targeted strategy is increasingly appreciated to effectively combat this devastating disease. Traditional Chinese medicine(TCM) has been widely used for treatment of dementia, and thus the advantages of the potential therapeutic features of TCM treatment and associated mechanisms should be well taken. The Amnesia Remedy Formula(ARF) was invented by one of the most influential Master of TCM SUN Si-miao, who lived for about 100 year old. The aim of this research is to characterize the time course changes of the cognitive behaviors post a ARF, and the mechanism underlying the effects, focusing on PKA-centered signaling for both enhancement of neural plasticity and clearance of the phosphorylated Tau. RESULTS We tested the efficacy of ARF on two animal models of AD, and examine the central role of PKA signaling in the enhancement of neural plasticity via PKA/CREB/BDNF pathway as well as clearance of toxic p Tau via PKA/GSK3β/p Tau pathway. In the scopolamine model, ARF effectively reversed the memory in Morris water maze(MWM) test, with some features superior to anti-AD drug donepezil. In a battery test of MWM, novel object recognition or T maze in 5-month-old senescenceaccelerated mouse prone 8(SAMP8) strain mice, two weeks of administration of ARF showed overall better improvement in memory loss than donepezil, and the effect lasted for at least 1 week after termination of administration of the formula. ARF increased expression of PKA/CREB/BDNF and synaptic proteins PSD95 expression, as well as enhanced Ser9 phosphorylation of GSK3β, thus reduced p Tau in the hippocampus. Blockade of PKA signaling blunted the anti-AD-like effect of ARF, with reversal of CREB/BDNF signaling. Transcriptomic analysis indicated some changes of novel molecules along this pathway may be part of the pathological and therapeutic mechanism, which warrants further investigation. CONCLUSION ARF may display some advantageous features in treating AD with early onset, via multi-targeted manner including enhancement of neural plasticity and reduction in Tau toxicity.

GENERALIZED INVERSE GROUP OF SIGNAL AND ITS IMPLEMENTATION WITH NEURAL NETWORKS

A new concept, the generalized inverse group (GIG) of signal, is firstly proposed and its properties, leaking coefficients and implementation with neural networks are presented. Theoretical analysis and computational simulation have shown that (1) there is a group of finite length of generalized inverse signals for any given finite signal, which forms the GIG; (2) each inverse group has different leaking coefficients, thus different abnormal states; (3) each GIG can be implemented by a grouped and improved single-layer perceptron which appears with fast convergence. When used in deconvolution, the proposed GIG can form a new parallel finite length of filtering deconvolution method. On off-line processing, the computational time is reduced to O(N) from O(N2). And the less the leaking coefficient is, the more reliable the deconvolution will be.

Physiological effects of amyloid precursor protein and its derivatives on neural stem cell biology and signaling pathways involved

The pathological implication of amyloid precursor protein(APP)in Alzheimer’s disease has been widely documented due to its involvement in the generation of amyloid-β peptide.However,the physiological functions of APP are still poorly understood.APP is considered a multimodal protein due to its role in a wide variety of processes,both in the embryo and in the adult brain.Specifically,APP seems to play a key role in the proliferation,differentiation and maturation of neural stem cells.In addition,APP can be processed through two canonical processing pathways,generating different functionally active fragments:soluble APP-α,soluble APP-β,amyloid-β peptide and the APP intracellular C-terminal domain.These fragments also appear to modulate various functions in neural stem cells,including the processes of proliferation,neurogenesis,gliogenesis or cell death.However,the molecular mechanisms involved in these effects are still unclear.In this review,we summarize the physiological functions of APP and its main proteolytic derivatives in neural stem cells,as well as the possible signaling pathways that could be implicated in these effects.The knowledge of these functions and signaling pathways involved in the onset or during the development of Alzheimer’s disease is essential to advance the understanding of the pathogenesis of Alzheimer’s disease,and in the search for potential therapeutic targets.

SENSOR FAILURE DETECTION AND SIGNAL RECOVERY BASED ON BP NEURAL NETWORK

A study is given on the application of BP neural network (BPNN) in sensorfailure detection in control systems, and on the networ architecture desgn, the redun-dancy,the quickness and the insensitivity to sensor noise of the BPNN based sensor detec-tion methed. Besules, an exploration is made into tbe factors accounting for the quality ofsignal recovery for failed sensor using BPNN. The results reveal clearly that BPNN can besuccessfully used in sensor failure detection and data recovery.

