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Sample records for adaptive critic neural

  1. Adaptive Critic Neural Network-Based Terminal Area Energy Management and Approach and Landing Guidance

    NASA Technical Reports Server (NTRS)

    Grantham, Katie

    2003-01-01

    Reusable Launch Vehicles (RLVs) have different mission requirements than the Space Shuttle, which is used for benchmark guidance design. Therefore, alternative Terminal Area Energy Management (TAEM) and Approach and Landing (A/L) Guidance schemes can be examined in the interest of cost reduction. A neural network based solution for a finite horizon trajectory optimization problem is presented in this paper. In this approach the optimal trajectory of the vehicle is produced by adaptive critic based neural networks, which were trained off-line to maintain a gradual glideslope.

  2. Optimal control of distributed parameter systems using adaptive critic neural networks

    NASA Astrophysics Data System (ADS)

    Padhi, Radhakant

    In this dissertation, two systematic optimal control synthesis techniques are presented for distributed parameter systems based on the adaptive critic neural networks. Following the philosophy of dynamic programming, this adaptive critic optimal control synthesis approach has many desirable features, viz. having a feedback form of the control, ability for on-line implementation, no need for approximating the nonlinear system dynamics, etc. More important, unlike the dynamic programming, it can accomplish these objectives without getting overwhelmed by the computational and storage requirements. First, an approximate dynamic programming based adaptive critic control synthesis formulation was carried out assuming an approximation of the system dynamics in a discrete form. A variety of example problems were solved using this proposed general approach. Next a different formulation is presented, which is capable of directly addressing the continuous form of system dynamics for control design. This was obtained following the methodology of Galerkin projection based weighted residual approximation using a set of orthogonal basis functions. The basis functions were designed by with the help of proper orthogonal decomposition, which leads to a very low-dimensional lumped parameter representation. The regulator problems of linear and nonlinear heat equations were revisited. Optimal controllers were synthesized first assuming a continuous controller and then a set of discrete controllers in the spatial domain. Another contribution of this study is the formulation of simplified adaptive critics for a large class of problems, which can be interpreted as a significant improvement of the existing adaptive critic technique.

  3. Phase transitions towards criticality in a neural system with adaptive interactions.

    PubMed

    Levina, Anna; Herrmann, J Michael; Geisel, Theo

    2009-03-20

    We analytically describe a transition scenario to self-organized criticality (SOC) that is new for physics as well as neuroscience; it combines the criticality of first and second-order phase transitions with a SOC phase. We consider a network of pulse-coupled neurons interacting via dynamical synapses, which exhibit depression and facilitation as found in experiments. We analytically show the coexistence of a SOC phase and a subcritical phase connected by a cusp bifurcation. Switching between the two phases can be triggered by varying the intensity of noisy inputs.

  4. The Adaptive Kernel Neural Network

    DTIC Science & Technology

    1989-10-01

    A neural network architecture for clustering and classification is described. The Adaptive Kernel Neural Network (AKNN) is a density estimation...classification layer. The AKNN retains the inherent parallelism common in neural network models. Its relationship to the kernel estimator allows the network to

  5. Generalized Adaptive Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Tawel, Raoul

    1993-01-01

    Mathematical model of supervised learning by artificial neural network provides for simultaneous adjustments of both temperatures of neurons and synaptic weights, and includes feedback as well as feedforward synaptic connections. Extension of mathematical model described in "Adaptive Neurons For Artificial Neural Networks" (NPO-17803). Dynamics of neural network represented in new model by less-restrictive continuous formalism.

  6. Critical Branching Neural Networks

    ERIC Educational Resources Information Center

    Kello, Christopher T.

    2013-01-01

    It is now well-established that intrinsic variations in human neural and behavioral activity tend to exhibit scaling laws in their fluctuations and distributions. The meaning of these scaling laws is an ongoing matter of debate between isolable causes versus pervasive causes. A spiking neural network model is presented that self-tunes to critical…

  7. An Adaptive Critic Approach to Reference Model Adaptation

    NASA Technical Reports Server (NTRS)

    Krishnakumar, K.; Limes, G.; Gundy-Burlet, K.; Bryant, D.

    2003-01-01

    Neural networks have been successfully used for implementing control architectures for different applications. In this work, we examine a neural network augmented adaptive critic as a Level 2 intelligent controller for a C- 17 aircraft. This intelligent control architecture utilizes an adaptive critic to tune the parameters of a reference model, which is then used to define the angular rate command for a Level 1 intelligent controller. The present architecture is implemented on a high-fidelity non-linear model of a C-17 aircraft. The goal of this research is to improve the performance of the C-17 under degraded conditions such as control failures and battle damage. Pilot ratings using a motion based simulation facility are included in this paper. The benefits of using an adaptive critic are documented using time response comparisons for severe damage situations.

  8. The Criticality Hypothesis in Neural Systems

    NASA Astrophysics Data System (ADS)

    Karimipanah, Yahya

    There is mounting evidence that neural networks of the cerebral cortex exhibit scale invariant dynamics. At the larger scale, fMRI recordings have shown evidence for spatiotemporal long range correlations. On the other hand, at the smaller scales this scale invariance is marked by the power law distribution of the size and duration of spontaneous bursts of activity, which are referred as neuronal avalanches. The existence of such avalanches has been confirmed by several studies in vitro and in vivo, among different species and across multiple scales, from spatial scale of MEG and EEG down to single cell resolution. This prevalent scale free nature of cortical activity suggests the hypothesis that the cortex resides at a critical state between two phases of order (short-lasting activity) and disorder (long-lasting activity). In addition, it has been shown, both theoretically and experimentally, that being at criticality brings about certain functional advantages for information processing. However, despite the plenty of evidence and plausibility of the neural criticality hypothesis, still very little is known on how the brain may leverage such criticality to facilitate neural coding. Moreover, the emergent functions that may arise from critical dynamics is poorly understood. In the first part of this thesis, we review several pieces of evidence for the neural criticality hypothesis at different scales, as well as some of the most popular theories of self-organized criticality (SOC). Thereafter, we will focus on the most prominent evidence from small scales, namely neuronal avalanches. We will explore the effect of adaptation and how it can maintain scale free dynamics even at the presence of external stimuli. Using calcium imaging we also experimentally demonstrate the existence of scale free activity at the cellular resolution in vivo. Moreover, by exploring the subsampling issue in neural data, we will find some fundamental constraints of the conventional methods

  9. Intrinsic adaptation in autonomous recurrent neural networks.

    PubMed

    Marković, Dimitrije; Gros, Claudius

    2012-02-01

    A massively recurrent neural network responds on one side to input stimuli and is autonomously active, on the other side, in the absence of sensory inputs. Stimuli and information processing depend crucially on the quality of the autonomous-state dynamics of the ongoing neural activity. This default neural activity may be dynamically structured in time and space, showing regular, synchronized, bursting, or chaotic activity patterns. We study the influence of nonsynaptic plasticity on the default dynamical state of recurrent neural networks. The nonsynaptic adaption considered acts on intrinsic neural parameters, such as the threshold and the gain, and is driven by the optimization of the information entropy. We observe, in the presence of the intrinsic adaptation processes, three distinct and globally attracting dynamical regimes: a regular synchronized, an overall chaotic, and an intermittent bursting regime. The intermittent bursting regime is characterized by intervals of regular flows, which are quite insensitive to external stimuli, interceded by chaotic bursts that respond sensitively to input signals. We discuss these findings in the context of self-organized information processing and critical brain dynamics.

  10. Criticality Maximizes Complexity in Neural Tissue.

    PubMed

    Timme, Nicholas M; Marshall, Najja J; Bennett, Nicholas; Ripp, Monica; Lautzenhiser, Edward; Beggs, John M

    2016-01-01

    The analysis of neural systems leverages tools from many different fields. Drawing on techniques from the study of critical phenomena in statistical mechanics, several studies have reported signatures of criticality in neural systems, including power-law distributions, shape collapses, and optimized quantities under tuning. Independently, neural complexity-an information theoretic measure-has been introduced in an effort to quantify the strength of correlations across multiple scales in a neural system. This measure represents an important tool in complex systems research because it allows for the quantification of the complexity of a neural system. In this analysis, we studied the relationships between neural complexity and criticality in neural culture data. We analyzed neural avalanches in 435 recordings from dissociated hippocampal cultures produced from rats, as well as neural avalanches from a cortical branching model. We utilized recently developed maximum likelihood estimation power-law fitting methods that account for doubly truncated power-laws, an automated shape collapse algorithm, and neural complexity and branching ratio calculation methods that account for sub-sampling, all of which are implemented in the freely available Neural Complexity and Criticality MATLAB toolbox. We found evidence that neural systems operate at or near a critical point and that neural complexity is optimized in these neural systems at or near the critical point. Surprisingly, we found evidence that complexity in neural systems is dependent upon avalanche profiles and neuron firing rate, but not precise spiking relationships between neurons. In order to facilitate future research, we made all of the culture data utilized in this analysis freely available online.

  11. Criticality Maximizes Complexity in Neural Tissue

    PubMed Central

    Timme, Nicholas M.; Marshall, Najja J.; Bennett, Nicholas; Ripp, Monica; Lautzenhiser, Edward; Beggs, John M.

    2016-01-01

    The analysis of neural systems leverages tools from many different fields. Drawing on techniques from the study of critical phenomena in statistical mechanics, several studies have reported signatures of criticality in neural systems, including power-law distributions, shape collapses, and optimized quantities under tuning. Independently, neural complexity—an information theoretic measure—has been introduced in an effort to quantify the strength of correlations across multiple scales in a neural system. This measure represents an important tool in complex systems research because it allows for the quantification of the complexity of a neural system. In this analysis, we studied the relationships between neural complexity and criticality in neural culture data. We analyzed neural avalanches in 435 recordings from dissociated hippocampal cultures produced from rats, as well as neural avalanches from a cortical branching model. We utilized recently developed maximum likelihood estimation power-law fitting methods that account for doubly truncated power-laws, an automated shape collapse algorithm, and neural complexity and branching ratio calculation methods that account for sub-sampling, all of which are implemented in the freely available Neural Complexity and Criticality MATLAB toolbox. We found evidence that neural systems operate at or near a critical point and that neural complexity is optimized in these neural systems at or near the critical point. Surprisingly, we found evidence that complexity in neural systems is dependent upon avalanche profiles and neuron firing rate, but not precise spiking relationships between neurons. In order to facilitate future research, we made all of the culture data utilized in this analysis freely available online. PMID:27729870

  12. Verification and Validation Methodology of Real-Time Adaptive Neural Networks for Aerospace Applications

    NASA Technical Reports Server (NTRS)

    Gupta, Pramod; Loparo, Kenneth; Mackall, Dale; Schumann, Johann; Soares, Fola

    2004-01-01

    Recent research has shown that adaptive neural based control systems are very effective in restoring stability and control of an aircraft in the presence of damage or failures. The application of an adaptive neural network with a flight critical control system requires a thorough and proven process to ensure safe and proper flight operation. Unique testing tools have been developed as part of a process to perform verification and validation (V&V) of real time adaptive neural networks used in recent adaptive flight control system, to evaluate the performance of the on line trained neural networks. The tools will help in certification from FAA and will help in the successful deployment of neural network based adaptive controllers in safety-critical applications. The process to perform verification and validation is evaluated against a typical neural adaptive controller and the results are discussed.

  13. Neural network with dynamically adaptable neurons

    NASA Technical Reports Server (NTRS)

    Tawel, Raoul (Inventor)

    1994-01-01

    This invention is an adaptive neuron for use in neural network processors. The adaptive neuron participates in the supervised learning phase of operation on a co-equal basis with the synapse matrix elements by adaptively changing its gain in a similar manner to the change of weights in the synapse IO elements. In this manner, training time is decreased by as much as three orders of magnitude.

  14. Optimal Control Problem of Feeding Adaptations of Daphnia and Neural Network Simulation

    NASA Astrophysics Data System (ADS)

    Kmet', Tibor; Kmet'ov, Mria

    2010-09-01

    A neural network based optimal control synthesis is presented for solving optimal control problems with control and state constraints and open final time. The optimal control problem is transcribed into nonlinear programming problem, which is implemented with adaptive critic neural network [9] and recurrent neural network for solving nonlinear proprojection equations [10]. The proposed simulation methods is illustrated by the optimal control problem of feeding adaptation of filter feeders of Daphnia. Results show that adaptive critic based systematic approach and neural network solving of nonlinear equations hold promise for obtaining the optimal control with control and state constraints and open final time.

  15. Adaptive Neurotechnology for Making Neural Circuits Functional .

    NASA Astrophysics Data System (ADS)

    Jung, Ranu

    2008-03-01

    Two of the most important trends in recent technological developments are that technology is increasingly integrated with biological systems and that it is increasingly adaptive in its capabilities. Neuroprosthetic systems that provide lost sensorimotor function after a neural disability offer a platform to investigate this interplay between biological and engineered systems. Adaptive neurotechnology (hardware and software) could be designed to be biomimetic, guided by the physical and programmatic constraints observed in biological systems, and allow for real-time learning, stability, and error correction. An example will present biomimetic neural-network hardware that can be interfaced with the isolated spinal cord of a lower vertebrate to allow phase-locked real-time neural control. Another will present adaptive neural network control algorithms for functional electrical stimulation of the peripheral nervous system to provide desired movements of paralyzed limbs in rodents or people. Ultimately, the frontier lies in being able to utilize the adaptive neurotechnology to promote neuroplasticity in the living system on a long-time scale under co-adaptive conditions.

  16. Neural adaptations to electrical stimulation strength training.

    PubMed

    Hortobágyi, Tibor; Maffiuletti, Nicola A

    2011-10-01

    This review provides evidence for the hypothesis that electrostimulation strength training (EST) increases the force of a maximal voluntary contraction (MVC) through neural adaptations in healthy skeletal muscle. Although electrical stimulation and voluntary effort activate muscle differently, there is substantial evidence to suggest that EST modifies the excitability of specific neural paths and such adaptations contribute to the increases in MVC force. Similar to strength training with voluntary contractions, EST increases MVC force after only a few sessions with some changes in muscle biochemistry but without overt muscle hypertrophy. There is some mixed evidence for spinal neural adaptations in the form of an increase in the amplitude of the interpolated twitch and in the amplitude of the volitional wave, with less evidence for changes in spinal excitability. Cross-sectional and exercise studies also suggest that the barrage of sensory and nociceptive inputs acts at the cortical level and can modify the motor cortical output and interhemispheric paths. The data suggest that neural adaptations mediate initial increases in MVC force after short-term EST.

  17. Adaptive neural control of aeroelastic response

    NASA Astrophysics Data System (ADS)

    Lichtenwalner, Peter F.; Little, Gerald R.; Scott, Robert C.

    1996-05-01

    The Adaptive Neural Control of Aeroelastic Response (ANCAR) program is a joint research and development effort conducted by McDonnell Douglas Aerospace (MDA) and the National Aeronautics and Space Administration, Langley Research Center (NASA LaRC) under a Memorandum of Agreement (MOA). The purpose of the MOA is to cooperatively develop the smart structure technologies necessary for alleviating undesirable vibration and aeroelastic response associated with highly flexible structures. Adaptive control can reduce aeroelastic response associated with buffet and atmospheric turbulence, it can increase flutter margins, and it may be able to reduce response associated with nonlinear phenomenon like limit cycle oscillations. By reducing vibration levels and loads, aircraft structures can have lower acquisition cost, reduced maintenance, and extended lifetimes. Phase I of the ANCAR program involved development and demonstration of a neural network-based semi-adaptive flutter suppression system which used a neural network for scheduling control laws as a function of Mach number and dynamic pressure. This controller was tested along with a robust fixed-gain control law in NASA's Transonic Dynamics Tunnel (TDT) utilizing the Benchmark Active Controls Testing (BACT) wing. During Phase II, a fully adaptive on-line learning neural network control system has been developed for flutter suppression which will be tested in 1996. This paper presents the results of Phase I testing as well as the development progress of Phase II.

  18. Critical Neural Substrates for Correcting Unexpected Trajectory Errors and Learning from Them

    ERIC Educational Resources Information Center

    Mutha, Pratik K.; Sainburg, Robert L.; Haaland, Kathleen Y.

    2011-01-01

    Our proficiency at any skill is critically dependent on the ability to monitor our performance, correct errors and adapt subsequent movements so that errors are avoided in the future. In this study, we aimed to dissociate the neural substrates critical for correcting unexpected trajectory errors and learning to adapt future movements based on…

  19. Adaptation to sensory input tunes visual cortex to criticality

    NASA Astrophysics Data System (ADS)

    Shew, Woodrow L.; Clawson, Wesley P.; Pobst, Jeff; Karimipanah, Yahya; Wright, Nathaniel C.; Wessel, Ralf

    2015-08-01

    A long-standing hypothesis at the interface of physics and neuroscience is that neural networks self-organize to the critical point of a phase transition, thereby optimizing aspects of sensory information processing. This idea is partially supported by strong evidence for critical dynamics observed in the cerebral cortex, but the impact of sensory input on these dynamics is largely unknown. Thus, the foundations of this hypothesis--the self-organization process and how it manifests during strong sensory input--remain unstudied experimentally. Here we show in visual cortex and in a computational model that strong sensory input initially elicits cortical network dynamics that are not critical, but adaptive changes in the network rapidly tune the system to criticality. This conclusion is based on observations of multifaceted scaling laws predicted to occur at criticality. Our findings establish sensory adaptation as a self-organizing mechanism that maintains criticality in visual cortex during sensory information processing.

  20. Applications of Neural Networks to Adaptive Control

    DTIC Science & Technology

    1989-12-01

    DTIC ;- E py 00 NAVAL POSTGRADUATE SCHOOL Monterey, California I.$ RDTIC IELECTE fl THESIS BEG7V°U APPLICATIONS OF NEURAL NETWORKS TO ADAPTIVE CONTROL...Second keader E . Robert Wood, Chairman, Department of Aeronautics and Astronautics Gordoii E . Schacher, Dean of Faculty and Graduate Education ii ABSTRACT...23: Network Dynamic Stability for q(t) . ............................. 55 ix Figure 24: Network Dynamic Stability for e (t

  1. Algebraic and adaptive learning in neural control systems

    NASA Astrophysics Data System (ADS)

    Ferrari, Silvia

    A systematic approach is developed for designing adaptive and reconfigurable nonlinear control systems that are applicable to plants modeled by ordinary differential equations. The nonlinear controller comprising a network of neural networks is taught using a two-phase learning procedure realized through novel techniques for initialization, on-line training, and adaptive critic design. A critical observation is that the gradients of the functions defined by the neural networks must equal corresponding linear gain matrices at chosen operating points. On-line training is based on a dual heuristic adaptive critic architecture that improves control for large, coupled motions by accounting for actual plant dynamics and nonlinear effects. An action network computes the optimal control law; a critic network predicts the derivative of the cost-to-go with respect to the state. Both networks are algebraically initialized based on prior knowledge of satisfactory pointwise linear controllers and continue to adapt on line during full-scale simulations of the plant. On-line training takes place sequentially over discrete periods of time and involves several numerical procedures. A backpropagating algorithm called Resilient Backpropagation is modified and successfully implemented to meet these objectives, without excessive computational expense. This adaptive controller is as conservative as the linear designs and as effective as a global nonlinear controller. The method is successfully implemented for the full-envelope control of a six-degree-of-freedom aircraft simulation. The results show that the on-line adaptation brings about improved performance with respect to the initialization phase during aircraft maneuvers that involve large-angle and coupled dynamics, and parameter variations.

  2. Neural Adaptation Effects in Conceptual Processing.

    PubMed

    Marino, Barbara F M; Borghi, Anna M; Gemmi, Luca; Cacciari, Cristina; Riggio, Lucia

    2015-07-31

    We investigated the conceptual processing of nouns referring to objects characterized by a highly typical color and orientation. We used a go/no-go task in which we asked participants to categorize each noun as referring or not to natural entities (e.g., animals) after a selective adaptation of color-edge neurons in the posterior LV4 region of the visual cortex was induced by means of a McCollough effect procedure. This manipulation affected categorization: the green-vertical adaptation led to slower responses than the green-horizontal adaptation, regardless of the specific color and orientation of the to-be-categorized noun. This result suggests that the conceptual processing of natural entities may entail the activation of modality-specific neural channels with weights proportional to the reliability of the signals produced by these channels during actual perception. This finding is discussed with reference to the debate about the grounded cognition view.

  3. Neural Adaptation Effects in Conceptual Processing

    PubMed Central

    Marino, Barbara F. M.; Borghi, Anna M.; Gemmi, Luca; Cacciari, Cristina; Riggio, Lucia

    2015-01-01

    We investigated the conceptual processing of nouns referring to objects characterized by a highly typical color and orientation. We used a go/no-go task in which we asked participants to categorize each noun as referring or not to natural entities (e.g., animals) after a selective adaptation of color-edge neurons in the posterior LV4 region of the visual cortex was induced by means of a McCollough effect procedure. This manipulation affected categorization: the green-vertical adaptation led to slower responses than the green-horizontal adaptation, regardless of the specific color and orientation of the to-be-categorized noun. This result suggests that the conceptual processing of natural entities may entail the activation of modality-specific neural channels with weights proportional to the reliability of the signals produced by these channels during actual perception. This finding is discussed with reference to the debate about the grounded cognition view. PMID:26264031

  4. Analytical investigation of self-organized criticality in neural networks.

    PubMed

    Droste, Felix; Do, Anne-Ly; Gross, Thilo

    2013-01-06

    Dynamical criticality has been shown to enhance information processing in dynamical systems, and there is evidence for self-organized criticality in neural networks. A plausible mechanism for such self-organization is activity-dependent synaptic plasticity. Here, we model neurons as discrete-state nodes on an adaptive network following stochastic dynamics. At a threshold connectivity, this system undergoes a dynamical phase transition at which persistent activity sets in. In a low-dimensional representation of the macroscopic dynamics, this corresponds to a transcritical bifurcation. We show analytically that adding activity-dependent rewiring rules, inspired by homeostatic plasticity, leads to the emergence of an attractive steady state at criticality and present numerical evidence for the system's evolution to such a state.

  5. Adaptive Control of Visually Guided Grasping in Neural Networks

    DTIC Science & Technology

    1990-03-12

    U01ITU S.WM NONnumsen Adaptive Control of Visually Guided Grasping in Neural Networks AFOSR-89-&CO030 88-NL-209 L AUTHOrSF 2313/A8 00 61102F (V) Dr...FINAL REPORT ADAPTIVE CONTROL OF VISUALLY GUIDED GRASPING IN NEURAL NETWORKS Neurogen Laboratories Inc. Project Summary Research performed for AFOSR...arm’s length in position and 6 degrees in orientation. Keywords: Neural Networks , Adaptive Motor Control, Sensory-Motor sensation Introduction The human

  6. Adaptive Filtering Using Recurrent Neural Networks

    NASA Technical Reports Server (NTRS)

    Parlos, Alexander G.; Menon, Sunil K.; Atiya, Amir F.

    2005-01-01

    A method for adaptive (or, optionally, nonadaptive) filtering has been developed for estimating the states of complex process systems (e.g., chemical plants, factories, or manufacturing processes at some level of abstraction) from time series of measurements of system inputs and outputs. The method is based partly on the fundamental principles of the Kalman filter and partly on the use of recurrent neural networks. The standard Kalman filter involves an assumption of linearity of the mathematical model used to describe a process system. The extended Kalman filter accommodates a nonlinear process model but still requires linearization about the state estimate. Both the standard and extended Kalman filters involve the often unrealistic assumption that process and measurement noise are zero-mean, Gaussian, and white. In contrast, the present method does not involve any assumptions of linearity of process models or of the nature of process noise; on the contrary, few (if any) assumptions are made about process models, noise models, or the parameters of such models. In this regard, the method can be characterized as one of nonlinear, nonparametric filtering. The method exploits the unique ability of neural networks to approximate nonlinear functions. In a given case, the process model is limited mainly by limitations of the approximation ability of the neural networks chosen for that case. Moreover, despite the lack of assumptions regarding process noise, the method yields minimum- variance filters. In that they do not require statistical models of noise, the neural- network-based state filters of this method are comparable to conventional nonlinear least-squares estimators.

  7. Adaptive Neural Network Motion Control of Manipulators with Experimental Evaluations

    PubMed Central

    Puga-Guzmán, S.; Moreno-Valenzuela, J.; Santibáñez, V.

    2014-01-01

    A nonlinear proportional-derivative controller plus adaptive neuronal network compensation is proposed. With the aim of estimating the desired torque, a two-layer neural network is used. Then, adaptation laws for the neural network weights are derived. Asymptotic convergence of the position and velocity tracking errors is proven, while the neural network weights are shown to be uniformly bounded. The proposed scheme has been experimentally validated in real time. These experimental evaluations were carried in two different mechanical systems: a horizontal two degrees-of-freedom robot and a vertical one degree-of-freedom arm which is affected by the gravitational force. In each one of the two experimental set-ups, the proposed scheme was implemented without and with adaptive neural network compensation. Experimental results confirmed the tracking accuracy of the proposed adaptive neural network-based controller. PMID:24574910

  8. Adaptive neural network motion control of manipulators with experimental evaluations.

    PubMed

    Puga-Guzmán, S; Moreno-Valenzuela, J; Santibáñez, V

    2014-01-01

    A nonlinear proportional-derivative controller plus adaptive neuronal network compensation is proposed. With the aim of estimating the desired torque, a two-layer neural network is used. Then, adaptation laws for the neural network weights are derived. Asymptotic convergence of the position and velocity tracking errors is proven, while the neural network weights are shown to be uniformly bounded. The proposed scheme has been experimentally validated in real time. These experimental evaluations were carried in two different mechanical systems: a horizontal two degrees-of-freedom robot and a vertical one degree-of-freedom arm which is affected by the gravitational force. In each one of the two experimental set-ups, the proposed scheme was implemented without and with adaptive neural network compensation. Experimental results confirmed the tracking accuracy of the proposed adaptive neural network-based controller.

  9. Adaptation tunes cortical dynamics to a critical regime during vision

    NASA Astrophysics Data System (ADS)

    Shew, Woodrow; Clawson, Wesley; Pobst, Jeff; Karimipanah, Yahya; Wright, Nathaniel; Wessel, Ralf; Shew Lab Team; Wessel Lab Team

    2015-03-01

    A long-standing hypothesis at the interface of physics and neuroscience is that neural networks self-organize to the critical point of a phase transition, thereby optimizing aspects of sensory information processing. This idea is partially supported by strong evidence for critical dynamics observed in the cerebral cortex, but has not been tested in systems with significant sensory input. Thus, the foundations of this hypothesis - the self-organization process and how it manifests during strong sensory input - remain unstudied experimentally. Here we report microelectrode array measurements from visual cortex of turtles during visual stimulation of the retina. We show experimentally and in a computational model that strong sensory input initially elicits cortical network dynamics that are not critical, but adaptive changes in the network rapidly tune the system to criticality. This conclusion is based on observations of multifaceted scaling laws predicted to occur at criticality. Our findings establish sensory adaptation as a self-organizing mechanism which maintains criticality in visual cortex during sensory information processing. Supported by NSF CRCNS Grant 1308174.

  10. Adaptive neural networks for mobile robotic control

    NASA Astrophysics Data System (ADS)

    Burnett, Jeff R.; Dagli, Cihan H.

    2001-03-01

    Movement of a differential drive robot has non-linear dependence on the current position and orientation. A controller must be able to deal with the non-linearity of the plant. The controller must either linearize the plant and deal with special cases, or be non-linear itself. Once the controller is designed, implementation on a real robotic platform presents challenges due to the varying parameters of the plant. Robots of the same model may have different motor frictions. The surface the robot maneuvers on may change e.g. carpet to tile. Batteries will drain, providing less power over time. A feed-forward neural network controller could overcome these challenges. The network could learn the non- linearities of the plant and monitor the error for parameter changes and adapt to them. In this manner, a single controller can be designed for an ideal robot, and then used to populate a multi-robot colony without manually fine tuning the controller for each robot. This paper shall demonstrate such a controller, outlining design in simulation and implementation on Khepera robotic platforms.

  11. Identification of Infinite Dimensional Systems via Adaptive Wavelet Neural Networks

    DTIC Science & Technology

    1993-01-01

    We consider identification of distributed systems via adaptive wavelet neural networks (AWNNs). We take advantage of the multiresolution property of...wavelet systems and the computational structure of neural networks to approximate the unknown plant successively. A systematic approach is developed

  12. Neural and Fuzzy Adaptive Control of Induction Motor Drives

    SciTech Connect

    Bensalem, Y.; Sbita, L.; Abdelkrim, M. N.

    2008-06-12

    This paper proposes an adaptive neural network speed control scheme for an induction motor (IM) drive. The proposed scheme consists of an adaptive neural network identifier (ANNI) and an adaptive neural network controller (ANNC). For learning the quoted neural networks, a back propagation algorithm was used to automatically adjust the weights of the ANNI and ANNC in order to minimize the performance functions. Here, the ANNI can quickly estimate the plant parameters and the ANNC is used to provide on-line identification of the command and to produce a control force, such that the motor speed can accurately track the reference command. By combining artificial neural network techniques with fuzzy logic concept, a neural and fuzzy adaptive control scheme is developed. Fuzzy logic was used for the adaptation of the neural controller to improve the robustness of the generated command. The developed method is robust to load torque disturbance and the speed target variations when it ensures precise trajectory tracking with the prescribed dynamics. The algorithm was verified by simulation and the results obtained demonstrate the effectiveness of the IM designed controller.

  13. Adaptive optimization and control using neural networks

    SciTech Connect

    Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.

    1993-10-22

    Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.

  14. Synaptic Plasticity Enables Adaptive Self-Tuning Critical Networks

    PubMed Central

    Stepp, Nigel; Plenz, Dietmar; Srinivasa, Narayan

    2015-01-01

    During rest, the mammalian cortex displays spontaneous neural activity. Spiking of single neurons during rest has been described as irregular and asynchronous. In contrast, recent in vivo and in vitro population measures of spontaneous activity, using the LFP, EEG, MEG or fMRI suggest that the default state of the cortex is critical, manifested by spontaneous, scale-invariant, cascades of activity known as neuronal avalanches. Criticality keeps a network poised for optimal information processing, but this view seems to be difficult to reconcile with apparently irregular single neuron spiking. Here, we simulate a 10,000 neuron, deterministic, plastic network of spiking neurons. We show that a combination of short- and long-term synaptic plasticity enables these networks to exhibit criticality in the face of intrinsic, i.e. self-sustained, asynchronous spiking. Brief external perturbations lead to adaptive, long-term modification of intrinsic network connectivity through long-term excitatory plasticity, whereas long-term inhibitory plasticity enables rapid self-tuning of the network back to a critical state. The critical state is characterized by a branching parameter oscillating around unity, a critical exponent close to -3/2 and a long tail distribution of a self-similarity parameter between 0.5 and 1. PMID:25590427

  15. Adaptive Neurons For Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Tawel, Raoul

    1990-01-01

    Training time decreases dramatically. In improved mathematical model of neural-network processor, temperature of neurons (in addition to connection strengths, also called weights, of synapses) varied during supervised-learning phase of operation according to mathematical formalism and not heuristic rule. Evidence that biological neural networks also process information at neuronal level.

  16. Emotional facial expressions reduce neural adaptation to face identity.

    PubMed

    Gerlicher, Anna M V; van Loon, Anouk M; Scholte, H Steven; Lamme, Victor A F; van der Leij, Andries R

    2014-05-01

    In human social interactions, facial emotional expressions are a crucial source of information. Repeatedly presented information typically leads to an adaptation of neural responses. However, processing seems sustained with emotional facial expressions. Therefore, we tested whether sustained processing of emotional expressions, especially threat-related expressions, would attenuate neural adaptation. Neutral and emotional expressions (happy, mixed and fearful) of same and different identity were presented at 3 Hz. We used electroencephalography to record the evoked steady-state visual potentials (ssVEP) and tested to what extent the ssVEP amplitude adapts to the same when compared with different face identities. We found adaptation to the identity of a neutral face. However, for emotional faces, adaptation was reduced, decreasing linearly with negative valence, with the least adaptation to fearful expressions. This short and straightforward method may prove to be a valuable new tool in the study of emotional processing.

  17. Avalanches in self-organized critical neural networks: a minimal model for the neural SOC universality class.

    PubMed

    Rybarsch, Matthias; Bornholdt, Stefan

    2014-01-01

    The brain keeps its overall dynamics in a corridor of intermediate activity and it has been a long standing question what possible mechanism could achieve this task. Mechanisms from the field of statistical physics have long been suggesting that this homeostasis of brain activity could occur even without a central regulator, via self-organization on the level of neurons and their interactions, alone. Such physical mechanisms from the class of self-organized criticality exhibit characteristic dynamical signatures, similar to seismic activity related to earthquakes. Measurements of cortex rest activity showed first signs of dynamical signatures potentially pointing to self-organized critical dynamics in the brain. Indeed, recent more accurate measurements allowed for a detailed comparison with scaling theory of non-equilibrium critical phenomena, proving the existence of criticality in cortex dynamics. We here compare this new evaluation of cortex activity data to the predictions of the earliest physics spin model of self-organized critical neural networks. We find that the model matches with the recent experimental data and its interpretation in terms of dynamical signatures for criticality in the brain. The combination of signatures for criticality, power law distributions of avalanche sizes and durations, as well as a specific scaling relationship between anomalous exponents, defines a universality class characteristic of the particular critical phenomenon observed in the neural experiments. Thus the model is a candidate for a minimal model of a self-organized critical adaptive network for the universality class of neural criticality. As a prototype model, it provides the background for models that may include more biological details, yet share the same universality class characteristic of the homeostasis of activity in the brain.

  18. Adaptive Neural Network Based Control of Noncanonical Nonlinear Systems.

    PubMed

    Zhang, Yanjun; Tao, Gang; Chen, Mou

    2016-09-01

    This paper presents a new study on the adaptive neural network-based control of a class of noncanonical nonlinear systems with large parametric uncertainties. Unlike commonly studied canonical form nonlinear systems whose neural network approximation system models have explicit relative degree structures, which can directly be used to derive parameterized controllers for adaptation, noncanonical form nonlinear systems usually do not have explicit relative degrees, and thus their approximation system models are also in noncanonical forms. It is well-known that the adaptive control of noncanonical form nonlinear systems involves the parameterization of system dynamics. As demonstrated in this paper, it is also the case for noncanonical neural network approximation system models. Effective control of such systems is an open research problem, especially in the presence of uncertain parameters. This paper shows that it is necessary to reparameterize such neural network system models for adaptive control design, and that such reparameterization can be realized using a relative degree formulation, a concept yet to be studied for general neural network system models. This paper then derives the parameterized controllers that guarantee closed-loop stability and asymptotic output tracking for noncanonical form neural network system models. An illustrative example is presented with the simulation results to demonstrate the control design procedure, and to verify the effectiveness of such a new design method.

  19. Adaptive Optimization of Aircraft Engine Performance Using Neural Networks

    NASA Technical Reports Server (NTRS)

    Simon, Donald L.; Long, Theresa W.

    1995-01-01

    Preliminary results are presented on the development of an adaptive neural network based control algorithm to enhance aircraft engine performance. This work builds upon a previous National Aeronautics and Space Administration (NASA) effort known as Performance Seeking Control (PSC). PSC is an adaptive control algorithm which contains a model of the aircraft's propulsion system which is updated on-line to match the operation of the aircraft's actual propulsion system. Information from the on-line model is used to adapt the control system during flight to allow optimal operation of the aircraft's propulsion system (inlet, engine, and nozzle) to improve aircraft engine performance without compromising reliability or operability. Performance Seeking Control has been shown to yield reductions in fuel flow, increases in thrust, and reductions in engine fan turbine inlet temperature. The neural network based adaptive control, like PSC, will contain a model of the propulsion system which will be used to calculate optimal control commands on-line. Hopes are that it will be able to provide some additional benefits above and beyond those of PSC. The PSC algorithm is computationally intensive, it is valid only at near steady-state flight conditions, and it has no way to adapt or learn on-line. These issues are being addressed in the development of the optimal neural controller. Specialized neural network processing hardware is being developed to run the software, the algorithm will be valid at steady-state and transient conditions, and will take advantage of the on-line learning capability of neural networks. Future plans include testing the neural network software and hardware prototype against an aircraft engine simulation. In this paper, the proposed neural network software and hardware is described and preliminary neural network training results are presented.

  20. Stable adaptive control using new critic designs

    NASA Astrophysics Data System (ADS)

    Werbos, Paul J.

    1999-03-01

    Classical adaptive control proves total-system stability for control of linear plants, but only for plants meeting very restrictive assumptions. Approximate Dynamic Programming (ADP) has the potential, in principle, to ensure stability without such tight restrictions. It also offers nonlinear and neural extensions for optimal control, with empirically supported links to what is seen in the brain. However, the relevant ADP methods in use today--TD, HDP, DHP, GDHP--and the Galerkin-based versions of these all have serious limitations when used here as parallel distributed real-time learning systems; either they do not possess quadratic unconditional stability (to be defined) or they lead to incorrect results in the stochastic case. (ADAC or Q- learning designs do not help.) After explaining these conclusions, this paper describes new ADP designs which overcome these limitations. It also addresses the Generalized Moving Target problem, a common family of static optimization problems, and describes a way to stabilize large-scale economic equilibrium models, such as the old long-term energy mode of DOE.

  1. Controlling a truck with an adaptive critic temporal difference CMAC design

    NASA Technical Reports Server (NTRS)

    Shelton, Robert O.; Peterson, James K.

    1993-01-01

    In this study, CMAC (Cerebellar Model Articulated Controller) neural architectures are shown to be viable for the purposes of real-time learning and control. An adaptive critic temporal difference neurocontrol design has been implemented that learns in real-time how to back up a trailer truck along a fixed straight line trajectory. The truck backer-upper experiment is a standard performance measure in the neural network literature, but previously the training of the controllers was done off-line. With the CMAC neural architectures, it was possible to train the neurocontrollers on-line in real-time on a MS-DOS PC 386.

  2. Adaptive neural control of spacecraft using control moment gyros

    NASA Astrophysics Data System (ADS)

    Leeghim, Henzeh; Kim, Donghoon

    2015-03-01

    An adaptive control technique is applied to reorient spacecraft with uncertainty using control moment gyros. A nonlinear quaternion feedback law is chosen as a baseline controller. An additional adaptive control input supported by neural networks can estimate and eliminate unknown terms adaptively. The normalized input neural networks are considered for reliable computation of the adaptive input. To prove the stability of the closed-loop dynamics with the control law, the Lyapunov stability theory is considered. Accordingly, the proposed approach results in the uniform ultimate boundedness in tracking error. For reorientation maneuvers, control moment gyros are utilized with a well-known singularity problem described in this work investigated by predicting one-step ahead singularity index. A momentum vector recovery approach using magnetic torquers is also introduced to evaluate the avoidance strategies indirectly. Finally, the suggested methods are demonstrated by numerical simulation studies.

  3. Dynamic Adaptive Neural Network Arrays: A Neuromorphic Architecture

    SciTech Connect

    Disney, Adam; Reynolds, John

    2015-01-01

    Dynamic Adaptive Neural Network Array (DANNA) is a neuromorphic hardware implementation. It differs from most other neuromorphic projects in that it allows for programmability of structure, and it is trained or designed using evolutionary optimization. This paper describes the DANNA structure, how DANNA is trained using evolutionary optimization, and an application of DANNA to a very simple classification task.

  4. Diminished Neural Adaptation during Implicit Learning in Autism

    PubMed Central

    Schipul, Sarah E.; Just, Marcel Adam

    2015-01-01

    Neuroimaging studies have shown evidence of disrupted neural adaptation during learning in individuals with autism spectrum disorder (ASD) in several types of tasks, potentially stemming from frontal-posterior cortical underconnectivity (Schipul et al., 2012). The aim of the current study was to examine neural adaptations in an implicit learning task that entails participation of frontal and posterior regions. Sixteen high-functioning adults with ASD and sixteen neurotypical control participants were trained on and performed an implicit dot pattern prototype learning task in a functional magnetic resonance imaging (fMRI) session. During the preliminary exposure to the type of implicit prototype learning task later to be used in the scanner, the ASD participants took longer than the neurotypical group to learn the task, demonstrating altered implicit learning in ASD. After equating task structure learning, the two groups’ brain activation differed during their learning of a new prototype in the subsequent scanning session. The main findings indicated that neural adaptations in a distributed task network were reduced in the ASD group, relative to the neurotypical group, and were related to ASD symptom severity. Functional connectivity was reduced and did not change as much during learning for the ASD group, and was related to ASD symptom severity. These findings suggest that individuals with ASD show altered neural adaptations during learning, as seen in both activation and functional connectivity measures. This finding suggests why many real-world implicit learning situations may pose special challenges for ASD. PMID:26484826

  5. Adaptation algorithms for 2-D feedforward neural networks.

    PubMed

    Kaczorek, T

    1995-01-01

    The generalized weight adaptation algorithms presented by J.G. Kuschewski et al. (1993) and by S.H. Zak and H.J. Sira-Ramirez (1990) are extended for 2-D madaline and 2-D two-layer feedforward neural nets (FNNs).

  6. Diminished neural adaptation during implicit learning in autism.

    PubMed

    Schipul, Sarah E; Just, Marcel Adam

    2016-01-15

    Neuroimaging studies have shown evidence of disrupted neural adaptation during learning in individuals with autism spectrum disorder (ASD) in several types of tasks, potentially stemming from frontal-posterior cortical underconnectivity (Schipul et al., 2012). The aim of the current study was to examine neural adaptations in an implicit learning task that entails participation of frontal and posterior regions. Sixteen high-functioning adults with ASD and sixteen neurotypical control participants were trained on and performed an implicit dot pattern prototype learning task in a functional magnetic resonance imaging (fMRI) session. During the preliminary exposure to the type of implicit prototype learning task later to be used in the scanner, the ASD participants took longer than the neurotypical group to learn the task, demonstrating altered implicit learning in ASD. After equating task structure learning, the two groups' brain activation differed during their learning of a new prototype in the subsequent scanning session. The main findings indicated that neural adaptations in a distributed task network were reduced in the ASD group, relative to the neurotypical group, and were related to ASD symptom severity. Functional connectivity was reduced and did not change as much during learning for the ASD group, and was related to ASD symptom severity. These findings suggest that individuals with ASD show altered neural adaptations during learning, as seen in both activation and functional connectivity measures. This finding suggests why many real-world implicit learning situations may pose special challenges for ASD.

  7. Neural adaptive control for vibration suppression in composite fin-tip of aircraft.

    PubMed

    Suresh, S; Kannan, N; Sundararajan, N; Saratchandran, P

    2008-06-01

    In this paper, we present a neural adaptive control scheme for active vibration suppression of a composite aircraft fin tip. The mathematical model of a composite aircraft fin tip is derived using the finite element approach. The finite element model is updated experimentally to reflect the natural frequencies and mode shapes very accurately. Piezo-electric actuators and sensors are placed at optimal locations such that the vibration suppression is a maximum. Model-reference direct adaptive neural network control scheme is proposed to force the vibration level within the minimum acceptable limit. In this scheme, Gaussian neural network with linear filters is used to approximate the inverse dynamics of the system and the parameters of the neural controller are estimated using Lyapunov based update law. In order to reduce the computational burden, which is critical for real-time applications, the number of hidden neurons is also estimated in the proposed scheme. The global asymptotic stability of the overall system is ensured using the principles of Lyapunov approach. Simulation studies are carried-out using sinusoidal force functions of varying frequency. Experimental results show that the proposed neural adaptive control scheme is capable of providing significant vibration suppression in the multiple bending modes of interest. The performance of the proposed scheme is better than the H(infinity) control scheme.

  8. Adaptive Neural Network Controller for ATM Traffic

    DTIC Science & Technology

    1996-12-01

    IEEE Communications Magazine (October 1995). 2. Baum, Eric B...Adaptive Control in ATM Networks," IEEE Communications Magazine (October 1995). 9. Evanowsky, John B. "Information for the Warrior," IEEE Communications Magazine (October...Network Applications in ATM," IEEE Communications Magazine (October 1995). 78 16. Imrich, et al. "A counter based congestion control for ATM

  9. Real-Time Adaptive Color Segmentation by Neural Networks

    NASA Technical Reports Server (NTRS)

    Duong, Tuan A.

    2004-01-01

    Artificial neural networks that would utilize the cascade error projection (CEP) algorithm have been proposed as means of autonomous, real-time, adaptive color segmentation of images that change with time. In the original intended application, such a neural network would be used to analyze digitized color video images of terrain on a remote planet as viewed from an uninhabited spacecraft approaching the planet. During descent toward the surface of the planet, information on the segmentation of the images into differently colored areas would be updated adaptively in real time to capture changes in contrast, brightness, and resolution, all in an effort to identify a safe and scientifically productive landing site and provide control feedback to steer the spacecraft toward that site. Potential terrestrial applications include monitoring images of crops to detect insect invasions and monitoring of buildings and other facilities to detect intruders. The CEP algorithm is reliable and is well suited to implementation in very-large-scale integrated (VLSI) circuitry. It was chosen over other neural-network learning algorithms because it is better suited to realtime learning: It provides a self-evolving neural-network structure, requires fewer iterations to converge and is more tolerant to low resolution (that is, fewer bits) in the quantization of neural-network synaptic weights. Consequently, a CEP neural network learns relatively quickly, and the circuitry needed to implement it is relatively simple. Like other neural networks, a CEP neural network includes an input layer, hidden units, and output units (see figure). As in other neural networks, a CEP network is presented with a succession of input training patterns, giving rise to a set of outputs that are compared with the desired outputs. Also as in other neural networks, the synaptic weights are updated iteratively in an effort to bring the outputs closer to target values. A distinctive feature of the CEP neural

  10. Neural responses to maternal criticism in healthy youth

    PubMed Central

    Siegle, Greg J.; Dahl, Ronald E.; Hooley, Jill M.; Silk, Jennifer S.

    2015-01-01

    Parental criticism can have positive and negative effects on children’s and adolescents’ behavior; yet, it is unclear how youth react to, understand and process parental criticism. We proposed that youth would engage three sets of neural processes in response to parental criticism including the following: (i) activating emotional reactions, (ii) regulating those reactions and (iii) social cognitive processing (e.g. understanding the parent’s mental state). To examine neural processes associated with both emotional and social processing of parental criticism in personally relevant and ecologically valid social contexts, typically developing youth were scanned while they listened to their mother providing critical, praising and neutral statements. In response to maternal criticism, youth showed increased brain activity in affective networks (e.g. subcortical–limbic regions including lentiform nucleus and posterior insula), but decreased activity in cognitive control networks (e.g. dorsolateral prefrontal cortex and caudal anterior cingulate cortex) and social cognitive networks (e.g. temporoparietal junction and posterior cingulate cortex/precuneus). These results suggest that youth may respond to maternal criticism with increased emotional reactivity but decreased cognitive control and social cognitive processing. A better understanding of children’s responses to parental criticism may provide insights into the ways that parental feedback can be modified to be more helpful to behavior and development in youth. PMID:25338632

  11. Neural Control Adaptation to Motor Noise Manipulation

    PubMed Central

    Hasson, Christopher J.; Gelina, Olga; Woo, Garrett

    2016-01-01

    Antagonistic muscular co-activation can compensate for movement variability induced by motor noise at the expense of increased energetic costs. Greater antagonistic co-activation is commonly observed in older adults, which could be an adaptation to increased motor noise. The present study tested this hypothesis by manipulating motor noise in 12 young subjects while they practiced a goal-directed task using a myoelectric virtual arm, which was controlled by their biceps and triceps muscle activity. Motor noise was increased by increasing the coefficient of variation (CV) of the myoelectric signals. As hypothesized, subjects adapted by increasing antagonistic co-activation, and this was associated with reduced noise-induced performance decrements. A second hypothesis was that a virtual decrease in motor noise, achieved by smoothing the myoelectric signals, would have the opposite effect: co-activation would decrease and motor performance would improve. However, the results showed that a decrease in noise made performance worse instead of better, with no change in co-activation. Overall, these findings suggest that the nervous system adapts to virtual increases in motor noise by increasing antagonistic co-activation, and this preserves motor performance. Reducing noise may have failed to benefit performance due to characteristics of the filtering process itself, e.g., delays are introduced and muscle activity bursts are attenuated. The observed adaptations to increased noise may explain in part why older adults and many patient populations have greater antagonistic co-activation, which could represent an adaptation to increased motor noise, along with a desire for increased joint stability. PMID:26973487

  12. Altered temporal dynamics of neural adaptation in the aging human auditory cortex.

    PubMed

    Herrmann, Björn; Henry, Molly J; Johnsrude, Ingrid S; Obleser, Jonas

    2016-09-01

    Neural response adaptation plays an important role in perception and cognition. Here, we used electroencephalography to investigate how aging affects the temporal dynamics of neural adaptation in human auditory cortex. Younger (18-31 years) and older (51-70 years) normal hearing adults listened to tone sequences with varying onset-to-onset intervals. Our results show long-lasting neural adaptation such that the response to a particular tone is a nonlinear function of the extended temporal history of sound events. Most important, aging is associated with multiple changes in auditory cortex; older adults exhibit larger and less variable response magnitudes, a larger dynamic response range, and a reduced sensitivity to temporal context. Computational modeling suggests that reduced adaptation recovery times underlie these changes in the aging auditory cortex and that the extended temporal stimulation has less influence on the neural response to the current sound in older compared with younger individuals. Our human electroencephalography results critically narrow the gap to animal electrophysiology work suggesting a compensatory release from cortical inhibition accompanying hearing loss and aging.

  13. The predictive roles of neural oscillations in speech motor adaptability.

    PubMed

    Sengupta, Ranit; Nasir, Sazzad M

    2016-06-01

    The human speech system exhibits a remarkable flexibility by adapting to alterations in speaking environments. While it is believed that speech motor adaptation under altered sensory feedback involves rapid reorganization of speech motor networks, the mechanisms by which different brain regions communicate and coordinate their activity to mediate adaptation remain unknown, and explanations of outcome differences in adaption remain largely elusive. In this study, under the paradigm of altered auditory feedback with continuous EEG recordings, the differential roles of oscillatory neural processes in motor speech adaptability were investigated. The predictive capacities of different EEG frequency bands were assessed, and it was found that theta-, beta-, and gamma-band activities during speech planning and production contained significant and reliable information about motor speech adaptability. It was further observed that these bands do not work independently but interact with each other suggesting an underlying brain network operating across hierarchically organized frequency bands to support motor speech adaptation. These results provide novel insights into both learning and disorders of speech using time frequency analysis of neural oscillations.

  14. An adaptive holographic implementation of a neural network

    NASA Technical Reports Server (NTRS)

    Downie, John D.; Hine, Butler P., III; Reid, Max B.

    1990-01-01

    A holographic implementation for neural networks is proposed and demonstrated as an alternative to the optical matrix-vector multiplier architecture. In comparison, the holographic architecture makes more efficient use of the system space-bandwidth product for certain types of neural networks. The principal network component is a thermoplastic hologram, used to provide both interconnection weights and beam direction. Given the updatable nature of this type of hologram, adaptivity or network learning is possible in the optical system. Two networks with fixed weights are experimentally implemented and verified, and for one of these examples the advantage of the holographic implementation with respect to the matrix-vector processor is demonstrated.

