Neural Compensation Research Articles

Neurons in Primary Motor Cortex Encode External Perturbations during an Orientation Reaching Task

When confronting an abrupt external perturbation force during movement, subjects continuously adjust their behaviors to adapt to changes. Such adaptation is of great importance for realizing flexible motor control in varied environments, but the potential cortical neuronal mechanisms behind it have not yet been elucidated. Aiming to reveal potential neural control system compensation for external disturbances, we applied an external orientation perturbation while monkeys performed an orientation reaching task and simultaneously recorded the neural activity in the primary motor cortex (M1). We found that a subpopulation of neurons in the primary motor cortex specially created a time-locked activity in response to a “go” signal in the adaptation phase of the impending orientation perturbation and did not react to a “go” signal under the normal task condition without perturbation. Such neuronal activity was amplified as the alteration was processed and retained in the extinction phase; then, the activity gradually faded out. The increases in activity during the adaptation to the orientation perturbation may prepare the system for the impending response. Our work provides important evidence for understanding how the motor cortex responds to external perturbations and should advance research about the neurophysiological mechanisms underlying motor learning and adaptation.

Robust adaptive neural network event-triggered compensation control for continuous stirred tank reactors with prescribed performance and actuator failures

This paper studies the event-triggered compensation tracking control problem of the Continuous Stirred Tank Reactor (CSTR) with actuator failures. By actuator redundancies, a novel adaptive neural network (NN) event-triggered controller (ETC) design scheme is proposed based on the switching threshold event-triggering mechanism (SWT-ETM). To constrain the maximum overshoot and the convergence rate within given specifications, a prescribed performance function (PPF) error transformation is employed. It is shown that the tracking error will exponentially converge to an adjustable neighborhood of zero with prescribed transient performance, despite the presence of failures, system nonlinearities, parametric uncertainties and external disturbances. Besides, the system’s burden is significantly alleviated requiring less system resources for signal transmission and actuator execution to decrease the occurrence rate of the actuator failures; and the minimum inter-event interval is guaranteed to be positive to avoid the Zeno phenomenon. Simulation results illustrate the effectiveness of the proposed adaptive NN ETC scheme.

Neural network compensation method for improving detection accuracy of rotor position from magnetic encoder

With the advantages of low cost, small size and high resolution, the magnetic encoder is suitable for permanent magnet synchronous motor. However, due to the installation accuracy and the motor vibration, the rotor position error from magnetic encoder can seriously impact the performance of the motor. Therefore, it is necessary to reduce the detection error of the rotor position from the encoder. In this letter, a new rotor position detection method is proposed based on neural network. The characteristic of the position error from magnetic encoder is analysed, and the backpropagation neural network is presented to obtain the error compensation function. On the basis of the compensation function, the rotor position error can be corrected and thus the precise rotor position can be detected. The experimental results show that not only the rotor position error but also the related total harmonic distortion of the phase current and the torque ripple can be reduced effectively.

Neural network adaptive backstepping control via uncertainty compensation for PMSG-based variable-speed wind turbine: Controller design and stability analysis

This paper proposes a design scheme along with stability analysis of a new adaptive backstepping controller designed for permanent magnet synchronous generator-based wind turbine, by using artificial neural network-based uncertainty compensation. The idea is to control the rotor speed and the mechanical power generated under internal and external nonlinear parametric uncertainties. An uncertain model of permanent magnet synchronous generator is designed. Then, two artificial neural network compensators are built to compensate such uncertainties in the current loops. The stability of the closed-loop system is studied according to the Lyapunov function. Simulations of the dynamic model are performed under both variable step and random wind speeds by using the DEV-C++ software, and the results are plotted with MATLAB. Compared to the classical direct torque control technique, the obtained results show the robustness of the proposed controller despite the presence of different uncertainties.