Effect of spinal cord extracts after spinal cord injury on proliferation of rat embryonic neural stem cells and Notch signal pathway in vitro

Objective:To investigate the effect of the spinal cord extracts(SCE)after spinal cord injuries(SCIs)on the proliferation of rat embryonic neural stem cells(NSCs)and the expressions of mRNA of Notch1 as well as of Hes1 in this process in vitro.Methods:The experiment was conducted in 4 different mediums:NSCs+PBS(Group A-blank control group),NSCs+SCE with healthy SD rats(Croup B-normal control group),NSCs+SCE with SD rats receiving sham-operation treatment(Croup C-sham-operation group)and NSCs+SCE with SCIs rats(Group D-paraplegic group).Proliferative abilities of 4 different groups were analyzed by MTT chromatometry after co-culture for 1,2,3,4 and 5 d,respectively.The expressions of Notch 1 and Hes1 mRNA were also detected with RT-PCR after co-culture for 24 and 48 h,respectively.Results:After co-culture for 1,2,3,4 and 5 d respectively,the MTT values of group D were significantly higher than those of group A,group B and group C(P<0.05).However,there were no significantly differences regarding MTT values between group A,group B and group C after co-culture for 1,2,3,4 and 5 d,respectively(P>0.05).Both the expressions of Notch1 and Hes1 mRNA of group D were significantly higher than those of other 3 groups after co-culture for 24 h and 48 h as well(P<0.05).But there was no difference oin expressions of Notch1 and Hes1 mRNA among group A,group B and group C after co-culture for 24 h and 48 h(P>0.05).There was no difference in expressions of Notch1and Hes1 mRNA between 24 h and 48 h treatment in group D.Conclusions:SCE could promote the proliferation of NSCs.It is demonstrated that the microenvironment of SCI may promote the proliferation of NSCs.Besides,SCE could increase the expression of Notch1 and Hes1 mRNA of NSC.It can be concluded that the Notch signaling pathway activation is one of the mechanisms that locally injured microenvironment contributes to the proliferation of ENSC after SCIs.This process may be performed by up-regulating the expressions of Notch1 and Hes1 gene.

Adaptive Learning with Large Variability of Teaching Signals for Neural Networks and Its Application to Motion Control of an Industrial Robot

Recently, various control methods represented by proportional-integral-derivative （PID） control are used for robotic control. To cope with the requirements for high response and precision, advanced feedforward controllers such as gravity compensator, Coriolis/centrifugal force compensator and friction compensators have been built in the controller. Generally, it causes heavy computational load when calculating the compensating value within a short sampling period. In this paper, integrated recurrent neural networks are applied as a feedforward controller for PUMA560 manipulator. The feedforward controller works instead of gravity and Coriolis/centrifugal force compensators. In the learning process of the neural network by using back propagation algorithm, the learning coefficient and gain of sigmoid function are tuned intuitively and empirically according to teaching signals. The tuning is complicated because it is being conducted by trial and error. Especially, when the scale of teaching signal is large, the problem becomes crucial. To cope with the problem which concerns the learning performance, a simple and adaptive learning technique for large scale teaching signals is proposed. The learning techniques and control effectiveness are evaluated through simulations using the dynamic model of PUMA560 manipulator.

On the Impact of Manufacturing Uncertainty in Structural Health Monitoring of Composite Structures: A Signal to Noise Weighted Neural Network Process

This article investigates the potential impact of manufacturing uncertainty in composite structures here in the form of thickness variation in laminate plies, on the robustness of commonly used Artificial Neural Networks (ANN) in Structural Health Monitoring (SHM). Namely, the robustness of an ANN SHM system is assessed through an airfoil case study based on the sensitivity of delamination location and size predictions, when the ANN is imposed to noisy input. In light of the observed poor performance of the original network, even when its architecture was carefully optimized, it had been proposed to weigh the input layer of the ANN by a set of signal-to-noise (SN) ratios and then trained the network. Both damage location and size predictions of the latter SHM approach were increased to above 90%. Practical aspects of the proposed robust SN-ANN SHM have also been discussed.