  15. Evolving RBF neural networks for adaptive soft-sensor design.

    PubMed

    Alexandridis, Alex

    2013-12-01

    This work presents an adaptive framework for building soft-sensors based on radial basis function (RBF) neural network models. The adaptive fuzzy means algorithm is utilized in order to evolve an RBF network, which approximates the unknown system based on input-output data from it. The methodology gradually builds the RBF network model, based on two separate levels of adaptation: On the first level, the structure of the hidden layer is modified by adding or deleting RBF centers, while on the second level, the synaptic weights are adjusted with the recursive least squares with exponential forgetting algorithm. The proposed approach is tested on two different systems, namely a simulated nonlinear DC Motor and a real industrial reactor. The results show that the produced soft-sensors can be successfully applied to model the two nonlinear systems. A comparison with two different adaptive modeling techniques, namely a dynamic evolving neural-fuzzy inference system (DENFIS) and neural networks trained with online backpropagation, highlights the advantages of the proposed methodology.

  16. Neural Basis of Adaptive Response Time Adjustment during Saccade Countermanding

    PubMed Central

    Pouget, Pierre; Logan, Gordon D.; Palmeri, Thomas J.; Boucher, Leanne; Paré, Martin; Schall, Jeffrey D.

    2011-01-01

    Humans and macaque monkeys adjust their response time adaptively in stop signal (countermanding) tasks, responding slower after stop-signal trials than after control trials with no stop signal. We investigated the neural mechanism underlying this adaptive response time adjustment in macaque monkeys performing a saccade countermanding task. Earlier research showed that movements are initiated when the random accumulation of presaccadic movement-related activity reaches a fixed threshold. We found that a systematic delay in response time after stop signal trials was accomplished not through a change of threshold, baseline, or accumulation rate, but instead through a change in the time when activity first began to accumulate. The neurons underlying movement initiation have been identified with mathematical accumulator models of response time performance. Therefore, this new result provides surprising new insights into the neural instantiation of stochastic accumulator models and the mechanisms through which executive control can be exerted. PMID:21880921

  17. Variable Neural Adaptive Robust Control: A Switched System Approach

    SciTech Connect

    Lian, Jianming; Hu, Jianghai; Zak, Stanislaw H.

    2015-05-01

    Variable neural adaptive robust control strategies are proposed for the output tracking control of a class of multi-input multi-output uncertain systems. The controllers incorporate a variable-structure radial basis function (RBF) network as the self-organizing approximator for unknown system dynamics. The variable-structure RBF network solves the problem of structure determination associated with fixed-structure RBF networks. It can determine the network structure on-line dynamically by adding or removing radial basis functions according to the tracking performance. The structure variation is taken into account in the stability analysis of the closed-loop system using a switched system approach with the aid of the piecewise quadratic Lyapunov function. The performance of the proposed variable neural adaptive robust controllers is illustrated with simulations.

  18. Neural control and adaptive neural forward models for insect-like, energy-efficient, and adaptable locomotion of walking machines.

    PubMed

    Manoonpong, Poramate; Parlitz, Ulrich; Wörgötter, Florentin

    2013-01-01

    Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs) and sensory feedback (afferent-based control) but also on internal forward models (efference copies). They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines.

  19. Neural control and adaptive neural forward models for insect-like, energy-efficient, and adaptable locomotion of walking machines

    PubMed Central

    Manoonpong, Poramate; Parlitz, Ulrich; Wörgötter, Florentin

    2013-01-01

    Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs) and sensory feedback (afferent-based control) but also on internal forward models (efference copies). They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines. PMID:23408775

  20. Adaptive conventional power system stabilizer based on artificial neural network

    SciTech Connect

    Kothari, M.L.; Segal, R.; Ghodki, B.K.

    1995-12-31

    This paper deals with an artificial neural network (ANN) based adaptive conventional power system stabilizer (PSS). The ANN comprises an input layer, a hidden layer and an output layer. The input vector to the ANN comprises real power (P) and reactive power (Q), while the output vector comprises optimum PSS parameters. A systematic approach for generating training set covering wide range of operating conditions, is presented. The ANN has been trained using back-propagation training algorithm. Investigations reveal that the dynamic performance of ANN based adaptive conventional PSS is quite insensitive to wide variations in loading conditions.

  1. Analysis and Synthesis of Adaptive Neural Elements and Assemblies.

    DTIC Science & Technology

    2007-11-02

    Form Approved REPORT DOCUMENTATION PAGE OMB No. 0704-0188 u,• thc renortc bsurden 4 ,r this collection of Information s estimated to averav i -our...motivational systems can influence behaviors, in part, by acting on motor systems, such as CPGs. Fourth, motor systems possess cellular mechanisms ...motor behaviors are governed by highly adaptive neural networks and help to explain how systems of nerve cells function to produce and modulate

  2. An adaptive neural fuzzy filter and its applications.

    PubMed

    Lin, C T; Juang, C F

    1997-01-01

    A new kind of nonlinear adaptive filter, the adaptive neural fuzzy filter (ANFF), based upon a neural network's learning ability and fuzzy if-then rule structure, is proposed in this paper. The ANFF is inherently a feedforward multilayered connectionist network which can learn by itself according to numerical training data or expert knowledge represented by fuzzy if-then rules. The adaptation here includes the construction of fuzzy if-then rules (structure learning), and the tuning of the free parameters of membership functions (parameter learning). In the structure learning phase, fuzzy rules are found based on the matching of input-output clusters. In the parameter learning phase, a backpropagation-like adaptation algorithm is developed to minimize the output error. There are no hidden nodes (i.e., no membership functions and fuzzy rules) initially, and both the structure learning and parameter learning are performed concurrently as the adaptation proceeds. However, if some linguistic information about the design of the filter is available, such knowledge can be put into the ANFF to form an initial structure with hidden nodes. Two major advantages of the ANFF can thus be seen: 1) a priori knowledge can be incorporated into the ANFF which makes the fusion of numerical data and linguistic information in the filter possible; and 2) no predetermination, like the number of hidden nodes, must be given, since the ANFF can find its optimal structure and parameters automatically.

  3. Critical role for resource constraints in neural models

    PubMed Central

    Roberts, James A.; Iyer, Kartik K.; Vanhatalo, Sampsa; Breakspear, Michael

    2014-01-01

    Criticality has emerged as a leading dynamical candidate for healthy and pathological neuronal activity. At the heart of criticality in neural systems is the need for parameters to be tuned to specific values or for the existence of self-organizing mechanisms. Existing models lack precise physiological descriptions for how the brain maintains its tuning near a critical point. In this paper we argue that a key ingredient missing from the field is a formulation of reciprocal coupling between neural activity and metabolic resources. We propose that the constraint of optimizing the balance between energy use and activity plays a major role in tuning brain states to lie near criticality. Important recent findings aligned with our viewpoint have emerged from analyses of disorders that involve severe metabolic disturbances and alter scale-free properties of brain dynamics, including burst suppression. Moreover, we argue that average shapes of neuronal avalanches are a signature of scale-free activity that offers sharper insights into underlying mechanisms than afforded by traditional analyses of avalanche statistics. PMID:25309349

  4. Short-Term Neural Adaptation to Simultaneous Bifocal Images

    PubMed Central

    Radhakrishnan, Aiswaryah; Dorronsoro, Carlos; Sawides, Lucie; Marcos, Susana

    2014-01-01

    Simultaneous vision is an increasingly used solution for the correction of presbyopia (the age-related loss of ability to focus near images). Simultaneous Vision corrections, normally delivered in the form of contact or intraocular lenses, project on the patient's retina a focused image for near vision superimposed with a degraded image for far vision, or a focused image for far vision superimposed with the defocused image of the near scene. It is expected that patients with these corrections are able to adapt to the complex Simultaneous Vision retinal images, although the mechanisms or the extent to which this happens is not known. We studied the neural adaptation to simultaneous vision by studying changes in the Natural Perceived Focus and in the Perceptual Score of image quality in subjects after exposure to Simultaneous Vision. We show that Natural Perceived Focus shifts after a brief period of adaptation to a Simultaneous Vision blur, similar to adaptation to Pure Defocus. This shift strongly correlates with the magnitude and proportion of defocus in the adapting image. The magnitude of defocus affects perceived quality of Simultaneous Vision images, with 0.5 D defocus scored lowest and beyond 1.5 D scored “sharp”. Adaptation to Simultaneous Vision shifts the Perceptual Score of these images towards higher rankings. Larger improvements occurred when testing simultaneous images with the same magnitude of defocus as the adapting images, indicating that wearing a particular bifocal correction improves the perception of images provided by that correction. PMID:24664087

  5. Patterns of interval correlations in neural oscillators with adaptation

    PubMed Central

    Schwalger, Tilo; Lindner, Benjamin

    2013-01-01

    Neural firing is often subject to negative feedback by adaptation currents. These currents can induce strong correlations among the time intervals between spikes. Here we study analytically the interval correlations of a broad class of noisy neural oscillators with spike-triggered adaptation of arbitrary strength and time scale. Our weak-noise theory provides a general relation between the correlations and the phase-response curve (PRC) of the oscillator, proves anti-correlations between neighboring intervals for adapting neurons with type I PRC and identifies a single order parameter that determines the qualitative pattern of correlations. Monotonically decaying or oscillating correlation structures can be related to qualitatively different voltage traces after spiking, which can be explained by the phase plane geometry. At high firing rates, the long-term variability of the spike train associated with the cumulative interval correlations becomes small, independent of model details. Our results are verified by comparison with stochastic simulations of the exponential, leaky, and generalized integrate-and-fire models with adaptation. PMID:24348372

  6. Dynamical criticality in the collective activity of a neural population

    NASA Astrophysics Data System (ADS)

    Mora, Thierry

    The past decade has seen a wealth of physiological data suggesting that neural networks may behave like critical branching processes. Concurrently, the collective activity of neurons has been studied using explicit mappings to classic statistical mechanics models such as disordered Ising models, allowing for the study of their thermodynamics, but these efforts have ignored the dynamical nature of neural activity. I will show how to reconcile these two approaches by learning effective statistical mechanics models of the full history of the collective activity of a neuron population directly from physiological data, treating time as an additional dimension. Applying this technique to multi-electrode recordings from retinal ganglion cells, and studying the thermodynamics of the inferred model, reveals a peak in specific heat reminiscent of a second-order phase transition.

  7. Complex Environmental Data Modelling Using Adaptive General Regression Neural Networks

    NASA Astrophysics Data System (ADS)

    Kanevski, Mikhail

    2015-04-01

    The research deals with an adaptation and application of Adaptive General Regression Neural Networks (GRNN) to high dimensional environmental data. GRNN [1,2,3] are efficient modelling tools both for spatial and temporal data and are based on nonparametric kernel methods closely related to classical Nadaraya-Watson estimator. Adaptive GRNN, using anisotropic kernels, can be also applied for features selection tasks when working with high dimensional data [1,3]. In the present research Adaptive GRNN are used to study geospatial data predictability and relevant feature selection using both simulated and real data case studies. The original raw data were either three dimensional monthly precipitation data or monthly wind speeds embedded into 13 dimensional space constructed by geographical coordinates and geo-features calculated from digital elevation model. GRNN were applied in two different ways: 1) adaptive GRNN with the resulting list of features ordered according to their relevancy; and 2) adaptive GRNN applied to evaluate all possible models N [in case of wind fields N=(2^13 -1)=8191] and rank them according to the cross-validation error. In both cases training were carried out applying leave-one-out procedure. An important result of the study is that the set of the most relevant features depends on the month (strong seasonal effect) and year. The predictabilities of precipitation and wind field patterns, estimated using the cross-validation and testing errors of raw and shuffled data, were studied in detail. The results of both approaches were qualitatively and quantitatively compared. In conclusion, Adaptive GRNN with their ability to select features and efficient modelling of complex high dimensional data can be widely used in automatic/on-line mapping and as an integrated part of environmental decision support systems. 1. Kanevski M., Pozdnoukhov A., Timonin V. Machine Learning for Spatial Environmental Data. Theory, applications and software. EPFL Press

  8. Pipelined recurrent fuzzy neural networks for nonlinear adaptive speech prediction.

    PubMed

    Stavrakoudis, Dimitris G; Theocharis, John B

    2007-10-01

    A class of pipelined recurrent fuzzy neural networks (PRFNNs) is proposed in this paper for nonlinear adaptive speech prediction. The PRFNNs are modular structures comprising a number of modules that are interconnected in a chained form. Each module is implemented by a small-scale recurrent fuzzy neural network (RFNN) with internal dynamics. Due to module nesting, the PRFNNs offer a number of desirable attributes, including decomposition of the modeling task, enhanced temporal processing capabilities, and multistage dynamic fuzzy inference. Tuning of the PRFNN adaptable parameters is accomplished by a series of gradient descent methods with different weighting of the modules and the decoupled extended Kalman filter (DEKF) algorithm, based on weight grouping. Extensive experimentation is carried out to evaluate the performance of the PRFNNs on the speech prediction platform. Comparative analysis shows that the PRFNNs outperform the single-RFNN models in terms of the prediction gains that are obtained and computational efficiency. Furthermore, PRFNNs provide considerably better performance compared to pipelined recurrent neural networks, for models with similar model complexity.

  9. Neural Network Based Intrusion Detection System for Critical Infrastructures

    SciTech Connect

    Todd Vollmer; Ondrej Linda; Milos Manic

    2009-07-01

    Resiliency and security in control systems such as SCADA and Nuclear plant’s in today’s world of hackers and malware are a relevant concern. Computer systems used within critical infrastructures to control physical functions are not immune to the threat of cyber attacks and may be potentially vulnerable. Tailoring an intrusion detection system to the specifics of critical infrastructures can significantly improve the security of such systems. The IDS-NNM – Intrusion Detection System using Neural Network based Modeling, is presented in this paper. The main contributions of this work are: 1) the use and analyses of real network data (data recorded from an existing critical infrastructure); 2) the development of a specific window based feature extraction technique; 3) the construction of training dataset using randomly generated intrusion vectors; 4) the use of a combination of two neural network learning algorithms – the Error-Back Propagation and Levenberg-Marquardt, for normal behavior modeling. The presented algorithm was evaluated on previously unseen network data. The IDS-NNM algorithm proved to be capable of capturing all intrusion attempts presented in the network communication while not generating any false alerts.

  10. Neural network payload estimation for adaptive robot control.

    PubMed

    Leahy, M R; Johnson, M A; Rogers, S K

    1991-01-01

    A concept is proposed for utilizing artificial neural networks to enhance the high-speed tracking accuracy of robotic manipulators. Tracking accuracy is a function of the controller's ability to compensate for disturbances produced by dynamical interactions between the links. A model-based control algorithm uses a nominal model of those dynamical interactions to reduce the disturbances. The problem is how to provide accurate dynamics information to the controller in the presence of payload uncertainty and modeling error. Neural network payload estimation uses a series of artificial neural networks to recognize the payload variation associated with a degradation in tracking performance. The network outputs are combined with a knowledge of nominal dynamics to produce a computationally efficient direct form of adaptive control. The concept is validated through experimentation and analysis on the first three links of a PUMA-560 manipulator. A multilayer perceptron architecture with two hidden layers is used. Integration of the principles of neural network pattern recognition and model-based control produces a tracking algorithm with enhanced robustness to incomplete dynamic information. Tracking efficacy and applicability to robust control algorithms are discussed.

  11. Adaptive evolutionary artificial neural networks for pattern classification.

    PubMed

    Oong, Tatt Hee; Isa, Nor Ashidi Mat

    2011-11-01

    This paper presents a new evolutionary approach called the hybrid evolutionary artificial neural network (HEANN) for simultaneously evolving an artificial neural networks (ANNs) topology and weights. Evolutionary algorithms (EAs) with strong global search capabilities are likely to provide the most promising region. However, they are less efficient in fine-tuning the search space locally. HEANN emphasizes the balancing of the global search and local search for the evolutionary process by adapting the mutation probability and the step size of the weight perturbation. This is distinguishable from most previous studies that incorporate EA to search for network topology and gradient learning for weight updating. Four benchmark functions were used to test the evolutionary framework of HEANN. In addition, HEANN was tested on seven classification benchmark problems from the UCI machine learning repository. Experimental results show the superior performance of HEANN in fine-tuning the network complexity within a small number of generations while preserving the generalization capability compared with other algorithms.

  12. Adaptive model predictive process control using neural networks

    DOEpatents

    Buescher, Kevin L.; Baum, Christopher C.; Jones, Roger D.

    1997-01-01

    A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data.

  13. Adaptive model predictive process control using neural networks

    DOEpatents

    Buescher, K.L.; Baum, C.C.; Jones, R.D.

    1997-08-19

    A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data. 46 figs.

  14. Distributed Adaptive Neural Control for Stochastic Nonlinear Multiagent Systems.

    PubMed

    Wang, Fang; Chen, Bing; Lin, Chong; Li, Xuehua

    2016-11-14

    In this paper, a consensus tracking problem of nonlinear multiagent systems is investigated under a directed communication topology. All the followers are modeled by stochastic nonlinear systems in nonstrict feedback form, where nonlinearities and stochastic disturbance terms are totally unknown. Based on the structural characteristic of neural networks (in Lemma 4), a novel distributed adaptive neural control scheme is put forward. The raised control method not only effectively handles unknown nonlinearities in nonstrict feedback systems, but also copes with the interactions among agents and coupling terms. Based on the stochastic Lyapunov functional method, it is indicated that all the signals of the closed-loop system are bounded in probability and all followers' outputs are convergent to a neighborhood of the output of leader. At last, the efficiency of the control method is testified by a numerical example.

  15. Adaptive critic learning techniques for engine torque and air-fuel ratio control.

    PubMed

    Liu, Derong; Javaherian, Hossein; Kovalenko, Olesia; Huang, Ting

    2008-08-01

    A new approach for engine calibration and control is proposed. In this paper, we present our research results on the implementation of adaptive critic designs for self-learning control of automotive engines. A class of adaptive critic designs that can be classified as (model-free) action-dependent heuristic dynamic programming is used in this research project. The goals of the present learning control design for automotive engines include improved performance, reduced emissions, and maintained optimum performance under various operating conditions. Using the data from a test vehicle with a V8 engine, we developed a neural network model of the engine and neural network controllers based on the idea of approximate dynamic programming to achieve optimal control. We have developed and simulated self-learning neural network controllers for both engine torque (TRQ) and exhaust air-fuel ratio (AFR) control. The goal of TRQ control and AFR control is to track the commanded values. For both control problems, excellent neural network controller transient performance has been achieved.

  16. An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries

    DTIC Science & Technology

    2010-10-01

    be scheduled in advance when needed. In this paper, an adaptive recurrent neural network (ARNN) is proposed for system dynamic state forecasting. The...developed ARNN is constructed based on the adaptive/recurrent neural network architecture and the network weights are adaptively optimized using the

  17. Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks

    NASA Technical Reports Server (NTRS)

    Jorgensen, Charles C.

    1997-01-01

    A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.

  18. Shaping Embodied Neural Networks for Adaptive Goal-directed Behavior

    PubMed Central

    Chao, Zenas C.; Bakkum, Douglas J.; Potter, Steve M.

    2008-01-01

    The acts of learning and memory are thought to emerge from the modifications of synaptic connections between neurons, as guided by sensory feedback during behavior. However, much is unknown about how such synaptic processes can sculpt and are sculpted by neuronal population dynamics and an interaction with the environment. Here, we embodied a simulated network, inspired by dissociated cortical neuronal cultures, with an artificial animal (an animat) through a sensory-motor loop consisting of structured stimuli, detailed activity metrics incorporating spatial information, and an adaptive training algorithm that takes advantage of spike timing dependent plasticity. By using our design, we demonstrated that the network was capable of learning associations between multiple sensory inputs and motor outputs, and the animat was able to adapt to a new sensory mapping to restore its goal behavior: move toward and stay within a user-defined area. We further showed that successful learning required proper selections of stimuli to encode sensory inputs and a variety of training stimuli with adaptive selection contingent on the animat's behavior. We also found that an individual network had the flexibility to achieve different multi-task goals, and the same goal behavior could be exhibited with different sets of network synaptic strengths. While lacking the characteristic layered structure of in vivo cortical tissue, the biologically inspired simulated networks could tune their activity in behaviorally relevant manners, demonstrating that leaky integrate-and-fire neural networks have an innate ability to process information. This closed-loop hybrid system is a useful tool to study the network properties intermediating synaptic plasticity and behavioral adaptation. The training algorithm provides a stepping stone towards designing future control systems, whether with artificial neural networks or biological animats themselves. PMID:18369432

  19. Adaptive PID control based on orthogonal endocrine neural networks.

    PubMed

    Milovanović, Miroslav B; Antić, Dragan S; Milojković, Marko T; Nikolić, Saša S; Perić, Staniša Lj; Spasić, Miodrag D

    2016-12-01

    A new intelligent hybrid structure used for online tuning of a PID controller is proposed in this paper. The structure is based on two adaptive neural networks, both with built-in Chebyshev orthogonal polynomials. First substructure network is a regular orthogonal neural network with implemented artificial endocrine factor (OENN), in the form of environmental stimuli, to its weights. It is used for approximation of control signals and for processing system deviation/disturbance signals which are introduced in the form of environmental stimuli. The output values of OENN are used to calculate artificial environmental stimuli (AES), which represent required adaptation measure of a second network-orthogonal endocrine adaptive neuro-fuzzy inference system (OEANFIS). OEANFIS is used to process control, output and error signals of a system and to generate adjustable values of proportional, derivative, and integral parameters, used for online tuning of a PID controller. The developed structure is experimentally tested on a laboratory model of the 3D crane system in terms of analysing tracking performances and deviation signals (error signals) of a payload. OENN-OEANFIS performances are compared with traditional PID and 6 intelligent PID type controllers. Tracking performance comparisons (in transient and steady-state period) showed that the proposed adaptive controller possesses performances within the range of other tested controllers. The main contribution of OENN-OEANFIS structure is significant minimization of deviation signals (17%-79%) compared to other controllers. It is recommended to exploit it when dealing with a highly nonlinear system which operates in the presence of undesirable disturbances.

  20. Adaptive control using neural networks and approximate models.

    PubMed

    Narendra, K S; Mukhopadhyay, S

    1997-01-01

    The NARMA model is an exact representation of the input-output behavior of finite-dimensional nonlinear discrete-time dynamical systems in a neighborhood of the equilibrium state. However, it is not convenient for purposes of adaptive control using neural networks due to its nonlinear dependence on the control input. Hence, quite often, approximate methods are used for realizing the neural controllers to overcome computational complexity. In this paper, we introduce two classes of models which are approximations to the NARMA model, and which are linear in the control input. The latter fact substantially simplifies both the theoretical analysis as well as the practical implementation of the controller. Extensive simulation studies have shown that the neural controllers designed using the proposed approximate models perform very well, and in many cases even better than an approximate controller designed using the exact NARMA model. In view of their mathematical tractability as well as their success in simulation studies, a case is made in this paper that such approximate input-output models warrant a detailed study in their own right.

  1. Real-time Adaptive Control Using Neural Generalized Predictive Control

    NASA Technical Reports Server (NTRS)

    Haley, Pam; Soloway, Don; Gold, Brian

    1999-01-01

    The objective of this paper is to demonstrate the feasibility of a Nonlinear Generalized Predictive Control algorithm by showing real-time adaptive control on a plant with relatively fast time-constants. Generalized Predictive Control has classically been used in process control where linear control laws were formulated for plants with relatively slow time-constants. The plant of interest for this paper is a magnetic levitation device that is nonlinear and open-loop unstable. In this application, the reference model of the plant is a neural network that has an embedded nominal linear model in the network weights. The control based on the linear model provides initial stability at the beginning of network training. In using a neural network the control laws are nonlinear and online adaptation of the model is possible to capture unmodeled or time-varying dynamics. Newton-Raphson is the minimization algorithm. Newton-Raphson requires the calculation of the Hessian, but even with this computational expense the low iteration rate make this a viable algorithm for real-time control.

  2. Research on PGNAA adaptive analysis method with BP neural network

    NASA Astrophysics Data System (ADS)

    Peng, Ke-Xin; Yang, Jian-Bo; Tuo, Xian-Guo; Du, Hua; Zhang, Rui-Xue

    2016-11-01

    A new approach method to dealing with the puzzle of spectral analysis in prompt gamma neutron activation analysis (PGNAA) is developed and demonstrated. It consists of utilizing BP neural network to PGNAA energy spectrum analysis which is based on Monte Carlo (MC) simulation, the main tasks which we will accomplish as follows: (1) Completing the MC simulation of PGNAA spectrum library, we respectively set mass fractions of element Si, Ca, Fe from 0.00 to 0.45 with a step of 0.05 and each sample is simulated using MCNP. (2) Establishing the BP model of adaptive quantitative analysis of PGNAA energy spectrum, we calculate peak areas of eight characteristic gamma rays that respectively correspond to eight elements in each individual of 1000 samples and that of the standard sample. (3) Verifying the viability of quantitative analysis of the adaptive algorithm where 68 samples were used successively. Results show that the precision when using neural network to calculate the content of each element is significantly higher than the MCLLS.

  3. On Mixed Data and Event Driven Design for Adaptive-Critic-Based Nonlinear $H∞ Control.

    PubMed

    Wang, Ding; Mu, Chaoxu; Liu, Derong; Ma, Hongwen

    2017-02-01

    In this paper, based on the adaptive critic learning technique, the H∞ control for a class of unknown nonlinear dynamic systems is investigated by adopting a mixed data and event driven design approach. The nonlinear H∞ control problem is formulated as a two-player zero-sum differential game and the adaptive critic method is employed to cope with the data-based optimization. The novelty lies in that the data driven learning identifier is combined with the event driven design formulation, in order to develop the adaptive critic controller, thereby accomplishing the nonlinear H∞ control. The event driven optimal control law and the time driven worst case disturbance law are approximated by constructing and tuning a critic neural network. Applying the event driven feedback control, the closed-loop system is built with stability analysis. Simulation studies are conducted to verify the theoretical results and illustrate the control performance. It is significant to observe that the present research provides a new avenue of integrating data-based control and event-triggering mechanism into establishing advanced adaptive critic systems.

  4. Adaptive significance of critical weight for metamorphosis in holometabolous insects.

    PubMed

    Hironaka, Ken-Ichi; Morishita, Yoshihiro

    2017-03-21

    Holometabolous insect larvae become committed to metamorphosis when they reach a critical weight. Although the physiological mechanisms involved in this process have been well-studied, the adaptive significance of the critical weight remains unclear. Here, we developed a life history model for holometabolous insects and evaluated it from the viewpoint of optimal energy allocation. We found that, without a priori assumptions about critical weight, the optimal growth schedule is always biphasic: larval tissues grow predominately until they reach a certain threshold, after which the imaginal tissues begin rapid growth, suggesting that the emergence of a critical weight as a phase-transition point is a natural consequence of optimal growth scheduling. Our model predicts the optimal timing of critical-weight attainment, in agreement with observations in phylogenetically-distinct species. Furthermore, it also predicts the scaling of growth scheduling against environmental change, i.e., the relative value and timing of the critical weight should be constant, thus providing a general interpretation of observed phenotypic plasticity. This scaling relationship allows the classification of adaptive responses in critical weight into five possible types that reflect the ecological features of focal insects. In this manner, our theory and its consistency with experimental observations demonstrate the adaptive significance of critical weight.

  5. Task-specific neural adaptations to isoinertial resistance training.

    PubMed

    Buckthorpe, M; Erskine, R M; Fletcher, G; Folland, J P

    2015-10-01

    This study aimed to delineate the contribution of adaptations in agonist, antagonist, and stabilizer muscle activation to changes in isometric and isoinertial lifting strength after short-term isoinertial resistance training (RT). Following familiarization, 45 men (23.2 ± 2.8 years) performed maximal isometric and isoinertial strength tests of the elbow flexors of their dominant arms before and after 3 weeks of isoinertial RT. During these tasks, surface electromyography (EMG) amplitude was recorded from the agonist (biceps brachii short and long heads), antagonist (triceps brachii lateral head), and stabilizer (anterior deltoid, pectoralis major) muscles and normalized to either Mmax (agonists) or to maximum EMG during relevant reference tasks (antagonist, stabilizers). After training, there was more than a twofold greater increase in training task-specific isoinertial than isometric strength (17% vs 7%). There were also task-specific adaptations in agonist EMG, with greater increases during the isoinertial than isometric strength task [analysis of variance (ANOVA), training × task, P = 0.005]. A novel finding of this study was that training increased stabilizer muscle activation during all the elbow flexion strength tasks (P < 0.001), although these were not task-specific training effects. RT elicited specific neural adaptations to the training task that appeared to explain the greater increase in isoinertial than isometric strength.

  6. Adaptive regularization network based neural modeling paradigm for nonlinear adaptive estimation of cerebral evoked potentials.

    PubMed

    Zhang, Jian-Hua; Böhme, Johann F

    2007-11-01

    In this paper we report an adaptive regularization network (ARN) approach to realizing fast blind separation of cerebral evoked potentials (EPs) from background electroencephalogram (EEG) activity with no need to make any explicit assumption on the statistical (or deterministic) signal model. The ARNs are proposed to construct nonlinear EEG and EP signal models. A novel adaptive regularization training (ART) algorithm is proposed to improve the generalization performance of the ARN. Two adaptive neural modeling methods based on the ARN are developed and their implementation and performance analysis are also presented. The computer experiments using simulated and measured visual evoked potential (VEP) data have shown that the proposed ARN modeling paradigm yields computationally efficient and more accurate VEP signal estimation owing to its intrinsic model-free and nonlinear processing characteristics.

  7. Adaptive neural coding: from biological to behavioral decision-making

    PubMed Central

    Louie, Kenway; Glimcher, Paul W.; Webb, Ryan

    2015-01-01

    Empirical decision-making in diverse species deviates from the predictions of normative choice theory, but why such suboptimal behavior occurs is unknown. Here, we propose that deviations from optimality arise from biological decision mechanisms that have evolved to maximize choice performance within intrinsic biophysical constraints. Sensory processing utilizes specific computations such as divisive normalization to maximize information coding in constrained neural circuits, and recent evidence suggests that analogous computations operate in decision-related brain areas. These adaptive computations implement a relative value code that may explain the characteristic context-dependent nature of behavioral violations of classical normative theory. Examining decision-making at the computational level thus provides a crucial link between the architecture of biological decision circuits and the form of empirical choice behavior. PMID:26722666

  8. Nonlinear adaptive trajectory tracking using dynamic neural networks.

    PubMed

    Poznyak, A S; Yu, W; Sanchez, E N; Perez, J P

    1999-01-01

    In this paper the adaptive nonlinear identification and trajectory tracking are discussed via dynamic neural networks. By means of a Lyapunov-like analysis we determine stability conditions for the identification error. Then we analyze the trajectory tracking error by a local optimal controller. An algebraic Riccati equation and a differential one are used for the identification and the tracking error analysis. As our main original contributions, we establish two theorems: the first one gives a bound for the identification error and the second one establishes a bound for the tracking error. We illustrate the effectiveness of these results by two examples: the second-order relay system with multiple isolated equilibrium points and the chaotic system given by Duffing equation.

  9. A recurrent neural network for adaptive beamforming and array correction.

    PubMed

    Che, Hangjun; Li, Chuandong; He, Xing; Huang, Tingwen

    2016-08-01

    In this paper, a recurrent neural network (RNN) is proposed for solving adaptive beamforming problem. In order to minimize sidelobe interference, the problem is described as a convex optimization problem based on linear array model. RNN is designed to optimize system's weight values in the feasible region which is derived from arrays' state and plane wave's information. The new algorithm is proven to be stable and converge to optimal solution in the sense of Lyapunov. So as to verify new algorithm's performance, we apply it to beamforming under array mismatch situation. Comparing with other optimization algorithms, simulations suggest that RNN has strong ability to search for exact solutions under the condition of large scale constraints.

  10. Adaptation to optimal cell growth through self-organized criticality.

    PubMed

    Furusawa, Chikara; Kaneko, Kunihiko

    2012-05-18

    A simple cell model consisting of a catalytic reaction network is studied to show that cellular states are self-organized in a critical state for achieving optimal growth; we consider the catalytic network dynamics over a wide range of environmental conditions, through the spontaneous regulation of nutrient transport into the cell. Furthermore, we find that the adaptability of cellular growth to reach a critical state depends only on the extent of environmental changes, while all chemical species in the cell exhibit correlated partial adaptation. These results are in remarkable agreement with the recent experimental observations of the present cells.

  11. Neural correlates of metacognition: A critical perspective on current tasks.

    PubMed

    Insabato, Andrea; Pannunzi, Mario; Deco, Gustavo

    2016-12-01

    Humans have a remarkable ability to reflect upon their behavior and mental processes, a capacity known as metacognition. Recent neurophysiological experiments have attempted to elucidate the neural correlates of metacognition in other species. Despite this increased attention, there is still no operational definition of metacognition and the ability of behavioral tasks to reflect metacognition is the subject of debate. The most widely used task for studying metacognition in animals, the uncertain-option task, has been criticized because it can be solved by simple associative mechanisms. Here we propose a broad perspective that generalizes those critiques to another task, post-decision wagering. Moreover, we extend this critical view to account for recent neurophysiological evidence. We argue these tasks are simple enough that any animal could solve them using very simple mechanisms such as sensory-motor associations. In this case, it is impossible to know whether all animals are metacognitive, or if the tasks are simply not appropriate. Therefore, we suggest using better defined concepts until a suitable task for metacognition is available.

  12. A Tool for Verification and Validation of Neural Network Based Adaptive Controllers for High Assurance Systems

    NASA Technical Reports Server (NTRS)

    Gupta, Pramod; Schumann, Johann

    2004-01-01

    High reliability of mission- and safety-critical software systems has been identified by NASA as a high-priority technology challenge. We present an approach for the performance analysis of a neural network (NN) in an advanced adaptive control system. This problem is important in the context of safety-critical applications that require certification, such as flight software in aircraft. We have developed a tool to measure the performance of the NN during operation by calculating a confidence interval (error bar) around the NN's output. Our tool can be used during pre-deployment verification as well as monitoring the network performance during operation. The tool has been implemented in Simulink and simulation results on a F-15 aircraft are presented.

  13. Adaptive Neural Network Nonparametric Identifier With Normalized Learning Laws.

    PubMed

    Chairez, Isaac

    2016-04-05

    This paper addresses the design of a normalized convergent learning law for neural networks (NNs) with continuous dynamics. The NN is used here to obtain a nonparametric model for uncertain systems described by a set of ordinary differential equations. The source of uncertainties is the presence of some external perturbations and poor knowledge of the nonlinear function describing the system dynamics. A new adaptive algorithm based on normalized algorithms was used to adjust the weights of the NN. The adaptive algorithm was derived by means of a nonstandard logarithmic Lyapunov function (LLF). Two identifiers were designed using two variations of LLFs leading to a normalized learning law for the first identifier and a variable gain normalized learning law. In the case of the second identifier, the inclusion of normalized learning laws yields to reduce the size of the convergence region obtained as solution of the practical stability analysis. On the other hand, the velocity of convergence for the learning laws depends on the norm of errors in inverse form. This fact avoids the peaking transient behavior in the time evolution of weights that accelerates the convergence of identification error. A numerical example demonstrates the improvements achieved by the algorithm introduced in this paper compared with classical schemes with no-normalized continuous learning methods. A comparison of the identification performance achieved by the no-normalized identifier and the ones developed in this paper shows the benefits of the learning law proposed in this paper.

  14. Separate neural substrates in the human cerebellum for sensory-motor adaptation of reactive and of scanning voluntary saccades.

    PubMed

    Alahyane, N; Fonteille, V; Urquizar, C; Salemme, R; Nighoghossian, N; Pelisson, D; Tilikete, C

    2008-01-01

    Sensory-motor adaptation processes are critically involved in maintaining accurate motor behavior throughout life. Yet their underlying neural substrates and task-dependency bases are still poorly understood. We address these issues here by studying adaptation of saccadic eye movements, a well-established model of sensory-motor plasticity. The cerebellum plays a major role in saccadic adaptation but it has not yet been investigated whether this role can account for the known specificity of adaptation to the saccade type (e.g., reactive versus voluntary). Two patients with focal lesions in different parts of the cerebellum were tested using the double-step target paradigm. Each patient was submitted to two separate sessions: one for reactive saccades (RS) triggered by the sudden appearance of a visual target and the second for scanning voluntary saccades (SVS) performed when exploring a more complex scene. We found that a medial cerebellar lesion impaired adaptation of reactive-but not of voluntary-saccades, whereas a lateral lesion affected adaptation of scanning voluntary saccades, but not of reactive saccades. These findings provide the first evidence of an involvement of the lateral cerebellum in saccadic adaptation, and extend the demonstrated role of the cerebellum in RS adaptation to adaptation of SVS. The double dissociation of adaptive abilities is also consistent with our previous hypothesis of the involvement in saccadic adaptation of partially separated cerebellar areas specific to the reactive or voluntary task (Alahyane et al. Brain Res 1135:107-121 (2007)).

  15. Adaptive Weibull Multiplicative Model and Multilayer Perceptron neural networks for dark-spot detection from SAR imagery.

    PubMed

    Taravat, Alireza; Oppelt, Natascha

    2014-12-02

    Oil spills represent a major threat to ocean ecosystems and their environmental status. Previous studies have shown that Synthetic Aperture Radar (SAR), as its recording is independent of clouds and weather, can be effectively used for the detection and classification of oil spills. Dark formation detection is the first and critical stage in oil-spill detection procedures. In this paper, a novel approach for automated dark-spot detection in SAR imagery is presented. A new approach from the combination of adaptive Weibull Multiplicative Model (WMM) and MultiLayer Perceptron (MLP) neural networks is proposed to differentiate between dark spots and the background. The results have been compared with the results of a model combining non-adaptive WMM and pulse coupled neural networks. The presented approach overcomes the non-adaptive WMM filter setting parameters by developing an adaptive WMM model which is a step ahead towards a full automatic dark spot detection. The proposed approach was tested on 60 ENVISAT and ERS2 images which contained dark spots. For the overall dataset, an average accuracy of 94.65% was obtained. Our experimental results demonstrate that the proposed approach is very robust and effective where the non-adaptive WMM & pulse coupled neural network (PCNN) model generates poor accuracies.

  16. Adaptive fuzzy-neural-network control for maglev transportation system.

    PubMed

    Wai, Rong-Jong; Lee, Jeng-Dao

    2008-01-01

    A magnetic-levitation (maglev) transportation system including levitation and propulsion control is a subject of considerable scientific interest because of highly nonlinear and unstable behaviors. In this paper, the dynamic model of a maglev transportation system including levitated electromagnets and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed first. Then, a model-based sliding-mode control (SMC) strategy is introduced. In order to alleviate chattering phenomena caused by the inappropriate selection of uncertainty bound, a simple bound estimation algorithm is embedded in the SMC strategy to form an adaptive sliding-mode control (ASMC) scheme. However, this estimation algorithm is always a positive value so that tracking errors introduced by any uncertainty will cause the estimated bound increase even to infinity with time. Therefore, it further designs an adaptive fuzzy-neural-network control (AFNNC) scheme by imitating the SMC strategy for the maglev transportation system. In the model-free AFNNC, online learning algorithms are designed to cope with the problem of chattering phenomena caused by the sign action in SMC design, and to ensure the stability of the controlled system without the requirement of auxiliary compensated controllers despite the existence of uncertainties. The outputs of the AFNNC scheme can be directly supplied to the electromagnets and LIM without complicated control transformations for relaxing strict constrains in conventional model-based control methodologies. The effectiveness of the proposed control schemes for the maglev transportation system is verified by numerical simulations, and the superiority of the AFNNC scheme is indicated in comparison with the SMC and ASMC strategies.

  17. A Bayesian regularized artificial neural network for adaptive optics forecasting

    NASA Astrophysics Data System (ADS)

    Sun, Zhi; Chen, Ying; Li, Xinyang; Qin, Xiaolin; Wang, Huiyong

    2017-01-01

    Real-time adaptive optics is a technology for enhancing the resolution of ground-based optical telescopes and overcoming the disturbance of atmospheric turbulence. The performance of the system is limited by delay errors induced by the servo system and photoelectrons noise of wavefront sensor. In order to cut these delay errors, this paper proposes a novel model to forecast the future control voltages of the deformable mirror. The predictive model is constructed by a multi-layered back propagation network with Bayesian regularization (BRBP). For the purpose of parallel computation and less disturbance, we adopt a number of sub-BP neural networks to substitute the whole network. The Bayesian regularized network assigns a probability to the network weights, allowing the network to automatically and optimally penalize excessively complex models. The simulation results show that the BRBP introduces smaller mean absolute percentage error (MAPE) and mean square errors (MSE) than other typical algorithms. Meanwhile, real data analysis results show that the BRBP model has strong generalization capability and parallelism.

  18. A complex-valued nonlinear neural adaptive filter with a gradient adaptive amplitude of the activation function.

    PubMed

    Hanna, Andrew I; Mandic, Danilo P

    2003-03-01

    A complex-valued nonlinear gradient descent (CNGD) learning algorithm for a simple finite impulse response (FIR) nonlinear neural adaptive filter with an adaptive amplitude of the complex activation function is proposed. This way the amplitude of the complex-valued analytic nonlinear activation function of a neuron in the learning algorithm is made gradient adaptive to give the complex-valued adaptive amplitude nonlinear gradient descent (CAANGD). Such an algorithm is beneficial when dealing with signals that have rich dynamical behavior. Simulations on the prediction of complex-valued coloured and nonlinear input signals show the gradient adaptive amplitude, CAANGD, outperforming the standard CNGD algorithm.

  19. Adaptation to different noninvasive ventilation masks in critically ill patients*

    PubMed Central

    da Silva, Renata Matos; Timenetsky, Karina Tavares; Neves, Renata Cristina Miranda; Shigemichi, Liane Hirano; Kanda, Sandra Sayuri; Maekawa, Carla; Silva, Eliezer; Eid, Raquel Afonso Caserta

    2013-01-01

    OBJECTIVE: To identify which noninvasive ventilation (NIV) masks are most commonly used and the problems related to the adaptation to such masks in critically ill patients admitted to a hospital in the city of São Paulo, Brazil. METHODS: An observational study involving patients ≥ 18 years of age admitted to intensive care units and submitted to NIV. The reason for NIV use, type of mask, NIV regimen, adaptation to the mask, and reasons for non-adaptation to the mask were investigated. RESULTS: We evaluated 245 patients, with a median age of 82 years. Acute respiratory failure was the most common reason for NIV use (in 71.3%). Total face masks were the most commonly used (in 74.7%), followed by full face masks and near-total face masks (in 24.5% and 0.8%, respectively). Intermittent NIV was used in 82.4% of the patients. Adequate adaptation to the mask was found in 76% of the patients. Masks had to be replaced by another type of mask in 24% of the patients. Adequate adaptation to total face masks and full face masks was found in 75.5% and 80.0% of the patients, respectively. Non-adaptation occurred in the 2 patients using near-total facial masks. The most common reason for non-adaptation was the shape of the face, in 30.5% of the patients. CONCLUSIONS: In our sample, acute respiratory failure was the most common reason for NIV use, and total face masks were the most commonly used. The most common reason for non-adaptation to the mask was the shape of the face, which was resolved by changing the type of mask employed. PMID:24068269

  20. AgRP Neural Circuits Mediate Adaptive Behaviors in the Starved State

    PubMed Central

    Padilla, Stephanie L.; Qiu, Jian; Soden, Marta E.; Sanz, Elisenda; Nestor, Casey C; Barker, Forrest D.; Quintana, Albert; Zweifel, Larry S.; Rønnekleiv, Oline K.; Kelly, Martin J.; Palmiter, Richard D.

    2016-01-01

    In the face of starvation animals will engage in high-risk behaviors that would normally be considered maladaptive. Starving rodents for example will forage in areas that are more susceptible to predators and will also modulate aggressive behavior within a territory of limited or depleted nutrients. The neural basis of these adaptive behaviors likely involves circuits that link innate feeding, aggression, and fear. Hypothalamic AgRP neurons are critically important for driving feeding and project axons to brain regions implicated in aggression and fear. Using circuit-mapping techniques, we define a disynaptic network originating from a subset of AgRP neurons that project to the medial nucleus of the amygdala and then to the principle bed nucleus of the stria terminalis, which plays a role in suppressing territorial aggression and reducing contextual fear. We propose that AgRP neurons serve as a master switch capable of coordinating behavioral decisions relative to internal state and environmental cues. PMID:27019015

  1. Adaptive Neural Control of Uncertain MIMO Nonlinear Systems With State and Input Constraints.

    PubMed

    Chen, Ziting; Li, Zhijun; Chen, C L Philip

    2016-03-17

    An adaptive neural control strategy for multiple input multiple output nonlinear systems with various constraints is presented in this paper. To deal with the nonsymmetric input nonlinearity and the constrained states, the proposed adaptive neural control is combined with the backstepping method, radial basis function neural network, barrier Lyapunov function (BLF), and disturbance observer. By ensuring the boundedness of the BLF of the closed-loop system, it is demonstrated that the output tracking is achieved with all states remaining in the constraint sets and the general assumption on nonsingularity of unknown control coefficient matrices has been eliminated. The constructed adaptive neural control has been rigorously proved that it can guarantee the semiglobally uniformly ultimate boundedness of all signals in the closed-loop system. Finally, the simulation studies on a 2-DOF robotic manipulator system indicate that the designed adaptive control is effective.

  2. Adaptive Control Law Development for Failure Compensation Using Neural Networks on a NASA F-15 Aircraft

    NASA Technical Reports Server (NTRS)

    Burken, John J.

    2005-01-01

    This viewgraph presentation covers the following topics: 1) Brief explanation of Generation II Flight Program; 2) Motivation for Neural Network Adaptive Systems; 3) Past/ Current/ Future IFCS programs; 4) Dynamic Inverse Controller with Explicit Model; 5) Types of Neural Networks Investigated; and 6) Brief example

  3. Neural correlates of priming and adaptation in familiar face perception.

    PubMed

    Walther, Christian; Schweinberger, Stefan R; Kaiser, Daniel; Kovács, Gyula

    2013-01-01

    Priming (PR) and adaptation-related aftereffects (AEs) are two phenomena when recent perceptual experiences alter face perception. While AEs are often reflected in contrastive perceptual biases, PR typically leads to behavioural facilitation. Previous research suggests that both phenomena modulate broadly similar components of the event-related potentials (ERPs). To disentangle the underlying neural mechanisms of PR and AE, we induced both effects within the same subjects and paradigm. We presented pairs of stimuli, where the first (S1) was a famous face (identity A, B or C), a morph between two famous faces (50/50% A/B), or a Fourier phase randomized face (as a control stimulus matched for low-level visual information) and the second (S2) was a face drawn from morph continua between identity A and B. Participants' performance in matching S2s to either A or B revealed contrastive aftereffects for ambiguous S2 faces, which were more likely perceived as identity B following the presentation of A and vice versa. Unambiguous S2 faces, however, showed PR, with significantly shorter response times, as well as higher classification performance, for identity-congruent than for incongruent S1-S2 pairs. Analyses of the simultaneously recorded ERPs revealed clear categorical adaptation at around 155-205 msec post-stimulus onset. We also found amplitude modulations for unambiguous S2 faces following identity-congruent S1 faces, related to PR, starting at 90 msec and being the most pronounced at around 205-255 msec. For ambiguous S2 faces, we also observed an ERP effect at around 205-255 msec that was correlated with behavioural AEs. Our results show that face PR and AEs are present simultaneously within a single paradigm, depending on the ambiguity of S2 faces and/or on the similarity of S1 and S2, and suggest that exclusive mechanisms might underlie both PR and AEs and that object-category and identity processing might run in parallel during face processing.

  4. Genetic algorithm based adaptive neural network ensemble and its application in predicting carbon flux

    USGS Publications Warehouse

    Xue, Y.; Liu, S.; Hu, Y.; Yang, J.; Chen, Q.

    2007-01-01

    To improve the accuracy in prediction, Genetic Algorithm based Adaptive Neural Network Ensemble (GA-ANNE) is presented. Intersections are allowed between different training sets based on the fuzzy clustering analysis, which ensures the diversity as well as the accuracy of individual Neural Networks (NNs). Moreover, to improve the accuracy of the adaptive weights of individual NNs, GA is used to optimize the cluster centers. Empirical results in predicting carbon flux of Duke Forest reveal that GA-ANNE can predict the carbon flux more accurately than Radial Basis Function Neural Network (RBFNN), Bagging NN ensemble, and ANNE. ?? 2007 IEEE.

  5. The insula: a critical neural substrate for craving and drug seeking under conflict and risk

    PubMed Central

    Naqvi, Nasir H.; Gaznick, Natassia; Tranel, Daniel; Bechara, Antoine

    2014-01-01

    Drug addiction is characterized by the inability to control drug use when it results in negative consequences or conflicts with more adaptive goals. Our previous work showed that damage to the insula disrupted addiction to cigarette smoking—the first time that the insula was shown to be a critical neural substrate for addiction. Here, we review those findings, as well as more recent studies that corroborate and extend them, demonstrating the role of the insula in (1) incentive motivational processes that drive addictive behavior, (2) control processes that moderate or inhibit addictive behavior, and (3) interoceptive processes that represent bodily states associated with drug use. We then describe a theoretical framework that attempts to integrate these seemingly disparate findings. In this framework, the insula functions in the recall of interoceptive drug effects during craving and drug seeking under specific conditions where drug taking is perceived as risky and/or where there is conflict between drug taking and more adaptive goals. We describe this framework in an evolutionary context and discuss its implications for understanding the mechanisms of behavior change in addiction treatments. PMID:24690001

  6. An adaptive neural swarm approach for intrusion defense in ad hoc networks

    NASA Astrophysics Data System (ADS)

    Cannady, James

    2011-06-01

    Wireless sensor networks (WSN) and mobile ad hoc networks (MANET) are being increasingly deployed in critical applications due to the flexibility and extensibility of the technology. While these networks possess numerous advantages over traditional wireless systems in dynamic environments they are still vulnerable to many of the same types of host-based and distributed attacks common to those systems. Unfortunately, the limited power and bandwidth available in WSNs and MANETs, combined with the dynamic connectivity that is a defining characteristic of the technology, makes it extremely difficult to utilize traditional intrusion detection techniques. This paper describes an approach to accurately and efficiently detect potentially damaging activity in WSNs and MANETs. It enables the network as a whole to recognize attacks, anomalies, and potential vulnerabilities in a distributive manner that reflects the autonomic processes of biological systems. Each component of the network recognizes activity in its local environment and then contributes to the overall situational awareness of the entire system. The approach utilizes agent-based swarm intelligence to adaptively identify potential data sources on each node and on adjacent nodes throughout the network. The swarm agents then self-organize into modular neural networks that utilize a reinforcement learning algorithm to identify relevant behavior patterns in the data without supervision. Once the modular neural networks have established interconnectivity both locally and with neighboring nodes the analysis of events within the network can be conducted collectively in real-time. The approach has been shown to be extremely effective in identifying distributed network attacks.

  7. Identification and Control of Aircrafts using Multiple Models and Adaptive Critics

    NASA Technical Reports Server (NTRS)

    Principe, Jose C.

    2007-01-01

    nutshell, the states of the reservoir of recurrent processing elements implement a projection space, where the desired response is optimally projected. This architecture trades training efficiency by a large increase in the dimension of the recurrent layer. However, the power of the recurrent neural networks can be brought to bear on practical difficult problems. Our goal was to implement an adaptive critic architecture implementing Bellman s approach to optimal control. However, we could only characterize the ESN performance as a critic in value function evaluation, which is just one of the pieces of the overall adaptive critic controller. The results were very convincing, and the simplicity of the implementation was unparalleled.