Path tracking control of an autonomous vehicle with model-free adaptive dynamic programming and RBF neural network disturbance compensation

The performance of the model-based controller is always affected by the uncertainty and nonlinearity of the model parameters in the vehicle path tracking process. To address this issue, a novel path tracking controller based on model-free adaptive dynamic programming (ADP) is proposed for autonomous vehicles in this paper. To be specific, the proposed controller obtains information from the online state and front-wheel angle input data which are repeatedly used to calculate the controller gain iteratively. So, this controller features not requiring accurate knowledge of vehicle model parameters for controller development. Meanwhile, the path tracking performance of the autonomous vehicle will be inevitably disturbed by unknown nonlinear external disturbance. To approximate this disturbance, the learning characteristics of Radial Basis Function Neural Network (RBFNN) are applied to generate compensation for the front-wheel angle. Afterward, the weight updating law of RBFNN is derived by Lyapunov function to ensure the stability and convergence of the whole system. Finally, Hardware in the loop (HIL) test results demonstrate that the proposed ADP-RBF controller can improve the comprehensive performance of the vehicle path tracking control system and achieve the balance between path tracking accuracy and minimum sideslip angle.

The Boundary Proportion Differential Control Method of Micro-Deformable Manipulator with Compensator Based on Partial Differential Equation Dynamic Model

It is challenging to accurately judge the actual end position of the manipulator—regarded as a rigid body—due to the influence of micro-deformation. Its precise and efficient control is a crucial problem. To solve the problem, the Hamilton principle was used to establish the partial differential equation (PDE) dynamic model of the manipulator system based on the infinite dimension of the working environment interference and the manipulator space. Hence, it resolves the common overflow instability problem in the micro-deformable manipulator system modeling. Furthermore, an infinite-dimensional radial basis function neural network compensator suitable for the dynamic model was proposed to compensate for boundary and uncertain external interference. Based on this compensation method, a distributed boundary proportional differential control method was designed to improve control accuracy and speed. The effectiveness of the proposed model and method was verified by theoretical analysis, numerical simulation, and experimental verification. The results show that the proposed method can effectively improve the response speed while ensuring accuracy.

Fiber-specific white matter alterations in early-stage tremor-dominant Parkinson\u2019s disease

Using a fixel-based analysis (FBA), we assessed the fiber-specific white matter (WM) alterations in nonmedicated patients with early-stage Parkinson’s disease (PD) with tremor-dominant (TD; n = 53; mean age, 61.7 ± 8.7 years) and postural instability and gait disorder (PIGD; n = 27; mean age, 57.8 ± 8.1 years) motor subtypes and age- and sex-matched healthy controls (HC; n = 43; mean age, 61.6 ± 9.2 years) from Parkinson’s Progression Markers Initiative dataset. FBA revealed significantly increased macrostructural fiber cross section and a combination of fiber density and cross section metrics within the corticospinal tract in patients with TD-PD compared with HC. Nonetheless, no significant changes in FBA-derived metrics were found in patients with PIGD-PD compared with HC or patients with TD-PD. Our results may provide evidence of WM neural compensation mechanisms in patients with TD-PD marked by increases in fiber bundle size and the ability to relay information between brain regions.

A Novel DSP-Based MPPT Control Design for Photovoltaic Systems Using Neural Network Compensator

In this study, based on the slope of power versus voltage, a novel maximum-power-point tracking algorithm using a neural network compensator was proposed and implemented on a TI TMS320F28335 digital signal processing chip, which can easily process the input signals conversion and the complex floating-point computation on the neural network of the proposed control scheme. Because the output power of the photovoltaic system is a function of the solar irradiation, cell temperature, and characteristics of the photovoltaic array, the analytic solution for obtaining the maximum power is difficult to obtain due to its complexity, nonlinearity, and uncertainties of parameters. The innovation of this work is to obtain the maximum power of the photovoltaic system using a neural network with the idea of transferring the maximum-power-point tracking problem into a proportional-integral current control problem despite the variation in solar irradiation, cell temperature, and the electrical load characteristics. The current controller parameters are determined via a genetic algorithm for finding the controller parameters by the minimization of a complicatedly nonlinear performance index function. The experimental result shows the output power of the photovoltaic system, which consists of the series connection of two 155-W TYN-155S5 modules, is 267.42 W at certain solar irradiation and ambient temperature. From the simulation and experimental results, the validity of the proposed controller was verified.