  8. Criticality as a Set-Point for Adaptive Behavior in Neuromorphic Hardware

    PubMed Central

    Srinivasa, Narayan; Stepp, Nigel D.; Cruz-Albrecht, Jose

    2015-01-01

    Neuromorphic hardware are designed by drawing inspiration from biology to overcome limitations of current computer architectures while forging the development of a new class of autonomous systems that can exhibit adaptive behaviors. Several designs in the recent past are capable of emulating large scale networks but avoid complexity in network dynamics by minimizing the number of dynamic variables that are supported and tunable in hardware. We believe that this is due to the lack of a clear understanding of how to design self-tuning complex systems. It has been widely demonstrated that criticality appears to be the default state of the brain and manifests in the form of spontaneous scale-invariant cascades of neural activity. Experiment, theory and recent models have shown that neuronal networks at criticality demonstrate optimal information transfer, learning and information processing capabilities that affect behavior. In this perspective article, we argue that understanding how large scale neuromorphic electronics can be designed to enable emergent adaptive behavior will require an understanding of how networks emulated by such hardware can self-tune local parameters to maintain criticality as a set-point. We believe that such capability will enable the design of truly scalable intelligent systems using neuromorphic hardware that embrace complexity in network dynamics rather than avoiding it. PMID:26648839

  9. Quantization-Based Adaptive Actor-Critic Tracking Control With Tracking Error Constraints.

    PubMed

    Fan, Quan-Yong; Yang, Guang-Hong; Ye, Dan

    2017-02-01

    In this paper, the problem of adaptive actor-critic (AC) tracking control is investigated for a class of continuous-time nonlinear systems with unknown nonlinearities and quantized inputs. Different from the existing results based on reinforcement learning, the tracking error constraints are considered and new critic functions are constructed to improve the performance further. To ensure that the tracking errors keep within the predefined time-varying boundaries, a tracking error transformation technique is used to constitute an augmented error system. Specific critic functions, rather than the long-term cost function, are introduced to supervise the tracking performance and tune the weights of the AC neural networks (NNs). A novel adaptive controller with a special structure is designed to reduce the effect of the NN reconstruction errors, input quantization, and disturbances. Based on the Lyapunov stability theory, the boundedness of the closed-loop signals and the desired tracking performance can be guaranteed. Finally, simulations on two connected inverted pendulums are given to illustrate the effectiveness of the proposed method.

  10. Adaptive Output-Feedback Neural Control of Switched Uncertain Nonlinear Systems With Average Dwell Time.

    PubMed

    Long, Lijun; Zhao, Jun

    2015-07-01

    This paper investigates the problem of adaptive neural tracking control via output-feedback for a class of switched uncertain nonlinear systems without the measurements of the system states. The unknown control signals are approximated directly by neural networks. A novel adaptive neural control technique for the problem studied is set up by exploiting the average dwell time method and backstepping. A switched filter and different update laws are designed to reduce the conservativeness caused by adoption of a common observer and a common update law for all subsystems. The proposed controllers of subsystems guarantee that all closed-loop signals remain bounded under a class of switching signals with average dwell time, while the output tracking error converges to a small neighborhood of the origin. As an application of the proposed design method, adaptive output feedback neural tracking controllers for a mass-spring-damper system are constructed.

  11. A study of interceptor attitude control based on adaptive wavelet neural networks

    NASA Astrophysics Data System (ADS)

    Li, Da; Wang, Qing-chao

    2005-12-01

    This paper engages to study the 3-DOF attitude control problem of the kinetic interceptor. When the kinetic interceptor enters into terminal guidance it has to maneuver with large angles. The characteristic of interceptor attitude system is nonlinearity, strong-coupling and MIMO. A kind of inverse control approach based on adaptive wavelet neural networks was proposed in this paper. Instead of using one complex neural network as the controller, the nonlinear dynamics of the interceptor can be approximated by three independent subsystems applying exact feedback-linearization firstly, and then controllers for each subsystem are designed using adaptive wavelet neural networks respectively. This method avoids computing a large amount of the weights and bias in one massive neural network and the control parameters can be adaptive changed online. Simulation results betray that the proposed controller performs remarkably well.

  12. An Adaptive-PSO-Based Self-Organizing RBF Neural Network.

    PubMed

    Han, Hong-Gui; Lu, Wei; Hou, Ying; Qiao, Jun-Fei

    2016-10-24

    In this paper, a self-organizing radial basis function (SORBF) neural network is designed to improve both accuracy and parsimony with the aid of adaptive particle swarm optimization (APSO). In the proposed APSO algorithm, to avoid being trapped into local optimal values, a nonlinear regressive function is developed to adjust the inertia weight. Furthermore, the APSO algorithm can optimize both the network size and the parameters of an RBF neural network simultaneously. As a result, the proposed APSO-SORBF neural network can effectively generate a network model with a compact structure and high accuracy. Moreover, the analysis of convergence is given to guarantee the successful application of the APSO-SORBF neural network. Finally, multiple numerical examples are presented to illustrate the effectiveness of the proposed APSO-SORBF neural network. The results demonstrate that the proposed method is more competitive in solving nonlinear problems than some other existing SORBF neural networks.

  13. A biomimetic adaptive algorithm and low-power architecture for implantable neural decoders.

    PubMed

    Rapoport, Benjamin I; Wattanapanitch, Woradorn; Penagos, Hector L; Musallam, Sam; Andersen, Richard A; Sarpeshkar, Rahul

    2009-01-01

    Algorithmically and energetically efficient computational architectures that operate in real time are essential for clinically useful neural prosthetic devices. Such devices decode raw neural data to obtain direct control signals for external devices. They can also perform data compression and vastly reduce the bandwidth and consequently power expended in wireless transmission of raw data from implantable brain-machine interfaces. We describe a biomimetic algorithm and micropower analog circuit architecture for decoding neural cell ensemble signals. The decoding algorithm implements a continuous-time artificial neural network, using a bank of adaptive linear filters with kernels that emulate synaptic dynamics. The filters transform neural signal inputs into control-parameter outputs, and can be tuned automatically in an on-line learning process. We provide experimental validation of our system using neural data from thalamic head-direction cells in an awake behaving rat.

  14. Online learning control using adaptive critic designs with sparse kernel machines.

    PubMed

    Xu, Xin; Hou, Zhongsheng; Lian, Chuanqiang; He, Haibo

    2013-05-01

    In the past decade, adaptive critic designs (ACDs), including heuristic dynamic programming (HDP), dual heuristic programming (DHP), and their action-dependent ones, have been widely studied to realize online learning control of dynamical systems. However, because neural networks with manually designed features are commonly used to deal with continuous state and action spaces, the generalization capability and learning efficiency of previous ACDs still need to be improved. In this paper, a novel framework of ACDs with sparse kernel machines is presented by integrating kernel methods into the critic of ACDs. To improve the generalization capability as well as the computational efficiency of kernel machines, a sparsification method based on the approximately linear dependence analysis is used. Using the sparse kernel machines, two kernel-based ACD algorithms, that is, kernel HDP (KHDP) and kernel DHP (KDHP), are proposed and their performance is analyzed both theoretically and empirically. Because of the representation learning and generalization capability of sparse kernel machines, KHDP and KDHP can obtain much better performance than previous HDP and DHP with manually designed neural networks. Simulation and experimental results of two nonlinear control problems, that is, a continuous-action inverted pendulum problem and a ball and plate control problem, demonstrate the effectiveness of the proposed kernel ACD methods.

  15. Adaptive artificial neural network for autonomous robot control

    NASA Technical Reports Server (NTRS)

    Arras, Michael K.; Protzel, Peter W.; Palumbo, Daniel L.

    1992-01-01

    The topics are presented in viewgraph form and include: neural network controller for robot arm positioning with visual feedback; initial training of the arm; automatic recovery from cumulative fault scenarios; and error reduction by iterative fine movements.

  16. Neural network-based adaptive dynamic surface control for permanent magnet synchronous motors.

    PubMed

    Yu, Jinpeng; Shi, Peng; Dong, Wenjie; Chen, Bing; Lin, Chong

    2015-03-01

    This brief considers the problem of neural networks (NNs)-based adaptive dynamic surface control (DSC) for permanent magnet synchronous motors (PMSMs) with parameter uncertainties and load torque disturbance. First, NNs are used to approximate the unknown and nonlinear functions of PMSM drive system and a novel adaptive DSC is constructed to avoid the explosion of complexity in the backstepping design. Next, under the proposed adaptive neural DSC, the number of adaptive parameters required is reduced to only one, and the designed neural controllers structure is much simpler than some existing results in literature, which can guarantee that the tracking error converges to a small neighborhood of the origin. Then, simulations are given to illustrate the effectiveness and potential of the new design technique.

  17. Direct Adaptive Neural Control for a Class of Uncertain Nonaffine Nonlinear Systems Based on Disturbance Observer.

    PubMed

    Chen, Mou; Ge, Shuzhi Sam

    2013-08-01

    In this paper, the direct adaptive neural control is proposed for a class of uncertain nonaffine nonlinear systems with unknown nonsymmetric input saturation. Based on the implicit function theorem and mean value theorem, both state feedback and output feedback direct adaptive controls are developed using neural networks (NNs) and a disturbance observer. A compounded disturbance is defined to take into account of the effect of the unknown external disturbance, the unknown nonsymmetric input saturation, and the approximation error of NN. Then, a disturbance observer is developed to estimate the unknown compounded disturbance, and it is established that the estimate error converges to a compact set if appropriate observer design parameters are chosen. Both state feedback and output feedback direct adaptive controls can guarantee semiglobal uniform boundedness of the closed-loop system signals as rigorously proved by Lyapunov analysis. Numerical simulation results are presented to illustrate the effectiveness of the proposed direct adaptive neural control techniques.

  18. Critical avalanches and subsampling in map-based neural networks coupled with noisy synapses.

    PubMed

    Girardi-Schappo, M; Kinouchi, O; Tragtenberg, M H R

    2013-08-01

    Many different kinds of noise are experimentally observed in the brain. Among them, we study a model of noisy chemical synapse and obtain critical avalanches for the spatiotemporal activity of the neural network. Neurons and synapses are modeled by dynamical maps. We discuss the relevant neuronal and synaptic properties to achieve the critical state. We verify that networks of functionally excitable neurons with fast synapses present power-law avalanches, due to rebound spiking dynamics. We also discuss the measuring of neuronal avalanches by subsampling our data, shedding light on the experimental search for self-organized criticality in neural networks.

  19. Establishing a Dynamic Self-Adaptation Learning Algorithm of the BP Neural Network and Its Applications

    NASA Astrophysics Data System (ADS)

    Li, Xiaofeng; Xiang, Suying; Zhu, Pengfei; Wu, Min

    2015-12-01

    In order to avoid the inherent deficiencies of the traditional BP neural network, such as slow convergence speed, that easily leading to local minima, poor generalization ability and difficulty in determining the network structure, the dynamic self-adaptive learning algorithm of the BP neural network is put forward to improve the function of the BP neural network. The new algorithm combines the merit of principal component analysis, particle swarm optimization, correlation analysis and self-adaptive model, hence can effectively solve the problems of selecting structural parameters, initial connection weights and thresholds and learning rates of the BP neural network. This new algorithm not only reduces the human intervention, optimizes the topological structures of BP neural networks and improves the network generalization ability, but also accelerates the convergence speed of a network, avoids trapping into local minima, and enhances network adaptation ability and prediction ability. The dynamic self-adaptive learning algorithm of the BP neural network is used to forecast the total retail sale of consumer goods of Sichuan Province, China. Empirical results indicate that the new algorithm is superior to the traditional BP network algorithm in predicting accuracy and time consumption, which shows the feasibility and effectiveness of the new algorithm.

  20. Artificial frame filling using adaptive neural fuzzy inference system for particle image velocimetry dataset

    NASA Astrophysics Data System (ADS)

    Akdemir, Bayram; Doǧan, Sercan; Aksoy, Muharrem H.; Canli, Eyüp; Özgören, Muammer

    2015-03-01

    Liquid behaviors are very important for many areas especially for Mechanical Engineering. Fast camera is a way to observe and search the liquid behaviors. Camera traces the dust or colored markers travelling in the liquid and takes many pictures in a second as possible as. Every image has large data structure due to resolution. For fast liquid velocity, there is not easy to evaluate or make a fluent frame after the taken images. Artificial intelligence has much popularity in science to solve the nonlinear problems. Adaptive neural fuzzy inference system is a common artificial intelligence in literature. Any particle velocity in a liquid has two dimension speed and its derivatives. Adaptive Neural Fuzzy Inference System has been used to create an artificial frame between previous and post frames as offline. Adaptive neural fuzzy inference system uses velocities and vorticities to create a crossing point vector between previous and post points. In this study, Adaptive Neural Fuzzy Inference System has been used to fill virtual frames among the real frames in order to improve image continuity. So this evaluation makes the images much understandable at chaotic or vorticity points. After executed adaptive neural fuzzy inference system, the image dataset increase two times and has a sequence as virtual and real, respectively. The obtained success is evaluated using R2 testing and mean squared error. R2 testing has a statistical importance about similarity and 0.82, 0.81, 0.85 and 0.8 were obtained for velocities and derivatives, respectively.

  1. Robust adaptive backstepping neural networks control for spacecraft rendezvous and docking with input saturation.

    PubMed

    Xia, Kewei; Huo, Wei

    2016-05-01

    This paper presents a robust adaptive neural networks control strategy for spacecraft rendezvous and docking with the coupled position and attitude dynamics under input saturation. Backstepping technique is applied to design a relative attitude controller and a relative position controller, respectively. The dynamics uncertainties are approximated by radial basis function neural networks (RBFNNs). A novel switching controller consists of an adaptive neural networks controller dominating in its active region combined with an extra robust controller to avoid invalidation of the RBFNNs destroying stability of the system outside the neural active region. An auxiliary signal is introduced to compensate the input saturation with anti-windup technique, and a command filter is employed to approximate derivative of the virtual control in the backstepping procedure. Globally uniformly ultimately bounded of the relative states is proved via Lyapunov theory. Simulation example demonstrates effectiveness of the proposed control scheme.

  2. Study on adaptive PID algorithm of hydraulic turbine governing system based on fuzzy neural network

    NASA Astrophysics Data System (ADS)

    Tang, Liangbao; Bao, Jumin

    2006-11-01

    The conventional hydraulic turbine governing system can't automatically modulate PID parameters according to the dynamic process of the system, the generator speed is unstable and the mains frequency fluctuation results in. To solve the above problem, the fuzzy neural network (FNN) and the adaptive control are combined to design an adaptive PID algorithm based on the fuzzy neural network which can effectively control the hydraulic turbine governing system. Finally, the improved mathematic model is simulated. The simulation results are compared with the conventional hydraulic turbine's. Thus the validity and superiority of the fuzzy neural network PID algorithm have been proved. The simulation results show that the algorithm not only retains the functions of fuzzy control, but also provides the ability to approach to the non-linear system. Also the dynamic process of the system can be reflected more precisely and the on-line adaptive control is implemented. The algorithm is superior to other methods in response and control effect.

  3. Analysis and Synthesis of Adaptive Neural Elements and Assembles

    DTIC Science & Technology

    1993-09-30

    responses. it was necessary to include elements representing recently described excitatory interneurons that elicit slow, decreased-conductance EPSPs ...incorporated into a small neural network and simulations examined the functions of interneurons and the consequences of plasticity at multiple sites. (4...complete model of the circuit underlying this reflex and simulations examined the contributions of additional interneurons and loci for plasticity to

  4. Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot.

    PubMed

    Grinke, Eduard; Tetzlaff, Christian; Wörgötter, Florentin; Manoonpong, Poramate

    2015-01-01

    Walking animals, like insects, with little neural computing can effectively perform complex behaviors. For example, they can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements to deal with an unknown situation. As a consequence, they successfully navigate through their complex environment. The versatile and adaptive abilities are the result of an integration of several ingredients embedded in their sensorimotor loop. Biological studies reveal that the ingredients include neural dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a many degrees-of-freedom (DOFs) walking robot is a challenging task. Thus, in this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, exteroceptive sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent neural network consisting of two fully connected neurons. Online correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a walking robot. The turning information is transmitted as descending steering signals to the neural locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations. The adaptation also enables the robot to effectively escape from sharp corners or deadlocks. Using backbone joint control embedded in the the locomotion control allows the robot to climb over small obstacles

  5. Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot

    PubMed Central

    Grinke, Eduard; Tetzlaff, Christian; Wörgötter, Florentin; Manoonpong, Poramate

    2015-01-01

    Walking animals, like insects, with little neural computing can effectively perform complex behaviors. For example, they can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements to deal with an unknown situation. As a consequence, they successfully navigate through their complex environment. The versatile and adaptive abilities are the result of an integration of several ingredients embedded in their sensorimotor loop. Biological studies reveal that the ingredients include neural dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a many degrees-of-freedom (DOFs) walking robot is a challenging task. Thus, in this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, exteroceptive sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent neural network consisting of two fully connected neurons. Online correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a walking robot. The turning information is transmitted as descending steering signals to the neural locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations. The adaptation also enables the robot to effectively escape from sharp corners or deadlocks. Using backbone joint control embedded in the the locomotion control allows the robot to climb over small obstacles

  6. Varying Timescales of Stimulus Integration Unite Neural Adaptation and Prototype Formation.

    PubMed

    Mattar, Marcelo G; Kahn, David A; Thompson-Schill, Sharon L; Aguirre, Geoffrey K

    2016-07-11

    Human visual perception is both stable and adaptive. Perception of complex objects, such as faces, is shaped by the long-term average of experience as well as immediate, comparative context. Measurements of brain activity have demonstrated corresponding neural mechanisms, including norm-based responses reflective of stored prototype representations, and adaptation induced by the immediately preceding stimulus. Here, we consider the possibility that these apparently separate phenomena can arise from a single mechanism of sensory integration operating over varying timescales. We used fMRI to measure neural responses from the fusiform gyrus while subjects observed a rapid stream of face stimuli. Neural activity at this cortical site was best explained by the integration of sensory experience over multiple sequential stimuli, following a decaying-exponential weighting function. Although this neural activity could be mistaken for immediate neural adaptation or long-term, norm-based responses, it in fact reflected a timescale of integration intermediate to both. We then examined the timescale of sensory integration across the cortex. We found a gradient that ranged from rapid sensory integration in early visual areas, to long-term, stable representations in higher-level, ventral-temporal cortex. These findings were replicated with a new set of face stimuli and subjects. Our results suggest that a cascade of visual areas integrate sensory experience, transforming highly adaptable responses at early stages to stable representations at higher levels.

  7. Studying the neural bases of prism adaptation using fMRI: A technical and design challenge.

    PubMed

    Bultitude, Janet H; Farnè, Alessandro; Salemme, Romeo; Ibarrola, Danielle; Urquizar, Christian; O'Shea, Jacinta; Luauté, Jacques

    2016-12-30

    Prism adaptation induces rapid recalibration of visuomotor coordination. The neural mechanisms of prism adaptation have come under scrutiny since the observations that the technique can alleviate hemispatial neglect following stroke, and can alter spatial cognition in healthy controls. Relative to non-imaging behavioral studies, fMRI investigations of prism adaptation face several challenges arising from the confined physical environment of the scanner and the supine position of the participants. Any researcher who wishes to administer prism adaptation in an fMRI environment must adjust their procedures enough to enable the experiment to be performed, but not so much that the behavioral task departs too much from true prism adaptation. Furthermore, the specific temporal dynamics of behavioral components of prism adaptation present additional challenges for measuring their neural correlates. We developed a system for measuring the key features of prism adaptation behavior within an fMRI environment. To validate our configuration, we present behavioral (pointing) and head movement data from 11 right-hemisphere lesioned patients and 17 older controls who underwent sham and real prism adaptation in an MRI scanner. Most participants could adapt to prismatic displacement with minimal head movements, and the procedure was well tolerated. We propose recommendations for fMRI studies of prism adaptation based on the design-specific constraints and our results.

  8. A Quasi-ARX Neural Network with Switching Mechanism to Adaptive Control of Nonlinear Systems

    NASA Astrophysics Data System (ADS)

    Wang, Lan; Cheng, Yu; Hu, Jinglu

    This paper introduces an improved quasi-ARX neural network and discusses its application to adaptive control of nonlinear systems. A switching mechanism is employed to improve the performance of the quasi-ARX neural network prediction model which has linear and nonlinear parts. An adaptive controller for a nonlinear system is established based on the proposed prediction model and some stability analysis of the control system is shown. Simulations are given to show the effectiveness of the proposed method both on stability and accuracy.

  9. Muscular, skeletal, and neural adaptations following spinal cord injury.

    PubMed

    Shields, Richard K

    2002-02-01

    Spinal cord injury is associated with adaptations to the muscular, skeletal, and spinal systems. Experimental data are lacking regarding the extent to which rehabilitative methods may influence these adaptations. An understanding of the plasticity of the muscular, skeletal, and spinal systems after paralysis may be important as new rehabilitative technologies emerge in the 21st century. Moreover, individuals injured today may become poor candidates for future scientific advancements (cure) if their neuromusculoskeletal systems are irreversibly impaired. The primary purpose of this paper is to explore the physiological properties of skeletal muscle as a result of spinal cord injury; secondarily, to consider associated changes at the skeletal and spinal levels. Muscular adaptations include a transformation to faster myosin, increased contractile speeds, shift to the right on the torque-frequency curve, increased fatigue, and enhanced doublet potentiation. These muscular adaptations may be prevented in individuals with acute paralysis and partially reversed in individuals with chronic paralysis. Moreover, the muscular changes may be coordinated with motor unit and spinal circuitry adaptations. Concurrently, skeletal adaptations, as measured by bone mineral density, show extensive loss within the first six months after paralysis. The underlying science governing neuromusculoskeletal adaptations after paralysis will help guide professionals as new rehabilitation strategies evolve in the future.

  10. Analysis and Synthesis of Adaptive Neural Elements and Assemblies

    DTIC Science & Technology

    1989-12-19

    learning into small neural networks, which include facilitatory and inhibitory interneurons , and we demonstrated the ability of these networks to...features of classical conditioning,. 3) to analyze the properties of facilitatory and inhibitory interneurons that might contribute to associative...individual sensory neurons represents separate pathways for conditioned stimuli (CS1 and CS2). The amplitude of the EPSPs at the sensory-to-motor synapses

  11. Multi-Agent Reinforcement Learning and Adaptive Neural Networks.

    DTIC Science & Technology

    2007-11-02

    learning method. The objective was to study the utility of reinforcement learning as an approach to complex decentralized control problems. The major...accomplishment was a detailed study of multi-agent reinforcement learning applied to a large-scale decentralized stochastic control problem. This study...included a very successful demonstration that a multi-agent reinforcement learning system using neural networks could learn high-performance

  12. Self-organized criticality as a fundamental property of neural systems.

    PubMed

    Hesse, Janina; Gross, Thilo

    2014-01-01

    The neural criticality hypothesis states that the brain may be poised in a critical state at a boundary between different types of dynamics. Theoretical and experimental studies show that critical systems often exhibit optimal computational properties, suggesting the possibility that criticality has been evolutionarily selected as a useful trait for our nervous system. Evidence for criticality has been found in cell cultures, brain slices, and anesthetized animals. Yet, inconsistent results were reported for recordings in awake animals and humans, and current results point to open questions about the exact nature and mechanism of criticality, as well as its functional role. Therefore, the criticality hypothesis has remained a controversial proposition. Here, we provide an account of the mathematical and physical foundations of criticality. In the light of this conceptual framework, we then review and discuss recent experimental studies with the aim of identifying important next steps to be taken and connections to other fields that should be explored.

  13. Self-organized criticality as a fundamental property of neural systems

    PubMed Central

    Hesse, Janina; Gross, Thilo

    2014-01-01

    The neural criticality hypothesis states that the brain may be poised in a critical state at a boundary between different types of dynamics. Theoretical and experimental studies show that critical systems often exhibit optimal computational properties, suggesting the possibility that criticality has been evolutionarily selected as a useful trait for our nervous system. Evidence for criticality has been found in cell cultures, brain slices, and anesthetized animals. Yet, inconsistent results were reported for recordings in awake animals and humans, and current results point to open questions about the exact nature and mechanism of criticality, as well as its functional role. Therefore, the criticality hypothesis has remained a controversial proposition. Here, we provide an account of the mathematical and physical foundations of criticality. In the light of this conceptual framework, we then review and discuss recent experimental studies with the aim of identifying important next steps to be taken and connections to other fields that should be explored. PMID:25294989

  14. Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system

    USGS Publications Warehouse

    Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.

    2000-01-01

    Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.

  15. Automated implementation of rule-based expert systems with neural networks for time-critical applications

    NASA Technical Reports Server (NTRS)

    Ramamoorthy, P. A.; Huang, Song; Govind, Girish

    1991-01-01

    In fault diagnosis, control and real-time monitoring, both timing and accuracy are critical for operators or machines to reach proper solutions or appropriate actions. Expert systems are becoming more popular in the manufacturing community for dealing with such problems. In recent years, neural networks have revived and their applications have spread to many areas of science and engineering. A method of using neural networks to implement rule-based expert systems for time-critical applications is discussed here. This method can convert a given rule-based system into a neural network with fixed weights and thresholds. The rules governing the translation are presented along with some examples. We also present the results of automated machine implementation of such networks from the given rule-base. This significantly simplifies the translation process to neural network expert systems from conventional rule-based systems. Results comparing the performance of the proposed approach based on neural networks vs. the classical approach are given. The possibility of very large scale integration (VLSI) realization of such neural network expert systems is also discussed.

  16. Neural network L1 adaptive control of MIMO systems with nonlinear uncertainty.

    PubMed

    Zhen, Hong-tao; Qi, Xiao-hui; Li, Jie; Tian, Qing-min

    2014-01-01

    An indirect adaptive controller is developed for a class of multiple-input multiple-output (MIMO) nonlinear systems with unknown uncertainties. This control system is comprised of an L 1 adaptive controller and an auxiliary neural network (NN) compensation controller. The L 1 adaptive controller has guaranteed transient response in addition to stable tracking. In this architecture, a low-pass filter is adopted to guarantee fast adaptive rate without generating high-frequency oscillations in control signals. The auxiliary compensation controller is designed to approximate the unknown nonlinear functions by MIMO RBF neural networks to suppress the influence of uncertainties. NN weights are tuned on-line with no prior training and the project operator ensures the weights bounded. The global stability of the closed-system is derived based on the Lyapunov function. Numerical simulations of an MIMO system coupled with nonlinear uncertainties are used to illustrate the practical potential of our theoretical results.

  17. Backstepping Design of Adaptive Neural Fault-Tolerant Control for MIMO Nonlinear Systems.

    PubMed

    Gao, Hui; Song, Yongduan; Wen, Changyun

    2016-08-24

    In this paper, an adaptive controller is developed for a class of multi-input and multioutput nonlinear systems with neural networks (NNs) used as a modeling tool. It is shown that all the signals in the closed-loop system with the proposed adaptive neural controller are globally uniformly bounded for any external input in L[₀,∞]. In our control design, the upper bound of the NN modeling error and the gains of external disturbance are characterized by unknown upper bounds, which is more rational to establish the stability in the adaptive NN control. Filter-based modification terms are used in the update laws of unknown parameters to improve the transient performance. Finally, fault-tolerant control is developed to accommodate actuator failure. An illustrative example applying the adaptive controller to control a rigid robot arm shows the validation of the proposed controller.

  18. Neural Network L1 Adaptive Control of MIMO Systems with Nonlinear Uncertainty

    PubMed Central

    Zhen, Hong-tao; Qi, Xiao-hui; Li, Jie; Tian, Qing-min

    2014-01-01

    An indirect adaptive controller is developed for a class of multiple-input multiple-output (MIMO) nonlinear systems with unknown uncertainties. This control system is comprised of an L1 adaptive controller and an auxiliary neural network (NN) compensation controller. The L1 adaptive controller has guaranteed transient response in addition to stable tracking. In this architecture, a low-pass filter is adopted to guarantee fast adaptive rate without generating high-frequency oscillations in control signals. The auxiliary compensation controller is designed to approximate the unknown nonlinear functions by MIMO RBF neural networks to suppress the influence of uncertainties. NN weights are tuned on-line with no prior training and the project operator ensures the weights bounded. The global stability of the closed-system is derived based on the Lyapunov function. Numerical simulations of an MIMO system coupled with nonlinear uncertainties are used to illustrate the practical potential of our theoretical results. PMID:25147871

  19. Adaptive exponential synchronization of complex-valued Cohen-Grossberg neural networks with known and unknown parameters.

    PubMed

    Hu, Jin; Zeng, Chunna

    2017-02-01

    The complex-valued Cohen-Grossberg neural network is a special kind of complex-valued neural network. In this paper, the synchronization problem of a class of complex-valued Cohen-Grossberg neural networks with known and unknown parameters is investigated. By using Lyapunov functionals and the adaptive control method based on parameter identification, some adaptive feedback schemes are proposed to achieve synchronization exponentially between the drive and response systems. The results obtained in this paper have extended and improved some previous works on adaptive synchronization of Cohen-Grossberg neural networks. Finally, two numerical examples are given to demonstrate the effectiveness of the theoretical results.

  20. Identification and adaptive neural network control of a DC motor system with dead-zone characteristics.

    PubMed

    Peng, Jinzhu; Dubay, Rickey

    2011-10-01

    In this paper, an adaptive control approach based on the neural networks is presented to control a DC motor system with dead-zone characteristics (DZC), where two neural networks are proposed to formulate the traditional identification and control approaches. First, a Wiener-type neural network (WNN) is proposed to identify the motor DZC, which formulates the Wiener model with a linear dynamic block in cascade with a nonlinear static gain. Second, a feedforward neural network is proposed to formulate the traditional PID controller, termed as PID-type neural network (PIDNN), which is then used to control and compensate for the DZC. In this way, the DC motor system with DZC is identified by the WNN identifier, which provides model information to the PIDNN controller in order to make it adaptive. Back-propagation algorithms are used to train both neural networks. Also, stability and convergence analysis are conducted using the Lyapunov theorem. Finally, experiments on the DC motor system demonstrated accurate identification and good compensation for dead-zone with improved control performance over the conventional PID control.

  1. Parameter adaptation in a simplified pulse-coupled neural network

    NASA Astrophysics Data System (ADS)

    Szekely, Geza; Lindblad, Thomas

    1999-03-01

    In a general purpose pulse coupled neural network (PCNN) algorithm the following parameters are used: 2 weight matrices, 3 time constants, 3 normalization factors and 2 further parameters. In a given application, one has to determine the near optimal parameter set to achieve the desired goal. Here a simplified PCNN is described which contains a parameter fitting part, in the least squares sense. Given input and a desired output image, the program is able to determine the optimal value of a selected PCNN parameter. This method can be extended to more general PCNN algorithms, because partial derivatives are not required for the fitting. Only the sum of squares of the differences is used.

  2. Adaptive neural network consensus based control of robot formations

    NASA Astrophysics Data System (ADS)

    Guzey, H. M.; Sarangapani, Jagannathan

    2013-05-01

    In this paper, adaptive consensus based formation control scheme is derived for mobile robots in a pre-defined formation when full dynamics of the robots which include inertia, Corolis, and friction vector are considered. It is shown that dynamic uncertainties of robots can make overall formation unstable when traditional consensus scheme is utilized. In order to estimate the affine nonlinear robot dynamics, a NN based adaptive scheme is utilized. In addition to this adaptive feedback control input, an additional control input is introduced based on the consensus approach to make the robots keep their desired formation. Subsequently, the outer consensus loop is redesigned for reduced communication. Lyapunov theory is used to show the stability of overall system. Simulation results are included at the end.

  3. Adaptive inverse control of neural spatiotemporal spike patterns with a reproducing kernel Hilbert space (RKHS) framework.

    PubMed

    Li, Lin; Park, Il Memming; Brockmeier, Austin; Chen, Badong; Seth, Sohan; Francis, Joseph T; Sanchez, Justin C; Príncipe, José C

    2013-07-01

    The precise control of spiking in a population of neurons via applied electrical stimulation is a challenge due to the sparseness of spiking responses and neural system plasticity. We pose neural stimulation as a system control problem where the system input is a multidimensional time-varying signal representing the stimulation, and the output is a set of spike trains; the goal is to drive the output such that the elicited population spiking activity is as close as possible to some desired activity, where closeness is defined by a cost function. If the neural system can be described by a time-invariant (homogeneous) model, then offline procedures can be used to derive the control procedure; however, for arbitrary neural systems this is not tractable. Furthermore, standard control methodologies are not suited to directly operate on spike trains that represent both the target and elicited system response. In this paper, we propose a multiple-input multiple-output (MIMO) adaptive inverse control scheme that operates on spike trains in a reproducing kernel Hilbert space (RKHS). The control scheme uses an inverse controller to approximate the inverse of the neural circuit. The proposed control system takes advantage of the precise timing of the neural events by using a Schoenberg kernel defined directly in the space of spike trains. The Schoenberg kernel maps the spike train to an RKHS and allows linear algorithm to control the nonlinear neural system without the danger of converging to local minima. During operation, the adaptation of the controller minimizes a difference defined in the spike train RKHS between the system and the target response and keeps the inverse controller close to the inverse of the current neural circuit, which enables adapting to neural perturbations. The results on a realistic synthetic neural circuit show that the inverse controller based on the Schoenberg kernel outperforms the decoding accuracy of other models based on the conventional rate

  4. An algorithmic approach to adaptive state filtering using recurrent neural networks.

    PubMed

    Parlos, A G; Menon, S K; Atiya, A

    2001-01-01

    Practical algorithms are presented for adaptive state filtering in nonlinear dynamic systems when the state equations are unknown. The state equations are constructively approximated using neural networks. The algorithms presented are based on the two-step prediction-update approach of the Kalman filter. The proposed algorithms make minimal assumptions regarding the underlying nonlinear dynamics and their noise statistics. Non-adaptive and adaptive state filtering algorithms are presented with both off-line and online learning stages. The algorithms are implemented using feedforward and recurrent neural network and comparisons are presented. Furthermore, extended Kalman filters (EKFs) are developed and compared to the filter algorithms proposed. For one of the case studies, the EKF converges but results in higher state estimation errors that the equivalent neural filters. For another, more complex case study with unknown system dynamics and noise statistics, the developed EKFs do not converge. The off-line trained neural state filters converge quite rapidly and exhibit acceptable performance. Online training further enhances the estimation accuracy of the developed adaptive filters, effectively decoupling the eventual filter accuracy from the accuracy of the process model.

  5. Multi-channel holographic birfurcative neural network system for real-time adaptive EOS data analysis

    NASA Technical Reports Server (NTRS)

    Liu, Hua-Kuang; Diep, J.; Huang, K.

    1991-01-01

    Viewgraphs on multi-channel holographic bifurcative neural network system for real-time adaptive Earth Observing System (EOS) data analysis are presented. The objective is to research and develop an optical bifurcating neuromorphic pattern recognition system for making optical data array comparisons and to evaluate the use of the system for EOS data classification, reduction, analysis, and other applications.

  6. An Application Specific Instruction Set Processor (ASIP) for Adaptive Filters in Neural Prosthetics.

    PubMed

    Xin, Yao; Li, Will X Y; Zhang, Zhaorui; Cheung, Ray C C; Song, Dong; Berger, Theodore W

    2015-01-01

    Neural coding is an essential process for neuroprosthetic design, in which adaptive filters have been widely utilized. In a practical application, it is needed to switch between different filters, which could be based on continuous observations or point process, when the neuron models, conditions, or system requirements have changed. As candidates of coding chip for neural prostheses, low-power general purpose processors are not computationally efficient especially for large scale neural population coding. Application specific integrated circuits (ASICs) do not have flexibility to switch between different adaptive filters while the cost for design and fabrication is formidable. In this research work, we explore an application specific instruction set processor (ASIP) for adaptive filters in neural decoding activity. The proposed architecture focuses on efficient computation for the most time-consuming matrix/vector operations among commonly used adaptive filters, being able to provide both flexibility and throughput. Evaluation and implementation results are provided to demonstrate that the proposed ASIP design is area-efficient while being competitive to commercial CPUs in computational performance.

  7. Manipulator adaptive control by neural networks in an orange picking robot.

    PubMed

    Cavalieri, S; Plebe, A

    1996-12-01

    The paper focuses on the use of neural networks for process identification in an orange-picking robot adaptive control system. The results that will be shown in the paper refer to a study carried out under the European Community ESPRIT project "CONNY", dealing with the application of neural networks to robotics. The aim of the research is to verify the possibility of integrating a neural identification module in a traditional system to control the movement of the manipulators of the robot. The paper illustrates integration of neural identification in the existing orange-picking robot control system, highlighting the improvement of performance obtainable. Although the proposal refers to a specific robot, it can be applied to any system with the same dynamic features.

  8. Quaternion-based adaptive output feedback attitude control of spacecraft using Chebyshev neural networks.

    PubMed

    Zou, An-Min; Dev Kumar, Krishna; Hou, Zeng-Guang

    2010-09-01

    This paper investigates the problem of output feedback attitude control of an uncertain spacecraft. Two robust adaptive output feedback controllers based on Chebyshev neural networks (CNN) termed adaptive neural networks (NN) controller-I and adaptive NN controller-II are proposed for the attitude tracking control of spacecraft. The four-parameter representations (quaternion) are employed to describe the spacecraft attitude for global representation without singularities. The nonlinear reduced-order observer is used to estimate the derivative of the spacecraft output, and the CNN is introduced to further improve the control performance through approximating the spacecraft attitude motion. The implementation of the basis functions of the CNN used in the proposed controllers depends only on the desired signals, and the smooth robust compensator using the hyperbolic tangent function is employed to counteract the CNN approximation errors and external disturbances. The adaptive NN controller-II can efficiently avoid the over-estimation problem (i.e., the bound of the CNNs output is much larger than that of the approximated unknown function, and hence, the control input may be very large) existing in the adaptive NN controller-I. Both adaptive output feedback controllers using CNN can guarantee that all signals in the resulting closed-loop system are uniformly ultimately bounded. For performance comparisons, the standard adaptive controller using the linear parameterization of spacecraft attitude motion is also developed. Simulation studies are presented to show the advantages of the proposed CNN-based output feedback approach over the standard adaptive output feedback approach.

  9. Improved methods in neural network-based adaptive output feedback control, with applications to flight control

    NASA Astrophysics Data System (ADS)

    Kim, Nakwan

    Utilizing the universal approximation property of neural networks, we develop several novel approaches to neural network-based adaptive output feedback control of nonlinear systems, and illustrate these approaches for several flight control applications. In particular, we address the problem of non-affine systems and eliminate the fixed point assumption present in earlier work. All of the stability proofs are carried out in a form that eliminates an algebraic loop in the neural network implementation. An approximate input/output feedback linearizing controller is augmented with a neural network using input/output sequences of the uncertain system. These approaches permit adaptation to both parametric uncertainty and unmodeled dynamics. All physical systems also have control position and rate limits, which may either deteriorate performance or cause instability for a sufficiently high control bandwidth. Here we apply a method for protecting an adaptive process from the effects of input saturation and time delays, known as "pseudo control hedging". This method was originally developed for the state feedback case, and we provide a stability analysis that extends its domain of applicability to the case of output feedback. The approach is illustrated by the design of a pitch-attitude flight control system for a linearized model of an R-50 experimental helicopter, and by the design of a pitch-rate control system for a 58-state model of a flexible aircraft consisting of rigid body dynamics coupled with actuator and flexible modes. A new approach to augmentation of an existing linear controller is introduced. It is especially useful when there is limited information concerning the plant model, and the existing controller. The approach is applied to the design of an adaptive autopilot for a guided munition. Design of a neural network adaptive control that ensures asymptotically stable tracking performance is also addressed.

  10. Physiological and Neural Adaptations to Eccentric Exercise: Mechanisms and Considerations for Training

    PubMed Central

    Hedayatpour, Nosratollah; Falla, Deborah

    2015-01-01

    Eccentric exercise is characterized by initial unfavorable effects such as subcellular muscle damage, pain, reduced fiber excitability, and initial muscle weakness. However, stretch combined with overload, as in eccentric contractions, is an effective stimulus for inducing physiological and neural adaptations to training. Eccentric exercise-induced adaptations include muscle hypertrophy, increased cortical activity, and changes in motor unit behavior, all of which contribute to improved muscle function. In this brief review, neuromuscular adaptations to different forms of exercise are reviewed, the positive training effects of eccentric exercise are presented, and the implications for training are considered. PMID:26543850

  11. Application of neural adaptive power system stabilizer in a multi-machine power system

    SciTech Connect

    Shamsollahi, P.; Malik, O.P.

    1999-09-01

    Application of a neural adaptive power system stabilizer (NAPSS) to a five-machine power system is described in this paper. The proposed NAPSS comprises two subnetworks. The adaptive neuro-identifier (ANI) to dynamically identify the non-linear plant, and the adaptive neuro-controller (ANC) to damp output oscillations. The back-propagation training method is used on-line to train these subnetworks. The effectiveness of the proposed NAPSS in damping both local and inter-area modes of oscillations and its self-coordination ability are demonstrated.

  12. Physiological and Neural Adaptations to Eccentric Exercise: Mechanisms and Considerations for Training.

    PubMed

    Hedayatpour, Nosratollah; Falla, Deborah

    2015-01-01

    Eccentric exercise is characterized by initial unfavorable effects such as subcellular muscle damage, pain, reduced fiber excitability, and initial muscle weakness. However, stretch combined with overload, as in eccentric contractions, is an effective stimulus for inducing physiological and neural adaptations to training. Eccentric exercise-induced adaptations include muscle hypertrophy, increased cortical activity, and changes in motor unit behavior, all of which contribute to improved muscle function. In this brief review, neuromuscular adaptations to different forms of exercise are reviewed, the positive training effects of eccentric exercise are presented, and the implications for training are considered.

  13. Image segmentation by EM-based adaptive pulse coupled neural networks in brain magnetic resonance imaging.

    PubMed

    Fu, J C; Chen, C C; Chai, J W; Wong, S T C; Li, I C

    2010-06-01

    We propose an automatic hybrid image segmentation model that integrates the statistical expectation maximization (EM) model and the spatial pulse coupled neural network (PCNN) for brain magnetic resonance imaging (MRI) segmentation. In addition, an adaptive mechanism is developed to fine tune the PCNN parameters. The EM model serves two functions: evaluation of the PCNN image segmentation and adaptive adjustment of the PCNN parameters for optimal segmentation. To evaluate the performance of the adaptive EM-PCNN, we use it to segment MR brain image into gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF). The performance of the adaptive EM-PCNN is compared with that of the non-adaptive EM-PCNN, EM, and Bias Corrected Fuzzy C-Means (BCFCM) algorithms. The result is four sets of boundaries for the GM and the brain parenchyma (GM+WM), the two regions of most interest in medical research and clinical applications. Each set of boundaries is compared with the golden standard to evaluate the segmentation performance. The adaptive EM-PCNN significantly outperforms the non-adaptive EM-PCNN, EM, and BCFCM algorithms in gray mater segmentation. In brain parenchyma segmentation, the adaptive EM-PCNN significantly outperforms the BCFCM only. However, the adaptive EM-PCNN is better than the non-adaptive EM-PCNN and EM on average. We conclude that of the three approaches, the adaptive EM-PCNN yields the best results for gray matter and brain parenchyma segmentation.

  14. The general critical analysis for continuous-time UPPAM recurrent neural networks.

    PubMed

    Qiao, Chen; Jing, Wen-Feng; Fang, Jian; Wang, Yu-Ping

    2016-01-29

    The uniformly pseudo-projection-anti-monotone (UPPAM) neural network model, which can be considered as the unified continuous-time neural networks (CNNs), includes almost all of the known CNNs individuals. Recently, studies on the critical dynamics behaviors of CNNs have drawn special attentions due to its importance in both theory and applications. In this paper, we will present the analysis of the UPPAM network under the general critical conditions. It is shown that the UPPAM network possesses the global convergence and asymptotical stability under the general critical conditions if the network satisfies one quasi-symmetric requirement on the connective matrices, which is easy to be verified and applied. The general critical dynamics have rarely been studied before, and this work is an attempt to gain an meaningful assurance of general critical convergence and stability of CNNs. Since UPPAM network is the unified model for CNNs, the results obtained here can generalize and extend the existing critical conclusions for CNNs individuals, let alone those non-critical cases. Moreover, the easily verified conditions for general critical convergence and stability can further promote the applications of CNNs.

  15. Sympathetic neural adaptations to exercise training in humans.

    PubMed

    Carter, Jason R; Ray, Chester A

    2015-03-01

    Physiological adaptations to exercise training are well recognized and contribute importantly to health and fitness. Cardiovascular diseases, such as hypertension and heart failure, are often associated with elevated activity of the sympathetic nervous system. This review aims to provide comprehensive overview on the role of exercise training on muscle sympathetic nerve activity (MSNA) regulation in humans, with a focus on recent advances in at-risk populations. Collectively, these studies converge to demonstrate that aerobic exercise training reduces resting MSNA in populations at heightened cardiovascular risk, but do not appear to alter resting MSNA in healthy adults. We provide directions for future research which might address gaps in our knowledge regarding sympathoneural adaptations to exercise training.

  16. Unipolar Terminal-Attractor Based Neural Associative Memory with Adaptive Threshold

    NASA Technical Reports Server (NTRS)

    Liu, Hua-Kuang (Inventor); Barhen, Jacob (Inventor); Farhat, Nabil H. (Inventor); Wu, Chwan-Hwa (Inventor)

    1996-01-01

    A unipolar terminal-attractor based neural associative memory (TABAM) system with adaptive threshold for perfect convergence is presented. By adaptively setting the threshold values for the dynamic iteration for the unipolar binary neuron states with terminal-attractors for the purpose of reducing the spurious states in a Hopfield neural network for associative memory and using the inner-product approach, perfect convergence and correct retrieval is achieved. Simulation is completed with a small number of stored states (M) and a small number of neurons (N) but a large M/N ratio. An experiment with optical exclusive-OR logic operation using LCTV SLMs shows the feasibility of optoelectronic implementation of the models. A complete inner-product TABAM is implemented using a PC for calculation of adaptive threshold values to achieve a unipolar TABAM (UIT) in the case where there is no crosstalk, and a crosstalk model (CRIT) in the case where crosstalk corrupts the desired state.

  17. Unipolar terminal-attractor based neural associative memory with adaptive threshold

    NASA Technical Reports Server (NTRS)

    Liu, Hua-Kuang (Inventor); Barhen, Jacob (Inventor); Farhat, Nabil H. (Inventor); Wu, Chwan-Hwa (Inventor)

    1993-01-01

    A unipolar terminal-attractor based neural associative memory (TABAM) system with adaptive threshold for perfect convergence is presented. By adaptively setting the threshold values for the dynamic iteration for the unipolar binary neuron states with terminal-attractors for the purpose of reducing the spurious states in a Hopfield neural network for associative memory and using the inner product approach, perfect convergence and correct retrieval is achieved. Simulation is completed with a small number of stored states (M) and a small number of neurons (N) but a large M/N ratio. An experiment with optical exclusive-OR logic operation using LCTV SLMs shows the feasibility of optoelectronic implementation of the models. A complete inner-product TABAM is implemented using a PC for calculation of adaptive threshold values to achieve a unipolar TABAM (UIT) in the case where there is no crosstalk, and a crosstalk model (CRIT) in the case where crosstalk corrupts the desired state.

  18. A neural learning classifier system with self-adaptive constructivism for mobile robot control.

    PubMed

    Hurst, Jacob; Bull, Larry

    2006-01-01

    For artificial entities to achieve true autonomy and display complex lifelike behavior, they will need to exploit appropriate adaptable learning algorithms. In this context adaptability implies flexibility guided by the environment at any given time and an open-ended ability to learn appropriate behaviors. This article examines the use of constructivism-inspired mechanisms within a neural learning classifier system architecture that exploits parameter self-adaptation as an approach to realize such behavior. The system uses a rule structure in which each rule is represented by an artificial neural network. It is shown that appropriate internal rule complexity emerges during learning at a rate controlled by the learner and that the structure indicates underlying features of the task. Results are presented in simulated mazes before moving to a mobile robot platform.

  19. A cerebellar neural network model for adaptative control of saccades implemented with MATLAB.

    PubMed

    Rodriguez Campos, Francisco A; Enderle, John

    2003-01-01

    This paper describes the implementation of a neural network for the adaptative control of the saccadic system. The model shows the cerebellum plays an important role in the adaptive control of the saccadic gain. Using only eye position input through the granule cells, the cerebellum projects this signal to the other cerebellar structures and then to motor neurons responsible for the saccade. The generation of an adjustment signal occurs in the inferior olive as a result of the error sensory signal created by the open loop saccade system from propioceptive position inputs from the last eye movement generated by the network until the movement towards the target is completed. In addition, a memory component has been defined in the error system to achieve the adaptation. This neural network involves only the horizontal saccade component modeled with Matrix Laboratory language (MATLAB), in conjunction with the Simulink tool.

  20. Neural Network Based Adaptive Flow Control for Maneuvering Vehicles

    DTIC Science & Technology

    2005-09-01

    effective nonlinear adaptive control of the aerodynamic flow about a dynamic body using a distributed array of synthetic jets for actuation. Design of a wind...possible coupling effects between actuation, the dynamics of flow field, and the rigid body dynamics of the model. The outcomes of simulation studies are...presented. The parameters were selected to have an adverse effect on the closed loop response, therefore representing a hypothetical worst-case

  1. Neural Adaptation across Viewpoint and Exemplar in Fusiform Cortex

    ERIC Educational Resources Information Center

    Harvey, Denise Y.; Burgund, E. Darcy

    2012-01-01

    The visual system has the remarkable ability to generalize across different viewpoints and exemplars to recognize abstract categories of objects, and to discriminate between different viewpoints and exemplars to recognize specific instances of particular objects. Behavioral experiments indicate the critical role of the right hemisphere in…

  2. Critical Neural Networks in Awake Surgery for Gliomas

    PubMed Central

    KINOSHITA, Masashi; MIYASHITA, Katsuyoshi; TSUTSUI, Taishi; FURUTA, Takuya; NAKADA, Mitsutoshi

    2016-01-01

    From the embarrassing character commonly infiltrating eloquent brain regions, the surgical resection of glioma remains challenging. Owing to the recent development of in vivo visualization techniques for the human brain, white matter regions can be delineated using diffusion tensor imaging (DTI) as a routine clinical practice in neurosurgery. In confirmation of the results of DTI tractography, a direct electrical stimulation (DES) substantially influences the investigation of cortico-subcortical networks, which can be identified via specific symptoms elicited in the concerned white matter tracts (eg., the arcuate fascicle, superior longitudinal fascicles, inferior fronto-occipital fascicle, inferior longitudinal fascicle, frontal aslant tract, sensori-motor tracts, optic radiation, and so forth). During awake surgery for glioma using DES, it is important to identify the anatomo-functional structure of white matter tracts to identify the surgical boundaries of brain regions not only to achieve maximal resection of the glioma but also to maximally preserve quality of life. However, the risk exists that neurosurgeons may be misled by the inability of DTI to visualize the actual anatomy of the white matter fibers, resulting in inappropriate decisions regarding surgical boundaries. This review article provides information of the critical neuronal network that is necessary to identify and understand in awake surgery for glioma, with special references to white matter tracts and the author’s experiences. PMID:27250817

  3. Adaptive Control Using Neural Network Augmentation for a Modified F-15 Aircraft

    NASA Technical Reports Server (NTRS)

    Burken, John J.; Williams-Hayes, Peggy; Karneshige, J. T.; Stachowiak, Susan J.

    2006-01-01

    Description of the performance of a simplified dynamic inversion controller with neural network augmentation follows. Simulation studies focus on the results with and without neural network adaptation through the use of an F-15 aircraft simulator that has been modified to include canards. Simulated control law performance with a surface failure, in addition to an aerodynamic failure, is presented. The aircraft, with adaptation, attempts to minimize the inertial cross-coupling effect of the failure (a control derivative anomaly associated with a jammed control surface). The dynamic inversion controller calculates necessary surface commands to achieve desired rates. The dynamic inversion controller uses approximate short period and roll axis dynamics. The yaw axis controller is a sideslip rate command system. Methods are described to reduce the cross-coupling effect and maintain adequate tracking errors for control surface failures. The aerodynamic failure destabilizes the pitching moment due to angle of attack. The results show that control of the aircraft with the neural networks is easier (more damped) than without the neural networks. Simulation results show neural network augmentation of the controller improves performance with aerodynamic and control surface failures in terms of tracking error and cross-coupling reduction.

  4. Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control

    NASA Technical Reports Server (NTRS)

    Hageman, Jacob J.; Smith, Mark S.; Stachowiak, Susan

    2003-01-01

    An indirect adaptive system has been constructed for robust control of an aircraft with uncertain aerodynamic characteristics. This system consists of a multilayer perceptron pre-trained neural network, online stability and control derivative identification, a dynamic cell structure online learning neural network, and a model following control system based on the stochastic optimal feedforward and feedback technique. The pre-trained neural network and model following control system have been flight-tested, but the online parameter identification and online learning neural network are new additions used for in-flight adaptation of the control system model. A description of the modification and integration of these two stand-alone software packages into the complete system in preparation for initial flight tests is presented. Open-loop results using both simulation and flight data, as well as closed-loop performance of the complete system in a nonlinear, six-degree-of-freedom, flight validated simulation, are analyzed. Results show that this online learning system, in contrast to the nonlearning system, has the ability to adapt to changes in aerodynamic characteristics in a real-time, closed-loop, piloted simulation, resulting in improved flying qualities.

  5. Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques

    NASA Astrophysics Data System (ADS)

    Lohani, A. K.; Kumar, Rakesh; Singh, R. D.

    2012-06-01

    SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.

  6. Unsupervised boundary delineation of spinal neural foramina using a multi-feature and adaptive spectral segmentation.

    PubMed

    He, Xiaoxu; Zhang, Heye; Landis, Mark; Sharma, Manas; Warrington, James; Li, Shuo

    2017-02-01

    As a common disease in the elderly, neural foramina stenosis (NFS) brings a significantly negative impact on the quality of life due to its symptoms including pain, disability, fall risk and depression. Accurate boundary delineation is essential to the clinical diagnosis and treatment of NFS. However, existing clinical routine is extremely tedious and inefficient due to the requirement of physicians' intensively manual delineation. Automated delineation is highly needed but faces big challenges from the complexity and variability in neural foramina images. In this paper, we propose a pure image-driven unsupervised boundary delineation framework for the automated neural foramina boundary delineation. This framework is based on a novel multi-feature and adaptive spectral segmentation (MFASS) algorithm. MFASS firstly utilizes the combination of region and edge features to generate reliable spectral features with a good separation between neural foramina and its surroundings, then estimates an optimal separation threshold for each individual image to separate neural foramina from its surroundings. This self-adjusted optimal separation threshold, estimated from spectral features, successfully overcome the diverse appearance and shape variations. With the robustness from the multi-feature fusion and the flexibility from the adaptively optimal separation threshold estimation, the proposed framework, based on MFASS, provides an automated and accurate boundary delineation. Validation was performed in 280 neural foramina MR images from 56 clinical subjects. Our method was benchmarked with manual boundary obtained by experienced physicians. Results demonstrate that the proposed method enjoys a high and stable consistency with experienced physicians (Dice: 90.58% ± 2.79%; SMAD: 0.5657 ± 0.1544 mm). Therefore, the proposed framework enables an efficient and accurate clinical tool in the diagnosis of neural foramina stenosis.

  7. Combining decoder design and neural adaptation in brain-machine interfaces.

    PubMed

    Shenoy, Krishna V; Carmena, Jose M

    2014-11-19

    Brain-machine interfaces (BMIs) aim to help people with paralysis by decoding movement-related neural signals into control signals for guiding computer cursors, prosthetic arms, and other assistive devices. Despite compelling laboratory experiments and ongoing FDA pilot clinical trials, system performance, robustness, and generalization remain challenges. We provide a perspective on how two complementary lines of investigation, that have focused on decoder design and neural adaptation largely separately, could be brought together to advance BMIs. This BMI paradigm should also yield new scientific insights into the function and dysfunction of the nervous system.

  8. Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV.

    PubMed

    Abbaspour, Alireza; Aboutalebi, Payam; Yen, Kang K; Sargolzaei, Arman

    2017-03-01

    A new online detection strategy is developed to detect faults in sensors and actuators of unmanned aerial vehicle (UAV) systems. In this design, the weighting parameters of the Neural Network (NN) are updated by using the Extended Kalman Filter (EKF). Online adaptation of these weighting parameters helps to detect abrupt, intermittent, and incipient faults accurately. We apply the proposed fault detection system to a nonlinear dynamic model of the WVU YF-22 unmanned aircraft for its evaluation. The simulation results show that the new method has better performance in comparison with conventional recurrent neural network-based fault detection strategies.

  9. The specificity of neural responses to music and their relation to voice processing: an fMRI-adaptation study.

    PubMed

    Armony, Jorge L; Aubé, William; Angulo-Perkins, Arafat; Peretz, Isabelle; Concha, Luis

    2015-04-23

    Several studies have identified, using functional magnetic resonance imaging (fMRI), a region within the superior temporal gyrus that preferentially responds to musical stimuli. However, in most cases, significant responses to other complex stimuli, particularly human voice, were also observed. Thus, it remains unknown if the same neurons respond to both stimulus types, albeit with different strengths, or whether the responses observed with fMRI are generated by distinct, overlapping neural populations. To address this question, we conducted an fMRI experiment in which short music excerpts and human vocalizations were presented in a pseudo-random order. Critically, we performed an adaptation-based analysis in which responses to the stimuli were analyzed taking into account the category of the preceding stimulus. Our results confirm the presence of a region in the anterior STG that responds more strongly to music than voice. Moreover, we found a music-specific adaptation effect in this area, consistent with the existence of music-preferred neurons. Lack of differences between musicians and non-musicians argues against an expertise effect. These findings provide further support for neural separability between music and speech within the temporal lobe.

  10. Cellular pulse-coupled neural network with adaptive weights for image segmentation and its VLSI implementation

    NASA Astrophysics Data System (ADS)

    Schreiter, Juerg; Ramacher, Ulrich; Heittmann, Arne; Matolin, Daniel; Schuffny, Rene

    2004-05-01

    We present a cellular pulse coupled neural network with adaptive weights and its analog VLSI implementation. The neural network operates on a scalar image feature, such as grey scale or the output of a spatial filter. It detects segments and marks them with synchronous pulses of the corresponding neurons. The network consists of integrate-and-fire neurons, which are coupled to their nearest neighbors via adaptive synaptic weights. Adaptation follows either one of two empirical rules. Both rules lead to spike grouping in wave like patterns. This synchronous activity binds groups of neurons and labels the corresponding image segments. Applications of the network also include feature preserving noise removal, image smoothing, and detection of bright and dark spots. The adaptation rules are insensitive for parameter deviations, mismatch and non-ideal approximation of the implied functions. That makes an analog VLSI implementation feasible. Simulations showed no significant differences in the synchronization properties between networks using the ideal adaptation rules and networks resembling implementation properties such as randomly distributed parameters and roughly implemented adaptation functions. A prototype is currently being designed and fabricated using an Infineon 130nm technology. It comprises a 128 × 128 neuron array, analog image memory, and an address event representation pulse output.

  11. Adaptive Neural Network Algorithm for Power Control in Nuclear Power Plants

    NASA Astrophysics Data System (ADS)

    Masri Husam Fayiz, Al

    2017-01-01

    The aim of this paper is to design, test and evaluate a prototype of an adaptive neural network algorithm for the power controlling system of a nuclear power plant. The task of power control in nuclear reactors is one of the fundamental tasks in this field. Therefore, researches are constantly conducted to ameliorate the power reactor control process. Currently, in the Department of Automation in the National Research Nuclear University (NRNU) MEPhI, numerous studies are utilizing various methodologies of artificial intelligence (expert systems, neural networks, fuzzy systems and genetic algorithms) to enhance the performance, safety, efficiency and reliability of nuclear power plants. In particular, a study of an adaptive artificial intelligent power regulator in the control systems of nuclear power reactors is being undertaken to enhance performance and to minimize the output error of the Automatic Power Controller (APC) on the grounds of a multifunctional computer analyzer (simulator) of the Water-Water Energetic Reactor known as Vodo-Vodyanoi Energetichesky Reaktor (VVER) in Russian. In this paper, a block diagram of an adaptive reactor power controller was built on the basis of an intelligent control algorithm. When implementing intelligent neural network principles, it is possible to improve the quality and dynamic of any control system in accordance with the principles of adaptive control. It is common knowledge that an adaptive control system permits adjusting the controller’s parameters according to the transitions in the characteristics of the control object or external disturbances. In this project, it is demonstrated that the propitious options for an automatic power controller in nuclear power plants is a control system constructed on intelligent neural network algorithms.

  12. Learning about stress: neural, endocrine and behavioral adaptations.

    PubMed

    McCarty, Richard

    2016-09-01

    In this review, nonassociative learning is advanced as an organizing principle to draw together findings from both sympathetic-adrenal medullary and hypothalamic-pituitary-adrenocortical (HPA) axis responses to chronic intermittent exposure to a variety of stressors. Studies of habituation, facilitation and sensitization of stress effector systems are reviewed and linked to an animal's prior experience with a given stressor, the intensity of the stressor and the appraisal by the animal of its ability to mobilize physiological systems to adapt to the stressor. Brain pathways that regulate physiological and behavioral responses to stress are discussed, especially in light of their regulation of nonassociative processes in chronic intermittent stress. These findings may have special relevance to various psychiatric diseases, including depression and post-traumatic stress disorder (PTSD).

  13. Adapting the Critical Thinking Assessment Test for Palestinian Universities

    ERIC Educational Resources Information Center

    Basha, Sami; Drane, Denise; Light, Gregory

    2016-01-01

    Critical thinking is a key learning outcome for Palestinian students. However, there are no validated critical thinking tests in Arabic. Suitability of the US developed Critical Thinking Assessment Test (CAT) for use in Palestine was assessed. The test was piloted with university students in English (n = 30) and 4 questions were piloted in Arabic…

  14. Neural self-tuning adaptive control of non-minimum phase system

    NASA Technical Reports Server (NTRS)

    Ho, Long T.; Bialasiewicz, Jan T.; Ho, Hai T.

    1993-01-01

    The motivation of this research came about when a neural network direct adaptive control scheme was applied to control the tip position of a flexible robotic arm. Satisfactory control performance was not attainable due to the inherent non-minimum phase characteristics of the flexible robotic arm tip. Most of the existing neural network control algorithms are based on the direct method and exhibit very high sensitivity, if not unstable, closed-loop behavior. Therefore, a neural self-tuning control (NSTC) algorithm is developed and applied to this problem and showed promising results. Simulation results of the NSTC scheme and the conventional self-tuning (STR) control scheme are used to examine performance factors such as control tracking mean square error, estimation mean square error, transient response, and steady state response.

  15. Training-specific functional, neural, and hypertrophic adaptations to explosive- vs. sustained-contraction strength training.

    PubMed

    Balshaw, Thomas G; Massey, Garry J; Maden-Wilkinson, Thomas M; Tillin, Neale A; Folland, Jonathan P

    2016-06-01

    Training specificity is considered important for strength training, although the functional and underpinning physiological adaptations to different types of training, including brief explosive contractions, are poorly understood. This study compared the effects of 12 wk of explosive-contraction (ECT, n = 13) vs. sustained-contraction (SCT, n = 16) strength training vs. control (n = 14) on the functional, neural, hypertrophic, and intrinsic contractile characteristics of healthy young men. Training involved 40 isometric knee extension repetitions (3 times/wk): contracting as fast and hard as possible for ∼1 s (ECT) or gradually increasing to 75% of maximum voluntary torque (MVT) before holding for 3 s (SCT). Torque and electromyography during maximum and explosive contractions, torque during evoked octet contractions, and total quadriceps muscle volume (QUADSVOL) were quantified pre and post training. MVT increased more after SCT than ECT [23 vs. 17%; effect size (ES) = 0.69], with similar increases in neural drive, but greater QUADSVOL changes after SCT (8.1 vs. 2.6%; ES = 0.74). ECT improved explosive torque at all time points (17-34%; 0.54 ≤ ES ≤ 0.76) because of increased neural drive (17-28%), whereas only late-phase explosive torque (150 ms, 12%; ES = 1.48) and corresponding neural drive (18%) increased after SCT. Changes in evoked torque indicated slowing of the contractile properties of the muscle-tendon unit after both training interventions. These results showed training-specific functional changes that appeared to be due to distinct neural and hypertrophic adaptations. ECT produced a wider range of functional adaptations than SCT, and given the lesser demands of ECT, this type of training provides a highly efficient means of increasing function.

  16. Self-organized adaptation of a simple neural circuit enables complex robot behaviour

    NASA Astrophysics Data System (ADS)

    Steingrube, Silke; Timme, Marc; Wörgötter, Florentin; Manoonpong, Poramate

    2010-03-01

    Controlling sensori-motor systems in higher animals or complex robots is a challenging combinatorial problem, because many sensory signals need to be simultaneously coordinated into a broad behavioural spectrum. To rapidly interact with the environment, this control needs to be fast and adaptive. Present robotic solutions operate with limited autonomy and are mostly restricted to few behavioural patterns. Here we introduce chaos control as a new strategy to generate complex behaviour of an autonomous robot. In the presented system, 18 sensors drive 18 motors by means of a simple neural control circuit, thereby generating 11 basic behavioural patterns (for example, orienting, taxis, self-protection and various gaits) and their combinations. The control signal quickly and reversibly adapts to new situations and also enables learning and synaptic long-term storage of behaviourally useful motor responses. Thus, such neural control provides a powerful yet simple way to self-organize versatile behaviours in autonomous agents with many degrees of freedom.

  17. Adaptive neural control for an uncertain robotic manipulator with joint space constraints

    NASA Astrophysics Data System (ADS)

    Tang, Zhong-Liang; Ge, Shuzhi Sam; Tee, Keng Peng; He, Wei

    2016-07-01

    In this paper, adaptive neural tracking control is proposed for a robotic manipulator with uncertainties in both manipulator dynamics and joint actuator dynamics. The manipulator joints are subject to inequality constraints, i.e., the joint angles are required to remain in some compact sets. Integral barrier Lyapunov functionals (iBLFs) are employed to address the joint space constraints directly without performing an additional mapping to the error space. Neural networks (NNs) are utilised to compensate for the unknown robot dynamics and external force. Adapting parameters are developed to estimate the unknown bounds on NN approximations. By the Lyapunov synthesis, the proposed control can guarantee the semi-global uniform ultimate boundedness of the closed-loop system, and the practical tracking of joint reference trajectory is achieved without the violation of predefined joint space constraints. Simulation results are given to validate the effectiveness of the proposed control scheme.

  18. Study on application of adaptive fuzzy control and neural network in the automatic leveling system

    NASA Astrophysics Data System (ADS)

    Xu, Xiping; Zhao, Zizhao; Lan, Weiyong; Sha, Lei; Qian, Cheng

    2015-04-01

    This paper discusses the adaptive fuzzy control and neural network BP algorithm in large flat automatic leveling control system application. The purpose is to develop a measurement system with a flat quick leveling, Make the installation on the leveling system of measurement with tablet, to be able to achieve a level in precision measurement work quickly, improve the efficiency of the precision measurement. This paper focuses on the automatic leveling system analysis based on fuzzy controller, Use of the method of combining fuzzy controller and BP neural network, using BP algorithm improve the experience rules .Construct an adaptive fuzzy control system. Meanwhile the learning rate of the BP algorithm has also been run-rate adjusted to accelerate convergence. The simulation results show that the proposed control method can effectively improve the leveling precision of automatic leveling system and shorten the time of leveling.

  19. Social categories shape the neural representation of emotion: evidence from a visual face adaptation task.

    PubMed

    Otten, Marte; Banaji, Mahzarin R

    2012-01-01

    A number of recent behavioral studies have shown that emotional expressions are differently perceived depending on the race of a face, and that perception of race cues is influenced by emotional expressions. However, neural processes related to the perception of invariant cues that indicate the identity of a face (such as race) are often described to proceed independently of processes related to the perception of cues that can vary over time (such as emotion). Using a visual face adaptation paradigm, we tested whether these behavioral interactions between emotion and race also reflect interdependent neural representation of emotion and race. We compared visual emotion aftereffects when the adapting face and ambiguous test face differed in race or not. Emotion aftereffects were much smaller in different race (DR) trials than same race (SR) trials, indicating that the neural representation of a facial expression is significantly different depending on whether the emotional face is black or white. It thus seems that invariable cues such as race interact with variable face cues such as emotion not just at a response level, but also at the level of perception and neural representation.

  20. Specific neural correlates of successful learning and adaptation during social exchanges.

    PubMed

    Smith-Collins, Adam P R; Fiorentini, Chiara; Kessler, Esther; Boyd, Harriet; Roberts, Fiona; Skuse, David H

    2013-12-01

    Cooperation and betrayal are universal features of social interactions, and knowing who to trust is vital in human society. Previous studies have identified brain regions engaged by decision making during social encounters, but the mechanisms supporting modification of future behaviour by utilizing social experience are not well characterized. Using functional magnetic resonance imaging (fMRI), we show that cooperation and betrayal during social exchanges elicit specific patterns of neural activity associated with future behaviour. Unanticipated cooperation leads to greater behavioural adaptation than unexpected betrayal, and is signalled by specific neural responses in the striatum and midbrain. Neural responses to betrayal and willingness to trust novel partners both decrease as the number of individuals encountered during repeated social encounters increases. We propose that, as social groups increase in size, uncooperative or untrustworthy behaviour becomes progressively less surprising, with cooperation becoming increasingly important as a stimulus for social learning. Effects on reputation of non-trusting decisions may also act to drive pro-social behaviour. Our findings characterize the dynamic neural processes underlying social adaptation, and suggest that the brain is optimized to cooperate with trustworthy partners, rather than avoiding those who might betray us.

  1. Robust adaptive neural network control of uncertain nonholonomic systems with strong nonlinear drifts.

    PubMed

    Wang, Z P; Ge, S S; Lee, T H

    2004-10-01

    In this paper, robust adaptive neural network (NN) control is presented to solve the control problem of nonholonomic systems in chained form with unknown virtual control coefficients and strong drift nonlinearities. The robust adaptive NN control laws are developed using state scaling and backstepping. Uniform ultimate boundedness of all the signals in the closed-loop are guaranteed, and the system states are proven to converge to a small neighborhood of zero. The control performance of the closed-loop system is guaranteed by appropriately choosing the design parameters. The proposed adaptive NN control is free of control singularity problem. An adaptive control based switching strategy is used to overcome the uncontrollability problem associated with x0 (t0) = 0. The simulation results demonstrate the effectiveness of the proposed controllers.

  2. Adaptive dynamic surface control of flexible-joint robots using self-recurrent wavelet neural networks.

    PubMed

    Yoo, Sung Jin; Park, Jin Bae; Choi, Yoon Ho

    2006-12-01

    A new method for the robust control of flexible-joint (FJ) robots with model uncertainties in both robot dynamics and actuator dynamics is proposed. The proposed control system is a combination of the adaptive dynamic surface control (DSC) technique and the self-recurrent wavelet neural network (SRWNN). The adaptive DSC technique provides the ability to overcome the "explosion of complexity" problem in backstepping controllers. The SRWNNs are used to observe the arbitrary model uncertainties of FJ robots, and all their weights are trained online. From the Lyapunov stability analysis, their adaptation laws are induced, and the uniformly ultimately boundedness of all signals in a closed-loop adaptive system is proved. Finally, simulation results for a three-link FJ robot are utilized to validate the good position tracking performance and robustness against payload uncertainties and external disturbances of the proposed control system.

  3. Dynamics of Dual Prism Adaptation: Relating Novel Experimental Results to a Minimalistic Neural Model

    PubMed Central

    Arévalo, Orlando; Bornschlegl, Mona A.; Eberhardt, Sven; Ernst, Udo; Pawelzik, Klaus; Fahle, Manfred

    2013-01-01

    In everyday life, humans interact with a dynamic environment often requiring rapid adaptation of visual perception and motor control. In particular, new visuo–motor mappings must be learned while old skills have to be kept, such that after adaptation, subjects may be able to quickly change between two different modes of generating movements (‘dual–adaptation’). A fundamental question is how the adaptation schedule determines the acquisition speed of new skills. Given a fixed number of movements in two different environments, will dual–adaptation be faster if switches (‘phase changes’) between the environments occur more frequently? We investigated the dynamics of dual–adaptation under different training schedules in a virtual pointing experiment. Surprisingly, we found that acquisition speed of dual visuo–motor mappings in a pointing task is largely independent of the number of phase changes. Next, we studied the neuronal mechanisms underlying this result and other key phenomena of dual–adaptation by relating model simulations to experimental data. We propose a simple and yet biologically plausible neural model consisting of a spatial mapping from an input layer to a pointing angle which is subjected to a global gain modulation. Adaptation is performed by reinforcement learning on the model parameters. Despite its simplicity, the model provides a unifying account for a broad range of experimental data: It quantitatively reproduced the learning rates in dual–adaptation experiments for both direct effect, i.e. adaptation to prisms, and aftereffect, i.e. behavior after removal of prisms, and their independence on the number of phase changes. Several other phenomena, e.g. initial pointing errors that are far smaller than the induced optical shift, were also captured. Moreover, the underlying mechanisms, a local adaptation of a spatial mapping and a global adaptation of a gain factor, explained asymmetric spatial transfer and generalization of

  4. Auditory to Visual Cross-Modal Adaptation for Emotion: Psychophysical and Neural Correlates.

    PubMed

    Wang, Xiaodong; Guo, Xiaotao; Chen, Lin; Liu, Yijun; Goldberg, Michael E; Xu, Hong

    2016-01-04

    Adaptation is fundamental in sensory processing and has been studied extensively within the same sensory modality. However, little is known about adaptation across sensory modalities, especially in the context of high-level processing, such as the perception of emotion. Previous studies have shown that prolonged exposure to a face exhibiting one emotion, such as happiness, leads to contrastive biases in the perception of subsequently presented faces toward the opposite emotion, such as sadness. Such work has shown the importance of adaptation in calibrating face perception based on prior visual exposure. In the present study, we showed for the first time that emotion-laden sounds, like laughter, adapt the visual perception of emotional faces, that is, subjects more frequently perceived faces as sad after listening to a happy sound. Furthermore, via electroencephalography recordings and event-related potential analysis, we showed that there was a neural correlate underlying the perceptual bias: There was an attenuated response occurring at ∼ 400 ms to happy test faces and a quickened response to sad test faces, after exposure to a happy sound. Our results provide the first direct evidence for a behavioral cross-modal adaptation effect on the perception of facial emotion, and its neural correlate.

  5. Feasability of adaptive vibration control of a space truss using modal filters and a neural network

    NASA Astrophysics Data System (ADS)

    Bosse, Albert; Fisher, Shalom; Shelley, Stuart J.; Lim, Tae W.

    1996-05-01

    An adaptive algorithm is proposed for the control of a large space truss structure which uses modal filters for independent modal space control and a simple neural network that provides an on-line system identification capability. The modal filters are computed off-line using measured frequency response functions and estimated pole values for the modes of interest, and provide a coordinate transformation that yields modal coordinates from physical response measurements. The time histories for the modal coordinates are then processed in real time by the neural network, which models a single degree of freedom system transfer function and provides estimates of modal parameters, namely, frequency, damping ratio and modal gain. The modal filters are used to implement independent modal space control on a 3.74 meter space truss using a single reaction-mass actuator and 32 accelerometers. The performance of the modal filter based controller is compared to that of a local rate feedback controller using the same actuator. The applicability of the adaptive filter to adaptive control is demonstrated by real time estimation of the modal parameters of the truss with and without control. Because the modal filter control gain can be adjusted to maintain a desired closed loop damping ratio, which is tracked by the adaptive filter, adaptive control of individual modes in a time-varying system is possible. The goal of this work is to field a control system which can maintain desired closed loop damping ratios for mode frequency variations as high as 10%.

  6. Adaptation to New Microphones Using Artificial Neural Networks With Trainable Activation Functions.

    PubMed

    Siniscalchi, Sabato Marco; Salerno, Valerio Mario

    2016-04-14

    Model adaptation is a key technique that enables a modern automatic speech recognition (ASR) system to adjust its parameters, using a small amount of enrolment data, to the nuances in the speech spectrum due to microphone mismatch in the training and test data. In this brief, we investigate four different adaptation schemes for connectionist (also known as hybrid) ASR systems that learn microphone-specific hidden unit contributions, given some adaptation material. This solution is made possible adopting one of the following schemes: 1) the use of Hermite activation functions; 2) the introduction of bias and slope parameters in the sigmoid activation functions; 3) the injection of an amplitude parameter specific for each sigmoid unit; or 4) the combination of 2) and 3). Such a simple yet effective solution allows the adapted model to be stored in a small-sized storage space, a highly desirable property of adaptation algorithms for deep neural networks that are suitable for large-scale online deployment. Experimental results indicate that the investigated approaches reduce word error rates on the standard Spoke 6 task of the Wall Street Journal corpus compared with unadapted ASR systems. Moreover, the proposed adaptation schemes all perform better than simple multicondition training and comparable favorably against conventional linear regression-based approaches while using up to 15 orders of magnitude fewer parameters. The proposed adaptation strategies are also effective when a single adaptation sentence is available.

  7. Construction of point process adaptive filter algorithms for neural systems using sequential Monte Carlo methods.

    PubMed

    Ergün, Ayla; Barbieri, Riccardo; Eden, Uri T; Wilson, Matthew A; Brown, Emery N

    2007-03-01

    The stochastic state point process filter (SSPPF) and steepest descent point process filter (SDPPF) are adaptive filter algorithms for state estimation from point process observations that have been used to track neural receptive field plasticity and to decode the representations of biological signals in ensemble neural spiking activity. The SSPPF and SDPPF are constructed using, respectively, Gaussian and steepest descent approximations to the standard Bayes and Chapman-Kolmogorov (BCK) system of filter equations. To extend these approaches for constructing point process adaptive filters, we develop sequential Monte Carlo (SMC) approximations to the BCK equations in which the SSPPF and SDPPF serve as the proposal densities. We term the two new SMC point process filters SMC-PPFs and SMC-PPFD, respectively. We illustrate the new filter algorithms by decoding the wind stimulus magnitude from simulated neural spiking activity in the cricket cercal system. The SMC-PPFs and SMC-PPFD provide more accurate state estimates at low number of particles than a conventional bootstrap SMC filter algorithm in which the state transition probability density is the proposal density. We also use the SMC-PPFs algorithm to track the temporal evolution of a spatial receptive field of a rat hippocampal neuron recorded while the animal foraged in an open environment. Our results suggest an approach for constructing point process adaptive filters using SMC methods.

  8. Epithelium-stroma classification via convolutional neural networks and unsupervised domain adaptation in histopathological images.

    PubMed

    Huang, Yue; Zheng, Han; Liu, Chi; Ding, Xinghao; Rohde, Gustavo

    2017-04-06

    Epithelium-stroma classification is a necessary preprocessing step in histopathological image analysis. Current deep learning based recognition methods for histology data require collection of large volumes of labeled data in order to train a new neural network when there are changes to the image acquisition procedure. However, it is extremely expensive for pathologists to manually label sufficient volumes of data for each pathology study in a professional manner, which results in limitations in real-world applications. A very simple but effective deep learning method, that introduces the concept of unsupervised domain adaptation to a simple convolutional neural network (CNN), has been proposed in this paper. Inspired by transfer learning, our work assumes that the training data and testing data follow different distributions, and there is an adaptation operation to more accurately estimate the kernels in CNN in feature extraction, in order to enhance performance by transferring knowledge from labeled data in source domain to unlabeled data in target domain. The model has been evaluated using three independent public epithelium-stroma datasets by cross-dataset validations. The experimental results demonstrate that for epithelium-stroma classification, the proposed framework outperforms the state-of-the-art deep neural network model, and it also achieves better performance than other existing deep domain adaptation methods. The proposed model can be considered to be a better option for real-world applications in histopathological image analysis, since there is no longer a requirement for large-scale labeled data in each specified domain.

  9. Neuroplasticity beyond Sounds: Neural Adaptations Following Long-Term Musical Aesthetic Experiences

    PubMed Central

    Reybrouck, Mark; Brattico, Elvira

    2015-01-01

    Capitalizing from neuroscience knowledge on how individuals are affected by the sound environment, we propose to adopt a cybernetic and ecological point of view on the musical aesthetic experience, which includes subprocesses, such as feature extraction and integration, early affective reactions and motor actions, style mastering and conceptualization, emotion and proprioception, evaluation and preference. In this perspective, the role of the listener/composer/performer is seen as that of an active “agent” coping in highly individual ways with the sounds. The findings concerning the neural adaptations in musicians, following long-term exposure to music, are then reviewed by keeping in mind the distinct subprocesses of a musical aesthetic experience. We conclude that these neural adaptations can be conceived of as the immediate and lifelong interactions with multisensorial stimuli (having a predominant auditory component), which result in lasting changes of the internal state of the “agent”. In a continuous loop, these changes affect, in turn, the subprocesses involved in a musical aesthetic experience, towards the final goal of achieving better perceptual, motor and proprioceptive responses to the immediate demands of the sounding environment. The resulting neural adaptations in musicians closely depend on the duration of the interactions, the starting age, the involvement of attention, the amount of motor practice and the musical genre played. PMID:25807006

  10. Self-organized criticality of a simple integrate-and-fire neural model

    NASA Astrophysics Data System (ADS)

    Choi, Hyung Wooc; Maeng, Seong Eun; Lee, Jae Woo

    2012-02-01

    We consider a simple integrate-and-fire neural model without synaptic plasticity. In this model, the membrane potential propagates to the nearest neighbor neurons when that potential is greater than a threshold value. When a neuron is fired, the propagating potential is leaky. Therefore, the sum of the received potential is less than the presynaptic potential. We simulated this simple model on a fully-connected network. We identified the critical membrane strength, J c = 4.71(1). At the critical membrane strength, we observed that the probability distribution function of the avalanche shows a power law, P( s) ≈ s -τ, with the critical exponent τ = 1.414(5). The lifetime of the avalanche also showed a power law. The power law behaviors imply that this model shows self-organized criticality.

  11. Fault Analysis of Space Station DC Power Systems-Using Neural Network Adaptive Wavelets to Detect Faults

    NASA Technical Reports Server (NTRS)

    Momoh, James A.; Wang, Yanchun; Dolce, James L.

    1997-01-01

    This paper describes the application of neural network adaptive wavelets for fault diagnosis of space station power system. The method combines wavelet transform with neural network by incorporating daughter wavelets into weights. Therefore, the wavelet transform and neural network training procedure become one stage, which avoids the complex computation of wavelet parameters and makes the procedure more straightforward. The simulation results show that the proposed method is very efficient for the identification of fault locations.

  12. Is the critical point for aperture crossing adapted to the person-plus-object system?

    PubMed

    Hackney, Amy L; Cinelli, Michael E; Frank, Jim S

    2014-01-01

    When passing through apertures, individuals scale their actions to their shoulder width and rotate their shoulders or avoid apertures that are deemed too small for straight passage. Carrying objects wider than the body produces a person-plus-object system that individuals must account for in order to pass through apertures safely. The present study aimed to determine whether individuals scale their critical point to the widest horizontal dimension (shoulder or object width). Two responses emerged: Fast adapters adapted to the person-plus-object system by maintaining a consistent critical point regardless of whether the object was carried while slow adapters initially increased their critical point (overestimated) before adapting back to their original critical point. The results suggest that individuals can account for increases in body width by scaling actions to the size of the object width but people adapt at different rates.

  13. Adapting Entry-Level Engineering Courses to Emphasize Critical Thinking

    ERIC Educational Resources Information Center

    Hagerty, D. Joseph; Rockaway, Thomas D.

    2012-01-01

    The University of Louisville recently developed a Quality Enhancement Plan (QEP) to improve undergraduate instruction across all disciplines as part of its ongoing accreditation requirements. Central elements of the plan are emphasis on critical thinking; integration of critical thinking throughout the curriculum; service learning for…

  14. fMRI-Adaptation Evidence of Overlapping Neural Representations for Objects Related in Function or Manipulation

    PubMed Central

    Yee, Eiling; Drucker, Daniel M.; Thompson-Schill, Sharon L.

    2010-01-01

    Sensorimotor-based theories of semantic memory contend that semantic information about an object is represented in the neural substrate invoked when we perceive or interact with it. We used fMRI adaptation to test this prediction, measuring brain activation as participants read pairs of words. Pairs shared function (flashlight–lantern), shape (marble–grape), both (pencil–pen), were unrelated (saucer–needle), or were identical (drill–drill). We observed adaptation for pairs with both function and shape similarity in left premotor cortex. Further, degree of function similarity was correlated with adaptation in three regions: two in the left temporal lobe (left medial temporal lobe, left middle temporal gyrus), which has been hypothesized to play a role in mutimodal integration, and one in left superior frontal gyrus. We also found that degree of manipulation (i.e., action) and function similarity were both correlated with adaptation in two regions: left premotor cortex and left intraparietal sulcus (involved in guiding actions). Additional considerations suggest that the adaptation in these two regions was driven by manipulation similarity alone; thus, these results imply that manipulation information about objects is encoded in brain regions involved in performing or guiding actions. Unexpectedly, these same two regions showed increased activation (rather than adaptation) for objects similar in shape. Overall, we found evidence (in the form of adaptation) that objects that share semantic features have overlapping representations. Further, the particular regions of overlap provide support for the existence of both sensorimotor and amodal/multimodal representations. PMID:20034582

  15. Adaptive critic autopilot design of bank-to-turn missiles using fuzzy basis function networks.

    PubMed

    Lin, Chuan-Kai

    2005-04-01

    A new adaptive critic autopilot design for bank-to-turn missiles is presented. In this paper, the architecture of adaptive critic learning scheme contains a fuzzy-basis-function-network based associative search element (ASE), which is employed to approximate nonlinear and complex functions of bank-to-turn missiles, and an adaptive critic element (ACE) generating the reinforcement signal to tune the associative search element. In the design of the adaptive critic autopilot, the control law receives signals from a fixed gain controller, an ASE and an adaptive robust element, which can eliminate approximation errors and disturbances. Traditional adaptive critic reinforcement learning is the problem faced by an agent that must learn behavior through trial-and-error interactions with a dynamic environment, however, the proposed tuning algorithm can significantly shorten the learning time by online tuning all parameters of fuzzy basis functions and weights of ASE and ACE. Moreover, the weight updating law derived from the Lyapunov stability theory is capable of guaranteeing both tracking performance and stability. Computer simulation results confirm the effectiveness of the proposed adaptive critic autopilot.

  16. Novel L1 neural network adaptive control architecture with guaranteed transient performance.

    PubMed

    Cao, Chengyu; Hovakimyan, Naira

    2007-07-01

    In this paper, we present a novel neural network (NN) adaptive control architecture with guaranteed transient performance. With this new architecture, both input and output signals of an uncertain nonlinear system follow a desired linear system during the transient phase, in addition to stable tracking. This new architecture uses a low-pass filter in the feedback loop, which consequently enables to enforce the desired transient performance by increasing the adaptation gain. For the guaranteed transient performance of both input and output signals of the uncertain nonlinear system, the L1 gain of a cascaded system, comprised of the low-pass filter and the closed-loop desired reference model, is required to be less than the inverse of the Lipschitz constant of the unknown nonlinearities in the system. The tools from this paper can be used to develop a theoretically justified verification and validation framework for NN adaptive controllers. Simulation results illustrate the theoretical findings.

  17. Learning from adaptive neural network output feedback control of a unicycle-type mobile robot.

    PubMed

    Zeng, Wei; Wang, Qinghui; Liu, Fenglin; Wang, Ying

    2016-03-01

    This paper studies learning from adaptive neural network (NN) output feedback control of nonholonomic unicycle-type mobile robots. The major difficulties are caused by the unknown robot system dynamics and the unmeasurable states. To overcome these difficulties, a new adaptive control scheme is proposed including designing a new adaptive NN output feedback controller and two high-gain observers. It is shown that the stability of the closed-loop robot system and the convergence of tracking errors are guaranteed. The unknown robot system dynamics can be approximated by radial basis function NNs. When repeating same or similar control tasks, the learned knowledge can be recalled and reused to achieve guaranteed stability and better control performance, thereby avoiding the tremendous repeated training process of NNs.

  18. Adaptive control of nonlinear system using online error minimum neural networks.

    PubMed

    Jia, Chao; Li, Xiaoli; Wang, Kang; Ding, Dawei

    2016-11-01

    In this paper, a new learning algorithm named OEM-ELM (Online Error Minimized-ELM) is proposed based on ELM (Extreme Learning Machine) neural network algorithm and the spreading of its main structure. The core idea of this OEM-ELM algorithm is: online learning, evaluation of network performance, and increasing of the number of hidden nodes. It combines the advantages of OS-ELM and EM-ELM, which can improve the capability of identification and avoid the redundancy of networks. The adaptive control based on the proposed algorithm OEM-ELM is set up which has stronger adaptive capability to the change of environment. The adaptive control of chemical process Continuous Stirred Tank Reactor (CSTR) is also given for application. The simulation results show that the proposed algorithm with respect to the traditional ELM algorithm can avoid network redundancy and improve the control performance greatly.

  19. Adaptive Neural Network Control for the Trajectory Tracking of the Furuta Pendulum.

    PubMed

    Moreno-Valenzuela, Javier; Aguilar-Avelar, Carlos; Puga-Guzman, Sergio A; Santibanez, Victor

    2016-12-01

    The purpose of this paper is to introduce a novel adaptive neural network-based control scheme for the Furuta pendulum, which is a two degree-of-freedom underactuated system. Adaptation laws for the input and output weights are also provided. The proposed controller is able to guarantee tracking of a reference signal for the arm while the pendulum remains in the upright position. The key aspect of the derivation of the controller is the definition of an output function that depends on the position and velocity errors. The internal and external dynamics are rigorously analyzed, thereby proving the uniform ultimate boundedness of the error trajectories. By using real-time experiments, the new scheme is compared with other control methodologies, therein demonstrating the improved performance of the proposed adaptive algorithm.

  20. Synchronization of Coupled Reaction-Diffusion Neural Networks With Directed Topology via an Adaptive Approach.

    PubMed

    Zhang, Hao; Sheng, Yin; Zeng, Zhigang

    2017-03-15

    This paper investigates the synchronization issue of coupled reaction-diffusion neural networks with directed topology via an adaptive approach. Due to the complexity of the network structure and the presence of space variables, it is difficult to design proper adaptive strategies on coupling weights to accomplish the synchronous goal. Under the assumptions of two kinds of special network structures, that is, directed spanning path and directed spanning tree, some novel edge-based adaptive laws, which utilized the local information of node dynamics fully are designed on the coupling weights for reaching synchronization. By constructing appropriate energy function, and utilizing some analytical techniques, several sufficient conditions are given. Finally, some simulation examples are given to verify the effectiveness of the obtained theoretical results.

  1. Neural Network Aided Adaptive Extended Kalman Filtering Approach for DGPS Positioning

    NASA Astrophysics Data System (ADS)

    Jwo, Dah-Jing; Huang, Hung-Chih

    2004-09-01

    The extended Kalman filter, when employed in the GPS receiver as the navigation state estimator, provides optimal solutions if the noise statistics for the measurement and system are completely known. In practice, the noise varies with time, which results in performance degradation. The covariance matching method is a conventional adaptive approach for estimation of noise covariance matrices. The technique attempts to make the actual filter residuals consistent with their theoretical covariance. However, this innovation-based adaptive estimation shows very noisy results if the window size is small. To resolve the problem, a multilayered neural network is trained to identify the measurement noise covariance matrix, in which the back-propagation algorithm is employed to iteratively adjust the link weights using the steepest descent technique. Numerical simulations show that based on the proposed approach the adaptation performance is substantially enhanced and the positioning accuracy is substantially improved.

  2. Adaptive and Adaptable Automation Design: A Critical Review of the Literature and Recommendations for Future Research

    NASA Technical Reports Server (NTRS)

    Prinzel, Lawrence J., III; Kaber, David B.

    2006-01-01

    This report presents a review of literature on approaches to adaptive and adaptable task/function allocation and adaptive interface technologies for effective human management of complex systems that are likely to be issues for the Next Generation Air Transportation System, and a focus of research under the Aviation Safety Program, Integrated Intelligent Flight Deck Project. Contemporary literature retrieved from an online database search is summarized and integrated. The major topics include the effects of delegation-type, adaptable automation on human performance, workload and situation awareness, the effectiveness of various automation invocation philosophies and strategies to function allocation in adaptive systems, and the role of user modeling in adaptive interface design and the performance implications of adaptive interface technology.

  3. Adaptive variability in the duration of critical windows of plasticity

    PubMed Central

    Wells, Jonathan C. K.

    2014-01-01

    Developmental plasticity underlies widespread associations between early-life exposures and many components of adult phenotype, including the risk of chronic diseases. Humans take almost two decades to reach reproductive maturity, and yet the ‘critical windows’ of physiological sensitivity that confer developmental plasticity tend to close during fetal life or infancy. While several explanations for lengthy human maturation have been offered, the brevity of physiological plasticity has received less attention. I argue that offspring plasticity is only viable within the niche of maternal care, and that as this protection is withdrawn, the offspring is obliged to canalize many developmental traits in order to minimize environmental disruptions. The schedule of maternal care may therefore shape the duration of critical windows, and since the duration of this care is subject to parent–offspring conflict, the resolution of this conflict may shape the duration of critical windows. This perspective may help understand (i) why windows close at different times for different traits, and (ii) why the duration of critical windows may vary across human populations. The issue is explored in relation to population differences in the association between infant weight gain and later body composition. The occupation of more stable environments by western populations may have favoured earlier closure of the critical window during which growth in lean mass is sensitive to nutritional intake. This may paradoxically have elevated the risk of obesity following rapid infant weight gain in such populations. PMID:25095791

  4. Adaptively combined FIR and functional link artificial neural network equalizer for nonlinear communication channel.

    PubMed

    Zhao, Haiquan; Zhang, Jiashu

    2009-04-01

    This paper proposes a novel computational efficient adaptive nonlinear equalizer based on combination of finite impulse response (FIR) filter and functional link artificial neural network (CFFLANN) to compensate linear and nonlinear distortions in nonlinear communication channel. This convex nonlinear combination results in improving the speed while retaining the lower steady-state error. In addition, since the CFFLANN needs not the hidden layers, which exist in conventional neural-network-based equalizers, it exhibits a simpler structure than the traditional neural networks (NNs) and can require less computational burden during the training mode. Moreover, appropriate adaptation algorithm for the proposed equalizer is derived by the modified least mean square (MLMS). Results obtained from the simulations clearly show that the proposed equalizer using the MLMS algorithm can availably eliminate various intensity linear and nonlinear distortions, and be provided with better anti-jamming performance. Furthermore, comparisons of the mean squared error (MSE), the bit error rate (BER), and the effect of eigenvalue ratio (EVR) of input correlation matrix are presented.

  5. Decentralized Adaptive Neural Output-Feedback DSC for Switched Large-Scale Nonlinear Systems.

    PubMed

    Long, Lijun; Zhao, Jun

    2016-03-08

    In this paper, for a class of switched large-scale uncertain nonlinear systems with unknown control coefficients and unmeasurable states, a switched-dynamic-surface-based decentralized adaptive neural output-feedback control approach is developed. The approach proposed extends the classical dynamic surface control (DSC) technique for nonswitched version to switched version by designing switched first-order filters, which overcomes the problem of multiple ``explosion of complexity.'' Also, a dual common coordinates transformation of all subsystems is exploited to avoid individual coordinate transformations for subsystems that are required when applying the backstepping recursive design scheme. Nussbaum-type functions are utilized to handle the unknown control coefficients, and a switched neural network observer is constructed to estimate the unmeasurable states. Combining with the average dwell time method and backstepping and the DSC technique, decentralized adaptive neural controllers of subsystems are explicitly designed. It is proved that the approach provided can guarantee the semiglobal uniformly ultimately boundedness for all the signals in the closed-loop system under a class of switching signals with average dwell time, and the tracking errors to a small neighborhood of the origin. A two inverted pendulums system is provided to demonstrate the effectiveness of the method proposed.

  6. Protein Secondary Structure Prediction Using Local Adaptive Techniques in Training Neural Networks

    NASA Astrophysics Data System (ADS)

    Aik, Lim Eng; Zainuddin, Zarita; Joseph, Annie

    2008-01-01

    One of the most significant problems in computer molecular biology today is how to predict a protein's three-dimensional structure from its one-dimensional amino acid sequence or generally call the protein folding problem and difficult to determine the corresponding protein functions. Thus, this paper involves protein secondary structure prediction using neural network in order to solve the protein folding problem. The neural network used for protein secondary structure prediction is multilayer perceptron (MLP) of the feed-forward variety. The training set are taken from the protein data bank which are 120 proteins while 60 testing set is the proteins which were chosen randomly from the protein data bank. Multiple sequence alignment (MSA) is used to get the protein similar sequence and Position Specific Scoring matrix (PSSM) is used for network input. The training process of the neural network involves local adaptive techniques. Local adaptive techniques used in this paper comprises Learning rate by sign changes, SuperSAB, Quickprop and RPROP. From the simulation, the performance for learning rate by Rprop and Quickprop are superior to all other algorithms with respect to the convergence time. However, the best result was obtained using Rprop algorithm.

  7. A novel nonlinear adaptive filter using a pipelined second-order Volterra recurrent neural network.

    PubMed

    Zhao, Haiquan; Zhang, Jiashu

    2009-12-01

    To enhance the performance and overcome the heavy computational complexity of recurrent neural networks (RNN), a novel nonlinear adaptive filter based on a pipelined second-order Volterra recurrent neural network (PSOVRNN) is proposed in this paper. A modified real-time recurrent learning (RTRL) algorithm of the proposed filter is derived in much more detail. The PSOVRNN comprises of a number of simple small-scale second-order Volterra recurrent neural network (SOVRNN) modules. In contrast to the standard RNN, these modules of a PSOVRNN can be performed simultaneously in a pipelined parallelism fashion, which can lead to a significant improvement in its total computational efficiency. Moreover, since each module of the PSOVRNN is a SOVRNN in which nonlinearity is introduced by the recursive second-order Volterra (RSOV) expansion, its performance can be further improved. Computer simulations have demonstrated that the PSOVRNN performs better than the pipelined recurrent neural network (PRNN) and RNN for nonlinear colored signals prediction and nonlinear channel equalization. However, the superiority of the PSOVRNN over the PRNN is at the cost of increasing computational complexity due to the introduced nonlinear expansion of each module.

  8. Neural adaptation to thin and fat bodies in the fusiform body area and middle occipital gyrus: an fMRI adaptation study.

    PubMed

    Hummel, Dennis; Rudolf, Anne K; Brandi, Marie-Luise; Untch, Karl-Heinz; Grabhorn, Ralph; Hampel, Harald; Mohr, Harald M

    2013-12-01

    Visual perception can be strongly biased due to exposure to specific stimuli in the environment, often causing neural adaptation and visual aftereffects. In this study, we investigated whether adaptation to certain body shapes biases the perception of the own body shape. Furthermore, we aimed to evoke neural adaptation to certain body shapes. Participants completed a behavioral experiment (n = 14) to rate manipulated pictures of their own bodies after adaptation to demonstratively thin or fat pictures of their own bodies. The same stimuli were used in a second experiment (n = 16) using functional magnetic resonance imaging (fMRI) adaptation. In the behavioral experiment, after adapting to a thin picture of the own body participants also judged a thinner than actual body picture to be the most realistic and vice versa, resembling a typical aftereffect. The fusiform body area (FBA) and the right middle occipital gyrus (rMOG) show neural adaptation to specific body shapes while the extrastriate body area (EBA) bilaterally does not. The rMOG cluster is highly selective for bodies and perhaps body parts. The findings of the behavioral experiment support the existence of a perceptual body shape aftereffect, resulting from a specific adaptation to thin and fat pictures of one's own body. The fMRI results imply that body shape adaptation occurs in the FBA and the rMOG. The role of the EBA in body shape processing remains unclear. The results are also discussed in the light of clinical body image disturbances.

  9. A Neural Mechanism for Time-Window Separation Resolves Ambiguity of Adaptive Coding

    PubMed Central

    Hildebrandt, K. Jannis; Ronacher, Bernhard; Hennig, R. Matthias; Benda, Jan

    2015-01-01

    The senses of animals are confronted with changing environments and different contexts. Neural adaptation is one important tool to adjust sensitivity to varying intensity ranges. For instance, in a quiet night outdoors, our hearing is more sensitive than when we are confronted with the plurality of sounds in a large city during the day. However, adaptation also removes available information on absolute sound levels and may thus cause ambiguity. Experimental data on the trade-off between benefits and loss through adaptation is scarce and very few mechanisms have been proposed to resolve it. We present an example where adaptation is beneficial for one task—namely, the reliable encoding of the pattern of an acoustic signal—but detrimental for another—the localization of the same acoustic stimulus. With a combination of neurophysiological data, modeling, and behavioral tests, we show that adaptation in the periphery of the auditory pathway of grasshoppers enables intensity-invariant coding of amplitude modulations, but at the same time, degrades information available for sound localization. We demonstrate how focusing the response of localization neurons to the onset of relevant signals separates processing of localization and pattern information temporally. In this way, the ambiguity of adaptive coding can be circumvented and both absolute and relative levels can be processed using the same set of peripheral neurons. PMID:25761097

  10. Neural network-based adaptive controller design of robotic manipulators with an observer.

    PubMed

    Sun, F; Sun, Z; Woo, P Y

    2001-01-01

    A neural network (NN)-based adaptive controller with an observer is proposed for the trajectory tracking of robotic manipulators with unknown dynamics nonlinearities. It is assumed that the robotic manipulator has only joint angle position measurements. A linear observer is used to estimate the robot joint angle velocity, while NNs are employed to further improve the control performance of the controlled system through approximating the modified robot dynamics function. The adaptive controller for robots with an observer can guarantee the uniform ultimate bounds of the tracking errors and the observer errors as well as the bounds of the NN weights. For performance comparisons, the conventional adaptive algorithm with an observer using linearity in parameters of the robot dynamics is also developed in the same control framework as the NN approach for online approximating unknown nonlinearities of the robot dynamics. Main theoretical results for designing such an observer-based adaptive controller with the NN approach using multilayer NNs with sigmoidal activation functions, as well as with the conventional adaptive approach using linearity in parameters of the robot dynamics are given. The performance comparisons between the NN approach and the conventional adaptation approach with an observer is carried out to show the advantages of the proposed control approaches through simulation studies.