Neural Compensation in a novel operant devaluation task in rats

The reinforcer devaluation task is often used to model flexible goal-directed action, the ability to adaptively modify behavior when the value of a reinforcer changes. Deficits in goal-directed action are reported in multiple neuropsychiatric conditions, including schizophrenia. However, dysfunction is not always apparent in early stages of schizophrenia, possibly due to neural compensation. We designed a novel devaluation task in which goal-directed action could be guided by stimulus-outcome (S-O) [presumably orbitofrontal cortex (OFC)-mediated] or response-outcome (R-O) associations [presumably prelimbic cortex (PL)-mediated] to maintain. Therefore, if our task is able to model neural compensation, damage to either PL or OFC should not impair devaluation because the non-damaged region can compensate for the loss using the alternate strategy. In Experiment 1, rats received bilateral OFC, PL, combined OFC+PL, or sham lesions and then completed our devaluation task. Sham, OFC, and PL lesioned rats showed intact devaluation, whereas the OFC+PL lesion group exhibited impaired devaluation. In Experiment 2, rats received bilateral PL or sham lesions and then completed behavioral training. During devaluation testing, rats were divided into Cue Normal or Cue Switch test conditions. In Cue Switch test conditions, the cuelight and spatial lever location predicted conflicting outcomes so we can determine whether rats are devaluing using a S-O or R-O strategy. Sham rats exhibited normal devaluation performance (based on the lever-location) in both Cue Normal and Cue Switch conditions. PL lesioned rats showed normal devaluation performance in the Cue Normal condition but a reverse devaluation effect in the Cue Switch condition suggesting the PL-lesioned rats were devaluing based on the outcome predicted by the cuelight. In Experiment 3, rats received unilateral infusions of cholera-toxin-b (CTb), a retrograde tracer, into OFC and either bilateral PL or sham lesions and then completed behavioral training. Rats were sacrificed on the last day of training to double-label for Arc and CTb in the mediodorsal thalamus (MD) and Arc in the OFC. We found increased Arc+CTb in MD when PL is lesioned and increased Arc+ neurons in OFC when PL is lesioned. There were no effect of PL lesion in the basolateral amygdala. Our results suggest that our devaluation task can successfully model neural compensation between OFC and PL and this compensation may involve MD. This research demonstrates a method to study how functional neural circuitry is subtly altered like in early stages of schizophrenia.

Anti-Sway and Positioning Adaptive Control of a Double-Pendulum Effect Crane System With Neural Network Compensation.

Cranes are widely used in the field of construction, logistics, and the manufacturing industry. Cranes that use wire ropes as the main lifting mechanism are deeply troubled by the swaying of heavy objects, which seriously restricts the working efficiency of the crane and even cause accidents. Compared with the single-pendulum crane, the double-pendulum effect crane model has stronger nonlinearity, and its controller design is challenging. In this paper, cranes with a double-pendulum effect are considered, and their nonlinear dynamical models are established. Then, a controller based on the radial basis function (RBF) neural network compensation adaptive method is designed, and a stability analysis is also presented. Finally, the hardware-in-the-loop experimental results show that the neural network compensation control can effectively improve the control performance of the controller in practice.

Event-Triggered Adaptive Neural Network Sensor Failure Compensation for Switched Interconnected Nonlinear Systems With Unknown Control Coefficients.

In this article, a decentralized adaptive neural network (NN) event-triggered sensor failure compensation control issue is investigated for nonlinear switched large-scale systems. Due to the presence of unknown control coefficients, output interactions, sensor faults, and arbitrary switchings, previous works cannot solve the investigated issue. First, to estimate unmeasured states, a novel observer is designed. Then, NNs are utilized for identifying both interconnected terms and unstructured uncertainties. A novel fault compensation mechanism is proposed to circumvent the obstacle caused by sensor faults, and a Nussbaum-type function is introduced to tackle unknown control coefficients. A novel switching threshold strategy is developed to balance communication constraints and system performance. Based on the common Lyapunov function (CLF) method, an event-triggered decentralized control scheme is proposed to guarantee that all closed-loop signals are bounded even if sensors undergo failures. It is shown that the Zeno behavior is avoided. Finally, simulation results are presented to show the validity of the proposed strategy.

An Effect of Education on Memory-Encoding Activation in Subjective Cognitive Decline.