  11. Advances in Adaptive Secure Message-Oriented Middleware for Distributed Business-Critical Systems

    NASA Astrophysics Data System (ADS)

    Abie, Habtamu; Savola, Reijo M.; Wang, Jinfu; Rotondi, Domenico

    2010-09-01

    Distributed business-critical systems are often implemented using distributed messaging infrastructures with increasingly stringent requirements with regard to resilience, security, adaptability, intelligence and scalability. Current systems have limited ability in meeting these requirements. This paper describes advances in adaptive security, security metrics, anomaly detection and resilience, and authentication architecture in such distributed messaging systems.

  12. Adaptive Neural Control of MIMO Nonstrict-Feedback Nonlinear Systems With Time Delay.

    PubMed

    Zhao, Xudong; Yang, Haijiao; Karimi, Hamid Reza; Zhu, Yanzheng

    2016-06-01

    In this paper, an adaptive neural output-feedback tracking controller is designed for a class of multiple-input and multiple-output nonstrict-feedback nonlinear systems with time delay. The system coefficient and uncertain functions of our considered systems are both unknown. By employing neural networks to approximate the unknown function entries, and constructing a new input-driven filter, a backstepping design method of tracking controller is developed for the systems under consideration. The proposed controller can guarantee that all the signals in the closed-loop systems are ultimately bounded, and the time-varying target signal can be tracked within a small error as well. The main contributions of this paper lie in that the systems under consideration are more general, and an effective design procedure of output-feedback controller is developed for the considered systems, which is more applicable in practice. Simulation results demonstrate the efficiency of the proposed algorithm.

  13. Multi-layer holographic bifurcative neural network system for real-time adaptive EOS data analysis

    NASA Technical Reports Server (NTRS)

    Liu, Hua-Kuang; Huang, K. S.; Diep, J.

    1993-01-01

    Optical data processing techniques have the inherent advantage of high data throughout, low weight and low power requirements. These features are particularly desirable for onboard spacecraft in-situ real-time data analysis and data compression applications. the proposed multi-layer optical holographic neural net pattern recognition technique will utilize the nonlinear photorefractive devices for real-time adaptive learning to classify input data content and recognize unexpected features. Information can be stored either in analog or digital form in a nonlinear photofractive device. The recording can be accomplished in time scales ranging from milliseconds to microseconds. When a system consisting of these devices is organized in a multi-layer structure, a feedforward neural net with bifurcating data classification capability is formed. The interdisciplinary research will involve the collaboration with top digital computer architecture experts at the University of Southern California.

  14. Multi-layer holographic bifurcative neural network system for real-time adaptive EOS data analysis

    NASA Technical Reports Server (NTRS)

    Liu, Hua-Kuang; Huang, K.; Diep, J.

    1992-01-01

    Optical data processing techniques have the inherent advantage of high data throughout, low weight and low power requirements. These features are particularly desirable for onboard spacecraft in-situ real-time data analysis and data compression applications. The proposed multi-layer optical holographic neural net pattern recognition technique will utilize the nonlinear photorefractive devices for real-time adaptive learning to classify input data content and recognize unexpected features. Information can be stored either in analog or digital form in a nonlinear photorefractive device. The recording can be accomplished in time scales ranging from milliseconds to microseconds. When a system consisting of these devices is organized in a multi-layer structure, a feed forward neural net with bifurcating data classification capability is formed. The interdisciplinary research will involve the collaboration with top digital computer architecture experts at the University of Southern California.

  15. Robust adaptive neural control for a class of uncertain MIMO nonlinear systems

    NASA Astrophysics Data System (ADS)

    Wang, Chenliang; Lin, Yan

    2015-08-01

    In this paper, a novel robust adaptive neural control scheme is proposed for a class of uncertain multi-input multi-output nonlinear systems. The proposed scheme has the following main features: (1) a kind of Hurwitz condition is introduced to handle the state-dependent control gain matrix and some assumptions in existing schemes are relaxed; (2) by introducing a novel matrix normalisation technique, it is shown that all bound restrictions imposed on the control gain matrix in existing schemes can be removed; (3) the singularity problem is avoided without any extra effort, which makes the control law quite simple. Besides, with the aid of the minimal learning parameter technique, only one parameter needs to be updated online regardless of the system input-output dimension and the number of neural network nodes. Simulation results are presented to illustrate the effectiveness of the proposed scheme.

  16. Adaptive pedestrian detection using convolutional neural network with dynamically adjusted classifier

    NASA Astrophysics Data System (ADS)

    Tang, Song; Ye, Mao; Zhu, Ce; Liu, Yiguang

    2017-01-01

    How to transfer the trained detector into the target scenarios has been an important topic for a long time in the field of computer vision. Unfortunately, most of the existing transfer methods need to keep source samples or label target samples in the detection phase. Therefore, they are difficult to apply to real applications. For this problem, we propose a framework that consists of a controlled convolutional neural network (CCNN) and a modulating neural network (MNN). In a CCNN, the parameters of the last layer, i.e., the classifier, are dynamically adjusted by a MNN. For each target sample, the CCNN adaptively generates a proprietary classifier. Our contributions include (1) the first detector-based unsupervised transfer method that is very suitable for real applications and (2) a new scheme of a dynamically adjusting classifier in which a new object function is invented. Experimental results confirm that our method can achieve state-of-the-art results on two pedestrian datasets.

  17. Adaptive nonlinear polynomial neural networks for control of boundary layer/structural interaction

    NASA Technical Reports Server (NTRS)

    Parker, B. Eugene, Jr.; Cellucci, Richard L.; Abbott, Dean W.; Barron, Roger L.; Jordan, Paul R., III; Poor, H. Vincent

    1993-01-01

    The acoustic pressures developed in a boundary layer can interact with an aircraft panel to induce significant vibration in the panel. Such vibration is undesirable due to the aerodynamic drag and structure-borne cabin noises that result. The overall objective of this work is to develop effective and practical feedback control strategies for actively reducing this flow-induced structural vibration. This report describes the results of initial evaluations using polynomial, neural network-based, feedback control to reduce flow induced vibration in aircraft panels due to turbulent boundary layer/structural interaction. Computer simulations are used to develop and analyze feedback control strategies to reduce vibration in a beam as a first step. The key differences between this work and that going on elsewhere are as follows: that turbulent and transitional boundary layers represent broadband excitation and thus present a more complex stochastic control scenario than that of narrow band (e.g., laminar boundary layer) excitation; and secondly, that the proposed controller structures are adaptive nonlinear infinite impulse response (IIR) polynomial neural network, as opposed to the traditional adaptive linear finite impulse response (FIR) filters used in most studies to date. The controllers implemented in this study achieved vibration attenuation of 27 to 60 dB depending on the type of boundary layer established by laminar, turbulent, and intermittent laminar-to-turbulent transitional flows. Application of multi-input, multi-output, adaptive, nonlinear feedback control of vibration in aircraft panels based on polynomial neural networks appears to be feasible today. Plans are outlined for Phase 2 of this study, which will include extending the theoretical investigation conducted in Phase 2 and verifying the results in a series of laboratory experiments involving both bum and plate models.

  18. Neural signatures of adaptive post-error adjustments in visual search.

    PubMed

    Steinhauser, Robert; Maier, Martin E; Steinhauser, Marco

    2017-02-22

    Errors in speeded choice tasks can lead to post-error adjustments both on the behavioral and on the neural level. There is an ongoing debate whether such adjustments result from adaptive processes that serve to optimize performance or whether they reflect interference from error monitoring or attentional orientation. The present study aimed at identifying adaptive adjustments in a two-stage visual search task, in which participants had to select and subsequently identify a target stimulus presented to the left or right visual hemifield. Target selection and identification can be measured by two distinct event-related potentials, the N2pc and the SPCN. Using a decoder analysis based on multivariate pattern analysis, we were able to isolate the processing stages related to error sources and post-error adjustments. Whereas errors were linked to deviations in the N2pc and the SPCN, only for the N2pc we identified a post-error adjustment, which exhibits key features of source-specific adaptivity. While errors were associated with an increased N2pc, post-error adjustments consisted in an N2pc decrease. We interpret this as an adaptive adjustment of target selection to prevent errors due to disproportionate processing of the task-irrelevant target location. Our study thus provides evidence for adaptive post-error adjustments in visual search.

  19. Discrete-time adaptive backstepping nonlinear control via high-order neural networks.

    PubMed

    Alanis, Alma Y; Sanchez, Edgar N; Loukianov, Alexander G

    2007-07-01

    This paper deals with adaptive tracking for discrete-time multiple-input-multiple-output (MIMO) nonlinear systems in presence of bounded disturbances. In this paper, a high-order neural network (HONN) structure is used to approximate a control law designed by the backstepping technique, applied to a block strict feedback form (BSFF). This paper also includes the respective stability analysis, on the basis of the Lyapunov approach, for the whole controlled system, including the extended Kalman filter (EKF)-based NN learning algorithm. Applicability of the scheme is illustrated via simulation for a discrete-time nonlinear model of an electric induction motor.

  20. Neural avalanches at the critical point between replay and non-replay of spatiotemporal patterns.

    PubMed

    Scarpetta, Silvia; de Candia, Antonio

    2013-01-01

    We model spontaneous cortical activity with a network of coupled spiking units, in which multiple spatio-temporal patterns are stored as dynamical attractors. We introduce an order parameter, which measures the overlap (similarity) between the activity of the network and the stored patterns. We find that, depending on the excitability of the network, different working regimes are possible. For high excitability, the dynamical attractors are stable, and a collective activity that replays one of the stored patterns emerges spontaneously, while for low excitability, no replay is induced. Between these two regimes, there is a critical region in which the dynamical attractors are unstable, and intermittent short replays are induced by noise. At the critical spiking threshold, the order parameter goes from zero to one, and its fluctuations are maximized, as expected for a phase transition (and as observed in recent experimental results in the brain). Notably, in this critical region, the avalanche size and duration distributions follow power laws. Critical exponents are consistent with a scaling relationship observed recently in neural avalanches measurements. In conclusion, our simple model suggests that avalanche power laws in cortical spontaneous activity may be the effect of a network at the critical point between the replay and non-replay of spatio-temporal patterns.

  1. Fault tolerance of artificial neural networks with applications in critical systems

    NASA Technical Reports Server (NTRS)

    Protzel, Peter W.; Palumbo, Daniel L.; Arras, Michael K.

    1992-01-01

    This paper investigates the fault tolerance characteristics of time continuous recurrent artificial neural networks (ANN) that can be used to solve optimization problems. The principle of operations and performance of these networks are first illustrated by using well-known model problems like the traveling salesman problem and the assignment problem. The ANNs are then subjected to 13 simultaneous 'stuck at 1' or 'stuck at 0' faults for network sizes of up to 900 'neurons'. The effects of these faults is demonstrated and the cause for the observed fault tolerance is discussed. An application is presented in which a network performs a critical task for a real-time distributed processing system by generating new task allocations during the reconfiguration of the system. The performance degradation of the ANN under the presence of faults is investigated by large-scale simulations, and the potential benefits of delegating a critical task to a fault tolerant network are discussed.

  2. Optimal system size for complex dynamics in random neural networks near criticality

    SciTech Connect

    Wainrib, Gilles; García del Molino, Luis Carlos

    2013-12-15

    In this article, we consider a model of dynamical agents coupled through a random connectivity matrix, as introduced by Sompolinsky et al. [Phys. Rev. Lett. 61(3), 259–262 (1988)] in the context of random neural networks. When system size is infinite, it is known that increasing the disorder parameter induces a phase transition leading to chaotic dynamics. We observe and investigate here a novel phenomenon in the sub-critical regime for finite size systems: the probability of observing complex dynamics is maximal for an intermediate system size when the disorder is close enough to criticality. We give a more general explanation of this type of system size resonance in the framework of extreme values theory for eigenvalues of random matrices.

  3. Nonlinear systems modeling based on self-organizing fuzzy-neural-network with adaptive computation algorithm.

    PubMed

    Han, Honggui; Wu, Xiao-Long; Qiao, Jun-Fei

    2014-04-01

    In this paper, a self-organizing fuzzy-neural-network with adaptive computation algorithm (SOFNN-ACA) is proposed for modeling a class of nonlinear systems. This SOFNN-ACA is constructed online via simultaneous structure and parameter learning processes. In structure learning, a set of fuzzy rules can be self-designed using an information-theoretic methodology. The fuzzy rules with high spiking intensities (SI) are divided into new ones. And the fuzzy rules with a small relative mutual information (RMI) value will be pruned in order to simplify the FNN structure. In parameter learning, the consequent part parameters are learned through the use of an ACA that incorporates an adaptive learning rate strategy into the learning process to accelerate the convergence speed. Then, the convergence of SOFNN-ACA is analyzed. Finally, the proposed SOFNN-ACA is used to model nonlinear systems. The modeling results demonstrate that this proposed SOFNN-ACA can model nonlinear systems effectively.

  4. A Neural Network Approach to Intention Modeling for User-Adapted Conversational Agents

    PubMed Central

    Griol, David

    2016-01-01

    Spoken dialogue systems have been proposed to enable a more natural and intuitive interaction with the environment and human-computer interfaces. In this contribution, we present a framework based on neural networks that allows modeling of the user's intention during the dialogue and uses this prediction to dynamically adapt the dialogue model of the system taking into consideration the user's needs and preferences. We have evaluated our proposal to develop a user-adapted spoken dialogue system that facilitates tourist information and services and provide a detailed discussion of the positive influence of our proposal in the success of the interaction, the information and services provided, and the quality perceived by the users. PMID:26819592

  5. Neural Network Control-Based Adaptive Learning Design for Nonlinear Systems With Full-State Constraints.

    PubMed

    Liu, Yan-Jun; Li, Jing; Tong, Shaocheng; Chen, C L Philip

    2016-07-01

    In order to stabilize a class of uncertain nonlinear strict-feedback systems with full-state constraints, an adaptive neural network control method is investigated in this paper. The state constraints are frequently emerged in the real-life plants and how to avoid the violation of state constraints is an important task. By introducing a barrier Lyapunov function (BLF) to every step in a backstepping procedure, a novel adaptive backstepping design is well developed to ensure that the full-state constraints are not violated. At the same time, one remarkable feature is that the minimal learning parameters are employed in BLF backstepping design. By making use of Lyapunov analysis, we can prove that all the signals in the closed-loop system are semiglobal uniformly ultimately bounded and the output is well driven to follow the desired output. Finally, a simulation is given to verify the effectiveness of the method.

  6. Crop classification by forward neural network with adaptive chaotic particle swarm optimization.

    PubMed

    Zhang, Yudong; Wu, Lenan

    2011-01-01

    This paper proposes a hybrid crop classifier for polarimetric synthetic aperture radar (SAR) images. The feature sets consisted of span image, the H/A/α decomposition, and the gray-level co-occurrence matrix (GLCM) based texture features. Then, the features were reduced by principle component analysis (PCA). Finally, a two-hidden-layer forward neural network (NN) was constructed and trained by adaptive chaotic particle swarm optimization (ACPSO). K-fold cross validation was employed to enhance generation. The experimental results on Flevoland sites demonstrate the superiority of ACPSO to back-propagation (BP), adaptive BP (ABP), momentum BP (MBP), Particle Swarm Optimization (PSO), and Resilient back-propagation (RPROP) methods. Moreover, the computation time for each pixel is only 1.08 × 10(-7) s.

  7. Modeling the behavioral substrates of associate learning and memory - Adaptive neural models

    NASA Technical Reports Server (NTRS)

    Lee, Chuen-Chien

    1991-01-01

    Three adaptive single-neuron models based on neural analogies of behavior modification episodes are proposed, which attempt to bridge the gap between psychology and neurophysiology. The proposed models capture the predictive nature of Pavlovian conditioning, which is essential to the theory of adaptive/learning systems. The models learn to anticipate the occurrence of a conditioned response before the presence of a reinforcing stimulus when training is complete. Furthermore, each model can find the most nonredundant and earliest predictor of reinforcement. The behavior of the models accounts for several aspects of basic animal learning phenomena in Pavlovian conditioning beyond previous related models. Computer simulations show how well the models fit empirical data from various animal learning paradigms.

  8. Crop Classification by Forward Neural Network with Adaptive Chaotic Particle Swarm Optimization

    PubMed Central

    Zhang, Yudong; Wu, Lenan

    2011-01-01

    This paper proposes a hybrid crop classifier for polarimetric synthetic aperture radar (SAR) images. The feature sets consisted of span image, the H/A/α decomposition, and the gray-level co-occurrence matrix (GLCM) based texture features. Then, the features were reduced by principle component analysis (PCA). Finally, a two-hidden-layer forward neural network (NN) was constructed and trained by adaptive chaotic particle swarm optimization (ACPSO). K-fold cross validation was employed to enhance generation. The experimental results on Flevoland sites demonstrate the superiority of ACPSO to back-propagation (BP), adaptive BP (ABP), momentum BP (MBP), Particle Swarm Optimization (PSO), and Resilient back-propagation (RPROP) methods. Moreover, the computation time for each pixel is only 1.08 × 10−7 s. PMID:22163872

  9. Adaptive Actor-Critic Design-Based Integral Sliding-Mode Control for Partially Unknown Nonlinear Systems With Input Disturbances.

    PubMed

    Fan, Quan-Yong; Yang, Guang-Hong

    2016-01-01

    This paper is concerned with the problem of integral sliding-mode control for a class of nonlinear systems with input disturbances and unknown nonlinear terms through the adaptive actor-critic (AC) control method. The main objective is to design a sliding-mode control methodology based on the adaptive dynamic programming (ADP) method, so that the closed-loop system with time-varying disturbances is stable and the nearly optimal performance of the sliding-mode dynamics can be guaranteed. In the first step, a neural network (NN)-based observer and a disturbance observer are designed to approximate the unknown nonlinear terms and estimate the input disturbances, respectively. Based on the NN approximations and disturbance estimations, the discontinuous part of the sliding-mode control is constructed to eliminate the effect of the disturbances and attain the expected equivalent sliding-mode dynamics. Then, the ADP method with AC structure is presented to learn the optimal control for the sliding-mode dynamics online. Reconstructed tuning laws are developed to guarantee the stability of the sliding-mode dynamics and the convergence of the weights of critic and actor NNs. Finally, the simulation results are presented to illustrate the effectiveness of the proposed method.

  10. Matched Behavioral and Neural Adaptations for Low Sound Level Echolocation in a Gleaning Bat, Antrozous pallidus

    PubMed Central

    Measor, Kevin R.; Leavell, Brian C.; Brewton, Dustin H.; Rumschlag, Jeffrey; Barber, Jesse R.

    2017-01-01

    Abstract In active sensing, animals make motor adjustments to match sensory inputs to specialized neural circuitry. Here, we describe an active sensing system for sound level processing. The pallid bat uses downward frequency-modulated (FM) sweeps as echolocation calls for general orientation and obstacle avoidance. The bat’s auditory cortex contains a region selective for these FM sweeps (FM sweep-selective region, FMSR). We show that the vast majority of FMSR neurons are sensitive and strongly selective for relatively low levels (30-60 dB SPL). Behavioral testing shows that when a flying bat approaches a target, it reduces output call levels to keep echo levels between ∼30 and 55 dB SPL. Thus, the pallid bat behaviorally matches echo levels to an optimized neural representation of sound levels. FMSR neurons are more selective for sound levels of FM sweeps than tones, suggesting that across-frequency integration enhances level tuning. Level-dependent timing of high-frequency sideband inhibition in the receptive field shapes increased level selectivity for FM sweeps. Together with previous studies, these data indicate that the same receptive field properties shape multiple filters (sweep direction, rate, and level) for FM sweeps, a sound common in multiple vocalizations, including human speech. The matched behavioral and neural adaptations for low-intensity echolocation in the pallid bat will facilitate foraging with reduced probability of acoustic detection by prey. PMID:28275715

  11. Pipelined chebyshev functional link artificial recurrent neural network for nonlinear adaptive filter.

    PubMed

    Zhao, Haiquan; Zhang, Jiashu

    2010-02-01

    A novel nonlinear adaptive filter with pipelined Chebyshev functional link artificial recurrent neural network (PCFLARNN) is presented in this paper, which uses a modification real-time recurrent learning algorithm. The PCFLARNN consists of a number of simple small-scale Chebyshev functional link artificial recurrent neural network (CFLARNN) modules. Compared to the standard recurrent neural network (RNN), those modules of PCFLARNN can simultaneously be performed in a pipelined parallelism fashion, and this would lead to a significant improvement in its total computational efficiency. Furthermore, contrasted with the architecture of a pipelined RNN (PRNN), each module of PCFLARNN is a CFLARNN whose nonlinearity is introduced by enhancing the input pattern with Chebyshev functional expansion, whereas the RNN of each module in PRNN utilizing linear input and first-order recurrent term only fails to utilize the high-order terms of inputs. Therefore, the performance of PCFLARNN can further be improved at the cost of a slightly increased computational complexity. In addition, due to the introduced nonlinear functional expansion of each module in PRNN, the number of input signals can be reduced. Computer simulations have demonstrated that the proposed filter performs better than PRNN and RNN for nonlinear colored signal prediction, nonstationary speech signal prediction, and chaotic time series prediction.

  12. Diagonal recurrent neural network based adaptive control of nonlinear dynamical systems using lyapunov stability criterion.

    PubMed

    Kumar, Rajesh; Srivastava, Smriti; Gupta, J R P

    2017-03-01

    In this paper adaptive control of nonlinear dynamical systems using diagonal recurrent neural network (DRNN) is proposed. The structure of DRNN is a modification of fully connected recurrent neural network (FCRNN). Presence of self-recurrent neurons in the hidden layer of DRNN gives it an ability to capture the dynamic behaviour of the nonlinear plant under consideration (to be controlled). To ensure stability, update rules are developed using lyapunov stability criterion. These rules are then used for adjusting the various parameters of DRNN. The responses of plants obtained with DRNN are compared with those obtained when multi-layer feed forward neural network (MLFFNN) is used as a controller. Also, in example 4, FCRNN is also investigated and compared with DRNN and MLFFNN. Robustness of the proposed control scheme is also tested against parameter variations and disturbance signals. Four simulation examples including one-link robotic manipulator and inverted pendulum are considered on which the proposed controller is applied. The results so obtained show the superiority of DRNN over MLFFNN as a controller.

  13. On adaptive trajectory tracking of a robot manipulator using inversion of its neural emulator.

    PubMed

    Behera, L; Gopal, M; Chaudhury, S

    1996-01-01

    This paper is concerned with the design of a neuro-adaptive trajectory tracking controller. The paper presents a new control scheme based on inversion of a feedforward neural model of a robot arm. The proposed control scheme requires two modules. The first module consists of an appropriate feedforward neural model of forward dynamics of the robot arm that continuously accounts for the changes in the robot dynamics. The second module implements an efficient network inversion algorithm that computes the control action by inverting the neural model. In this paper, a new extended Kalman filter (EKF) based network inversion scheme is proposed. The scheme is evaluated through comparison with two other schemes of network inversion: gradient search in input space and Lyapunov function approach. Using these three inversion schemes the proposed controller was implemented for trajectory tracking control of a two-link manipulator. Simulation results in all cases confirm the efficacy of control input prediction using network inversion. Comparison of the inversion algorithms in terms of tracking accuracy showed the superior performance of the EKF based inversion scheme over others.

  14. Adaptive Resonance Theory Neural Networks for Astronomical Region of Interest Detection and Noise Characterization

    NASA Astrophysics Data System (ADS)

    Young, R. J.; Ritthaler, M. Healy, M.; Caudell, T. P. Zimmer, P.; McGraw, J.

    2007-10-01

    While learning algorithms have been used for astronomical data analysis, the vast majority of those algorithms have used supervised learning. We examine the use of two types of Adaptive Resonance Theory (ART) (Carpenter & Grossberg 1987) neural networks which use unsupervised learning for this task. Using synthetic astronomical data from SkyMaker which was designed to mimic the dynamic range of the CTI-II telescope, we compared the ability of the ART-1 neural network and the ART-1 neural network with a category theoretic modification to detect regions of interest and to characterize noise. We use the program SExtractor to pinpoint clusters that contain either many or no object hits. We then show that there are more targets in the clusters with many SExtractor hits than SExtractor finds. We also show that ART clusters together input regions that are dominated by noise that can be used to characterize the noise in an image. The results provided show that unsupervised learning algorithms should not be overlooked for astronomical data analysis.

  15. Low-complexity nonlinear adaptive filter based on a pipelined bilinear recurrent neural network.

    PubMed

    Zhao, Haiquan; Zeng, Xiangping; He, Zhengyou

    2011-09-01

    To reduce the computational complexity of the bilinear recurrent neural network (BLRNN), a novel low-complexity nonlinear adaptive filter with a pipelined bilinear recurrent neural network (PBLRNN) is presented in this paper. The PBLRNN, inheriting the modular architectures of the pipelined RNN proposed by Haykin and Li, comprises a number of BLRNN modules that are cascaded in a chained form. Each module is implemented by a small-scale BLRNN with internal dynamics. Since those modules of the PBLRNN can be performed simultaneously in a pipelined parallelism fashion, it would result in a significant improvement of computational efficiency. Moreover, due to nesting module, the performance of the PBLRNN can be further improved. To suit for the modular architectures, a modified adaptive amplitude real-time recurrent learning algorithm is derived on the gradient descent approach. Extensive simulations are carried out to evaluate the performance of the PBLRNN on nonlinear system identification, nonlinear channel equalization, and chaotic time series prediction. Experimental results show that the PBLRNN provides considerably better performance compared to the single BLRNN and RNN models.

  16. Distributed Adaptive Coordinated Control of Multi-Manipulator Systems Using Neural Networks

    NASA Astrophysics Data System (ADS)

    Hou, Zeng-Guang; Cheng, Long; Tan, Min; Wang, Xu

    On many occasions, all the manipulators in the multi-manipulator system need to achieve the same joint configuration to fulfill certain coordination tasks. In this chapter, a distributed adaptive approach is proposed for solving this coordination problem based on the leader-follower strategy. The proposed algorithm is distributed because the controller for each follower manipulator is solely based on the information of connected neighbor manipulators, and the joint value of leader manipulator is only accessible to partial follower manipulators. The uncertain term in the manipulator's dynamics is considered in the controller design, and it is approximated by the adaptive neural network scheme. The neural network weight matrix is adjusted on-line by the projection method, and the pre-training phase is no longer required. Effects of approximation error and external disturbances are counteracted by employing the robustness signal. According to the theoretical analysis, all the joints of follower manipulators can be regulated into an arbitrary small neighborhood of the value of leader's joint. Finally, simulation results are given to demonstrate the satisfactory performance of the proposed method.

  17. Short-term training for explosive strength causes neural and mechanical adaptations.

    PubMed

    Tillin, Neale A; Pain, Matthew T G; Folland, Jonathan P

    2012-05-01

    This study investigated the neural and peripheral adaptations to short-term training for explosive force production. Ten men trained the knee extensors with unilateral explosive isometric contractions (1 s 'fast and hard') for 4 weeks. Before and after training, force was recorded at 50-ms intervals from force onset (F(50), F(100) and F(150)) during both voluntary and involuntary (supramaximal evoked octet; eight pulses at 300 Hz) explosive isometric contractions. Neural drive during the explosive voluntary contractions was measured with the ratio of voluntary/octet force, and average EMG normalized to the peak-to-peak M-wave of the three superficial quadriceps. Maximal voluntary force (MVF) was also measured, and ultrasonic images of the vastus lateralis were recorded during ramped contractions to assess muscle-tendon unit stiffness between 50 and 90% MVF. There was an increase in voluntary F(50) (+54%), F(100) (+15%) and F(150) (+14%) and in octet F(50) (+7%) and F(100) (+10%). Voluntary F(100) and F(150), and octet F(50) and F(100) increased proportionally with MVF (+11%). However, the increase in voluntary F(50) was +37% even after normalization to MVF, and coincided with a 42% increase in both voluntary/octet force and agonist-normalized EMG over the first 50 ms. Muscle-tendon unit stiffness between 50 and 90% MVF also increased. In conclusion, enhanced agonist neural drive and MVF accounted for improved explosive voluntary force production in the early and late phases of the contraction, respectively. The increases in explosive octet force and muscle-tendon unit stiffness provide novel evidence of peripheral adaptations within merely 4 weeks of training for explosive force production.

  18. Motor learning and cross-limb transfer rely upon distinct neural adaptation processes.

    PubMed

    Stöckel, Tino; Carroll, Timothy J; Summers, Jeffery J; Hinder, Mark R

    2016-08-01

    Performance benefits conferred in the untrained limb after unilateral motor practice are termed cross-limb transfer. Although the effect is robust, the neural mechanisms remain incompletely understood. In this study we used noninvasive brain stimulation to reveal that the neural adaptations that mediate motor learning in the trained limb are distinct from those that underlie cross-limb transfer to the opposite limb. Thirty-six participants practiced a ballistic motor task with their right index finger (150 trials), followed by intermittent theta-burst stimulation (iTBS) applied to the trained (contralateral) primary motor cortex (cM1 group), the untrained (ipsilateral) M1 (iM1 group), or the vertex (sham group). After stimulation, another 150 training trials were undertaken. Motor performance and corticospinal excitability were assessed before motor training, pre- and post-iTBS, and after the second training bout. For all groups, training significantly increased performance and excitability of the trained hand, and performance, but not excitability, of the untrained hand, indicating transfer at the level of task performance. The typical facilitatory effect of iTBS on MEPs was reversed for cM1, suggesting homeostatic metaplasticity, and prior performance gains in the trained hand were degraded, suggesting that iTBS interfered with learning. In stark contrast, iM1 iTBS facilitated both performance and excitability for the untrained hand. Importantly, the effects of cM1 and iM1 iTBS on behavior were exclusive to the hand contralateral to stimulation, suggesting that adaptations within the untrained M1 contribute to cross-limb transfer. However, the neural processes that mediate learning in the trained hemisphere vs. transfer in the untrained hemisphere appear distinct.

  19. Adaptive neural tracking control for a class of nonstrict-feedback stochastic nonlinear systems with unknown backlash-like hysteresis.

    PubMed

    Wang, Huanqing; Chen, Bing; Liu, Kefu; Liu, Xiaoping; Lin, Chong

    2014-05-01

    This paper considers the problem of adaptive neural control of stochastic nonlinear systems in nonstrict-feedback form with unknown backlash-like hysteresis nonlinearities. To overcome the design difficulty of nonstrict-feedback structure, variable separation technique is used to decompose the unknown functions of all state variables into a sum of smooth functions of each error dynamic. By combining radial basis function neural networks' universal approximation capability with an adaptive backstepping technique, an adaptive neural control algorithm is proposed. It is shown that the proposed controller guarantees that all the signals in the closed-loop system are four-moment semiglobally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood of the origin in the sense of mean quartic value. Simulation results further show the effectiveness of the presented control scheme.

  20. Prescribed performance synchronization controller design of fractional-order chaotic systems: An adaptive neural network control approach

    NASA Astrophysics Data System (ADS)

    Li, Yuan; Lv, Hui; Jiao, Dongxiu

    2017-03-01

    In this study, an adaptive neural network synchronization (NNS) approach, capable of guaranteeing prescribed performance (PP), is designed for non-identical fractional-order chaotic systems (FOCSs). For PP synchronization, we mean that the synchronization error converges to an arbitrary small region of the origin with convergence rate greater than some function given in advance. Neural networks are utilized to estimate unknown nonlinear functions in the closed-loop system. Based on the integer-order Lyapunov stability theorem, a fractional-order adaptive NNS controller is designed, and the PP can be guaranteed. Finally, simulation results are presented to confirm our results.

  1. Anti-synchronization for stochastic memristor-based neural networks with non-modeled dynamics via adaptive control approach

    NASA Astrophysics Data System (ADS)

    Zhao, Hui; Li, Lixiang; Peng, Haipeng; Kurths, Jürgen; Xiao, Jinghua; Yang, Yixian

    2015-05-01

    In this paper, exponential anti-synchronization in mean square of an uncertain memristor-based neural network is studied. The uncertain terms include non-modeled dynamics with boundary and stochastic perturbations. Based on the differential inclusions theory, linear matrix inequalities, Gronwall's inequality and adaptive control technique, an adaptive controller with update laws is developed to realize the exponential anti-synchronization. Adaptive controller can adjust itself behavior to get the best performance, according to the environment is changing or the environment has changed, which has the ability to adapt to environmental change. Furthermore, a numerical example is provided to validate the effectiveness of the proposed method.

  2. Distributed recurrent neural forward models with synaptic adaptation and CPG-based control for complex behaviors of walking robots

    PubMed Central

    Dasgupta, Sakyasingha; Goldschmidt, Dennis; Wörgötter, Florentin; Manoonpong, Poramate

    2015-01-01

    Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of (1) central pattern generator based control for generating basic rhythmic patterns and coordinated movements, (2) distributed (at each leg) recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and (3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex locomotive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps, leg damage adaptations, as well as climbing over high obstacles. Furthermore, we demonstrate that the newly developed recurrent network based approach to online forward models outperforms the adaptive neuron forward models

  3. The Neural Dynamics of Conflict Adaptation within a Look-to-Do Transition

    PubMed Central

    Tang, Dandan; Hu, Li; Li, Hong; Zhang, Qinglin; Chen, Antao

    2013-01-01

    Background For optimal performance in conflict situations, conflict adaptation (conflict detection and adjustment) is necessary. However, the neural dynamics of conflict adaptation is still unclear. Methods In the present study, behavioral and electroencephalography (EEG) data were recorded from seventeen healthy participants during performance of a color-word Stroop task with a novel look-to-do transition. Within this transition, participants looked at the Stroop stimuli but no responses were required in the ‘look’ trials; or made manual responses to the Stroop stimuli in the ‘do’ trials. Results In the ‘look’ trials, the amplitude modulation of N450 occurred exclusively in the right-frontal region. Subsequently, the amplitude modulation of sustained potential (SP) emerged in the posterior parietal and right-frontal regions. A significantly positive correlation between the modulation of reconfiguration in the ‘look’ trials and the behavioral conflict adaptation in the ‘do’ trials was observed. Specially, a stronger information flow from right-frontal region to posterior parietal region in the beta band was observed for incongruent condition than for congruent condition. In the ‘do’ trials, the conflict of ‘look’ trials enhanced the amplitude modulations of N450 in the right-frontal and posterior parietal regions, but decreased the amplitude modulations of SP in these regions. Uniquely, a stronger information flow from centro-parietal region to right-frontal region in the theta band was observed for iI condition than for cI condition. Conclusion All these findings showed that top-down conflict adaptation is implemented by: (1) enhancing the sensitivity to conflict detection and the adaptation to conflict resolution; (2) modulating the effective connectivity between parietal region and right-frontal region. PMID:23469102

  4. Insulin concentration is critical in culturing human neural stem cells and neurons

    PubMed Central

    Rhee, Y-H; Choi, M; Lee, H-S; Park, C-H; Kim, S-M; Yi, S-H; Oh, S-M; Cha, H-J; Chang, M-Y; Lee, S-H

    2013-01-01

    Cell culture of human-derived neural stem cells (NSCs) is a useful tool that contributes to our understanding of human brain development and allows for the development of therapies for intractable human brain disorders. Human NSC (hNSC) cultures, however, are not commonly used, mainly because of difficulty with consistently maintaining the cells in a healthy state. In this study, we show that hNSC cultures, unlike NSCs of rodent origins, are extremely sensitive to insulin, an indispensable culture supplement, and that the previously reported difficulty in culturing hNSCs is likely because of a lack of understanding of this relationship. Like other neural cell cultures, insulin is required for hNSC growth, as withdrawal of insulin supplementation results in massive cell death and delayed cell growth. However, severe apoptotic cell death was also detected in insulin concentrations optimized to rodent NSC cultures. Thus, healthy hNSC cultures were only produced in a narrow range of relatively low insulin concentrations. Insulin-mediated cell death manifested not only in all human NSCs tested, regardless of origin, but also in differentiated human neurons. The underlying cell death mechanism at high insulin concentrations was similar to insulin resistance, where cells became less responsive to insulin, resulting in a reduction in the activation of the PI3K/Akt pathway critical to cell survival signaling. PMID:23928705

  5. Intelligent adaptive nonlinear flight control for a high performance aircraft with neural networks.

    PubMed

    Savran, Aydogan; Tasaltin, Ramazan; Becerikli, Yasar

    2006-04-01

    This paper describes the development of a neural network (NN) based adaptive flight control system for a high performance aircraft. The main contribution of this work is that the proposed control system is able to compensate the system uncertainties, adapt to the changes in flight conditions, and accommodate the system failures. The underlying study can be considered in two phases. The objective of the first phase is to model the dynamic behavior of a nonlinear F-16 model using NNs. Therefore a NN-based adaptive identification model is developed for three angular rates of the aircraft. An on-line training procedure is developed to adapt the changes in the system dynamics and improve the identification accuracy. In this procedure, a first-in first-out stack is used to store a certain history of the input-output data. The training is performed over the whole data in the stack at every stage. To speed up the convergence rate and enhance the accuracy for achieving the on-line learning, the Levenberg-Marquardt optimization method with a trust region approach is adapted to train the NNs. The objective of the second phase is to develop intelligent flight controllers. A NN-based adaptive PID control scheme that is composed of an emulator NN, an estimator NN, and a discrete time PID controller is developed. The emulator NN is used to calculate the system Jacobian required to train the estimator NN. The estimator NN, which is trained on-line by propagating the output error through the emulator, is used to adjust the PID gains. The NN-based adaptive PID control system is applied to control three angular rates of the nonlinear F-16 model. The body-axis pitch, roll, and yaw rates are fed back via the PID controllers to the elevator, aileron, and rudder actuators, respectively. The resulting control system has learning, adaptation, and fault-tolerant abilities. It avoids the storage and interpolation requirements for the too many controller parameters of a typical flight control

  6. An adaptive workspace hypothesis about the neural correlates of consciousness: insights from neuroscience and meditation studies.

    PubMed

    Raffone, Antonino; Srinivasan, Narayanan

    2009-01-01

    While enormous progress has been made to identify neural correlates of consciousness (NCC), crucial NCC aspects are still very controversial. A major hurdle is the lack of an adequate definition and characterization of different aspects of conscious experience and also its relationship to attention and metacognitive processes like monitoring. In this paper, we therefore attempt to develop a unitary theoretical framework for NCC, with an interdependent characterization of endogenous attention, access consciousness, phenomenal awareness, metacognitive consciousness, and a non-referential form of unified consciousness. We advance an adaptive workspace hypothesis about the NCC based on the global workspace model emphasizing transient resonant neurodynamics and prefrontal cortex function, as well as meditation-related characterizations of conscious experiences. In this hypothesis, transient dynamic links within an adaptive coding net in prefrontal cortex, especially in anterior prefrontal cortex, and between it and the rest of the brain, in terms of ongoing intrinsic and long-range signal exchanges, flexibly regulate the interplay between endogenous attention, access consciousness, phenomenal awareness, and metacognitive consciousness processes. Such processes are established in terms of complementary aspects of an ongoing transition between context-sensitive global workspace assemblies, modulated moment-to-moment by body and environment states. Brain regions associated to momentary interoceptive and exteroceptive self-awareness, or first-person experiential perspective as emphasized in open monitoring meditation, play an important modulatory role in adaptive workspace transitions.

  7. Adaptive Estimation of Active Contour Parameters Using Convolutional Neural Networks and Texture Analysis.

    PubMed

    Hoogi, Assaf; Subramaniam, Arjun; Veerapaneni, Rishi; Rubin, Daniel

    2016-11-11

    In this paper, we propose a generalization of the level set segmentation approach by supplying a novel method for adaptive estimation of active contour parameters. The presented segmentation method is fully automatic once the lesion has been detected. First, the location of the level set contour relative to the lesion is estimated using a convolutional neural network (CNN). The CNN has two convolutional layers for feature extraction, which lead into dense layers for classification. Second, the output CNN probabilities are then used to adaptively calculate the parameters of the active contour functional during the segmentation process. Finally, the adaptive window size surrounding each contour point is re-estimated by an iterative process that considers lesion size and spatial texture. We demonstrate the capabilities of our method on a dataset of 164 MRI and 112 CT images of liver lesions that includes low contrast and heterogeneous lesions as well as noisy images. To illustrate the strength of our method, we evaluated it against state of the art CNNbased and active contour techniques. For all cases, our method, as assessed by Dice similarity coefficients, performed significantly better than currently available methods. An average Dice improvement of 0.27 was found across the entire dataset over all comparisons. We also analyzed two challenging subsets of lesions and obtained a significant Dice improvement of ����.�������� with our method (p < 0.001, Wilcoxon).

  8. Adaptive Estimation of Active Contour Parameters Using Convolutional Neural Networks and Texture Analysis.

    PubMed

    Hoogi, Assaf; Subramaniam, Arjun; Veerapaneni, Rishi; Rubin, Daniel

    2016-11-11

    In this paper, we propose a generalization of the level set segmentation approach by supplying a novel method for adaptive estimation of active contour parameters. The presented segmentation method is fully automatic once the lesion has been detected. First, the location of the level set contour relative to the lesion is estimated using a convolutional neural network (CNN). The CNN has two convolutional layers for feature extraction, which lead into dense layers for classification. Second, the output CNN probabilities are then used to adaptively calculate the parameters of the active contour functional during the segmentation process. Finally, the adaptive window size surrounding each contour point is re-estimated by an iterative process that considers lesion size and spatial texture. We demonstrate the capabilities of our method on a dataset of 164 MRI and 112 CT images of liver lesions that includes low contrast and heterogeneous lesions as well as noisy images. To illustrate the strength of our method, we evaluated it against state of the art CNNbased and active contour techniques. For all cases, our method, as assessed by Dice similarity coefficients, performed significantly better than currently available methods. An average Dice improvement of 0.27 was found across the entire dataset over all comparisons. We also analyzed two challenging subsets of lesions and obtained a significant Dice improvement of 0.24 with our method (p < 0.001, Wilcoxon).

  9. Sustained activity in hierarchical modular neural networks: self-organized criticality and oscillations.

    PubMed

    Wang, Sheng-Jun; Hilgetag, Claus C; Zhou, Changsong

    2011-01-01

    Cerebral cortical brain networks possess a number of conspicuous features of structure and dynamics. First, these networks have an intricate, non-random organization. In particular, they are structured in a hierarchical modular fashion, from large-scale regions of the whole brain, via cortical areas and area subcompartments organized as structural and functional maps to cortical columns, and finally circuits made up of individual neurons. Second, the networks display self-organized sustained activity, which is persistent in the absence of external stimuli. At the systems level, such activity is characterized by complex rhythmical oscillations over a broadband background, while at the cellular level, neuronal discharges have been observed to display avalanches, indicating that cortical networks are at the state of self-organized criticality (SOC). We explored the relationship between hierarchical neural network organization and sustained dynamics using large-scale network modeling. Previously, it was shown that sparse random networks with balanced excitation and inhibition can sustain neural activity without external stimulation. We found that a hierarchical modular architecture can generate sustained activity better than random networks. Moreover, the system can simultaneously support rhythmical oscillations and SOC, which are not present in the respective random networks. The mechanism underlying the sustained activity is that each dense module cannot sustain activity on its own, but displays SOC in the presence of weak perturbations. Therefore, the hierarchical modular networks provide the coupling among subsystems with SOC. These results imply that the hierarchical modular architecture of cortical networks plays an important role in shaping the ongoing spontaneous activity of the brain, potentially allowing the system to take advantage of both the sensitivity of critical states and the predictability and timing of oscillations for efficient information

  10. Neural adaptations to resistive exercise: mechanisms and recommendations for training practices.

    PubMed

    Gabriel, David A; Kamen, Gary; Frost, Gail

    2006-01-01

    It is generally accepted that neural factors play an important role in muscle strength gains. This article reviews the neural adaptations in strength, with the goal of laying the foundations for practical applications in sports medicine and rehabilitation. An increase in muscular strength without noticeable hypertrophy is the first line of evidence for neural involvement in acquisition of muscular strength. The use of surface electromyographic (SEMG) techniques reveal that strength gains in the early phase of a training regimen are associated with an increase in the amplitude of SEMG activity. This has been interpreted as an increase in neural drive, which denotes the magnitude of efferent neural output from the CNS to active muscle fibres. However, SEMG activity is a global measure of muscle activity. Underlying alterations in SEMG activity are changes in motor unit firing patterns as measured by indwelling (wire or needle) electrodes. Some studies have reported a transient increase in motor unit firing rate. Training-related increases in the rate of tension development have also been linked with an increased probability of doublet firing in individual motor units. A doublet is a very short interspike interval in a motor unit train, and usually occurs at the onset of a muscular contraction. Motor unit synchronisation is another possible mechanism for increases in muscle strength, but has yet to be definitely demonstrated. There are several lines of evidence for central control of training-related adaptation to resistive exercise. Mental practice using imagined contractions has been shown to increase the excitability of the cortical areas involved in movement and motion planning. However, training using imagined contractions is unlikely to be as effective as physical training, and it may be more applicable to rehabilitation. Retention of strength gains after dissipation of physiological effects demonstrates a strong practice effect. Bilateral contractions are

  11. Adaptive neural network control for a class of low-triangular-structured nonlinear systems.

    PubMed

    Du, Hongbin; Shao, Huihe; Yao, Pingjing

    2006-03-01

    In this paper, a class of unknown perturbed nonlinear systems is theoretically stabilized by using adaptive neural network control. The systems, with disturbances and nonaffine unknown functions, have low triangular structure, which generalizes both strict-feedback uncertain systems and pure-feedback ones. There do not exist any effective methods to stabilize this kind of systems. With some new conclusions for Nussbaum-Gain functions (NGF) and the idea of backstepping, semiglobal, uniformal, and ultimate boundedness of all the signals in the closed-loop is proved at equilibrium point. The two problems, control directions and control singularity, are well dealt with. The effectiveness of proposed scheme is shown by simulation on a proper nonlinear system.

  12. Stochastic Oscillation in Self-Organized Critical States of Small Systems: Sensitive Resting State in Neural Systems

    NASA Astrophysics Data System (ADS)

    Wang, Sheng-Jun; Ouyang, Guang; Guang, Jing; Zhang, Mingsha; Wong, K. Y. Michael; Zhou, Changsong

    2016-01-01

    Self-organized critical states (SOCs) and stochastic oscillations (SOs) are simultaneously observed in neural systems, which appears to be theoretically contradictory since SOCs are characterized by scale-free avalanche sizes but oscillations indicate typical scales. Here, we show that SOs can emerge in SOCs of small size systems due to temporal correlation between large avalanches at the finite-size cutoff, resulting from the accumulation-release process in SOCs. In contrast, the critical branching process without accumulation-release dynamics cannot exhibit oscillations. The reconciliation of SOCs and SOs is demonstrated both in the sandpile model and robustly in biologically plausible neuronal networks. The oscillations can be suppressed if external inputs eliminate the prominent slow accumulation process, providing a potential explanation of the widely studied Berger effect or event-related desynchronization in neural response. The features of neural oscillations and suppression are confirmed during task processing in monkey eye-movement experiments. Our results suggest that finite-size, columnar neural circuits may play an important role in generating neural oscillations around the critical states, potentially enabling functional advantages of both SOCs and oscillations for sensitive response to transient stimuli.

  13. Fast response and high sensitivity to microsaccades in a cascading-adaptation neural network with short-term synaptic depression.

    PubMed

    Yuan, Wu-Jie; Zhou, Jian-Fang; Zhou, Changsong

    2016-04-01

    Microsaccades are very small eye movements during fixation. Experimentally, they have been found to play an important role in visual information processing. However, neural responses induced by microsaccades are not yet well understood and are rarely studied theoretically. Here we propose a network model with a cascading adaptation including both retinal adaptation and short-term depression (STD) at thalamocortical synapses. In the neural network model, we compare the microsaccade-induced neural responses in the presence of STD and those without STD. It is found that the cascading with STD can give rise to faster and sharper responses to microsaccades. Moreover, STD can enhance response effectiveness and sensitivity to microsaccadic spatiotemporal changes, suggesting improved detection of small eye movements (or moving visual objects). We also explore the mechanism of the response properties in the model. Our studies strongly indicate that STD plays an important role in neural responses to microsaccades. Our model considers simultaneously retinal adaptation and STD at thalamocortical synapses in the study of microsaccade-induced neural activity, and may be useful for further investigation of the functional roles of microsaccades in visual information processing.

  14. Neural adaptive chaotic control with constrained input using state and output feedback

    NASA Astrophysics Data System (ADS)

    Gao, Shi-Gen; Dong, Hai-Rong; Sun, Xu-Bin; Ning, Bin

    2015-01-01

    This paper presents neural adaptive control methods for a class of chaotic nonlinear systems in the presence of constrained input and unknown dynamics. To attenuate the influence of constrained input caused by actuator saturation, an effective auxiliary system is constructed to prevent the stability of closed loop system from being destroyed. Radial basis function neural networks (RBF-NNs) are used in the online learning of the unknown dynamics, which do not require an off-line training phase. Both state and output feedback control laws are developed. In the output feedback case, high-order sliding mode (HOSM) observer is utilized to estimate the unmeasurable system states. Simulation results are presented to verify the effectiveness of proposed schemes. Project supported by the National High Technology Research and Development Program of China (Grant No. 2012AA041701), the Fundamental Research Funds for Central Universities of China (Grant No. 2013JBZ007), the National Natural Science Foundation of China (Grant Nos. 61233001, 61322307, 61304196, and 61304157), and the Research Program of Beijing Jiaotong University, China (Grant No. RCS2012ZZ003).

  15. Self: an adaptive pressure arising from self-organization, chaotic dynamics, and neural Darwinism.

    PubMed

    Bruzzo, Angela Alessia; Vimal, Ram Lakhan Pandey

    2007-12-01

    In this article, we establish a model to delineate the emergence of "self" in the brain making recourse to the theory of chaos. Self is considered as the subjective experience of a subject. As essential ingredients of subjective experiences, our model includes wakefulness, re-entry, attention, memory, and proto-experiences. The stability as stated by chaos theory can potentially describe the non-linear function of "self" as sensitive to initial conditions and can characterize it as underlying order from apparently random signals. Self-similarity is discussed as a latent menace of a pathological confusion between "self" and "others". Our test hypothesis is that (1) consciousness might have emerged and evolved from a primordial potential or proto-experience in matter, such as the physical attractions and repulsions experienced by electrons, and (2) "self" arises from chaotic dynamics, self-organization and selective mechanisms during ontogenesis, while emerging post-ontogenically as an adaptive pressure driven by both volume and synaptic-neural transmission and influencing the functional connectivity of neural nets (structure).

  16. Nonlinear model identification and adaptive model predictive control using neural networks.

    PubMed

    Akpan, Vincent A; Hassapis, George D

    2011-04-01

    This paper presents two new adaptive model predictive control algorithms, both consisting of an on-line process identification part and a predictive control part. Both parts are executed at each sampling instant. The predictive control part of the first algorithm is the Nonlinear Model Predictive Control strategy and the control part of the second algorithm is the Generalized Predictive Control strategy. In the identification parts of both algorithms the process model is approximated by a series-parallel neural network structure which is trained by a recursive least squares (ARLS) method. The two control algorithms have been applied to: 1) the temperature control of a fluidized bed furnace reactor (FBFR) of a pilot plant and 2) the auto-pilot control of an F-16 aircraft. The training and validation data of the neural network are obtained from the open-loop simulation of the FBFR and the nonlinear F-16 aircraft models. The identification and control simulation results show that the first algorithm outperforms the second one at the expense of extra computation time.

  17. Prediction of Tensile Strength of Friction Stir Weld Joints with Adaptive Neuro-Fuzzy Inference System (ANFIS) and Neural Network

    NASA Technical Reports Server (NTRS)

    Dewan, Mohammad W.; Huggett, Daniel J.; Liao, T. Warren; Wahab, Muhammad A.; Okeil, Ayman M.

    2015-01-01

    Friction-stir-welding (FSW) is a solid-state joining process where joint properties are dependent on welding process parameters. In the current study three critical process parameters including spindle speed (??), plunge force (????), and welding speed (??) are considered key factors in the determination of ultimate tensile strength (UTS) of welded aluminum alloy joints. A total of 73 weld schedules were welded and tensile properties were subsequently obtained experimentally. It is observed that all three process parameters have direct influence on UTS of the welded joints. Utilizing experimental data, an optimized adaptive neuro-fuzzy inference system (ANFIS) model has been developed to predict UTS of FSW joints. A total of 1200 models were developed by varying the number of membership functions (MFs), type of MFs, and combination of four input variables (??,??,????,??????) utilizing a MATLAB platform. Note EFI denotes an empirical force index derived from the three process parameters. For comparison, optimized artificial neural network (ANN) models were also developed to predict UTS from FSW process parameters. By comparing ANFIS and ANN predicted results, it was found that optimized ANFIS models provide better results than ANN. This newly developed best ANFIS model could be utilized for prediction of UTS of FSW joints.

  18. Depression as a Mediator in the Relationship between Perceived Familial Criticism and College Adaptation

    ERIC Educational Resources Information Center

    Puff, Jayme; Kolomeyer, Ellen; McSwiggan, Meagan; Pearte, Catherine; Lauer, Brea-Anne; Renk, Kimberly

    2016-01-01

    Objective: This study examined relationships among emerging adults' perceived familial criticism, their depressive symptoms, and their college adaptation. Participants: The current study examined the responses of 412 emerging adults (300 females and 112 males) who were college students at a large southeastern university. The majority of these…

  19. An Online Adaptive Learning Environment for Critical-Thinking-Infused English Literacy Instruction

    ERIC Educational Resources Information Center

    Yang, Ya-Ting Carolyn; Gamble, Jeffrey Hugh; Hung, Yu-Wan; Lin, Tzu-Yun

    2014-01-01

    Critical thinking (CT) and English literacy are two essential 21st century competencies that are a priority for teaching and learning in an increasingly digital learning environment. Taking advantage of innovations in educational technology, this study empirically investigates the effectiveness of CT-infused adaptive English literacy instruction…

  20. Adaptive neural tracking control of a class of MIMO pure-feedback time-delay nonlinear systems with input saturation

    NASA Astrophysics Data System (ADS)

    Yang, Yang; Yue, Dong; Yuan, Deming

    2016-11-01

    Considering interconnections among subsystems, we propose an adaptive neural tracking control scheme for a class of multiple-input-multiple-output (MIMO) non-affine pure-feedback time-delay nonlinear systems with input saturation. Neural networks (NNs) are employed to approximate unknown functions in the design procedure, and the separation technology is introduced here to tackle the problem induced from unknown time-delay items. The adaptive neural tracking control scheme is constructed by combining Lyapunov-Krasovskii functionals, NNs, the auxiliary system, the implicit function theory and the mean value theorem along with the dynamic surface control technique. Also, it is proven that the strategy guarantees tracking errors converge to a small neighbourhood around the origin by appropriate choice of design parameters and all signals in the closed-loop system uniformly ultimately bounded. Numerical simulation results are presented to demonstrate the effectiveness of the proposed control strategy.

  1. Behavioral and neural correlates of visuomotor adaptation observed through a brain-computer interface in primary motor cortex.

    PubMed

    Chase, Steven M; Kass, Robert E; Schwartz, Andrew B

    2012-07-01

    Brain-computer interfaces (BCIs) provide a defined link between neural activity and devices, allowing a detailed study of the neural adaptive responses generating behavioral output. We trained monkeys to perform two-dimensional center-out movements of a computer cursor using a BCI. We then applied a perturbation by randomly selecting a subset of the recorded units and rotating their directional contributions to cursor movement by a consistent angle. Globally, this perturbation mimics a visuomotor transformation, and in the first part of this article we characterize the psychophysical indications of motor adaptation and compare them with known results from adaptation of natural reaching movements. Locally, however, only a subset of the neurons in the population actually contributes to error, allowing us to probe for signatures of neural adaptation that might be specific to the subset of neurons we perturbed. One compensation strategy would be to selectively adapt the subset of cells responsible for the error. An alternate strategy would be to globally adapt the entire population to correct the error. Using a recently developed mathematical technique that allows us to differentiate these two mechanisms, we found evidence of both strategies in the neural responses. The dominant strategy we observed was global, accounting for ∼86% of the total error reduction. The remaining 14% came from local changes in the tuning functions of the perturbed units. Interestingly, these local changes were specific to the details of the applied rotation: in particular, changes in the depth of tuning were only observed when the percentage of perturbed cells was small. These results imply that there may be constraints on the network's adaptive capabilities, at least for perturbations lasting only a few hundreds of trials.

  2. Enhanced neural function in highly aberrated eyes following perceptual learning with adaptive optics.

    PubMed

    Sabesan, Ramkumar; Barbot, Antoine; Yoon, Geunyoung

    2017-03-01

    Highly aberrated keratoconic (KC) eyes do not elicit the expected visual advantage from customized optical corrections. This is attributed to the neural insensitivity arising from chronic visual experience with poor retinal image quality, dominated by low spatial frequencies. The goal of this study was to investigate if targeted perceptual learning with adaptive optics (AO) can stimulate neural plasticity in these highly aberrated eyes. The worse eye of 2 KC subjects was trained in a contrast threshold test under AO correction. Prior to training, tumbling 'E' visual acuity and contrast sensitivity at 4, 8, 12, 16, 20, 24 and 28 c/deg were measured in both the trained and untrained eyes of each subject with their routine prescription and with AO correction for a 6mm pupil. The high spatial frequency requiring 50% contrast for detection with AO correction was picked as the training frequency. Subjects were required to train on a contrast detection test with AO correction for 1h for 5 consecutive days. During each training session, threshold contrast measurement at the training frequency with AO was conducted. Pre-training measures were repeated after the 5 training sessions in both eyes (i.e., post-training). After training, contrast sensitivity under AO correction improved on average across spatial frequency by a factor of 1.91 (range: 1.77-2.04) and 1.75 (1.22-2.34) for the two subjects. This improvement in contrast sensitivity transferred to visual acuity with the two subjects improving by 1.5 and 1.3 lines respectively with AO following training. One of the two subjects denoted an interocular transfer of training and an improvement in performance with their routine prescription post-training. This training-induced visual benefit demonstrates the potential of AO as a tool for neural rehabilitation in patients with abnormal corneas. Moreover, it reveals a sufficient degree of neural plasticity in normally developed adults who have a long history of abnormal visual

  3. Adaptive FIR neural model for centroid learning in self-organizing maps.

    PubMed

    Tucci, Mauro; Raugi, Marco

    2010-06-01

    In this paper, a training method for the formation of topology preserving maps is introduced. The proposed approach presents a sequential formulation of the self-organizing map (SOM), which is based on a new model of the neuron, or processing unit. Each neuron acts as a finite impulse response (FIR) system, and the coefficients of the filters are adaptively estimated during the sequential learning process, in order to minimize a distortion measure of the map. The proposed FIR-SOM model deals with static distributions and it computes an ordered set of centroids. Additionally, the FIR-SOM estimates the learning dynamic of each prototype using an adaptive FIR model. A noteworthy result is that the optimized coefficients of the FIR processes tend to represent a moving average filter, regardless of the underlying input distribution. The convergence of the resulting model is analyzed numerically and shows good properties with respect to the classic SOM and other unsupervised neural models. Finally, the optimal FIR coefficients are shown to be useful for visualizing the cluster densities.

  4. An adaptive wavelet neural network for spatio-temporal system identification.

    PubMed

    Wei, H L; Billings, S A; Zhao, Y F; Guo, L Z

    2010-12-01

    Starting from the basic concept of coupled map lattices, a new family of adaptive wavelet neural networks (AWNN) is introduced for spatio-temporal system identification, by combining an efficient wavelet representation with a coupled map lattice model. A new orthogonal projection pursuit (OPP) method, coupled with a particle swarm optimization (PSO) algorithm, is proposed for augmenting the proposed network. A novel two-stage hybrid training scheme is developed for constructing a parsimonious network model. In the first stage, by applying the orthogonal projection pursuit algorithm, significant wavelet neurons are adaptively and successively recruited into the network, where adjustable parameters of the associated wavelet neurons are optimized using a particle swarm optimizer. The resultant network model, obtained in the first stage, may however be redundant. In the second stage, an orthogonal least squares algorithm is then applied to refine and improve the initially trained network by removing redundant wavelet neurons from the network. The proposed two-stage hybrid training procedure can generally produce a parsimonious network model, where a ranked list of wavelet neurons, according to the capability of each neuron to represent the total variance in the system output signal is produced. Two spatio-temporal system identification examples are presented to demonstrate the performance of the proposed new modelling framework.

  5. Implications of movement-related cortical potential for understanding neural adaptations in muscle strength tasks.

    PubMed

    Lattari, Eduardo; Arias-Carrión, Oscar; Monteiro-Junior, Renato Sobral; Mello Portugal, Eduardo Matta; Paes, Flávia; Menéndez-González, Manuel; Silva, Adriana Cardoso; Nardi, Antonio Egidio; Machado, Sergio

    2014-03-06

    This systematic review aims to provide information about the implications of the movement-related cortical potential (MRCP) in acute and chronic responses to the counter resistance training. The structuring of the methods of this study followed the proposals of the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses). It was performed an electronically search in Pubmed/Medline and ISI Web of Knowledge data bases, from 1987 to 2013, besides the manual search in the selected references. The following terms were used: Bereitschaftspotential, MRCP, strength and force. The logical operator "AND" was used to combine descriptors and terms used to search publications. At the end, 11 studies attended all the eligibility criteria and the results demonstrated that the behavior of MRCP is altered because of different factors such as: force level, rate of force development, fatigue induced by exercise, and the specific phase of muscular action, leading to an increase in the amplitude in eccentric actions compared to concentric actions, in acute effects. The long-term adaptations demonstrated that the counter resistance training provokes an attenuation in the amplitude in areas related to the movement, which may be caused by neural adaptation occurred in the motor cortex.

  6. Adaptive Movable Neural Interfaces for Monitoring Single Neurons in the Brain

    PubMed Central

    Muthuswamy, Jit; Anand, Sindhu; Sridharan, Arati

    2011-01-01

    Implantable microelectrodes that are currently used to monitor neuronal activity in the brain in vivo have serious limitations both in acute and chronic experiments. Movable microelectrodes that adapt their position in the brain to maximize the quality of neuronal recording have been suggested and tried as a potential solution to overcome the challenges with the current fixed implantable microelectrodes. While the results so far suggest that movable microelectrodes improve the quality and stability of neuronal recordings from the brain in vivo, the bulky nature of the technologies involved in making these movable microelectrodes limits the throughput (number of neurons that can be recorded from at any given time) of these implantable devices. Emerging technologies involving the use of microscale motors and electrodes promise to overcome this limitation. This review summarizes some of the most recent efforts in developing movable neural interfaces using microscale technologies that adapt their position in response to changes in the quality of the neuronal recordings. Key gaps in our understanding of the brain–electrode interface are highlighted. Emerging discoveries in these areas will lead to success in the development of a reliable and stable interface with single neurons that will impact basic neurophysiological studies and emerging cortical prosthetic technologies. PMID:21927593

  7. Implications of movement-related cortical potential for understanding neural adaptations in muscle strength tasks

    PubMed Central

    2014-01-01

    This systematic review aims to provide information about the implications of the movement-related cortical potential (MRCP) in acute and chronic responses to the counter resistance training. The structuring of the methods of this study followed the proposals of the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses). It was performed an electronically search in Pubmed/Medline and ISI Web of Knowledge data bases, from 1987 to 2013, besides the manual search in the selected references. The following terms were used: Bereitschaftspotential, MRCP, strength and force. The logical operator “AND” was used to combine descriptors and terms used to search publications. At the end, 11 studies attended all the eligibility criteria and the results demonstrated that the behavior of MRCP is altered because of different factors such as: force level, rate of force development, fatigue induced by exercise, and the specific phase of muscular action, leading to an increase in the amplitude in eccentric actions compared to concentric actions, in acute effects. The long-term adaptations demonstrated that the counter resistance training provokes an attenuation in the amplitude in areas related to the movement, which may be caused by neural adaptation occurred in the motor cortex. PMID:24602228

  8. Adaptive neural control design for nonlinear distributed parameter systems with persistent bounded disturbances.

    PubMed

    Wu, Huai-Ning; Li, Han-Xiong

    2009-10-01

    In this paper, an adaptive neural network (NN) control with a guaranteed L(infinity)-gain performance is proposed for a class of parabolic partial differential equation (PDE) systems with unknown nonlinearities and persistent bounded disturbances. Initially, Galerkin method is applied to the PDE system to derive a low-order ordinary differential equation (ODE) system that accurately describes the dynamics of the dominant (slow) modes of the PDE system. Subsequently, based on the low-order slow model and the Lyapunov technique, an adaptive modal feedback controller is developed such that the closed-loop slow system is semiglobally input-to-state practically stable (ISpS) with an L(infinity)-gain performance. In the proposed control scheme, a radial basis function (RBF) NN is employed to approximate the unknown term in the derivative of the Lyapunov function due to the unknown system nonlinearities. The outcome of the adaptive L(infinity)-gain control problem is formulated as a linear matrix inequality (LMI) problem. Moreover, by using the existing LMI optimization technique, a suboptimal controller is obtained in the sense of minimizing an upper bound of the L(infinity)-gain, while control constraints are respected. Furthermore, it is shown that the proposed controller can ensure the semiglobal input-to-state practical stability and L(infinity)-gain performance of the closed-loop PDE system. Finally, by applying the developed design method to the temperature profile control of a catalytic rod, the achieved simulation results show the effectiveness of the proposed controller.

  9. Learning from adaptive neural dynamic surface control of strict-feedback systems.

    PubMed

    Wang, Min; Wang, Cong

    2015-06-01

    Learning plays an essential role in autonomous control systems. However, how to achieve learning in the nonstationary environment for nonlinear systems is a challenging problem. In this paper, we present learning method for a class of n th-order strict-feedback systems by adaptive dynamic surface control (DSC) technology, which achieves the human-like ability of learning by doing and doing with learned knowledge. To achieve the learning, this paper first proposes stable adaptive DSC with auxiliary first-order filters, which ensures the boundedness of all the signals in the closed-loop system and the convergence of tracking errors in a finite time. With the help of DSC, the derivative of the filter output variable is used as the neural network (NN) input instead of traditional intermediate variables. As a result, the proposed adaptive DSC method reduces greatly the dimension of NN inputs, especially for high-order systems. After the stable DSC design, we decompose the stable closed-loop system into a series of linear time-varying perturbed subsystems. Using a recursive design, the recurrent property of NN input variables is easily verified since the complexity is overcome using DSC. Subsequently, the partial persistent excitation condition of the radial basis function NN is satisfied. By combining a state transformation, accurate approximations of the closed-loop system dynamics are recursively achieved in a local region along recurrent orbits. Then, the learning control method using the learned knowledge is proposed to achieve the closed-loop stability and the improved control performance. Simulation studies are performed to demonstrate the proposed scheme can not only reuse the learned knowledge to achieve the better control performance with the faster tracking convergence rate and the smaller tracking error but also greatly alleviate the computational burden because of reducing the number and complexity of NN input variables.

  10. Adaptive pattern recognition by mini-max neural networks as a part of an intelligent processor

    NASA Technical Reports Server (NTRS)

    Szu, Harold H.

    1990-01-01

    In this decade and progressing into 21st Century, NASA will have missions including Space Station and the Earth related Planet Sciences. To support these missions, a high degree of sophistication in machine automation and an increasing amount of data processing throughput rate are necessary. Meeting these challenges requires intelligent machines, designed to support the necessary automations in a remote space and hazardous environment. There are two approaches to designing these intelligent machines. One of these is the knowledge-based expert system approach, namely AI. The other is a non-rule approach based on parallel and distributed computing for adaptive fault-tolerances, namely Neural or Natural Intelligence (NI). The union of AI and NI is the solution to the problem stated above. The NI segment of this unit extracts features automatically by applying Cauchy simulated annealing to a mini-max cost energy function. The feature discovered by NI can then be passed to the AI system for future processing, and vice versa. This passing increases reliability, for AI can follow the NI formulated algorithm exactly, and can provide the context knowledge base as the constraints of neurocomputing. The mini-max cost function that solves the unknown feature can furthermore give us a top-down architectural design of neural networks by means of Taylor series expansion of the cost function. A typical mini-max cost function consists of the sample variance of each class in the numerator, and separation of the center of each class in the denominator. Thus, when the total cost energy is minimized, the conflicting goals of intraclass clustering and interclass segregation are achieved simultaneously.

  11. Neuroplasticity in dynamic neural networks comprised of neurons attached to adaptive base plate.

    PubMed

    Joghataie, Abdolreza; Shafiei Dizaji, Mehrdad

    2016-03-01

    In this paper, a learning algorithm is developed for Dynamic Plastic Continuous Neural Networks (DPCNNs) to improve their learning of highly nonlinear time dependent problems. A DPCNN is comprised of a base medium, which is nonlinear and plastic, and a number of neurons that are attached to the base by wire-like connections similar to perceptrons. The information is distributed within DPCNNs gradually and through wave propagation mechanism. While a DPCNN is adaptive due to its connection weights, the material properties of its base medium can also be adjusted to improve its learning. The material of the medium is plastic and can contribute to memorizing the history of input-response similar to neuroplasticity in natural brain. The results obtained from numerical simulation of DPCNNs have been encouraging. Nonlinear plastic finite element modeling has been used for numerical simulation of dynamic behavior and wave propagation in the medium. Two significant differences of DPCNNs with other types of neural networks are that: (1) there is a medium to which the neurons are attached where the medium can contribute to the learning, (2) the input layer is not made of nodes but it is an edge terminal which is capable of receiving a continuous function over the input edge, though it is discretized in the finite element model. A DPCNN is reduced to a perceptron if the medium is removed and the neurons are connected to each other only by wires. Continuity of the input lets the discretization of data take place intrinsically within the DPCNN instead of being applied by the user.

  12. The changing brain--insights into the mechanisms of neural and behavioral adaptation to the environment.

    PubMed

    Bergersen, L H; Bramham, C R; Hugdahl, K; Sander, M; Storm-Mathisen, J

    2013-09-05

    The Kavli Prize in Neuroscience was awarded for the third time in September 2012, by the Norwegian Academy of Science and Letters in Oslo. The accompanying Kavli Prize Symposium on Neuroscience, held in Bergen and Trondheim, was a showcase of excellence in neuroscience research. The common theme of the Symposium presentations was the mechanisms by which animals adapt to their environment. The symposium speakers--Michael Greenberg, Erin Schuman, Chiara Cirelli, Michael Meaney, Catherine Dulac, Hopi Hoekstra, and Stanislas Dehaene--covered topics ranging from the molecular and cellular levels to the systems level and behavior. Thus a single amino acid change in a transcriptional repressor can disrupt gene regulation through neural activity (Greenberg). Deep sequencing analysis of the neuropil transcriptome indicates that a large fraction of the synaptic proteome is synthesized in situ in axons and dendrites, permitting local regulation (Schuman). The nature of the 'reset' function that makes animals dependent of sleep is being revealed (Cirelli). Maternal behavior can cause changes in gene expression that stably modify behavior in the offspring (Meaney). Removal of a single sensory channel protein in the vomero-nasal organ can switch off male-specific and switch on female-specific innate behavior of mice in response to environmental stimulation (Dulac). Innate behaviors can be stably transmitted from parent to offspring through generations even when those behaviors cannot be expressed, as illustrated by the elaborate burrowing behavior in a rodent species, in which independent genetic regions regulate distinct modules of the burrowing pattern (Hoekstra). Finally, at the other extreme of the nature-nurture scale, functional magnetic resonance imaging (fMRI) analysis in children and adults identified a brain area specifically involved in reading (Dehaene). As the area must originally have developed for a purpose other than reading, such as shape recognition, this

  13. Adaptive attitude controller for a satellite based on neural network in the presence of unknown external disturbances and actuator faults

    NASA Astrophysics Data System (ADS)

    Fazlyab, Ali Reza; Fani Saberi, Farhad; Kabganian, Mansour

    2016-01-01

    In this paper, an adaptive attitude control algorithm is developed based on neural network for a satellite. The proposed attitude control is based on nonlinear modified Rodrigues parameters feedback control in the presence of unknown terms like external disturbances and actuator faults. In order to eliminate the effect of the uncertainties, a multilayer neural network with a new learning rule will be designed appropriately. In this method, asymptotic stability of the proposed algorithm has been proven in the presence of unknown terms based on Lyapunov stability theorem. Finally, the performance of the designed attitude controller is investigated by simulations.

  14. Entry Abort Determination Using Non-Adaptive Neural Networks for Mars Precision Landers

    NASA Technical Reports Server (NTRS)

    Graybeal, Sarah R.; Kranzusch, Kara M.

    2005-01-01

    The 2009 Mars Science Laboratory (MSL) will attempt the first precision landing on Mars using a modified version of the Apollo Earth entry guidance program. The guidance routine, Entry Terminal Point Controller (ETPC), commands the deployment of a supersonic parachute after converging the range to the landing target. For very dispersed cases, ETPC may not converge the range to the target and safely command parachute deployment within Mach number and dynamic pressure constraints. A full-lift up abort can save 85% of these failed trajectories while abandoning the precision landing objective. Though current MSL requirements do not call for an abort capability, an autonomous abort capability may be desired, for this mission or future Mars precision landers, to make the vehicle more robust. The application of artificial neural networks (NNs) as an abort determination technique was evaluated by personnel at the National Aeronautics and Space Administration (NASA) Johnson Space Center (JSC). In order to implement an abort, a failed trajectory needs to be recognized in real time. Abort determination is dependent upon several trajectory parameters whose relationships to vehicle survival are not well understood, and yet the lander must be trained to recognize unsafe situations. Artificial neural networks (NNs) provide a way to model these parameters and can provide MSL with the artificial intelligence necessary to independently declare an abort. Using the 2009 Mars Science Laboratory (MSL) mission as a case study, a non-adaptive NN was designed, trained and tested using Monte Carlo simulations of MSL descent and incorporated into ETPC. Neural network theory, the development history of the MSL NN, and initial testing with severe dust storm entry trajectory cases are discussed in Reference 1 and will not be repeated here. That analysis demonstrated that NNs are capable of recognizing failed descent trajectories and can significantly increase the survivability of MSL for very

  15. Small-Aperture Monovision and the Pulfrich Experience: Absence of Neural Adaptation Effects

    PubMed Central

    Plainis, Sotiris; Petratou, Dionysia; Giannakopoulou, Trisevgeni; Radhakrishnan, Hema; Pallikaris, Ioannis G.; Charman, W. Neil

    2013-01-01

    Purpose To explore whether adaptation reduces the interocular visual latency differences and the induced Pulfrich effect caused by the anisocoria implicit in small-aperture monovision. Methods Anisocoric vision was simulated in two adults by wearing in the non-dominant eye for 7 successive days, while awake, an opaque soft contact lens (CL) with a small, central, circular aperture. This was repeated with aperture diameters of 1.5 and 2.5 mm. Each day, monocular and binocular pattern-reversal Visual Evoked Potentials (VEP) were recorded. Additionally, the Pulfrich effect was measured: the task of the subject was to state whether a a 2-deg spot appeared in front or behind the plane of a central cross when moved left-to-right or right-to-left on a display screen. The retinal illuminance of the dominant eye was varied using neutral density (ND) filters to establish the ND value which eliminated the Pulfrich effect for each lens. All experiments were performed at luminance levels of 5 and 30 cd/m2. Results Interocular differences in monocular VEP latency (at 30 cd/m2) rose to about 12–15 ms and 20–25 ms when the CL aperture was 2.5 and 1.5 mm, respectively. The effect was more pronounced at 5 cd/m2 (i.e. with larger natural pupils). A strong Pulfrich effect was observed under all conditions, with the effect being less striking for the 2.5 mm aperture. No neural adaptation appeared to occur: neither the interocular differences in VEP latency nor the ND value required to null the Pulfrich effect reduced over each 7-day period of anisocoric vision. Conclusions Small-aperture monovision produced marked interocular differences in visual latency and a Pulfrich experience. These were not reduced by adaptation, perhaps because the natural pupil diameter of the dominant eye was continually changing throughout the day due to varying illumination and other factors, making adaptation difficult. PMID:24155881

  16. An Investigation of the Application of Artificial Neural Networks to Adaptive Optics Imaging Systems

    DTIC Science & Technology

    1991-12-01

    Recurrent and feedforward artificial neural networks are developed as wavefront reconstructors. The recurrent neural network studied is the Hopfield...input features are just the wavefront sensor slope outputs. Both artificial neural networks use their inputs to calculate deformable mirror actuator commands. The effects of training are examined.

  17. Design of a Mobile Agent-Based Adaptive Communication Middleware for Federations of Critical Infrastructure Simulations

    NASA Astrophysics Data System (ADS)

    Görbil, Gökçe; Gelenbe, Erol

    The simulation of critical infrastructures (CI) can involve the use of diverse domain specific simulators that run on geographically distant sites. These diverse simulators must then be coordinated to run concurrently in order to evaluate the performance of critical infrastructures which influence each other, especially in emergency or resource-critical situations. We therefore describe the design of an adaptive communication middleware that provides reliable and real-time one-to-one and group communications for federations of CI simulators over a wide-area network (WAN). The proposed middleware is composed of mobile agent-based peer-to-peer (P2P) overlays, called virtual networks (VNets), to enable resilient, adaptive and real-time communications over unreliable and dynamic physical networks (PNets). The autonomous software agents comprising the communication middleware monitor their performance and the underlying PNet, and dynamically adapt the P2P overlay and migrate over the PNet in order to optimize communications according to the requirements of the federation and the current conditions of the PNet. Reliable communications is provided via redundancy within the communication middleware and intelligent migration of agents over the PNet. The proposed middleware integrates security methods in order to protect the communication infrastructure against attacks and provide privacy and anonymity to the participants of the federation. Experiments with an initial version of the communication middleware over a real-life networking testbed show that promising improvements can be obtained for unicast and group communications via the agent migration capability of our middleware.

  18. Fuzzy clustering: critical analysis of the contextual mechanisms employed by three neural network models

    NASA Astrophysics Data System (ADS)

    Baraldi, Andrea; Parmiggiani, Flavio

    1996-06-01

    cognitive tasks. In this paper, the FLVQ model is critically analyzed in order to stress the meaning of a fuzzy learning mechanism. This study leads to the development of a new NN model, termed the fuzzy competitive/cooperative Kohonen (FCCK) model, which replaces FLVQ. Then, the architectural differences amongst three NN algorithms and the relationships between their fuzzy clustering properties are discussed. These models, which all perform on-line learning, are: (1) SOM; (2) FCCK; and (3) improved neural-gas (INC).

  19. An indirect adaptive neural control of a visual-based quadrotor robot for pursuing a moving target.

    PubMed

    Shirzadeh, Masoud; Amirkhani, Abdollah; Jalali, Aliakbar; Mosavi, Mohammad R

    2015-11-01

    This paper aims to use a visual-based control mechanism to control a quadrotor type aerial robot which is in pursuit of a moving target. The nonlinear nature of a quadrotor, on the one hand, and the difficulty of obtaining an exact model for it, on the other hand, constitute two serious challenges in designing a controller for this UAV. A potential solution for such problems is the use of intelligent control methods such as those that rely on artificial neural networks and other similar approaches. In addition to the two mentioned problems, another problem that emerges due to the moving nature of a target is the uncertainty that exists in the target image. By employing an artificial neural network with a Radial Basis Function (RBF) an indirect adaptive neural controller has been designed for a quadrotor robot in search of a moving target. The results of the simulation for different paths show that the quadrotor has efficiently tracked the moving target.

  20. Improving Robustness of Deep Neural Network Acoustic Models via Speech Separation and Joint Adaptive Training

    PubMed Central

    Narayanan, Arun; Wang, DeLiang

    2015-01-01

    Although deep neural network (DNN) acoustic models are known to be inherently noise robust, especially with matched training and testing data, the use of speech separation as a frontend and for deriving alternative feature representations has been shown to improve performance in challenging environments. We first present a supervised speech separation system that significantly improves automatic speech recognition (ASR) performance in realistic noise conditions. The system performs separation via ratio time-frequency masking; the ideal ratio mask (IRM) is estimated using DNNs. We then propose a framework that unifies separation and acoustic modeling via joint adaptive training. Since the modules for acoustic modeling and speech separation are implemented using DNNs, unification is done by introducing additional hidden layers with fixed weights and appropriate network architecture. On the CHiME-2 medium-large vocabulary ASR task, and with log mel spectral features as input to the acoustic model, an independently trained ratio masking frontend improves word error rates by 10.9% (relative) compared to the noisy baseline. In comparison, the jointly trained system improves performance by 14.4%. We also experiment with alternative feature representations to augment the standard log mel features, like the noise and speech estimates obtained from the separation module, and the standard feature set used for IRM estimation. Our best system obtains a word error rate of 15.4% (absolute), an improvement of 4.6 percentage points over the next best result on this corpus. PMID:26973851

  1. Neural self-adapting architecture for video-on-radio devices

    NASA Astrophysics Data System (ADS)

    Basti, Gianfranco; Perrone, Antonio L.

    2002-03-01

    In this paper we have sketched some technical details of an FM sub-carrier technology called Multi Purpose Radio Communication Channel (MPRC). This technology delivers actually data at maximum data rate of around 40 kbs using a proprietary codec algorithm: Subsidiary Communication Channel (SCC). A core device of this codec algorithm is a DWT compressor with a proprietary pre-processing, constituted by a neural self-adapting filter, the Dynamic Perceptron Algorithm (DPA), able to detect edges and to extract objects from the moving images flow, so to optimize the overall compression rate and the image quality. As a result it is possible to obtain video transmission in QCIF format at roughly 8/12 fps using 35 kHz of the 100 kHz available for a commercial FM radio station in Europe. This allows transmitting video on FM radio together with the usual radio broadcasting. On the contrary, if we use all the available 100 kHz., we obtain, after the charge related to the error protocol, a channel for compressed video transmission of about 113 kbit, allowing high quality 640x480 (zoomed or not zoomed) video images.

  2. Measures of complexity in neural spike-trains of the slowly adapting stretch receptor organs.

    PubMed

    Jiménez-Montaño, M A; Penagos, H; Hernández Torres, A; Diez-Martínez, O

    2000-01-01

    Discrete sequence analysis methods were applied to study spike-trains generated by the isolated neuron of the slowly adapting stretch receptor organ. Calculation of the algorithmic complexity and block entropies of digitized individual spike-train forms allowed us to distinguish different classes of neural behavior. While some spike-trains exhibited significant structure, others displayed diverse degrees of randomness. The sequences recorded during the stimulated portions of the intermittent and walk-through forms, differed considerably from their randomly shuffled surrogates. Informational and grammar complexity measures (in two, four and eight-letter alphabets), tell us things about the structure of spike-trains that are not obtained with conventional spike analysis. Comparison of the conditional entropies for the digitized signals showed that the method distinguishes between different stimulated conditions. Additionally, comparison of the different stimulated conditions with their corresponding surrogates showed that, both, conditional entropies and complexities were significantly different for the two groups. Although the original and the randomly shuffled sequences had the same distribution and average firing rate, their complexity values were different. The results obtained with both measures of sequence structure were quite consistent.

  3. Adaptive neural control for cooperative path following of marine surface vehicles: state and output feedback

    NASA Astrophysics Data System (ADS)

    Wang, H.; Wang, D.; Peng, Z. H.

    2016-01-01

    This paper addresses the cooperative path-following problem of multiple marine surface vehicles subject to dynamical uncertainties and ocean disturbances induced by unknown wind, wave and ocean current. The control design falls neatly into two parts. One is to steer individual marine surface vehicle to track a predefined path and the other is to synchronise the along-path speed and path variables under the constraints of an underlying communication network. Within these two formulations, a robust adaptive path-following controller is first designed for individual vehicles based on backstepping and neural network techniques. Then, a decentralised synchronisation control law is derived by means of consensus on along-path speed and path variables based on graph theory. The distinct feature of this design lies in that synchronised path following can be reached for any undirected connected communication graphs without accurate knowledge of the model. This result is further extended to the output feedback case, where an observer-based cooperative path-following controller is developed without measuring the velocity of each vehicle. For both designs, rigorous theoretical analysis demonstrate that all signals in the closed-loop system are semi-global uniformly ultimately bounded. Simulation results validate the performance and robustness improvement of the proposed strategy.

  4. Learning from experience: Event-related potential correlates of reward processing, neural adaptation, and behavioral choice

    PubMed Central

    Walsh, Matthew M.; Anderson, John R.

    2012-01-01

    To behave adaptively, we must learn from the consequences of our actions. Studies using event-related potentials (ERPs) have been informative with respect to the question of how such learning occurs. These studies have revealed a frontocentral negativity termed the feedback-related negativity (FRN) that appears after negative feedback. According to one prominent theory, the FRN tracks the difference between the values of actual and expected outcomes, or reward prediction errors. As such, the FRN provides a tool for studying reward valuation and decision making. We begin this review by examining the neural significance of the FRN. We then examine its functional significance. To understand the cognitive processes that occur when the FRN is generated, we explore variables that influence its appearance and amplitude. Specifically, we evaluate four hypotheses: (1) the FRN encodes a quantitative reward prediction error; (2) the FRN is evoked by outcomes and by stimuli that predict outcomes; (3) the FRN and behavior change with experience; and (4) the system that produces the FRN is maximally engaged by volitional actions. PMID:22683741

  5. Indoor location system based on discriminant-adaptive neural network in IEEE 802.11 environments.

    PubMed

    Fang, Shih-Hau; Lin, Tsung-Nan

    2008-11-01

    This brief paper presents a novel localization algorithm, named discriminant-adaptive neural network (DANN), which takes the received signal strength (RSS) from the access points (APs) as inputs to infer the client position in the wireless local area network (LAN) environment. We extract the useful information into discriminative components (DCs) for network learning. The nonlinear relationship between RSS and the position is then accurately constructed by incrementally inserting the DCs and recursively updating the weightings in the network until no further improvement is required. Our localization system is developed in a real-world wireless LAN WLAN environment, where the realistic RSS measurement is collected. We implement the traditional approaches on the same test bed, including weighted kappa-nearest neighbor (WKNN), maximum likelihood (ML), and multilayer perceptron (MLP), and compare the results. The experimental results indicate that the proposed algorithm is much higher in accuracy compared with other examined techniques. The improvement can be attributed to that only the useful information is efficiently extracted for positioning while the redundant information is regarded as noise and discarded. Finally, the analysis shows that our network intelligently accomplishes learning while the inserted DCs provide sufficient information.

  6. Pedestrian tracking and navigation using an adaptive knowledge system based on neural networks

    NASA Astrophysics Data System (ADS)

    Grejner-Brzezinska, Dorota A.; Toth, Charles; Moafipoor, Shahram

    2007-11-01

    The primary objective of the research presented here is to develop theoretical foundations and implementation algorithms, which integrate the Global Positioning System (GPS), micro-electromechanical inertial measurement unit (MEMS IMU), digital barometer, electronic compass, and human pedometry to provide navigation and tracking of military and rescue ground personnel. This paper discusses the design, implementation and the performance analyses of the personal navigator prototype, with a special emphasis on dead-reckoning (DR) navigation supported by the human locomotion model. The adaptive knowledge system, based on the Artificial Neural Networks (ANN), is implemented to support this functionality. The knowledge system is trained during the GPS signal reception and is used to support navigation under GPS-denied conditions. The human locomotion parameters, step frequency (SF) and step length (SL), are extracted from GPS-timed impact switches (step frequency) and GPS/IMU data (step length), respectively, during the system calibration period. SL is correlated with several data types, such as acceleration, acceleration variation, SF, terrain slope, etc. that constitute the input parameters to the ANN-based knowledge system. The ANN-predicted SL, together with the heading information from the compass and gyro, support DR navigation. The current target accuracy of the system is 3-5 m CEP (circular error probable) 50%.

  7. Adaptation of the Critical Care Family Need Inventory to the Turkish population and its psychometric properties

    PubMed Central

    Büyükçoban, Sibel; Çiçeklioğlu, Meltem; Demiral Yılmaz, Nilüfer

    2015-01-01

    In the complex environment of intensive care units, needs of patients’ relatives might be seen as the lowest priority. On the other hand, because of their patients’ critical and often uncertain conditions, stress levels of relatives are quite high. This study aims to adapt the Critical Care Family Need Inventory, which assesses the needs of patients’ relatives, for use with the Turkish-speaking population and to assess psychometric properties of the resulting inventory. The study was conducted in a state hospital with the participation of 191 critical care patient relatives. Content validity was assessed by expert opinions, and construct validity was examined by exploratory factor analysis (EFA). Cronbach’s alpha coefficient was used to determine internal consistency. The translated inventory has a content validity ratio higher than the minimum acceptable level. Its construct validity was established by the EFA. Cronbach’s alpha coefficient for the entire scale was 0.93 and higher than 0.80 for subscales, thus demonstrating the translated version’s reliability. The Turkish adaptation appropriately reflects all dimensions of needs in the original CCFNI, and its psychometric properties were acceptable. The revised tool could be useful for helping critical care healthcare workers provide services in a holistic approach and for policymakers to improve quality of service. PMID:26357593

  8. Robust adaptive control for a class of uncertain non-affine nonlinear systems using affine-type neural networks

    NASA Astrophysics Data System (ADS)

    Zhao, Shitie; Gao, Xianwen

    2016-08-01

    A robust adaptive control is proposed for a class of single-input single-output non-affine nonlinear systems. In order to approximate the unknown nonlinear function, a novel affine-type neural network is used, and then to compensate the approximation error and external disturbance a robust control term is employed. By Lyapunov stability analysis for the closed-loop system, it is proved that tracking errors asymptotically converge to zero. Moreover, an observer is designed to estimate the system states because all the states may not be available for measurements. Furthermore, the adaptation laws of neural networks and the robust controller are given out based on the Lyapunov stability theory. Finally, two simulation examples are presented to demonstrate the effectiveness of the proposed control method.

  9. Novel adaptive neural control design for a constrained flexible air-breathing hypersonic vehicle based on actuator compensation

    NASA Astrophysics Data System (ADS)

    Bu, Xiangwei; Wu, Xiaoyan; He, Guangjun; Huang, Jiaqi

    2016-03-01

    This paper investigates the design of a novel adaptive neural controller for the longitudinal dynamics of a flexible air-breathing hypersonic vehicle with control input constraints. To reduce the complexity of controller design, the vehicle dynamics is decomposed into the velocity subsystem and the altitude subsystem, respectively. For each subsystem, only one neural network is utilized to approach the lumped unknown function. By employing a minimal-learning parameter method to estimate the norm of ideal weight vectors rather than their elements, there are only two adaptive parameters required for neural approximation. Thus, the computational burden is lower than the ones derived from neural back-stepping schemes. Specially, to deal with the control input constraints, additional systems are exploited to compensate the actuators. Lyapunov synthesis proves that all the closed-loop signals involved are uniformly ultimately bounded. Finally, simulation results show that the adopted compensation scheme can tackle actuator constraint effectively and moreover velocity and altitude can stably track their reference trajectories even when the physical limitations on control inputs are in effect.

  10. Pedagogical Challenges and Possibilities: Adapting Critical Literacy in EFL College Writing Using a Video-Authoring Project

    ERIC Educational Resources Information Center

    Park, Sunyoung

    2010-01-01

    This thesis is a critical action research to explore the pedagogical challenges and possibilities by adapting a critical literacy in an EFL college writing class supported by a video-authoring project. Mainly, it focuses on three parts of the study; 1) How will develop the theoretical notions of a critical literacy in an EFL classroom, in…

  11. Distribution of language-related Cntnap2 protein in neural circuits critical for vocal learning.

    PubMed

    Condro, Michael C; White, Stephanie A

    2014-01-01

    Variants of the contactin associated protein-like 2 (Cntnap2) gene are risk factors for language-related disorders including autism spectrum disorder, specific language impairment, and stuttering. Songbirds are useful models for study of human speech disorders due to their shared capacity for vocal learning, which relies on similar cortico-basal ganglia circuitry and genetic factors. Here we investigate Cntnap2 protein expression in the brain of the zebra finch, a songbird species in which males, but not females, learn their courtship songs. We hypothesize that Cntnap2 has overlapping functions in vocal learning species, and expect to find protein expression in song-related areas of the zebra finch brain. We further expect that the distribution of this membrane-bound protein may not completely mirror its mRNA distribution due to the distinct subcellular localization of the two molecular species. We find that Cntnap2 protein is enriched in several song control regions relative to surrounding tissues, particularly within the adult male, but not female, robust nucleus of the arcopallium (RA), a cortical song control region analogous to human layer 5 primary motor cortex. The onset of this sexually dimorphic expression coincides with the onset of sensorimotor learning in developing males. Enrichment in male RA appears due to expression in projection neurons within the nucleus, as well as to additional expression in nerve terminals of cortical projections to RA from the lateral magnocellular nucleus of the nidopallium. Cntnap2 protein expression in zebra finch brain supports the hypothesis that this molecule affects neural connectivity critical for vocal learning across taxonomic classes.

  12. Progress Toward Adaptive Integration and Optimization of Automated and Neural Processing Systems: Establishing Neural and Behavioral Benchmarks of Optimized Performance

    DTIC Science & Technology

    2014-11-01

    into more-realistic multitasking environments. Behind the work are basic questions about the utility of rapid serial visual presentation (RSVP) and... multitasking simulator with integrated real-time electroencephalogram (EEG) processing, RSVP performance was measured. Figure 2a shows the display for the...classification of their neural signals, all within the multitasking simulation environment. These studies and the results are described in the following

  13. An adaptive Hinfinity controller design for bank-to-turn missiles using ridge Gaussian neural networks.

    PubMed

    Lin, Chuan-Kai; Wang, Sheng-De

    2004-11-01

    A new autopilot design for bank-to-turn (BTT) missiles is presented. In the design of autopilot, a ridge Gaussian neural network with local learning capability and fewer tuning parameters than Gaussian neural networks is proposed to model the controlled nonlinear systems. We prove that the proposed ridge Gaussian neural network, which can be a universal approximator, equals the expansions of rotated and scaled Gaussian functions. Although ridge Gaussian neural networks can approximate the nonlinear and complex systems accurately, the small approximation errors may affect the tracking performance significantly. Therefore, by employing the Hinfinity control theory, it is easy to attenuate the effects of the approximation errors of the ridge Gaussian neural networks to a prescribed level. Computer simulation results confirm the effectiveness of the proposed ridge Gaussian neural networks-based autopilot with Hinfinity stabilization.

  14. Critical Incident Stress Management (CISM) in complex systems: cultural adaptation and safety impacts in healthcare.

    PubMed

    Müller-Leonhardt, Alice; Mitchell, Shannon G; Vogt, Joachim; Schürmann, Tim

    2014-07-01

    In complex systems, such as hospitals or air traffic control operations, critical incidents (CIs) are unavoidable. These incidents can not only become critical for victims but also for professionals working at the "sharp end" who may have to deal with critical incident stress (CIS) reactions that may be severe and impede emotional, physical, cognitive and social functioning. These CIS reactions may occur not only under exceptional conditions but also during every-day work and become an important safety issue. In contrast to air traffic management (ATM) operations in Europe, which have readily adopted critical incident stress management (CISM), most hospitals have not yet implemented comprehensive peer support programs. This survey was conducted in 2010 at the only European general hospital setting which implemented CISM program since 2004. The aim of the article is to describe possible contribution of CISM in hospital settings framed from the perspective of organizational safety and individual health for healthcare professionals. Findings affirm that daily work related incidents also can become critical for healthcare professionals. Program efficiency appears to be influenced by the professional culture, as well as organizational structure and policies. Overall, findings demonstrate that the adaptation of the CISM program in general hospitals takes time but, once established, it may serve as a mechanism for changing professional culture, thereby permitting the framing of even small incidents or near misses as an opportunity to provide valuable feedback to the system.

  15. An Adaptive Landscape Classification Procedure using Geoinformatics and Artificial Neural Networks

    SciTech Connect

    Coleman, Andre Michael

    2008-06-01

    The Adaptive Landscape Classification Procedure (ALCP), which links the advanced geospatial analysis capabilities of Geographic Information Systems (GISs) and Artificial Neural Networks (ANNs) and particularly Self-Organizing Maps (SOMs), is proposed as a method for establishing and reducing complex data relationships. Its adaptive and evolutionary capability is evaluated for situations where varying types of data can be combined to address different prediction and/or management needs such as hydrologic response, water quality, aquatic habitat, groundwater recharge, land use, instrumentation placement, and forecast scenarios. The research presented here documents and presents favorable results of a procedure that aims to be a powerful and flexible spatial data classifier that fuses the strengths of geoinformatics and the intelligence of SOMs to provide data patterns and spatial information for environmental managers and researchers. This research shows how evaluation and analysis of spatial and/or temporal patterns in the landscape can provide insight into complex ecological, hydrological, climatic, and other natural and anthropogenic-influenced processes. Certainly, environmental management and research within heterogeneous watersheds provide challenges for consistent evaluation and understanding of system functions. For instance, watersheds over a range of scales are likely to exhibit varying levels of diversity in their characteristics of climate, hydrology, physiography, ecology, and anthropogenic influence. Furthermore, it has become evident that understanding and analyzing these diverse systems can be difficult not only because of varying natural characteristics, but also because of the availability, quality, and variability of spatial and temporal data. Developments in geospatial technologies, however, are providing a wide range of relevant data, and in many cases, at a high temporal and spatial resolution. Such data resources can take the form of high

  16. Neural Adaptations Associated with Interlimb Transfer in a Ballistic Wrist Flexion Task

    PubMed Central

    Ruddy, Kathy L.; Rudolf, Anne K.; Kalkman, Barbara; King, Maedbh; Daffertshofer, Andreas; Carroll, Timothy J.; Carson, Richard G.

    2016-01-01

    Cross education is the process whereby training of one limb gives rise to increases in the subsequent performance of its opposite counterpart. The execution of many unilateral tasks is associated with increased excitability of corticospinal projections from primary motor cortex (M1) to the opposite limb. It has been proposed that these effects are causally related. Our aim was to establish whether changes in corticospinal excitability (CSE) arising from prior training of the opposite limb determine levels of interlimb transfer. We used three vision conditions shown previously to modulate the excitability of corticospinal projections to the inactive (right) limb during wrist flexion movements performed by the training (left) limb. These were: (1) mirrored visual feedback of the training limb; (2) no visual feedback of either limb; and (3) visual feedback of the inactive limb. Training comprised 300 discrete, ballistic wrist flexion movements executed as rapidly as possible. Performance of the right limb on the same task was assessed prior to, at the mid point of, and following left limb training. There was no evidence that variations in the excitability of corticospinal projections (assessed by transcranial magnetic stimulation (TMS)) to the inactive limb were associated with, or predictive of, the extent of interlimb transfer that was expressed. There were however associations between alterations in muscle activation dynamics observed for the untrained limb, and the degree of positive transfer that arose from training of the opposite limb. The results suggest that the acute adaptations that mediate the bilateral performance gains realized through unilateral practice of this ballistic wrist flexion task are mediated by neural elements other than those within M1 that are recruited at rest by single-pulse TMS. PMID:27199722

  17. Hybrid feedback feedforward: An efficient design of adaptive neural network control.

    PubMed

    Pan, Yongping; Liu, Yiqi; Xu, Bin; Yu, Haoyong

    2016-04-01

    This paper presents an efficient hybrid feedback feedforward (HFF) adaptive approximation-based control (AAC) strategy for a class of uncertain Euler-Lagrange systems. The control structure includes a proportional-derivative (PD) control term in the feedback loop and a radial-basis-function (RBF) neural network (NN) in the feedforward loop, which mimics the human motor learning control mechanism. At the presence of discontinuous friction, a sigmoid-jump-function NN is incorporated to improve control performance. The major difference of the proposed HFF-AAC design from the traditional feedback AAC (FB-AAC) design is that only desired outputs, rather than both tracking errors and desired outputs, are applied as RBF-NN inputs. Yet, such a slight modification leads to several attractive properties of HFF-AAC, including the convenient choice of an approximation domain, the decrease of the number of RBF-NN inputs, and semiglobal practical asymptotic stability dominated by control gains. Compared with previous HFF-AAC approaches, the proposed approach possesses the following two distinctive features: (i) all above attractive properties are achieved by a much simpler control scheme; (ii) the bounds of plant uncertainties are not required to be known. Consequently, the proposed approach guarantees a minimum configuration of the control structure and a minimum requirement of plant knowledge for the AAC design, which leads to a sharp decrease of implementation cost in terms of hardware selection, algorithm realization and system debugging. Simulation results have demonstrated that the proposed HFF-AAC can perform as good as or even better than the traditional FB-AAC under much simpler control synthesis and much lower computational cost.

  18. Using Multivariate Adaptive Regression Spline and Artificial Neural Network to Simulate Urbanization in Mumbai, India

    NASA Astrophysics Data System (ADS)

    Ahmadlou, M.; Delavar, M. R.; Tayyebi, A.; Shafizadeh-Moghadam, H.

    2015-12-01

    Land use change (LUC) models used for modelling urban growth are different in structure and performance. Local models divide the data into separate subsets and fit distinct models on each of the subsets. Non-parametric models are data driven and usually do not have a fixed model structure or model structure is unknown before the modelling process. On the other hand, global models perform modelling using all the available data. In addition, parametric models have a fixed structure before the modelling process and they are model driven. Since few studies have compared local non-parametric models with global parametric models, this study compares a local non-parametric model called multivariate adaptive regression spline (MARS), and a global parametric model called artificial neural network (ANN) to simulate urbanization in Mumbai, India. Both models determine the relationship between a dependent variable and multiple independent variables. We used receiver operating characteristic (ROC) to compare the power of the both models for simulating urbanization. Landsat images of 1991 (TM) and 2010 (ETM+) were used for modelling the urbanization process. The drivers considered for urbanization in this area were distance to urban areas, urban density, distance to roads, distance to water, distance to forest, distance to railway, distance to central business district, number of agricultural cells in a 7 by 7 neighbourhoods, and slope in 1991. The results showed that the area under the ROC curve for MARS and ANN was 94.77% and 95.36%, respectively. Thus, ANN performed slightly better than MARS to simulate urban areas in Mumbai, India.

  19. Neural networks-based adaptive control for nonlinear time-varying delays systems with unknown control direction.

    PubMed

    Wen, Yuntong; Ren, Xuemei

    2011-10-01

    This paper investigates a neural network (NN) state observer-based adaptive control for a class of time-varying delays nonlinear systems with unknown control direction. An adaptive neural memoryless observer, in which the knowledge of time-delay is not used, is designed to estimate the system states. Furthermore, by applying the property of the function tanh(2)(ϑ/ε)/ϑ (the function can be defined at ϑ = 0) and introducing a novel type appropriate Lyapunov-Krasovskii functional, an adaptive output feedback controller is constructed via backstepping method which can efficiently avoid the problem of controller singularity and compensate for the time-delay. It is highly proven that the closed-loop systems controller designed by the NN-basis function property, new kind parameter adaptive law and Nussbaum function in detecting the control direction is able to guarantee the semi-global uniform ultimate boundedness of all signals and the tracking error can converge to a small neighborhood of zero. The characteristic of the proposed approach is that it relaxes any restrictive assumptions of Lipschitz condition for the unknown nonlinear continuous functions. And the proposed scheme is suitable for the systems with mismatching conditions and unmeasurable states. Finally, two simulation examples are given to illustrate the effectiveness and applicability of the proposed approach.