Subjective cognitive decline (SCD) may be an early manifestation of pre-clinical Alzheimer's disease. Elevated amyloid-β (Aβ) is a correlate of SCD symptoms in some individuals. The underlying neural correlates of SCD symptoms and their association with Aβ is unknown. SCD is a heterogeneous condition, and cognitive reserve may explain individual differences in its neural correlates. We investigated the association between brain activation during memory encoding and SCD symptoms, as well as with Aβ, among older individuals. We also tested the moderating role of education (an index of cognitive reserve) on the associations. We measured brain activation during the "face-name" memory-encoding fMRI task and Aβ deposition with Pittsburgh Compound-B (PiB)-PET among cognitively normal older individuals (n = 63, mean age 73.1 ± 7.4 years). We tested associations between activation and SCD symptoms by self-report measures, Aβ, and interactions with education. Activation was not directly associated with SCD symptoms or Aβ. However, education moderated the association between activation and SCD symptoms in the executive control network, salience network, and subcortical regions. Greater SCD symptoms were associated with greater activation in those with higher education, but with lower activation in those with lower education. SCD symptoms were associated with different patterns of brain activation in the extended memory system depending on level of cognitive reserve. Greater SCD symptoms may represent a saturation of neural compensation in individuals with greater cognitive reserve, while it may reflect diminishing neural resources in individuals with lower cognitive reserve.

Modal space neural network compensation control for Gough-Stewart robot with uncertain load

This paper investigates the motion control of a Stewart parallel robot with uncertain load. The external disturbances caused by load can trigger dynamic coupling among six degree-of-freedoms and have a significant impact on control precision. In practice, the dynamic parameters of load is difficult to be precedent identified and the robot with load is a multi-input multi-output(MIMO) system in physical space, which makes the dynamic coupling problem difficult to solve effectively and the high-performance control is hard to achieve. In this paper, the dynamic effect of the load is first analyzed. Then a novel motion control method, modal space neural network compensation control, is designed with the aim of reducing the effect of the load disturbances. This controller implements the neural network to compensate for the load disturbances. In addition, the modal space control theory is introduced to realize the independent control of each control channel. The feasibility of the proposed controller is evaluated in numerical simulations. Results reveal that the proposed control method gives exceptional tracking performance and dynamic coupling suppression capability.

UWB localization algorithm based on BP neural network compensation extended Kalman filter

When UWB technology is applied to indoor positioning, there will be a lot of noise interfering with its positioning accuracy. In order to improve the positioning accuracy, the improved extended incremental Kalman filtering algorithm and BP neural network algorithm are proposed to eliminate the positioning errors. In this paper, the extended incremental Kalman filter algorithm is first used to denoise the distance measured by UWB to reduce the errors caused by environment, equipment and other factors, then the classical Chan positioning algorithm is used to get the positioning results of tags, and finally the BP neural network algorithm is used to compensate the positioning results.

Event-Related Potentials, Inhibition, and Risk for Alzheimer's Disease Among Cognitively Intact Elders.

Despite advances in understanding Alzheimer's disease (AD), prediction of AD prior to symptom onset remains severely limited, even when primary risk factors such as the apolipoprotein E (APOE) ɛ4 allele are known. Although executive dysfunction is highly prevalent and is a primary contributor to loss of independence in those with AD, few studies have examined neural differences underlying executive functioning as indicators of risk for AD prior to symptom onset, when intervention might be effective. This study examined event-related potential (ERP) differences during inhibitory control in 44 cognitively intact older adults (20 ɛ4+, 24 ɛ4-), relative to 41 young adults. All participants completed go/no-go and stop-signal tasks. Overall, both older adult groups exhibited slower reaction times and longer ERP latencies compared to young adults. Older adults also had generally smaller N200 and P300 amplitudes, except at frontal electrodes and for N200 stop-signal amplitudes, which were larger in older adults. Considered with intact task accuracy, these findings suggest age-related neural compensation. Although ɛ4 did not distinguish elders during go or no-go tasks, this study uniquely showed that the more demanding stop-signal task was sensitive to ɛ4 differences, despite comparable task and neuropsychological performance with non-carriers. Specifically, ɛ4+ elders had slower frontal N200 latency and larger N200 amplitude, which was most robust at frontal sites, compared with ɛ4-. N200 during a stop-signal task is sensitive to AD risk, prior to any evidence of cognitive dysfunction, suggesting that stop-signal ERPs may be an important protocol addition to neuropsychological testing.