  20. Advancing interconnect density for spiking neural network hardware implementations using traffic-aware adaptive network-on-chip routers.

    PubMed

    Carrillo, Snaider; Harkin, Jim; McDaid, Liam; Pande, Sandeep; Cawley, Seamus; McGinley, Brian; Morgan, Fearghal

    2012-09-01

    The brain is highly efficient in how it processes information and tolerates faults. Arguably, the basic processing units are neurons and synapses that are interconnected in a complex pattern. Computer scientists and engineers aim to harness this efficiency and build artificial neural systems that can emulate the key information processing principles of the brain. However, existing approaches cannot provide the dense interconnect for the billions of neurons and synapses that are required. Recently a reconfigurable and biologically inspired paradigm based on network-on-chip (NoC) and spiking neural networks (SNNs) has been proposed as a new method of realising an efficient, robust computing platform. However, the use of the NoC as an interconnection fabric for large-scale SNNs demands a good trade-off between scalability, throughput, neuron/synapse ratio and power consumption. This paper presents a novel traffic-aware, adaptive NoC router, which forms part of a proposed embedded mixed-signal SNN architecture called EMBRACE (EMulating Biologically-inspiRed ArChitectures in hardwarE). The proposed adaptive NoC router provides the inter-neuron connectivity for EMBRACE, maintaining router communication and avoiding dropped router packets by adapting to router traffic congestion. Results are presented on throughput, power and area performance analysis of the adaptive router using a 90 nm CMOS technology which outperforms existing NoCs in this domain. The adaptive behaviour of the router is also verified on a Stratix II FPGA implementation of a 4 × 2 router array with real-time traffic congestion. The presented results demonstrate the feasibility of using the proposed adaptive NoC router within the EMBRACE architecture to realise large-scale SNNs on embedded hardware.

  1. The Adaptation of East Asian Masters Students to Western Norms of Critical Thinking and Argumentation in the UK

    ERIC Educational Resources Information Center

    Durkin, Kathy

    2008-01-01

    The paper explores the adaptation experiences of East Asian masters students in the UK in dealing with western academic norms of critical thinking and debate. Through in-depth interviewing, students' perceptions of their learning experiences were explored, and stages in this adaptation process were identified, with various entry and exit routes.…

  2. Adaptation of the phase of the human linear vestibulo-ocular reflex (LVOR) and effects on the oculomotor neural integrator

    NASA Technical Reports Server (NTRS)

    Hegemann, S.; Shelhamer, M.; Kramer, P. D.; Zee, D. S.

    2000-01-01

    The phase of the translational linear VOR (LVOR) can be adaptively modified by exposure to a visual-vestibular mismatch. We extend here our earlier work on LVOR phase adaptation, and discuss the role of the oculomotor neural integrator. Ten subjects were oscillated laterally at 0.5 Hz, 0.3 g peak acceleration, while sitting upright on a linear sled. LVOR was assessed before and after adaptation with subjects tracking the remembered location of a target at 1 m in the dark. Phase and gain were measured by fitting sine waves to the desaccaded eye movements, and comparing sled and eye position. To adapt LVOR phase, the subject viewed a computer-generated stereoscopic visual display, at a virtual distance of 1 m, that moved so as to require either a phase lead or a phase lag of 53 deg. Adaptation lasted 20 min, during which subjects were oscillated at 0.5 Hz/0.3 g. Four of five subjects produced an adaptive change in the lag condition (range 4-45 deg), and each of five produced a change in the lead condition (range 19-56 deg), as requested. Changes in drift on eccentric gaze suggest that the oculomotor velocity-to-position integrator may be involved in the phase changes.

  3. Part 2: Adaptation of Gait Kinematics in Unilateral Cerebral Palsy Demonstrates Preserved Independent Neural Control of Each Limb

    PubMed Central

    Bulea, Thomas C.; Stanley, Christopher J.; Damiano, Diane L.

    2017-01-01

    Motor adaptation, or alteration of neural control in response to a perturbation, is a potential mechanism to facilitate motor learning for rehabilitation. Central nervous system deficits are known to affect locomotor adaptation; yet we demonstrated that similar to adults following stroke, children with unilateral brain injuries can adapt step length in response to unilateral leg weighting. Here, we extend our analysis to explore kinematic strategies underlying step length adaptation and utilize dynamical systems approaches to elucidate how neural control may differ in those with hemiplegic CP across legs and compared to typically developing controls. Ten participants with hemiplegic CP and ten age-matched controls participated in this study. Knee and hip joint kinematics were analyzed during unilateral weighting of each leg in treadmill walking to assess adaptation and presence and persistence of after-effects. Peak joint angle displacement was used to represent changes in joint angles during walking. We examined baseline and task-specific variability and local dynamic stability to evaluate neuromuscular control across groups and legs. In contrast to controls, children with unilateral CP had asymmetries in joint angle variability and local dynamic stability at baseline, showing increased variability and reduced stability in the dominant limb. Kinematic variability increased and local stability decreased during weighting of ipsilateral and contralateral limbs in both groups compared to baseline. After weight removal both measures returned to baseline. Analogous to the temporal-spatial results, children with unilateral CP demonstrated similar capability as controls to adapt kinematics to unilateral leg weighting, however, the group with CP differed across sides after weight removal with dominant limb after-effects fading more quickly than in controls. The change in kinematics did not completely return to baseline in the non-dominant limb of the CP group, producing a

  4. Self-Adaptive Prediction of Cloud Resource Demands Using Ensemble Model and Subtractive-Fuzzy Clustering Based Fuzzy Neural Network

    PubMed Central

    Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong

    2015-01-01

    In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands. PMID:25691896

  5. Self-adaptive prediction of cloud resource demands using ensemble model and subtractive-fuzzy clustering based fuzzy neural network.

    PubMed

    Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong

    2015-01-01

    In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands.

  6. ELM-based adaptive backstepping neural control for a class of uncertain MIMO nonlinear systems with predefined tracking accuracy

    NASA Astrophysics Data System (ADS)

    Elkoteshy, Yasser; Jiao, L. C.; Chen, Weisheng

    2014-05-01

    In this work, the adaptive backstepping neural control technique is proposed for a class of uncertain multi-input multi-output nonlinear systems in block-triangular form with the ultimate tracking accuracy assumed to be known a priori. The stability analysis of the closed-loop control system is derived based on Barbalat's Lemma instead of Lyapunov stability theory. Semi-global uniform ultimate boundedness of all the signals in the closed-loop system is achieved and after a sufficiently large interval of time, the outputs of the system are proven to converge to the predefined value. A single hidden layer feed-forward neural network based on the extreme learning machine is used in this work to approximate the unknown nonlinear functions in the control laws. Two simulation examples, including a mathematical one and a practical one, are given to verify the effectiveness of the proposed controller and its superiority over the existing techniques.

  7. Complex ordering in spin networks: Critical role of adaptation rate for dynamically evolving interactions

    NASA Astrophysics Data System (ADS)

    Pathak, Anand; Sinha, Sitabhra

    2015-09-01

    Many complex systems can be represented as networks of dynamical elements whose states evolve in response to interactions with neighboring elements, noise and external stimuli. The collective behavior of such systems can exhibit remarkable ordering phenomena such as chimera order corresponding to coexistence of ordered and disordered regions. Often, the interactions in such systems can also evolve over time responding to changes in the dynamical states of the elements. Link adaptation inspired by Hebbian learning, the dominant paradigm for neuronal plasticity, has been earlier shown to result in structural balance by removing any initial frustration in a system that arises through conflicting interactions. Here we show that the rate of the adaptive dynamics for the interactions is crucial in deciding the emergence of different ordering behavior (including chimera) and frustration in networks of Ising spins. In particular, we observe that small changes in the link adaptation rate about a critical value result in the system exhibiting radically different energy landscapes, viz., smooth landscape corresponding to balanced systems seen for fast learning, and rugged landscapes corresponding to frustrated systems seen for slow learning.

  8. Global Squeeze: Assessing Climate-Critical Resource Constraints for Coastal Climate Adaptation

    NASA Astrophysics Data System (ADS)

    Chase, N. T.; Becker, A.; Schwegler, B.; Fischer, M.

    2014-12-01

    The projected impacts of climate change in the coastal zone will require local planning and local resources to adapt to increasing risks of social, environmental, and economic consequences from extreme events. This means that, for the first time in human history, aggregated local demands could outpace global supply of certain "climate-critical resources." For example, construction materials such as sand and gravel, steel, and cement may be needed to fortify many coastal locations at roughly the same point in time if decision makers begin to construct new storm barriers or elevate coastal lands. Where might adaptation bottlenecks occur? Can the world produce enough cement to armour the world's seaports as flood risks increase due to sea-level rise and more intense storms? Just how many coastal engineers would multiple such projects require? Understanding such global implications of adaptation requires global datasets—such as bathymetry, coastal topography, local sea-level rise and storm surge projections, and construction resource production capacity—that are currently unavailable at a resolution appropriate for a global-scale analysis. Our research group has identified numerous gaps in available data necessary to make such estimates on both the supply and demand sides of this equation. This presentation examines the emerging need and current availability of these types of datasets and argues for new coordinated efforts to develop and share such data.

  9. An FPGA Implementation of a Polychronous Spiking Neural Network with Delay Adaptation

    PubMed Central

    Wang, Runchun; Cohen, Gregory; Stiefel, Klaus M.; Hamilton, Tara Julia; Tapson, Jonathan; van Schaik, André

    2013-01-01

    We present an FPGA implementation of a re-configurable, polychronous spiking neural network with a large capacity for spatial-temporal patterns. The proposed neural network generates delay paths de novo, so that only connections that actually appear in the training patterns will be created. This allows the proposed network to use all the axons (variables) to store information. Spike Timing Dependent Delay Plasticity is used to fine-tune and add dynamics to the network. We use a time multiplexing approach allowing us to achieve 4096 (4k) neurons and up to 1.15 million programmable delay axons on a Virtex 6 FPGA. Test results show that the proposed neural network is capable of successfully recalling more than 95% of all spikes for 96% of the stored patterns. The tests also show that the neural network is robust to noise from random input spikes. PMID:23408739

  10. An FPGA Implementation of a Polychronous Spiking Neural Network with Delay Adaptation.

    PubMed

    Wang, Runchun; Cohen, Gregory; Stiefel, Klaus M; Hamilton, Tara Julia; Tapson, Jonathan; van Schaik, André

    2013-01-01

    We present an FPGA implementation of a re-configurable, polychronous spiking neural network with a large capacity for spatial-temporal patterns. The proposed neural network generates delay paths de novo, so that only connections that actually appear in the training patterns will be created. This allows the proposed network to use all the axons (variables) to store information. Spike Timing Dependent Delay Plasticity is used to fine-tune and add dynamics to the network. We use a time multiplexing approach allowing us to achieve 4096 (4k) neurons and up to 1.15 million programmable delay axons on a Virtex 6 FPGA. Test results show that the proposed neural network is capable of successfully recalling more than 95% of all spikes for 96% of the stored patterns. The tests also show that the neural network is robust to noise from random input spikes.

  11. Research of Recurrent Dynamic Neural Networks for Adaptive Control of Complex Dynamic Systems

    DTIC Science & Technology

    2010-07-08

    Recognition 7.1. General description of experiment 7.2. Gesture recognition system on the base of single recurrent neural network 7.3. Experiment...results for gesture recognition system on the base of single recurrent neural network 7.4. Gesture recognition system on the base of multimodular...of non-linear effects increases an advantage of neurocontrol on linear control methods. 7. Experiments related to Gesture Recognition 7.1. General

  12. Neural responses to maternal praise and criticism: Relationship to depression and anxiety symptoms in high-risk adolescent girls

    PubMed Central

    Aupperle, Robin L.; Morris, Amanda S.; Silk, Jennifer S.; Criss, Michael M.; Judah, Matt R.; Eagleton, Sally G.; Kirlic, Namik; Byrd-Craven, Jennifer; Phillips, Raquel; Alvarez, Ruben P.

    2016-01-01

    Background The parent-child relationship may be an important factor in the development of adolescent depressive and anxious symptoms. In adults, depressive symptoms relate to increased amygdala and attenuated prefrontal activation to maternal criticism. The current pilot study examined how depressive and anxiety symptoms in a high-risk adolescent population relate to neural responses to maternal feedback. Given previous research relating oxytocin to maternal behavior, we conducted exploratory analyses using oxytocin receptor (OXTR) genotype. Methods Eighteen females (ages 12–16) listened to maternal praise, neutral, and critical statements during functional magnetic resonance imaging. Participants completed the Mood and Feelings Questionnaire and the Screen for Child Anxiety Related Emotional Disorders. The OXTR single nucleotide polymorphism, rs53576, was genotyped. Linear mixed models were used to identify symptom or allele (GG, AA/AG) by condition (critical, neutral, praise) interaction effects on brain activation. Results Greater symptoms related to greater right amygdala activation for criticism and reduced activation to praise. For left amygdala, greater symptoms related to reduced activation to both conditions. Anxiety symptoms related to differences in superior medial PFC activation patterns. Parental OXTR AA/AG allele related to reduced activation to criticism and greater activation to praise within the right amygdala. Conclusions Results support a relationship between anxiety and depressive symptoms and prefrontal-amygdala responses to maternal feedback. The lateralization of amygdala findings suggests separate neural targets for interventions reducing reactivity to negative feedback or increasing salience of positive feedback. Exploratory analyses suggest that parents' OXTR genetic profile influences parent-child interactions and related adolescent brain responses. PMID:27158587

  13. An adaptive drug delivery design using neural networks for effective treatment of infectious diseases: a simulation study.

    PubMed

    Padhi, Radhakant; Bhardhwaj, Jayender R

    2009-06-01

    An adaptive drug delivery design is presented in this paper using neural networks for effective treatment of infectious diseases. The generic mathematical model used describes the coupled evolution of concentration of pathogens, plasma cells, antibodies and a numerical value that indicates the relative characteristic of a damaged organ due to the disease under the influence of external drugs. From a system theoretic point of view, the external drugs can be interpreted as control inputs, which can be designed based on control theoretic concepts. In this study, assuming a set of nominal parameters in the mathematical model, first a nonlinear controller (drug administration) is designed based on the principle of dynamic inversion. This nominal drug administration plan was found to be effective in curing "nominal model patients" (patients whose immunological dynamics conform to the mathematical model used for the control design exactly. However, it was found to be ineffective in curing "realistic model patients" (patients whose immunological dynamics may have off-nominal parameter values and possibly unwanted inputs) in general. Hence, to make the drug delivery dosage design more effective for realistic model patients, a model-following adaptive control design is carried out next by taking the help of neural networks, that are trained online. Simulation studies indicate that the adaptive controller proposed in this paper holds promise in killing the invading pathogens and healing the damaged organ even in the presence of parameter uncertainties and continued pathogen attack. Note that the computational requirements for computing the control are very minimal and all associated computations (including the training of neural networks) can be carried out online. However it assumes that the required diagnosis process can be carried out at a sufficient faster rate so that all the states are available for control computation.

  14. Neural Signature of DCD: A Critical Review of MRI Neuroimaging Studies

    PubMed Central

    Biotteau, Maëlle; Chaix, Yves; Blais, Mélody; Tallet, Jessica; Péran, Patrice; Albaret, Jean-Michel

    2016-01-01

    The most common neurodevelopmental disorders (e.g., developmental dyslexia (DD), autism, attention-deficit hyperactivity disorder (ADHD)) have been the subject of numerous neuroimaging studies, leading to certain brain regions being identified as neural correlates of these conditions, referring to a neural signature of disorders. Developmental coordination disorder (DCD), however, remains one of the least understood and studied neurodevelopmental disorders. Given the acknowledged link between motor difficulties and brain features, it is surprising that so few research studies have systematically explored the brains of children with DCD. The aim of the present review was to ascertain whether it is currently possible to identify a neural signature for DCD, based on the 14 magnetic resonance imaging neuroimaging studies that have been conducted in DCD to date. Our results indicate that several brain areas are unquestionably linked to DCD: cerebellum, basal ganglia, parietal lobe, and parts of the frontal lobe (medial orbitofrontal cortex and dorsolateral prefrontal cortex). However, research has been too sparse and studies have suffered from several limitations that constitute a serious obstacle to address the question of a well-established neural signature for DCD. PMID:28018285

  15. Ontogenetic influence on neural spine bifurcation in Diplodocoidea (Dinosauria: Sauropoda): a critical phylogenetic character.

    PubMed

    Woodruff, D Cary; Fowler, Denver W

    2012-07-01

    Within Diplodocoidea (Dinosauria: Sauropoda), phylogenetic position of the three subclades Rebbachisauridae, Dicraeosauridae, and Diplodocidae is strongly influenced by a relatively small number of characters. Neural spine bifurcation, especially within the cervical vertebrae, is considered to be a derived character, with taxa that lack this feature regarded as relatively basal. Our analysis of dorsal and cervical vertebrae from small-sized diplodocoids (representing at least 18 individuals) reveals that neural spine bifurcation is less well developed or absent in smaller specimens. New preparation of the roughly 200-cm long diplodocid juvenile Sauriermuseum Aathal 0009 reveals simple nonbifurcated cervical neural spines, strongly reminiscent of more basal sauropods such as Omeisaurus. An identical pattern of ontogenetically linked bifurcation has also been observed in several specimens of the basal macronarian Camarasaurus, suggesting that this is characteristic of several clades of Sauropoda. We suggest that neural spine bifurcation performs a biomechanical function related to horizontal positioning of the neck that may become significant only at the onset of a larger body size, hence, its apparent absence or weaker development in smaller specimens. These results have significant implications for the taxonomy and phylogenetic position of taxa described from specimens of small body size. On the basis of shallow bifurcation of its cervical and dorsal neural spines, the small diplodocid Suuwassea is more parsimoniously interpreted as an immature specimen of an already recognized diplodocid taxon. Our findings emphasize the view that nonmature dinosaurs often exhibit morphologies more similar to their ancestral state and may therefore occupy a more basal position in phylogenetic analyses than would mature specimens of the same species. In light of this, we stress the need for phylogenetic reanalysis of sauropod clades where vital characters may be ontogenetically

  16. An asymmetric image cryptosystem based on the adaptive synchronization of an uncertain unified chaotic system and a cellular neural network

    NASA Astrophysics Data System (ADS)

    Cheng, Chao-Jung; Cheng, Chi-Bin

    2013-10-01

    Chaotic dynamics provide a fast and simple means to create an excellent image cryptosystem, because it is extremely sensitive to initial conditions and system parameters, pseudorandomness, and non-periodicity. However, most chaos-based image encryption schemes are symmetric cryptographic techniques, which have been proven to be more vulnerable, compared to an asymmetric cryptosystem. This paper develops an asymmetric image cryptosystem, based on the adaptive synchronization of two different chaotic systems, namely a unified chaotic system and a cellular neural network. An adaptive controller with parameter update laws is formulated, using the Lyapunov stability theory, to asymptotically synchronize the two chaotic systems. The synchronization controller is embedded in the image cryptosystem and generates a pair of asymmetric keys, for image encryption and decryption. Using numerical simulations, three sets of experiments are conducted to evaluate the feasibility and reliability of the proposed chaos-based image cryptosystem.

  17. Development of a FPGA based fuzzy neural network system for early diagnosis of critical health condition of a patient.

    PubMed

    Chowdhury, Shubhajit Roy; Saha, Hiranmay

    2010-02-01

    The paper describes the design and training of a fuzzy neural network used for early diagnosis of a patient through an FPGA based implementation of a smart instrument. The system employs a fuzzy interface cascaded with a feed-forward neural network. In order to obtain an optimum decision regarding the future pathophysiological state of a patient, the optimal weights of the synapses between the neurons have been determined by using inverse delayed function model of neurons. The neurons that are considered in the proposed network are devoid of self connections instead of commonly used self connected neurons. The current work also find out the optimal number of neurons in the hidden layer for accurate diagnosis as against the available number of CLB in the FPGA. The system has been trained and tested with renal data of patients taken at 10 days interval of time. Applying the methodology, the chance of attainment of critical renal condition of a patient has been predicted with an accuracy of 95.2%, 30 days ahead of actually attaining the critical condition. The system has also been tested for pathophysiological state prediction of patients at multiple time steps ahead and the prediction at the next instant of time stands out to be the most accurate.

  18. Prediction of the Wrist Joint Position During a Postural Tremor Using Neural Oscillators and an Adaptive Controller

    PubMed Central

    Kobravi, Hamid Reza; Ali, Sara Hemmati; Vatandoust, Masood; Marvi, Rasoul

    2016-01-01

    The prediction of the joint angle position, especially during tremor bursts, can be useful for detecting, tracking, and forecasting tremors. Thus, this research proposes a new model for predicting the wrist joint position during rhythmic bursts and inter-burst intervals. Since a tremor is an approximately rhythmic and roughly sinusoidal movement, neural oscillators have been selected to underlie the proposed model. Two neural oscillators were adopted. Electromyogram (EMG) signals were recorded from the extensor carpi radialis and flexor carpi radialis muscles concurrent with the joint angle signals of a stroke subject in an arm constant-posture. The output frequency of each oscillator was equal to the frequency corresponding to the maximum value of power spectrum related to the rhythmic wrist joint angle signals which had been recorded during a postural tremor. The phase shift between the outputs of the two oscillators was equal to the phase shift between the muscle activation of the wrist flexor and extensor muscles. The difference between the two oscillators’ output signals was considered the main pattern. Along with a proportional compensator, an adaptive neural controller has adjusted the amplitude of the main pattern in such a way so as to minimize the wrist joint prediction error during a stroke patient's tremor burst and a healthy subject's generated artificial tremor. In regard to the range of wrist joint movement during the observed rhythmic motions, a calculated prediction error is deemed acceptable. PMID:27186540

  19. Neural crest cell signaling pathways critical to cranial bone development and pathology.

    PubMed

    Mishina, Yuji; Snider, Taylor Nicholas

    2014-07-15

    Neural crest cells appear early during embryogenesis and give rise to many structures in the mature adult. In particular, a specific population of neural crest cells migrates to and populates developing cranial tissues. The ensuing differentiation of these cells via individual complex and often intersecting signaling pathways is indispensible to growth and development of the craniofacial complex. Much research has been devoted to this area of development with particular emphasis on cell signaling events required for physiologic development. Understanding such mechanisms will allow researchers to investigate ways in which they can be exploited in order to treat a multitude of diseases affecting the craniofacial complex. Knowing how these multipotent cells are driven towards distinct fates could, in due course, allow patients to receive regenerative therapies for tissues lost to a variety of pathologies. In order to realize this goal, nucleotide sequencing advances allowing snapshots of entire genomes and exomes are being utilized to identify molecular entities associated with disease states. Once identified, these entities can be validated for biological significance with other methods. A crucial next step is the integration of knowledge gleaned from observations in disease states with normal physiology to generate an explanatory model for craniofacial development. This review seeks to provide a current view of the landscape on cell signaling and fate determination of the neural crest and to provide possible avenues of approach for future research.

  20. An adaptive PID like controller using mix locally recurrent neural network for robotic manipulator with variable payload.

    PubMed

    Sharma, Richa; Kumar, Vikas; Gaur, Prerna; Mittal, A P

    2016-05-01

    Being complex, non-linear and coupled system, the robotic manipulator cannot be effectively controlled using classical proportional-integral-derivative (PID) controller. To enhance the effectiveness of the conventional PID controller for the nonlinear and uncertain systems, gains of the PID controller should be conservatively tuned and should adapt to the process parameter variations. In this work, a mix locally recurrent neural network (MLRNN) architecture is investigated to mimic a conventional PID controller which consists of at most three hidden nodes which act as proportional, integral and derivative node. The gains of the mix locally recurrent neural network based PID (MLRNNPID) controller scheme are initialized with a newly developed cuckoo search algorithm (CSA) based optimization method rather than assuming randomly. A sequential learning based least square algorithm is then investigated for the on-line adaptation of the gains of MLRNNPID controller. The performance of the proposed controller scheme is tested against the plant parameters uncertainties and external disturbances for both links of the two link robotic manipulator with variable payload (TL-RMWVP). The stability of the proposed controller is analyzed using Lyapunov stability criteria. A performance comparison is carried out among MLRNNPID controller, CSA optimized NNPID (OPTNNPID) controller and CSA optimized conventional PID (OPTPID) controller in order to establish the effectiveness of the MLRNNPID controller.

  1. Cognitive flexibility in adolescence: neural and behavioral mechanisms of reward prediction error processing in adaptive decision making during development.

    PubMed

    Hauser, Tobias U; Iannaccone, Reto; Walitza, Susanne; Brandeis, Daniel; Brem, Silvia

    2015-01-01

    Adolescence is associated with quickly changing environmental demands which require excellent adaptive skills and high cognitive flexibility. Feedback-guided adaptive learning and cognitive flexibility are driven by reward prediction error (RPE) signals, which indicate the accuracy of expectations and can be estimated using computational models. Despite the importance of cognitive flexibility during adolescence, only little is known about how RPE processing in cognitive flexibility deviates between adolescence and adulthood. In this study, we investigated the developmental aspects of cognitive flexibility by means of computational models and functional magnetic resonance imaging (fMRI). We compared the neural and behavioral correlates of cognitive flexibility in healthy adolescents (12-16years) to adults performing a probabilistic reversal learning task. Using a modified risk-sensitive reinforcement learning model, we found that adolescents learned faster from negative RPEs than adults. The fMRI analysis revealed that within the RPE network, the adolescents had a significantly altered RPE-response in the anterior insula. This effect seemed to be mainly driven by increased responses to negative prediction errors. In summary, our findings indicate that decision making in adolescence goes beyond merely increased reward-seeking behavior and provides a developmental perspective to the behavioral and neural mechanisms underlying cognitive flexibility in the context of reinforcement learning.

  2. A system identification analysis of neural adaptation dynamics and nonlinear responses in the local reflex control of locust hind limbs.

    PubMed

    Dewhirst, Oliver P; Angarita-Jaimes, Natalia; Simpson, David M; Allen, Robert; Newland, Philip L

    2013-02-01

    Nonlinear type system identification models coupled with white noise stimulation provide an experimentally convenient and quick way to investigate the often complex and nonlinear interactions between the mechanical and neural elements of reflex limb control systems. Previous steady state analysis has allowed the neurons in such systems to be categorised by their sensitivity to position, velocity or acceleration (dynamics) and has improved our understanding of network function. These neurons, however, are known to adapt their output amplitude or spike firing rate during repetitive stimulation and this transient response may be more important than the steady state response for reflex control. In the current study previously used system identification methods are developed and applied to investigate both steady state and transient dynamic and nonlinear changes in the neural circuit responsible for controlling reflex movements of the locust hind limbs. Through the use of a parsimonious model structure and Monte Carlo simulations we conclude that key system dynamics remain relatively unchanged during repetitive stimulation while output amplitude adaptation is occurring. Whilst some evidence of a significant change was found in parts of the systems nonlinear response, the effect was small and probably of little physiological relevance. Analysis using biologically more realistic stimulation reinforces this conclusion.

  3. An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox

    PubMed Central

    Jing, Luyang; Wang, Taiyong; Zhao, Ming; Wang, Peng

    2017-01-01

    A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment. PMID:28230767

  4. An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox.

    PubMed

    Jing, Luyang; Wang, Taiyong; Zhao, Ming; Wang, Peng

    2017-02-21

    A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment.

  5. Adaptive inverse control of linear and nonlinear systems using dynamic neural networks.

    PubMed

    Plett, G L

    2003-01-01

    In this paper, we see adaptive control as a three-part adaptive-filtering problem. First, the dynamical system we wish to control is modeled using adaptive system-identification techniques. Second, the dynamic response of the system is controlled using an adaptive feedforward controller. No direct feedback is used, except that the system output is monitored and used by an adaptive algorithm to adjust the parameters of the controller. Third, disturbance canceling is performed using an additional adaptive filter. The canceler does not affect system dynamics, but feeds back plant disturbance in a way that minimizes output disturbance power. The techniques work to control minimum-phase or nonminimum-phase, linear or nonlinear, single-input-single-output (SISO) or multiple-input-multiple-ouput (MIMO), stable or stabilized systems. Constraints may additionally be placed on control effort for a practical implementation. Simulation examples are presented to demonstrate that the proposed methods work very well.

  6. Feed Forward Neural Network and Optimal Control Problem with Control and State Constraints

    NASA Astrophysics Data System (ADS)

    Kmet', Tibor; Kmet'ová, Mária

    2009-09-01

    A feed forward neural network based optimal control synthesis is presented for solving optimal control problems with control and state constraints. The paper extends adaptive critic neural network architecture proposed by [5] to the optimal control problems with control and state constraints. The optimal control problem is transcribed into a nonlinear programming problem which is implemented with adaptive critic neural network. The proposed simulation method is illustrated by the optimal control problem of nitrogen transformation cycle model. Results show that adaptive critic based systematic approach holds promise for obtaining the optimal control with control and state constraints.

  7. Adaptive evolution of anti-predator ability promotes the diversity of prey species: critical function analysis.

    PubMed

    Zu, Jian; Takeuchi, Yasuhiro

    2012-08-01

    In this paper, with the method of adaptive dynamics and critical function analysis, we investigate the evolutionary diversification of prey species. We assume that prey species can evolve safer strategies such that it can reduce the predation risk, but this has a cost in terms of its reproduction. First, by using the method of critical function analysis, we identify the general properties of trade-off functions that allow for continuously stable strategy and evolutionary branching in the prey strategy. It is found that if the trade-off curve is globally concave, then the evolutionarily singular strategy is continuously stable. However, if the trade-off curve is concave-convex-concave and the prey's sensitivity to crowding is not strong, then the evolutionarily singular strategy may be an evolutionary branching point, near which the resident and mutant prey can coexist and diverge in their strategies. Second, we find that after branching has occurred in the prey strategy, if the trade-off curve is concave-convex-concave, the prey population will eventually evolve into two different types, which can coexist on the long-term evolutionary timescale. The algebraical analysis reveals that an attractive dimorphism will always be evolutionarily stable and that no further branching is possible for the concave-convex-concave trade-off relationship.

  8. Functional cross-talk between the cellular prion protein and the neural cell adhesion molecule is critical for neuronal differentiation of neural stem/precursor cells.

    PubMed

    Prodromidou, Kanella; Papastefanaki, Florentia; Sklaviadis, Theodoros; Matsas, Rebecca

    2014-06-01

    Cellular prion protein (PrP) is prominently expressed in brain, in differentiated neurons but also in neural stem/precursor cells (NPCs). The misfolding of PrP is a central event in prion diseases, yet the physiological function of PrP is insufficiently understood. Although PrP has been reported to associate with the neural cell adhesion molecule (NCAM), the consequences of concerted PrP-NCAM action in NPC physiology are unknown. Here, we generated NPCs from the subventricular zone (SVZ) of postnatal day 5 wild-type and PrP null (-/-) mice and observed that PrP is essential for proper NPC proliferation and neuronal differentiation. Moreover, we found that PrP is required for the NPC response to NCAM-induced neuronal differentiation. In the absence of PrP, NCAM not only fails to promote neuronal differentiation but also induces an accumulation of doublecortin-positive neuronal progenitors at the proliferation stage. In agreement, we noted an increase in cycling neuronal progenitors in the SVZ of PrP-/- mice compared with PrP+/+ mice, as evidenced by double labeling for the proliferation marker Ki67 and doublecortin as well as by 5-bromo-2'-deoxyuridine incorporation experiments. Additionally, fewer newly born neurons were detected in the rostral migratory stream of PrP-/- mice. Analysis of the migration of SVZ cells in microexplant cultures from wild-type and PrP-/- mice revealed no differences between genotypes or a role for NCAM in this process. Our data demonstrate that PrP plays a critical role in neuronal differentiation of NPCs and suggest that this function is, at least in part, NCAM-dependent.

  9. Chaos control of the brushless direct current motor using adaptive dynamic surface control based on neural network with the minimum weights

    SciTech Connect

    Luo, Shaohua; Wu, Songli; Gao, Ruizhen

    2015-07-15

    This paper investigates chaos control for the brushless DC motor (BLDCM) system by adaptive dynamic surface approach based on neural network with the minimum weights. The BLDCM system contains parameter perturbation, chaotic behavior, and uncertainty. With the help of radial basis function (RBF) neural network to approximate the unknown nonlinear functions, the adaptive law is established to overcome uncertainty of the control gain. By introducing the RBF neural network and adaptive technology into the dynamic surface control design, a robust chaos control scheme is developed. It is proved that the proposed control approach can guarantee that all signals in the closed-loop system are globally uniformly bounded, and the tracking error converges to a small neighborhood of the origin. Simulation results are provided to show that the proposed approach works well in suppressing chaos and parameter perturbation.

  10. Chaos control of the brushless direct current motor using adaptive dynamic surface control based on neural network with the minimum weights

    NASA Astrophysics Data System (ADS)

    Luo, Shaohua; Wu, Songli; Gao, Ruizhen

    2015-07-01

    This paper investigates chaos control for the brushless DC motor (BLDCM) system by adaptive dynamic surface approach based on neural network with the minimum weights. The BLDCM system contains parameter perturbation, chaotic behavior, and uncertainty. With the help of radial basis function (RBF) neural network to approximate the unknown nonlinear functions, the adaptive law is established to overcome uncertainty of the control gain. By introducing the RBF neural network and adaptive technology into the dynamic surface control design, a robust chaos control scheme is developed. It is proved that the proposed control approach can guarantee that all signals in the closed-loop system are globally uniformly bounded, and the tracking error converges to a small neighborhood of the origin. Simulation results are provided to show that the proposed approach works well in suppressing chaos and parameter perturbation.

  11. Chaos control of the brushless direct current motor using adaptive dynamic surface control based on neural network with the minimum weights.

    PubMed

    Luo, Shaohua; Wu, Songli; Gao, Ruizhen

    2015-07-01

    This paper investigates chaos control for the brushless DC motor (BLDCM) system by adaptive dynamic surface approach based on neural network with the minimum weights. The BLDCM system contains parameter perturbation, chaotic behavior, and uncertainty. With the help of radial basis function (RBF) neural network to approximate the unknown nonlinear functions, the adaptive law is established to overcome uncertainty of the control gain. By introducing the RBF neural network and adaptive technology into the dynamic surface control design, a robust chaos control scheme is developed. It is proved that the proposed control approach can guarantee that all signals in the closed-loop system are globally uniformly bounded, and the tracking error converges to a small neighborhood of the origin. Simulation results are provided to show that the proposed approach works well in suppressing chaos and parameter perturbation.

  12. An Adaptive Neural Mechanism for Acoustic Motion Perception with Varying Sparsity

    PubMed Central

    Shaikh, Danish; Manoonpong, Poramate

    2017-01-01

    Biological motion-sensitive neural circuits are quite adept in perceiving the relative motion of a relevant stimulus. Motion perception is a fundamental ability in neural sensory processing and crucial in target tracking tasks. Tracking a stimulus entails the ability to perceive its motion, i.e., extracting information about its direction and velocity. Here we focus on auditory motion perception of sound stimuli, which is poorly understood as compared to its visual counterpart. In earlier work we have developed a bio-inspired neural learning mechanism for acoustic motion perception. The mechanism extracts directional information via a model of the peripheral auditory system of lizards. The mechanism uses only this directional information obtained via specific motor behaviour to learn the angular velocity of unoccluded sound stimuli in motion. In nature however the stimulus being tracked may be occluded by artefacts in the environment, such as an escaping prey momentarily disappearing behind a cover of trees. This article extends the earlier work by presenting a comparative investigation of auditory motion perception for unoccluded and occluded tonal sound stimuli with a frequency of 2.2 kHz in both simulation and practice. Three instances of each stimulus are employed, differing in their movement velocities–0.5°/time step, 1.0°/time step and 1.5°/time step. To validate the approach in practice, we implement the proposed neural mechanism on a wheeled mobile robot and evaluate its performance in auditory tracking. PMID:28337137

  13. An Adaptive Neural Mechanism for Acoustic Motion Perception with Varying Sparsity.

    PubMed

    Shaikh, Danish; Manoonpong, Poramate

    2017-01-01

    Biological motion-sensitive neural circuits are quite adept in perceiving the relative motion of a relevant stimulus. Motion perception is a fundamental ability in neural sensory processing and crucial in target tracking tasks. Tracking a stimulus entails the ability to perceive its motion, i.e., extracting information about its direction and velocity. Here we focus on auditory motion perception of sound stimuli, which is poorly understood as compared to its visual counterpart. In earlier work we have developed a bio-inspired neural learning mechanism for acoustic motion perception. The mechanism extracts directional information via a model of the peripheral auditory system of lizards. The mechanism uses only this directional information obtained via specific motor behaviour to learn the angular velocity of unoccluded sound stimuli in motion. In nature however the stimulus being tracked may be occluded by artefacts in the environment, such as an escaping prey momentarily disappearing behind a cover of trees. This article extends the earlier work by presenting a comparative investigation of auditory motion perception for unoccluded and occluded tonal sound stimuli with a frequency of 2.2 kHz in both simulation and practice. Three instances of each stimulus are employed, differing in their movement velocities-0.5°/time step, 1.0°/time step and 1.5°/time step. To validate the approach in practice, we implement the proposed neural mechanism on a wheeled mobile robot and evaluate its performance in auditory tracking.

  14. Creating a New Model for Mainstreaming Climate Change Adaptation for Critical Infrastructure: The New York City Climate Change Adaptation Task Force and the NYC Panel on Climate Change

    NASA Astrophysics Data System (ADS)

    Rosenzweig, C.; Solecki, W. D.; Freed, A. M.

    2008-12-01

    The New York City Climate Change Adaptation Task Force, launched in August 2008, aims to secure the city's critical infrastructure against rising seas, higher temperatures and fluctuating water supplies projected to result from climate change. The Climate Change Adaptation Task Force is part of PlaNYC, the city's long- term sustainability plan, and is composed of over 30 city and state agencies, public authorities and companies that operate the region's roads, bridges, tunnels, mass transit, and water, sewer, energy and telecommunications systems - all with critical infrastructure identified as vulnerable. It is one of the most comprehensive adaptation efforts yet launched by an urban region. To guide the effort, Mayor Michael Bloomberg has formed the New York City Panel on Climate Change (NPCC), modeled on the Intergovernmental Panel on Climate Change (IPCC). Experts on the panel include climatologists, sea-level rise specialists, adaptation experts, and engineers, as well as representatives from the insurance and legal sectors. The NPCC is developing planning tools for use by the Task Force members that provide information about climate risks, adaptation and risk assessment, prioritization frameworks, and climate protection levels. The advisory panel is supplying climate change projections, helping to identify at- risk infrastructure, and assisting the Task Force in developing adaptation strategies and guidelines for design of new structures. The NPCC will also publish an assessment report in 2009 that will serve as the foundation for climate change adaptation in the New York City region, similar to the IPCC reports. Issues that the Climate Change Adaptation Task Force and the NPCC are addressing include decision- making under climate change uncertainty, effective ways for expert knowledge to be incorporated into public actions, and strategies for maintaining consistent and effective attention to long-term climate change even as municipal governments cycle

  15. Neural activity in the dorsal medial superior temporal area of monkeys represents retinal error during adaptive motor learning

    PubMed Central

    Takemura, Aya; Ofuji, Tomoyo; Miura, Kenichiro; Kawano, Kenji

    2017-01-01

    To adapt to variable environments, humans regulate their behavior by modulating gains in sensory-to-motor processing. In this study, we measured a simple eye movement, the ocular following response (OFR), in monkeys to study the neuronal basis of adaptive motor learning in the visuomotor processing stream. The medial superior temporal (MST) area of the cerebral cortex is a critical site for contextual gain modulation of the OFR. However, the role of MST neurons in adaptive gain modulation of the OFR remains unknown. We adopted a velocity step-down sequence paradigm that was designed to promote adaptive gain modulation of the OFR to investigate the role of the dorsal MST (MSTd) in adaptive motor learning. In the initial learning stage, we observed a reduction in the OFR but no significant change in the “open-loop” responses for the majority of the MSTd neurons. However, in the late learning stage, some MSTd neurons exhibited significantly enhanced “closed-loop” responses in association with increases in retinal error velocity. These results indicate that the MSTd area primarily encodes visual motion, suggesting that MSTd neurons function upstream of the motor learning site to provide sensory signals to the downstream structures involved in adaptive motor learning. PMID:28102342

  16. Crossing the threshold: gene flow, dominance and the critical level of standing genetic variation required for adaptation to novel environments.

    PubMed

    Nuismer, S L; MacPherson, A; Rosenblum, E B

    2012-12-01

    Genetic architecture plays an important role in the process of adaptation to novel environments. One example is the role of allelic dominance, where advantageous recessive mutations have a lower probability of fixation than advantageous dominant mutations. This classic observation, termed 'Haldane's sieve', has been well explored theoretically for single isolated populations adapting to new selective regimes. However, the role of dominance is less well understood for peripheral populations adapting to novel environments in the face of recurrent and maladaptive gene flow. Here, we use a combination of analytical approximations and individual-based simulations to explore how dominance influences the likelihood of adaptation to novel peripheral environments. We demonstrate that in the face of recurrent maladaptive gene flow, recessive alleles can fuel adaptation only when their frequency exceeds a critical threshold within the ancestral range.

  17. Adapting for endocytosis: roles for endocytic sorting adaptors in directing neural development

    PubMed Central

    Yap, Chan Choo; Winckler, Bettina

    2015-01-01

    Proper cortical development depends on the orchestrated actions of a multitude of guidance receptors and adhesion molecules and their downstream signaling. The levels of these receptors on the surface and their precise locations can greatly affect guidance outcomes. Trafficking of receptors to a particular surface locale and removal by endocytosis thus feed crucially into the final guidance outcomes. In addition, endocytosis of receptors can affect downstream signaling (both quantitatively and qualitatively) and regulated endocytosis of guidance receptors is thus an important component of ensuring proper neural development. We will discuss the cell biology of regulated endocytosis and the impact on neural development. We focus our discussion on endocytic accessory proteins (EAPs) (such as numb and disabled) and how they regulate endocytosis and subsequent post-endocytic trafficking of their cognate receptors (such as Notch, TrkB, β-APP, VLDLR, and ApoER2). PMID:25904845

  18. Adapting for endocytosis: roles for endocytic sorting adaptors in directing neural development.

    PubMed

    Yap, Chan Choo; Winckler, Bettina

    2015-01-01

    Proper cortical development depends on the orchestrated actions of a multitude of guidance receptors and adhesion molecules and their downstream signaling. The levels of these receptors on the surface and their precise locations can greatly affect guidance outcomes. Trafficking of receptors to a particular surface locale and removal by endocytosis thus feed crucially into the final guidance outcomes. In addition, endocytosis of receptors can affect downstream signaling (both quantitatively and qualitatively) and regulated endocytosis of guidance receptors is thus an important component of ensuring proper neural development. We will discuss the cell biology of regulated endocytosis and the impact on neural development. We focus our discussion on endocytic accessory proteins (EAPs) (such as numb and disabled) and how they regulate endocytosis and subsequent post-endocytic trafficking of their cognate receptors (such as Notch, TrkB, β-APP, VLDLR, and ApoER2).

  19. A mixed-signal implementation of a polychronous spiking neural network with delay adaptation

    PubMed Central

    Wang, Runchun M.; Hamilton, Tara J.; Tapson, Jonathan C.; van Schaik, André

    2014-01-01

    We present a mixed-signal implementation of a re-configurable polychronous spiking neural network capable of storing and recalling spatio-temporal patterns. The proposed neural network contains one neuron array and one axon array. Spike Timing Dependent Delay Plasticity is used to fine-tune delays and add dynamics to the network. In our mixed-signal implementation, the neurons and axons have been implemented as both analog and digital circuits. The system thus consists of one FPGA, containing the digital neuron array and the digital axon array, and one analog IC containing the analog neuron array and the analog axon array. The system can be easily configured to use different combinations of each. We present and discuss the experimental results of all combinations of the analog and digital axon arrays and the analog and digital neuron arrays. The test results show that the proposed neural network is capable of successfully recalling more than 85% of stored patterns using both analog and digital circuits. PMID:24672422

  20. Neural representations for the generation of inventive conceptions inspired by adaptive feature optimization of biological species.

    PubMed

    Zhang, Hao; Liu, Jia; Zhang, Qinglin

    2014-01-01

    Inventive conceptions amount to creative ideas for designing devices that are both original and useful. The generation of inventive conceptions is a key element of the inventive process. However, neural mechanisms of the inventive process remain poorly understood. Here we employed functional feature association tasks and event-related functional magnetic resonance imaging (MRI) to investigate neural substrates for the generation of inventive conceptions. The functional MRI (fMRI) data revealed significant activations at Brodmann area (BA) 47 in the left inferior frontal gyrus and at BA 18 in the left lingual gyrus, when participants performed biological functional feature association tasks compared with non-biological functional feature association tasks. Our results suggest that the left inferior frontal gyrus (BA 47) is associated with novelty-based representations formed by the generation and selection of semantic relatedness, and the left lingual gyrus (BA 18) is involved in relevant visual imagery in processing of semantic relatedness. The findings might shed light on neural mechanisms underlying the inventive process.

  1. Closed loop adaptive control of spectrum-producing step using neural networks

    DOEpatents

    Fu, C.Y.

    1998-11-24

    Characteristics of the plasma in a plasma-based manufacturing process step are monitored directly and in real time by observing the spectrum which it produces. An artificial neural network analyzes the plasma spectrum and generates control signals to control one or more of the process input parameters in response to any deviation of the spectrum beyond a narrow range. In an embodiment, a plasma reaction chamber forms a plasma in response to input parameters such as gas flow, pressure and power. The chamber includes a window through which the electromagnetic spectrum produced by a plasma in the chamber, just above the subject surface, may be viewed. The spectrum is conducted to an optical spectrometer which measures the intensity of the incoming optical spectrum at different wavelengths. The output of optical spectrometer is provided to an analyzer which produces a plurality of error signals, each indicating whether a respective one of the input parameters to the chamber is to be increased or decreased. The microcontroller provides signals to control respective controls, but these lines are intercepted and first added to the error signals, before being provided to the controls for the chamber. The analyzer can include a neural network and an optional spectrum preprocessor to reduce background noise, as well as a comparator which compares the parameter values predicted by the neural network with a set of desired values provided by the microcontroller. 7 figs.

  2. Closed loop adaptive control of spectrum-producing step using neural networks

    DOEpatents

    Fu, Chi Yung

    1998-01-01

    Characteristics of the plasma in a plasma-based manufacturing process step are monitored directly and in real time by observing the spectrum which it produces. An artificial neural network analyzes the plasma spectrum and generates control signals to control one or more of the process input parameters in response to any deviation of the spectrum beyond a narrow range. In an embodiment, a plasma reaction chamber forms a plasma in response to input parameters such as gas flow, pressure and power. The chamber includes a window through which the electromagnetic spectrum produced by a plasma in the chamber, just above the subject surface, may be viewed. The spectrum is conducted to an optical spectrometer which measures the intensity of the incoming optical spectrum at different wavelengths. The output of optical spectrometer is provided to an analyzer which produces a plurality of error signals, each indicating whether a respective one of the input parameters to the chamber is to be increased or decreased. The microcontroller provides signals to control respective controls, but these lines are intercepted and first added to the error signals, before being provided to the controls for the chamber. The analyzer can include a neural network and an optional spectrum preprocessor to reduce background noise, as well as a comparator which compares the parameter values predicted by the neural network with a set of desired values provided by the microcontroller.

  3. Stochastic slowly adapting ionic currents may provide a decorrelation mechanism for neural oscillators by causing wander in the intrinsic period.

    PubMed

    Norman, Sharon E; Butera, Robert J; Canavier, Carmen C

    2016-09-01

    Oscillatory neurons integrate their synaptic inputs in fundamentally different ways than normally quiescent neurons. We show that the oscillation period of invertebrate endogenous pacemaker neurons wanders, producing random fluctuations in the interspike intervals (ISI) on a time scale of seconds to minutes, which decorrelates pairs of neurons in hybrid circuits constructed using the dynamic clamp. The autocorrelation of the ISI sequence remained high for many ISIs, but the autocorrelation of the ΔISI series had on average a single nonzero value, which was negative at a lag of one interval. We reproduced these results using a simple integrate and fire (IF) model with a stochastic population of channels carrying an adaptation current with a stochastic component that was integrated with a slow time scale, suggesting that a similar population of channels underlies the observed wander in the period. Using autoregressive integrated moving average (ARIMA) models, we found that a single integrator and a single moving average with a negative coefficient could simulate both the experimental data and the IF model. Feeding white noise into an integrator with a slow time constant is sufficient to produce the autocorrelation structure of the ISI series. Moreover, the moving average clearly accounted for the autocorrelation structure of the ΔISI series and is biophysically implemented in the IF model using slow stochastic adaptation. The observed autocorrelation structure may be a neural signature of slow stochastic adaptation, and wander generated in this manner may be a general mechanism for limiting episodes of synchronized activity in the nervous system.

  4. Neural mechanisms for the cannabinoid modulation of cognition and affect in man: a critical review of neuroimaging studies.

    PubMed

    Bhattacharyya, Sagnik; Atakan, Zerrin; Martin-Santos, Rocio; Crippa, Jose A; McGuire, Philip K

    2012-01-01

    Pharmacological challenge in conjunction with neuroimaging techniques has been employed for over two decades now to understand the neural basis of the cognitive, emotional and symptomatic effects of the main ingredients of cannabis, the most widely used illicit drug in the world. This selective critical review focuses on the human neuroimaging studies investigating the effects of delta-9- tetrahydrocannabinol (THC) and cannabidiol (CBD), the two main cannabinoids of interest present in the extract of the cannabis plant. These studies suggest that consistent with the polymorphic and heterogeneous nature of the effects of cannabis, THC and CBD have distinct and often opposing effects on widely distributed neural networks that include medial temporal and prefrontal cortex and striatum, brain regions that are rich in cannabinoid receptors and implicated in the pathophysiology of psychosis. They help elucidate the neurocognitive mechanisms underlying the acute induction of psychotic symptoms by cannabis and provide mechanistic understanding underlying the potential role of CBD as an anxiolytic and antipsychotic. Although there are ethical and methodological caveats, pharmacological neuroimaging studies such as those reviewed here may not only help model different aspects of the psychopathology of mental disorders such as schizophrenia and offer insights into their underlying mechanisms, but may suggest potentially new therapeutic targets for drug discovery.

  5. Simulation of photosynthetic production using neural network

    NASA Astrophysics Data System (ADS)

    Kmet, Tibor; Kmetova, Maria

    2013-10-01

    This paper deals with neural network based optimal control synthesis for solving optimal control problems with control and state constraints and discrete time delay. The optimal control problem is transcribed into nonlinear programming problem which is implemented with adaptive critic neural network. This approach is applicable to a wide class of nonlinear systems. The proposed simulation methods is illustrated by the optimal control problem of photosynthetic production described by discrete time delay differential equations. Results show that adaptive critic based systematic approach holds promise for obtaining the optimal control with control and state constraints.