An improved neural network method for aeromagnetic compensation

Aeromagnetic compensation plays an important role in aeromagnetic data processing and can eliminate the magnetic interference generated by aircraft. Currently, aeromagnetic compensation algorithms are mostly based on linear regression equations, but linear regression has insufficient fitting function capabilities compared to neural networks. Williams proposed a compensation model from the perspective of a neural network, but this model has the problem of overfitting. Combining these two methods, this paper proposes a neural network compensation method with strong generalization. This method is based on regression equations and uses generalized regression neural networks (GRNNs) for neural network regression. Analysis of the probability density function that constitutes the GRNN shows that proper smoothing factors and multiple training sets can improve the generalization of the GRNN compensation model, thereby weakening the effect of overfitting.

Bilingualism's Effects on Resting-State Functional Connectivity in Mild Cognitive Impairment.

Background: Bilingualism is considered a cognitive reserve (CR) factor, due to the delay in the onset of dementia in bilinguals compared with monolinguals. Two neural mechanisms have been suggested to underlie CR: neural reserve and neural compensation. However, it is still unclear how bilingualism contributes to these mechanisms. Methods: In this study, we used cognitive tests, functional connectivity (FC), regional homogeneity, and fractional amplitude of low-frequency fluctuations (fALFF) measures to study resting-state brain patterns in a sample of bilingual and monolingual subjects with mild cognitive impairment. Results: We found no significant differences between the groups in age, sex, education, or cognitive level, but bilinguals showed higher FC than monolinguals between the posterior part of the superior temporal gyrus and the precuneus, positively correlated with Mini-Mental State Examination scores, and higher fALFF in the thalamus bilaterally. Conclusions: Our results suggest that bilingualism may act as a CR factor that protects against dementia through neural compensation.

The enigma of cerebral blood flow and cognition.

The enigma of cerebral blood flow and cognition.

Effects of vestibular neurectomy and neural compensation on head movements in patients undergoing vestibular schwannoma resection

The vestibular system is vital for maintaining balance and stabilizing gaze and vestibular damage causes impaired postural and gaze control. Here we examined the effects of vestibular loss and subsequent compensation on head motion kinematics during voluntary behavior. Head movements were measured in vestibular schwannoma patients before, and then 6 weeks and 6 months after surgical tumor removal, requiring sectioning of the involved vestibular nerve (vestibular neurectomy). Head movements were recorded in six dimensions using a small head-mounted sensor while patients performed the Functional Gait Assessment (FGA). Kinematic measures differed between patients (at all three time points) and normal subjects on several challenging FGA tasks, indicating that vestibular damage (caused by the tumor or neurectomy) alters head movements in a manner that is not normalized by central compensation. Kinematics measured at different time points relative to vestibular neurectomy differed substantially between pre-operative and 6-week post-operative states but changed little between 6-week and > 6-month post-operative states, demonstrating that compensation affecting head kinematics is relatively rapid. Our results indicate that quantifying head kinematics during self-generated gait tasks provides valuable information about vestibular damage and compensation, suggesting that early changes in patient head motion strategy may be maladaptive for long-term vestibular compensation.

Two cortical representations of voice control are differentially involved in speech fluency.

Recent studies have identified two distinct cortical representations of voice control in humans, the ventral and the dorsal laryngeal motor cortex. Strikingly, while persistent developmental stuttering has been linked to a white-matter deficit in the ventral laryngeal motor cortex, intensive fluency-shaping intervention modulated the functional connectivity of the dorsal laryngeal motor cortical network. Currently, it is unknown whether the underlying structural network organization of these two laryngeal representations is distinct or differently shaped by stuttering intervention. Using probabilistic diffusion tractography in 22 individuals who stutter and participated in a fluency shaping intervention, in 18 individuals who stutter and did not participate in the intervention and in 28 control participants, we here compare structural networks of the dorsal laryngeal motor cortex and the ventral laryngeal motor cortex and test intervention-related white-matter changes. We show (i) that all participants have weaker ventral laryngeal motor cortex connections compared to the dorsal laryngeal motor cortex network, regardless of speech fluency, (ii) connections of the ventral laryngeal motor cortex were stronger in fluent speakers, (iii) the connectivity profile of the ventral laryngeal motor cortex predicted stuttering severity (iv) but the ventral laryngeal motor cortex network is resistant to a fluency shaping intervention. Our findings substantiate a weaker structural organization of the ventral laryngeal motor cortical network in developmental stuttering and imply that assisted recovery supports neural compensation rather than normalization. Moreover, the resulting dissociation provides evidence for functionally segregated roles of the ventral laryngeal motor cortical and dorsal laryngeal motor cortical networks.