  6. Safe, Advanced, Adaptable Isolation System Eliminates the Need for Critical Lifts

    NASA Technical Reports Server (NTRS)

    Ginn, Starr

    2011-01-01

    The Starr Soft Support isolation system incorporates an automatically reconfigurable aircraft jack into NASA's existing 1-Hertz isolators. This enables an aircraft to float in mid-air without the need for a critical lift during ground vibration testing (GVT), significantly reducing testing risk, time, and costs. Currently incorporating the most advanced technology available, the 60,000-poundcapacity (27-metric-ton) isolation system is used for weight and measurement tests, control-surface free-play tests, and structural mode interaction tests without the need for any major reconfiguration, often saving days of time and significantly reducing labor costs. The Starr Soft Support isolation system consists of an aircraft-jacking device with three jacking points, each of which has an individual motor and accommodates up to 20,000 pounds (9 metric tons) for a total 60,000-pound (27-metric-ton) capacity. The system can be transported to the aircraft by forklift and placed at its jacking points using a pallet jack. The motors power the electric actuators, raising the aircraft above the ground until the landing gear can retract. Inflatable isolators then deploy, enabling the aircraft to float in mid-air, simulating a 1-Hertz free-free boundary condition. Inflatable isolators have been in use at NASA for years, enabling aircraft to literally float unsupported for highly accurate GVT. These isolators must be placed underneath the aircraft for this to occur. Traditionally, this is achieved by a critical lift a high-risk procedure in which a crane and flexible cord system are used to lift the aircraft. In contrast, the Starr Soft Support isolation system eliminates the need for critical lift by integrating the inflatable isolators into an aircraft jacking system. The system maintains vertical and horizontal isolating capabilities. The aircraft can be rolled onto the system, jacked up, and then the isolators can be inflated and positioned without any personnel needing to work

  7. Online training course on critical appraisal for nurses: adaptation and assessment

    PubMed Central

    2014-01-01

    Background Research is an essential activity for improving quality and efficiency in healthcare. The objective of this study was to train nurses from the public Basque Health Service (Osakidetza) in critical appraisal, promoting continuous training and the use of research in clinical practice. Methods This was a prospective pre-post test study. The InfoCritique course on critical appraisal was translated and adapted. A sample of 50 nurses and 3 tutors was recruited. Educational strategies and assessment instruments were established for the course. A course website was created that contained contact details of the teaching team and coordinator, as well as a course handbook and videos introducing the course. Assessment comprised the administration of questionnaires before and after the course, in order to explore the main intervention outcomes: knowledge acquired and self-learning readiness. Satisfaction was also measured at the end of the course. Results Of the 50 health professionals recruited, 3 did not complete the course for personal or work-related reasons. The mean score on the pre-course knowledge questionnaire was 70.5 out of 100, with a standard deviation of 11.96. In general, participants’ performance on the knowledge questionnaire improved after the course, as reflected in the notable increase of the mean score, to 86.6, with a standard deviation of 10.00. Further, analyses confirmed statistically significant differences between pre- and post-course results (p < 0.001). With regard to self-learning readiness, after the course, participants reported a greater readiness and ability for self-directed learning. Lastly, in terms of level of satisfaction with the course, the mean score was 7 out of 10. Conclusions Participants significantly improved their knowledge score and self-directed learning readiness after the educational intervention, and they were overall satisfied with the course. For the health system and nursing professionals, this type of

  8. Military service member and veteran reintegration: A critical review and adapted ecological model.

    PubMed

    Elnitsky, Christine A; Blevins, Cara L; Fisher, Michael P; Magruder, Kathryn

    2017-01-01

    Returning military service members and veterans (MSMVs) experience a wide range of stress-related disorders in addition to social and occupational difficulties when reintegrating to the community. Facilitating reintegration of MSMVs following deployment is a societal priority. With an objective of identifying challenges and facilitators for reintegration of MSMVs of the current war era, we critically review and identify gaps in the literature. We searched 8 electronic databases and identified 1,764 articles. Screening of abstracts and full-text review based on our inclusion/exclusion criteria, yielded 186 articles for review. Two investigators evaluating relevant articles independently found a lack of clear definition or comprehensive theorizing about MSMV reintegration. To address these gaps, we linked the findings from the literature to provide a unified definition of reintegration and adapted the social ecological systems theory to guide research and practice aimed at MSMV reintegration. Furthermore, we identified individual, interpersonal, community, and societal challenges related to reintegration. The 186 studies published from 2001 (the start of the current war era) to 2015 included 6 experimental studies or clinical trials. Most studies do not adequately account for context or more than a narrow set of potential influences on MSMV reintegration. Little evidence was found that evaluated interventions for health conditions, rehabilitation, and employment, or effective models of integrated delivery systems. We recommend an ecological model of MSMV reintegration to advance research and practice processes and outcomes at 4 levels (individual, interpersonal, organizational, and societal). (PsycINFO Database Record

  9. Simulation of SiGe:C HBTs using neural network and adaptive neuro-fuzzy inference system for RF applications

    NASA Astrophysics Data System (ADS)

    Karimi, Gholamreza; Banitalebi, Roza; Babaei Sedaghat, Sedigheh

    2013-07-01

    In this article, the small-signal equivalent circuit model of SiGe:C heterojunction bipolar transistors (HBTs) has directly been extracted from S-parameter data. Moreover, in this article, we present a new modelling approach using ANFIS (adaptive neuro-fuzzy inference system), which in general has a high degree of accuracy, simplicity and novelty (independent approach). Then measured and model-calculated data show an excellent agreement with less than 1.68 × 10-5% discrepancy in the frequency range of higher than 300 GHz over a wide range of bias points in ANFIS. The results show ANFIS model is better than ANN (artificial neural network) for redeveloping the model and increasing the input parameters.

  10. Adaptive neural control for dual-arm coordination of humanoid robot with unknown nonlinearities in output mechanism.

    PubMed

    Liu, Zhi; Chen, Ci; Zhang, Yun; Chen, C L P

    2015-03-01

    To achieve an excellent dual-arm coordination of the humanoid robot, it is essential to deal with the nonlinearities existing in the system dynamics. The literatures so far on the humanoid robot control have a common assumption that the problem of output hysteresis could be ignored. However, in the practical applications, the output hysteresis is widely spread; and its existing limits the motion/force performances of the robotic system. In this paper, an adaptive neural control scheme, which takes the unknown output hysteresis and computational efficiency into account, is presented and investigated. In the controller design, the prior knowledge of system dynamics is assumed to be unknown. The motion error is guaranteed to converge to a small neighborhood of the origin by Lyapunov's stability theory. Simultaneously, the internal force is kept bounded and its error can be made arbitrarily small.

  11. Dyslexics’ faster decay of implicit memory for sounds and words is manifested in their shorter neural adaptation

    PubMed Central

    Jaffe-Dax, Sagi; Frenkel, Or; Ahissar, Merav

    2017-01-01

    Dyslexia is a prevalent reading disability whose underlying mechanisms are still disputed. We studied the neural mechanisms underlying dyslexia using a simple frequency-discrimination task. Though participants were asked to compare the two tones in each trial, implicit memory of previous trials affected their responses. We hypothesized that implicit memory decays faster among dyslexics. We tested this by increasing the temporal intervals between consecutive trials, and by measuring the behavioral impact and ERP responses from the auditory cortex. Dyslexics showed a faster decay of implicit memory effects on both measures, with similar time constants. Finally, faster decay of implicit memory also characterized the impact of sound regularities in benefitting dyslexics' oral reading rate. Their benefit decreased faster as a function of the time interval from the previous reading of the same non-word. We propose that dyslexics’ shorter neural adaptation paradoxically accounts for their longer reading times, since it reduces their temporal window of integration of past stimuli, resulting in noisier and less reliable predictions for both simple and complex stimuli. Less reliable predictions limit their acquisition of reading expertise. DOI: http://dx.doi.org/10.7554/eLife.20557.001 PMID:28115055

  12. A nonsynonymous mutation in the transcriptional regulator lbh is associated with cichlid craniofacial adaptation and neural crest cell development.

    PubMed

    Powder, Kara E; Cousin, Hélène; McLinden, Gretchen P; Craig Albertson, R

    2014-12-01

    Since the time of Darwin, biologists have sought to understand the origins and maintenance of life's diversity of form. However, the nature of the exact DNA mutations and molecular mechanisms that result in morphological differences between species remains unclear. Here, we characterize a nonsynonymous mutation in a transcriptional coactivator, limb bud and heart homolog (lbh), which is associated with adaptive variation in the lower jaw of cichlid fishes. Using both zebrafish and Xenopus, we demonstrate that lbh mediates migration of cranial neural crest cells, the cellular source of the craniofacial skeleton. A single amino acid change that is alternatively fixed in cichlids with differing facial morphologies results in discrete shifts in migration patterns of this multipotent cell type that are consistent with both embryological and adult craniofacial phenotypes. Among animals, this polymorphism in lbh represents a rare example of a coding change that is associated with continuous morphological variation. This work offers novel insights into the development and evolution of the craniofacial skeleton, underscores the evolutionary potential of neural crest cells, and extends our understanding of the genetic nature of mutations that underlie divergence in complex phenotypes.

  13. Novel image fusion method based on adaptive pulse coupled neural network and discrete multi-parameter fractional random transform

    NASA Astrophysics Data System (ADS)

    Lang, Jun; Hao, Zhengchao

    2014-01-01

    In this paper, we first propose the discrete multi-parameter fractional random transform (DMPFRNT), which can make the spectrum distributed randomly and uniformly. Then we introduce this new spectrum transform into the image fusion field and present a new approach for the remote sensing image fusion, which utilizes both adaptive pulse coupled neural network (PCNN) and the discrete multi-parameter fractional random transform in order to meet the requirements of both high spatial resolution and low spectral distortion. In the proposed scheme, the multi-spectral (MS) and panchromatic (Pan) images are converted into the discrete multi-parameter fractional random transform domains, respectively. In DMPFRNT spectrum domain, high amplitude spectrum (HAS) and low amplitude spectrum (LAS) components carry different informations of original images. We take full advantage of the synchronization pulse issuance characteristics of PCNN to extract the HAS and LAS components properly, and give us the PCNN ignition mapping images which can be used to determine the fusion parameters. In the fusion process, local standard deviation of the amplitude spectrum is chosen as the link strength of pulse coupled neural network. Numerical simulations are performed to demonstrate that the proposed method is more reliable and superior than several existing methods based on Hue Saturation Intensity representation, Principal Component Analysis, the discrete fractional random transform etc.

  14. A Nonsynonymous Mutation in the Transcriptional Regulator lbh Is Associated with Cichlid Craniofacial Adaptation and Neural Crest Cell Development

    PubMed Central

    Powder, Kara E.; Cousin, Hélène; McLinden, Gretchen P.; Craig Albertson, R.

    2014-01-01

    Since the time of Darwin, biologists have sought to understand the origins and maintenance of life’s diversity of form. However, the nature of the exact DNA mutations and molecular mechanisms that result in morphological differences between species remains unclear. Here, we characterize a nonsynonymous mutation in a transcriptional coactivator, limb bud and heart homolog (lbh), which is associated with adaptive variation in the lower jaw of cichlid fishes. Using both zebrafish and Xenopus, we demonstrate that lbh mediates migration of cranial neural crest cells, the cellular source of the craniofacial skeleton. A single amino acid change that is alternatively fixed in cichlids with differing facial morphologies results in discrete shifts in migration patterns of this multipotent cell type that are consistent with both embryological and adult craniofacial phenotypes. Among animals, this polymorphism in lbh represents a rare example of a coding change that is associated with continuous morphological variation. This work offers novel insights into the development and evolution of the craniofacial skeleton, underscores the evolutionary potential of neural crest cells, and extends our understanding of the genetic nature of mutations that underlie divergence in complex phenotypes. PMID:25234704

  15. Adaptive fuzzy neural network control design via a T-S fuzzy model for a robot manipulator including actuator dynamics.

    PubMed

    Wai, Rong-Jong; Yang, Zhi-Wei

    2008-10-01

    This paper focuses on the development of adaptive fuzzy neural network control (AFNNC), including indirect and direct frameworks for an n-link robot manipulator, to achieve high-precision position tracking. In general, it is difficult to adopt a model-based design to achieve this control objective due to the uncertainties in practical applications, such as friction forces, external disturbances, and parameter variations. In order to cope with this problem, an indirect AFNNC (IAFNNC) scheme and a direct AFNNC (DAFNNC) strategy are investigated without the requirement of prior system information. In these model-free control topologies, a continuous-time Takagi-Sugeno (T-S) dynamic fuzzy model with online learning ability is constructed to represent the system dynamics of an n-link robot manipulator. In the IAFNNC, an FNN estimator is designed to tune the nonlinear dynamic function vector in fuzzy local models, and then, the estimative vector is used to indirectly develop a stable IAFNNC law. In the DAFNNC, an FNN controller is directly designed to imitate a predetermined model-based stabilizing control law, and then, the stable control performance can be achieved by only using joint position information. All the IAFNNC and DAFNNC laws and the corresponding adaptive tuning algorithms for FNN weights are established in the sense of Lyapunov stability analyses to ensure the stable control performance. Numerical simulations and experimental results of a two-link robot manipulator actuated by dc servomotors are given to verify the effectiveness and robustness of the proposed methodologies. In addition, the superiority of the proposed control schemes is indicated in comparison with proportional-differential control, fuzzy-model-based control, T-S-type FNN control, and robust neural fuzzy network control systems.

  16. The neural dynamics of somatosensory processing and adaptation across childhood: a high-density electrical mapping study

    PubMed Central

    Uppal, Neha; Foxe, John J.; Butler, John S.; Acluche, Frantzy

    2016-01-01

    Young children are often hyperreactive to somatosensory inputs hardly noticed by adults, as exemplified by irritation to seams or labels in clothing. The neurodevelopmental mechanisms underlying changes in sensory reactivity are not well understood. Based on the idea that neurodevelopmental changes in somatosensory processing and/or changes in sensory adaptation might underlie developmental differences in somatosensory reactivity, high-density electroencephalography was used to examine how the nervous system responds and adapts to repeated vibrotactile stimulation over childhood. Participants aged 6–18 yr old were presented with 50-ms vibrotactile stimuli to the right wrist over the median nerve at 5 blocked interstimulus intervals (ranging from ∼7 to ∼1 stimulus per second). Somatosensory evoked potentials (SEPs) revealed three major phases of activation within the first 200 ms, with scalp topographies suggestive of neural generators in contralateral somatosensory cortex. Although overall SEPs were highly similar for younger, middle, and older age groups (6.1–9.8, 10.0–12.9, and 13.0–17.8 yr old), there were significant age-related amplitude differences in initial and later phases of the SEP. In contrast, robust adaptation effects for fast vs. slow presentation rates were observed that did not differ as a function of age. A greater amplitude response in the later portion of the SEP was observed for the youngest group and may be related to developmental changes in responsivity to somatosensory stimuli. These data suggest the protracted development of the somatosensory system over childhood, whereas adaptation, as assayed in this study, is largely in place by ∼7 yr of age. PMID:26763781

  17. Asynchronous BCI and local neural classifiers: an overview of the Adaptive Brain Interface project.

    PubMed

    Millán, José del R; Mouriño, Josep

    2003-06-01

    In this communication, we give an overview of our work on an asynchronous brain-computer interface (where the subject makes self-paced decisions on when to switch from one mental task to the next) that responds every 0.5 s. A local neural classifier tries to recognize three different mental tasks; it may also respond "unknown" for uncertain samples as the classifier has incorporated statistical rejection criteria. We report our experience with 15 subjects. We also briefly describe two brain-actuated applications we have developed: a virtual keyboard and a mobile robot (emulating a motorized wheelchair).

  18. The effects of spike frequency adaptation and negative feedback on the synchronization of neural oscillators.

    PubMed

    Ermentrout, B; Pascal, M; Gutkin, B

    2001-06-01

    There are several different biophysical mechanisms for spike frequency adaptation observed in recordings from cortical neurons. The two most commonly used in modeling studies are a calcium-dependent potassium current I(ahp) and a slow voltage-dependent potassium current, I(m). We show that both of these have strong effects on the synchronization properties of excitatorily coupled neurons. Furthermore, we show that the reasons for these effects are different. We show through an analysis of some standard models, that the M-current adaptation alters the mechanism for repetitive firing, while the afterhyperpolarization adaptation works via shunting the incoming synapses. This latter mechanism applies with a network that has recurrent inhibition. The shunting behavior is captured in a simple two-variable reduced model that arises near certain types of bifurcations. A one-dimensional map is derived from the simplified model.

  19. Neural Networks

    DTIC Science & Technology

    1990-01-01

    FUNDING NUMBERS PROGRAM PROJECT TASK WORK UNIT ELEMENT NO. NO. NO. ACCESSION NO 11 TITLE (Include Security Classification) NEURAL NETWORKS 12. PERSONAL...SUB-GROUP Neural Networks Optical Architectures Nonlinear Optics Adaptation 19. ABSTRACT (Continue on reverse if necessary and identify by block number...341i Y C-odes , lo iii/(iv blank) 1. INTRODUCTION Neural networks are a type of distributed processing system [1

  20. Neural Substrates Related to Motor Memory with Multiple Timescales in Sensorimotor Adaptation

    PubMed Central

    Lv, Jinchi; Schweighofer, Nicolas; Imamizu, Hiroshi

    2015-01-01

    Recent computational and behavioral studies suggest that motor adaptation results from the update of multiple memories with different timescales. Here, we designed a model-based functional magnetic resonance imaging (fMRI) experiment in which subjects adapted to two opposing visuomotor rotations. A computational model of motor adaptation with multiple memories was fitted to the behavioral data to generate time-varying regressors of brain activity. We identified regional specificity to timescales: in particular, the activity in the inferior parietal region and in the anterior-medial cerebellum was associated with memories for intermediate and long timescales, respectively. A sparse singular value decomposition analysis of variability in specificities to timescales over the brain identified four components, two fast, one middle, and one slow, each associated with different brain networks. Finally, a multivariate decoding analysis showed that activity patterns in the anterior-medial cerebellum progressively represented the two rotations. Our results support the existence of brain regions associated with multiple timescales in adaptation and a role of the cerebellum in storing multiple internal models. PMID:26645916

  1. Adaptive training using an artificial neural network and EEG metrics for within- and cross-task workload classification.

    PubMed

    Baldwin, Carryl L; Penaranda, B N

    2012-01-02

    Adaptive training using neurophysiological measures requires efficient classification of mental workload in real time as a learner encounters new and increasingly difficult levels of tasks. Previous investigations have shown that artificial neural networks (ANNs) can accurately classify workload, but only when trained on neurophysiological exemplars from experienced operators on specific tasks. The present study examined classification accuracies for ANNs trained on electroencephalographic (EEG) activity recorded while participants performed the same (within task) and different (cross) tasks for short periods of time with little or no prior exposure to the tasks. Participants performed three working memory tasks at two difficulty levels with order of task and difficulty level counterbalanced. Within-task classification accuracies were high when ANNs were trained on exemplars from the same task or a set containing the to-be-classified task, (M=87.1% and 85.3%, respectively). Cross-task classification accuracies were significantly lower (average 44.8%) indicating consistent systematic misclassification for certain tasks in some individuals. Results are discussed in terms of their implications for developing neurophysiologically driven adaptive training platforms.

  2. Improving pattern discovery and visualization of SAGE data through poisson-based self-adaptive neural networks.

    PubMed

    Zheng, Huiru; Wang, Haiying; Azuaje, Francisco

    2008-07-01

    Serial analysis of gene expression (SAGE) allows a detailed, simultaneous analysis of thousands of genes without the need for prior, complete gene sequence information. However, due to its inherent complexity and the lack of complete structural and function knowledge, mining vast collections of SAGE data to extract useful knowledge poses great challenges to traditional analytical techniques. Moreover, SAGE data are characterized by a specific statistical model that has not been incorporated into traditional data analysis techniques. The analysis of SAGE data requires advanced, intelligent computational techniques, which consider the underlying biology and the statistical nature of SAGE data. By addressing the statistical properties demonstrated by SAGE data, this paper presents a new self-adaptive neural network, Poisson-based growing self-organizing map (PGSOM), which implements novel weight adaptation and neuron growing strategies. An empirical study of key dynamic mechanisms of PGSOM is presented. It was tested on three datasets, including synthetic and experimental SAGE data. The results indicate that, in comparison to traditional techniques, the PGSOM offers significant advantages in the context of pattern discovery and visualization in SAGE data. The pattern discovery and visualization platform discussed in this paper can be applied to other problem domains where the data are better approximated by a Poisson distribution.

  3. Neural response in vestibular organ of Helix aspersa to centrifugation and re-adaptation to normal gravity.

    PubMed

    Popova, Yekaterina; Boyle, Richard

    2015-07-01

    Gravity plays a key role in shaping the vestibular sensitivity (VS) of terrestrial organisms. We studied VS changes in the statocyst of the gastropod Helix aspersa immediately after 4-, 16-, and 32-day exposures to a 1.4G hypergravic field or following a 7-day recovery period. In the same animals we measured latencies of behavioral "negative gravitaxis" responses to a head-down pitch before and after centrifugation and found significant delays after 16- and 32-day runs. In an isolated neural preparation we recorded the electrophysiological responses of the statocyst nerve to static tilt (±19°) and sinusoids (±12°; 0.1 Hz). Spike sorting software was used to separate individual sensory cells' patterns out of a common trace. In correspondence with behavior we observed a VS decrease in animals after 16- (p < 0.05) and 32-day (p < 0.01) centrifugations. These findings reveal the capability of statoreceptors to adjust their sensitivity in response to a prolonged change in the force of gravity. Interestingly, background discharge rate increased after 16 and 32 days in hypergravity and continued to rise through the recovery period. This result indicates that adaptive mechanisms to novel gravity levels were long lasting, and re-adaptation from hypergravity is a more complex process than just "return to normal".

  4. Normal perception of Mooney faces in developmental prosopagnosia: Evidence from the N170 component and rapid neural adaptation.

    PubMed

    Towler, John; Gosling, Angela; Duchaine, Bradley; Eimer, Martin

    2016-03-01

    Individuals with developmental prosopagnosia (DP) have a severe difficulty recognizing the faces of known individuals in the absence of any history of neurological damage. These recognition problems may be linked to selective deficits in the holistic/configural processing of faces. We used two-tone Mooney images to study the processing of faces versus non-face objects in DP when it is based on holistic information (or the facial gestalt) in the absence of obvious local cues about facial features. A rapid adaptation procedure was employed for a group of 16 DPs. Naturalistic photographs of upright faces were preceded by upright or inverted Mooney faces or by Mooney houses. DPs showed face-sensitive N170 components in response to Mooney faces versus houses, and N170 amplitude reductions for inverted as compared to upright Mooney faces. They also showed the typical pattern of N170 adaptation effects, with reduced N170 components when upright naturalistic test faces were preceded by upright Mooney faces, demonstrating that the perception of Mooney and naturalistic faces recruits shared neural populations. Our findings demonstrate that individuals with DP can utilize global information about face configurations for categorical discriminations between faces and non-face objects, and suggest that face processing deficits emerge primarily at more fine-grained higher level stages of face perception.

  5. Neural network-based optimal adaptive output feedback control of a helicopter UAV.

    PubMed

    Nodland, David; Zargarzadeh, Hassan; Jagannathan, Sarangapani

    2013-07-01

    Helicopter unmanned aerial vehicles (UAVs) are widely used for both military and civilian operations. Because the helicopter UAVs are underactuated nonlinear mechanical systems, high-performance controller design for them presents a challenge. This paper introduces an optimal controller design via an output feedback for trajectory tracking of a helicopter UAV, using a neural network (NN). The output-feedback control system utilizes the backstepping methodology, employing kinematic and dynamic controllers and an NN observer. The online approximator-based dynamic controller learns the infinite-horizon Hamilton-Jacobi-Bellman equation in continuous time and calculates the corresponding optimal control input by minimizing a cost function, forward-in-time, without using the value and policy iterations. Optimal tracking is accomplished by using a single NN utilized for the cost function approximation. The overall closed-loop system stability is demonstrated using Lyapunov analysis. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control design for trajectory tracking.

  6. Neural network-based adaptive consensus tracking control for multi-agent systems under actuator faults

    NASA Astrophysics Data System (ADS)

    Zhao, Lin; Jia, Yingmin

    2016-06-01

    In this paper, a distributed output feedback consensus tracking control scheme is proposed for second-order multi-agent systems in the presence of uncertain nonlinear dynamics, external disturbances, input constraints, and partial loss of control effectiveness. The proposed controllers incorporate reduced-order filters to account for the unmeasured states, and the neural networks technique is implemented to approximate the uncertain nonlinear dynamics in the synthesis of control algorithms. In order to compensate the partial loss of actuator effectiveness faults, fault-tolerant parts are included in controllers. Using the Lyapunov approach and graph theory, it is proved that the controllers guarantee a group of agents that simultaneously track a common time-varying state of leader, even when the state of leader is available only to a subset of the members of a group. Simulation results are provided to demonstrate the effectiveness of the proposed consensus tracking method.

  7. Automated detection of semagram-laden images using adaptive neural networks

    NASA Astrophysics Data System (ADS)

    Cerkez, Paul S.; Cannady, James D.

    2012-04-01

    Digital steganography is gaining wide acceptance in the world of electronic copyright stamping. Digital media that are easy to steal, such as graphics, photos and audio files, are being tagged with both visible and invisible copyright stamps (known as digital watermarking). However, these same techniques can also be used to hide communications between actors in criminal or covert activities. An inherent difficulty in detecting steganography is overcoming the variety of methods for hiding a message and the multitude of choices of available media. Another problem in steganography defense is the issue of detection speed since the encoded data is frequently time-sensitive. When a message is visually transmitted in a non-textual format (i.e., in an image) it is referred to as a semagram. Semagrams are relatively easy to create, but very difficult to detect. While steganography can often be identified by detecting digital modifications to an image's structure, an image-based semagram is more difficult because the message is the image itself. The work presented describes the creation of a novel, computer-based application, which uses hybrid hierarchical neural network architecture to detect the likely presence of a semagram message in an image. The prototype system was used to detect semagrams containing Morse Code messages. Based on the results of these experiments our approach provides a significant advance in the detection of complex semagram patterns. Specific results of the experiments and the potential practical applications of the neural network-based technology are discussed. This presentation provides the final results of our research experiments.

  8. Neural Architectures for Control

    NASA Technical Reports Server (NTRS)

    Peterson, James K.

    1991-01-01

    The cerebellar model articulated controller (CMAC) neural architectures are shown to be viable for the purposes of real-time learning and control. Software tools for the exploration of CMAC performance are developed for three hardware platforms, the MacIntosh, the IBM PC, and the SUN workstation. All algorithm development was done using the C programming language. These software tools were then used to implement an adaptive critic neuro-control design that learns in real-time how to back up a trailer truck. The truck backer-upper experiment is a standard performance measure in the neural network literature, but previously the training of the controllers was done off-line. With the CMAC neural architectures, it was possible to train the neuro-controllers on-line in real-time on a MS-DOS PC 386. CMAC neural architectures are also used in conjunction with a hierarchical planning approach to find collision-free paths over 2-D analog valued obstacle fields. The method constructs a coarse resolution version of the original problem and then finds the corresponding coarse optimal path using multipass dynamic programming. CMAC artificial neural architectures are used to estimate the analog transition costs that dynamic programming requires. The CMAC architectures are trained in real-time for each obstacle field presented. The coarse optimal path is then used as a baseline for the construction of a fine scale optimal path through the original obstacle array. These results are a very good indication of the potential power of the neural architectures in control design. In order to reach as wide an audience as possible, we have run a seminar on neuro-control that has met once per week since 20 May 1991. This seminar has thoroughly discussed the CMAC architecture, relevant portions of classical control, back propagation through time, and adaptive critic designs.

  9. Behavioral and neural Darwinism: selectionist function and mechanism in adaptive behavior dynamics.

    PubMed

    McDowell, J J

    2010-05-01

    An evolutionary theory of behavior dynamics and a theory of neuronal group selection share a common selectionist framework. The theory of behavior dynamics instantiates abstractly the idea that behavior is selected by its consequences. It implements Darwinian principles of selection, reproduction, and mutation to generate adaptive behavior in virtual organisms. The behavior generated by the theory has been shown to be quantitatively indistinguishable from that of live organisms. The theory of neuronal group selection suggests a mechanism whereby the abstract principles of the evolutionary theory may be implemented in the nervous systems of biological organisms. According to this theory, groups of neurons subserving behavior may be selected by synaptic modifications that occur when the consequences of behavior activate value systems in the brain. Together, these theories constitute a framework for a comprehensive account of adaptive behavior that extends from brain function to the behavior of whole organisms in quantitative detail.

  10. Stress, sex and neural adaptation to a changing environment: mechanisms of neuronal remodeling

    PubMed Central

    McEwen, Bruce S.

    2010-01-01

    The adult brain is much more resilient and adaptable than previously believed, and adaptive structural plasticity involves growth and shrinkage of dendritic trees, turnover of synapses and limited amounts of neurogenesis in the forebrain, especially the dentate gyrus of the hippocampal formation. Stress and sex hormones help to mediate adaptive structural plasticity, which has been extensively investigated in hippocampus and to a lesser extent in prefrontal cortex and amygdala, all brain regions that are involved in cognitive and emotional functions. Stress and sex hormones exert their effects on brain structural remodeling through both classical genomic as well as non-genomic mechanisms, and they do so in collaboration with neurotransmitters and other intra- and extracellular mediators. This review will illustrate the actions of estrogen on synapse formation in the hippocampus and the process of stress-induced remodelling of dendrites and synapses in the hippocampus, amygdala and prefrontal cortex. The influence of early developmental epigenetic events, such as early life stress and brain sexual differentiation, is noted along with the interactions between sex hormones and the effects of stress on the brain. Because hormones influence brain structure and function and because hormone secretion is governed by the brain, applied molecular neuroscience techniques can begin to reveal the role of hormones in brain-related disorders and the treatment of these diseases. A better understanding of hormone-brain interactions should promote more flexible approaches to the treatment of psychiatric disorders, as well as their prevention through both behavioral and pharmaceutical interventions. PMID:20840167

  11. How Play Makes for a More Adaptable Brain: A Comparative and Neural Perspective

    ERIC Educational Resources Information Center

    Pellis, Sergio M.; Pellis, Vivien C.; Himmler, Brett T.

    2014-01-01

    Studies of rats and some primates show that rough-and-tumble play among juveniles improves social competence, cognition, and emotional regulation later in life. Most critically, such play makes animals better able to respond to unexpected situations. But not all animals engage in play, and not all animals that play appear to gain these benefits.…

  12. Adaptor Identity Modulates Adaptation Effects in Familiar Face Identification and Their Neural Correlates

    PubMed Central

    Walther, Christian; Schweinberger, Stefan R.; Kovács, Gyula

    2013-01-01

    Adaptation-related aftereffects (AEs) show how face perception can be altered by recent perceptual experiences. Along with contrastive behavioural biases, modulations of the early event-related potentials (ERPs) were typically reported on categorical levels. Nevertheless, the role of the adaptor stimulus per se for face identity-specific AEs is not completely understood and was therefore investigated in the present study. Participants were adapted to faces (S1s) varying systematically on a morphing continuum between pairs of famous identities (identities A and B), or to Fourier phase-randomized faces, and had to match the subsequently presented ambiguous faces (S2s; 50/50% identity A/B) to one of the respective original faces. We found that S1s identical with or near to the original identities led to strong contrastive biases with more identity B responses following A adaptation and vice versa. In addition, the closer S1s were to the 50/50% S2 on the morphing continuum, the smaller the magnitude of the AE was. The relation between S1s and AE was, however, not linear. Additionally, stronger AEs were accompanied by faster reaction times. Analyses of the simultaneously recorded ERPs revealed categorical adaptation effects starting at 100 ms post-stimulus onset, that were most pronounced at around 125–240 ms for occipito-temporal sites over both hemispheres. S1-specific amplitude modulations were found at around 300–400 ms. Response-specific analyses of ERPs showed reduced voltages starting at around 125 ms when the S1 biased perception in a contrastive way as compared to when it did not. Our results suggest that face identity AEs do not only depend on physical differences between S1 and S2, but also on perceptual factors, such as the ambiguity of S1. Furthermore, short-term plasticity of face identity processing might work in parallel to object-category processing, and is reflected in the first 400 ms of the ERP. PMID:23990908

  13. Adaptor identity modulates adaptation effects in familiar face identification and their neural correlates.

    PubMed

    Walther, Christian; Schweinberger, Stefan R; Kovács, Gyula

    2013-01-01

    Adaptation-related aftereffects (AEs) show how face perception can be altered by recent perceptual experiences. Along with contrastive behavioural biases, modulations of the early event-related potentials (ERPs) were typically reported on categorical levels. Nevertheless, the role of the adaptor stimulus per se for face identity-specific AEs is not completely understood and was therefore investigated in the present study. Participants were adapted to faces (S1s) varying systematically on a morphing continuum between pairs of famous identities (identities A and B), or to Fourier phase-randomized faces, and had to match the subsequently presented ambiguous faces (S2s; 50/50% identity A/B) to one of the respective original faces. We found that S1s identical with or near to the original identities led to strong contrastive biases with more identity B responses following A adaptation and vice versa. In addition, the closer S1s were to the 50/50% S2 on the morphing continuum, the smaller the magnitude of the AE was. The relation between S1s and AE was, however, not linear. Additionally, stronger AEs were accompanied by faster reaction times. Analyses of the simultaneously recorded ERPs revealed categorical adaptation effects starting at 100 ms post-stimulus onset, that were most pronounced at around 125-240 ms for occipito-temporal sites over both hemispheres. S1-specific amplitude modulations were found at around 300-400 ms. Response-specific analyses of ERPs showed reduced voltages starting at around 125 ms when the S1 biased perception in a contrastive way as compared to when it did not. Our results suggest that face identity AEs do not only depend on physical differences between S1 and S2, but also on perceptual factors, such as the ambiguity of S1. Furthermore, short-term plasticity of face identity processing might work in parallel to object-category processing, and is reflected in the first 400 ms of the ERP.

  14. Neural correlates of adaptive working memory training in a glycogen storage disease type-IV patient.

    PubMed

    Lee, Kristin; Ernst, Thomas; Løhaugen, Gro; Zhang, Xin; Chang, Linda

    2017-03-01

    Glycogen storage disease type-IV has varied clinical presentations and subtypes. We evaluated a 38-year-old man with memory complaints, common symptoms in adult polyglucosan body disease subtype, and investigated cognitive and functional MRI changes associated with two 25-sessions of adaptive working memory training. He showed improved trained and nontrained working memory up to 6-months after the training sessions. On functional MRI, he showed increased cortical activation 1-3 months after training, but both increased and decreased activation 6-months later. Working memory training appears to be beneficial to patients with adult polyglucosan body disease, although continued training may be required to maintain improvements.

  15. Toolbox or Adjustable Spanner? A Critical Comparison of Two Metaphors for Adaptive Decision Making

    ERIC Educational Resources Information Center

    Söllner, Anke; Bröder, Arndt

    2016-01-01

    For multiattribute decision tasks, different metaphors exist that describe the process of decision making and its adaptation to diverse problems and situations. Multiple strategy models (MSMs) assume that decision makers choose adaptively from a set of different strategies (toolbox metaphor), whereas evidence accumulation models (EAMs) hold that a…

  16. Neural adaptations for processing the two-note call of the Puerto Rican treefrog, Eleutherodactylus coqui.

    PubMed

    Narins, P M; Capranica, R R

    1980-01-01

    Male Puerto Rican treefrogs, Eleutherodactylus coqui, produce a two-note call: a 100-msec constant frequency 'Co' note, followed by a longer, upward sweeping 'Qui' note. Previous behavioral studies have shown that males respond selectively to natural and synthetic call notes of 100 msec duration, whereas preliminary results suggest that females respond preferentially to the second note in the male's call. In the present study, we first show that the basilar papilla in the inner ear is tuned differentially in males and females. Comparisons were next made between cells in the eighth nerve and midbrain torus semicircularis of firing rate vs. duration functions in order to help determine the underlying neural mechanisms responsible for the behavioral selectivity to notes of 100 msec duration. A model for detection of vocalizations of specific durations is postulated and discussed in the light of the observed calling behavior of the male as well as the response properties of a class of cells found in the torus semicircularis.

  17. Neural-network-based adaptive UPFC for improving transient stability performance of power system.

    PubMed

    Mishra, Sukumar

    2006-03-01

    This paper uses the recently proposed H(infinity)-learning method, for updating the parameter of the radial basis function neural network (RBFNN) used as a control scheme for the unified power flow controller (UPFC) to improve the transient stability performance of a multimachine power system. The RBFNN uses a single neuron architecture whose input is proportional to the difference in error and the updating of its parameters is carried via a proportional value of the error. Also, the coefficients of the difference of error, error, and auxiliary signal used for improving damping performance are depicted by a genetic algorithm. The performance of the newly designed controller is evaluated in a four-machine power system subjected to different types of disturbances. The newly designed single-neuron RBFNN-based UPFC exhibits better damping performance compared to the conventional PID as well as the extended Kalman filter (EKF) updating-based RBFNN scheme, making the unstable cases stable. Its simple architecture reduces the computational burden, thereby making it attractive for real-time implementation. Also, all the machines are being equipped with the conventional power system stabilizer (PSS) to study the coordinated effect of UPFC and PSS in the system.

  18. Adaptive control of uncertain nonaffine nonlinear systems with input saturation using neural networks.

    PubMed

    Esfandiari, Kasra; Abdollahi, Farzaneh; Talebi, Heidar Ali

    2015-10-01

    This paper presents a tracking control methodology for a class of uncertain nonlinear systems subject to input saturation constraint and external disturbances. Unlike most previous approaches on saturated systems, which assumed affine nonlinear systems, in this paper, tracking control problem is solved for uncertain nonaffine nonlinear systems with input saturation. To deal with the saturation constraint, an auxiliary system is constructed and a modified tracking error is defined. Then, by employing implicit function theorem, mean value theorem, and modified tracking error, updating rules are derived based on the well-known back-propagation (BP) algorithm, which has been proven to be the most relevant updating rule to control problems. However, most of the previous approaches on BP algorithm suffer from lack of stability analysis. By injecting a damping term to the standard BP algorithm, uniformly ultimately boundedness of all the signals of the closed-loop system is ensured via Lyapunov's direct method. Furthermore, the presented approach employs nonlinear in parameter neural networks. Hence, the proposed scheme is applicable to systems with higher degrees of nonlinearity. Using a high-gain observer to reconstruct the states of the system, an output feedback controller is also presented. Finally, the simulation results performed on a Duffing-Holmes chaotic system, a generalized pendulum-type system, and a numerical system are presented to demonstrate the effectiveness of the suggested state and output feedback control schemes.

  19. The effects of accentuated eccentric loading on strength, muscle hypertrophy, and neural adaptations in trained individuals.

    PubMed

    Brandenburg, Jason P; Docherty, David

    2002-02-01

    The purpose of this study was to compare the strength and neuromuscular adaptations for dynamic constant external resistance (DCER) training and dynamic accentuated external resistance (DAER) training (resistance training employing an accentuated load during eccentric actions). Male subjects active in resistance training were assigned to either a DCER training group (n = 10) or a DAER training group (n = 8) for 9 weeks. Subjects in the DCER group performed 4 sets of 10 repetitions with a load of 75% concentric 1 repetition maximum (RM). Subjects in the DAER group performed 3 sets of 10 repetitions with a concentric load of 75% of 1RM and an eccentric load of approximately 120% of concentric 1RM. Three measures reflecting adaptation of elbow flexors and extensors were recorded pretraining and posttraining: concentric 1RM, muscle cross-sectional area (CSA), and specific tension. Strength was assessed at midtraining periods. No significant changes in muscle CSA were observed in either group. Both training groups experienced significant increases in concentric 1RM and specific tension of both the elbow flexors and extensors, but compared with DCER training, DAER training produced significantly greater increases in concentric 1RM of the elbow extensors. These results suggest that, for some exercises, DAER training may be more effective than DCER training in developing strength within a 9-week training phase. However, for trained subjects, neither protocol is effective in eliciting muscle hypertrophy.

  20. Task-oriented quality assessment and adaptation in real-time mission critical video streaming applications

    NASA Astrophysics Data System (ADS)

    Nightingale, James; Wang, Qi; Grecos, Christos

    2015-02-01

    In recent years video traffic has become the dominant application on the Internet with global year-on-year increases in video-oriented consumer services. Driven by improved bandwidth in both mobile and fixed networks, steadily reducing hardware costs and the development of new technologies, many existing and new classes of commercial and industrial video applications are now being upgraded or emerging. Some of the use cases for these applications include areas such as public and private security monitoring for loss prevention or intruder detection, industrial process monitoring and critical infrastructure monitoring. The use of video is becoming commonplace in defence, security, commercial, industrial, educational and health contexts. Towards optimal performances, the design or optimisation in each of these applications should be context aware and task oriented with the characteristics of the video stream (frame rate, spatial resolution, bandwidth etc.) chosen to match the use case requirements. For example, in the security domain, a task-oriented consideration may be that higher resolution video would be required to identify an intruder than to simply detect his presence. Whilst in the same case, contextual factors such as the requirement to transmit over a resource-limited wireless link, may impose constraints on the selection of optimum task-oriented parameters. This paper presents a novel, conceptually simple and easily implemented method of assessing video quality relative to its suitability for a particular task and dynamically adapting videos streams during transmission to ensure that the task can be successfully completed. Firstly we defined two principle classes of tasks: recognition tasks and event detection tasks. These task classes are further subdivided into a set of task-related profiles, each of which is associated with a set of taskoriented attributes (minimum spatial resolution, minimum frame rate etc.). For example, in the detection class

  1. Dynamics of On-Off Neural Firing Patterns and Stochastic Effects near a Sub-Critical Hopf Bifurcation

    PubMed Central

    Huaguang, Gu; Zhiguo, Zhao; Bing, Jia; Shenggen, Chen

    2015-01-01

    On-off firing patterns, in which repetition of clusters of spikes are interspersed with epochs of subthreshold oscillations or quiescent states, have been observed in various nervous systems, but the dynamics of this event remain unclear. Here, we report that on-off firing patterns observed in three experimental models (rat sciatic nerve subject to chronic constrictive injury, rat CA1 pyramidal neuron, and rabbit blood pressure baroreceptor) appeared as an alternation between quiescent state and burst containing multiple period-1 spikes over time. Burst and quiescent state had various durations. The interspike interval (ISI) series of on-off firing pattern was suggested as stochastic using nonlinear prediction and autocorrelation function. The resting state was changed to a period-1 firing pattern via on-off firing pattern as the potassium concentration, static pressure, or depolarization current was changed. During the changing process, the burst duration of on-off firing pattern increased and the duration of the quiescent state decreased. Bistability of a limit cycle corresponding to period-1 firing and a focus corresponding to resting state was simulated near a sub-critical Hopf bifurcation point in the deterministic Morris—Lecar (ML) model. In the stochastic ML model, noise-induced transitions between the coexisting regimes formed an on-off firing pattern, which closely matched that observed in the experiment. In addition, noise-induced exponential change in the escape rate from the focus, and noise-induced coherence resonance were identified. The distinctions between the on-off firing pattern and stochastic firing patterns generated near three other types of bifurcations of equilibrium points, as well as other viewpoints on the dynamics of on-off firing pattern, are discussed. The results not only identify the on-off firing pattern as noise-induced stochastic firing pattern near a sub-critical Hopf bifurcation point, but also offer practical indicators to

  2. "DRY" immersion induces neural and contractile adaptations in the human triceps surae muscle.

    PubMed

    Koryak, Yuri

    2002-12-01

    The effects of 7-days of simulated spaceflight, achieved with the technique of "dry" water immersion, on human triceps surae muscle function have been investigated in six subjects. After immersion, the maximal voluntary contraction (MVC) was reduced by 19% (p<0.01), and the electrically evoked (150 Hz) maximal tetanic contraction (Po) was reduced by 8% (p>0.05). The difference between Po and MVC expressed as a percentage of Po and referred to as force deficiency has also been calculated. The force deficiency increased by 44% (p<0.01) after immersion. The decrease in Po was associated with increased maximal rates of tension development (7.2%) and of tension relaxation. The twitch time-to-peak was not significantly changed, and half relaxation and total contraction time were decreased by 5% and 3%, respectively, but the twitch tension (Pt) was not significantly changed and the Pt/Po ratio was decreased by 9%. The 60-s intermittent contractions (50 Hz) decreased tetanic force to 57% (p<0.05) of initial values, but force reduction was not significantly different in the two fatigue tests: fatigue index was 36.2 +/- 5.4% vs. 38.6 +/- 2.8%, respectively (p>0.05). While identical force reduction was present in the two fatigue test it would appear that concomitant electrical failure was considerably different. Comparison of the electrical and mechanical responses alterations recorded during voluntary contractions, and in contractions evoked by electrical stimulation of the motor nerve, suggests that immersion not only modifies the peripheral processes associated with contraction, but also changes central and/or neural command of the contraction. At peripheral sites, it is proposed that the intracellular processes of contraction play a role in the contractile impairment recorded during immersion.

  3. Distributed neural network control for adaptive synchronization of uncertain dynamical multiagent systems.

    PubMed

    Peng, Zhouhua; Wang, Dan; Zhang, Hongwei; Sun, Gang

    2014-08-01

    This paper addresses the leader-follower synchronization problem of uncertain dynamical multiagent systems with nonlinear dynamics. Distributed adaptive synchronization controllers are proposed based on the state information of neighboring agents. The control design is developed for both undirected and directed communication topologies without requiring the accurate model of each agent. This result is further extended to the output feedback case where a neighborhood observer is proposed based on relative output information of neighboring agents. Then, distributed observer-based synchronization controllers are derived and a parameter-dependent Riccati inequality is employed to prove the stability. This design has a favorable decouple property between the observer and the controller designs for nonlinear multiagent systems. For both cases, the developed controllers guarantee that the state of each agent synchronizes to that of the leader with bounded residual errors. Two illustrative examples validate the efficacy of the proposed methods.

  4. A Critical Analysis of the Behavioral Adaptation Explanation of the Probing Effect.

    ERIC Educational Resources Information Center

    Levine, Timothy R.; McCornack, Steven A.

    1996-01-01

    Documents three problems with the behavioral adaption explanation (BAE) that, taken together, suggest that it cannot account for the probing effect, i.e., the finding that sources interrogatively probed appear more honest to message recipients than nonprobed sources. (TB)

  5. The Adaptation of Four Year Undergraduate Colleges to Current Fiscal and Enrollment Pressures: An Exploration of Critical Event Cycles at Seventeen Campuses. ASHE 1983 Annual Meeting Paper.

    ERIC Educational Resources Information Center

    Finkelstein, Martin; Farrar, David

    The results of analyzing institutional change profiles for 17 four-year undergraduate colleges are discussed. Using critical event cycles as a unit of analysis, attention was focused on distinctive patterns of institutional adaptation. Based on site visits, a list of 33 critical events at the 17 campuses was developed, from which 13 critical event…

  6. Adaptive predictive control using neural network for a class of pure-feedback systems in discrete time.

    PubMed

    Ge, Shuzhi Sam; Yang, Chenguang; Lee, Tong Heng

    2008-09-01

    In this paper, adaptive neural network (NN) control is investigated for a class of nonlinear pure-feedback discrete-time systems. By using prediction functions of future states, the pure-feedback system is transformed into an n-step-ahead predictor, based on which state feedback NN control is synthesized. Next, by investigating the relationship between outputs and states, the system is transformed into an input-output predictor model, and then, output feedback control is constructed. To overcome the difficulty of nonaffine appearance of the control input, implicit function theorem is exploited in the control design and NN is employed to approximate the unknown function in the control. In both state feedback and output feedback control, only a single NN is used and the controller singularity is completely avoided. The closed-loop system achieves semiglobal uniform ultimate boundedness (SGUUB) stability and the output tracking error is made within a neighborhood around zero. Simulation results are presented to show the effectiveness of the proposed control approach.

  7. Modeling Pb (II) adsorption from aqueous solution by ostrich bone ash using adaptive neural-based fuzzy inference system.

    PubMed

    Amiri, Mohammad J; Abedi-Koupai, Jahangir; Eslamian, Sayed S; Mousavi, Sayed F; Hasheminejad, Hasti

    2013-01-01

    To evaluate the performance of Adaptive Neural-Based Fuzzy Inference System (ANFIS) model in estimating the efficiency of Pb (II) ions removal from aqueous solution by ostrich bone ash, a batch experiment was conducted. Five operational parameters including adsorbent dosage (C(s)), initial concentration of Pb (II) ions (C(o)), initial pH, temperature (T) and contact time (t) were taken as the input data and the adsorption efficiency (AE) of bone ash as the output. Based on the 31 different structures, 5 ANFIS models were tested against the measured adsorption efficiency to assess the accuracy of each model. The results showed that ANFIS5, which used all input parameters, was the most accurate (RMSE = 2.65 and R(2) = 0.95) and ANFIS1, which used only the contact time input, was the worst (RMSE = 14.56 and R(2) = 0.46). In ranking the models, ANFIS4, ANFIS3 and ANFIS2 ranked second, third and fourth, respectively. The sensitivity analysis revealed that the estimated AE is more sensitive to the contact time, followed by pH, initial concentration of Pb (II) ions, adsorbent dosage, and temperature. The results showed that all ANFIS models overestimated the AE. In general, this study confirmed the capabilities of ANFIS model as an effective tool for estimation of AE.

  8. Prediction of matching condition for a microstrip subsystem using artificial neural network and adaptive neuro-fuzzy inference system

    NASA Astrophysics Data System (ADS)

    Salehi, Mohammad Reza; Noori, Leila; Abiri, Ebrahim

    2016-11-01

    In this paper, a subsystem consisting of a microstrip bandpass filter and a microstrip low noise amplifier (LNA) is designed for WLAN applications. The proposed filter has a small implementation area (49 mm2), small insertion loss (0.08 dB) and wide fractional bandwidth (FBW) (61%). To design the proposed LNA, the compact microstrip cells, an field effect transistor, and only a lumped capacitor are used. It has a low supply voltage and a low return loss (-40 dB) at the operation frequency. The matching condition of the proposed subsystem is predicted using subsystem analysis, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). To design the proposed filter, the transmission matrix of the proposed resonator is obtained and analysed. The performance of the proposed ANN and ANFIS models is tested using the numerical data by four performance measures, namely the correlation coefficient (CC), the mean absolute error (MAE), the average percentage error (APE) and the root mean square error (RMSE). The obtained results show that these models are in good agreement with the numerical data, and a small error between the predicted values and numerical solution is obtained.

  9. Recurrent-Neural-Network-Based Multivariable Adaptive Control for a Class of Nonlinear Dynamic Systems With Time-Varying Delay.

    PubMed

    Hwang, Chih-Lyang; Jan, Chau

    2016-02-01

    At the beginning, an approximate nonlinear autoregressive moving average (NARMA) model is employed to represent a class of multivariable nonlinear dynamic systems with time-varying delay. It is known that the disadvantages of robust control for the NARMA model are as follows: 1) suitable control parameters for larger time delay are more sensitive to achieving desirable performance; 2) it only deals with bounded uncertainty; and 3) the nominal NARMA model must be learned in advance. Due to the dynamic feature of the NARMA model, a recurrent neural network (RNN) is online applied to learn it. However, the system performance becomes deteriorated due to the poor learning of the larger variation of system vector functions. In this situation, a simple network is employed to compensate the upper bound of the residue caused by the linear parameterization of the approximation error of RNN. An e -modification learning law with a projection for weight matrix is applied to guarantee its boundedness without persistent excitation. Under suitable conditions, the semiglobally ultimately bounded tracking with the boundedness of estimated weight matrix is obtained by the proposed RNN-based multivariable adaptive control. Finally, simulations are presented to verify the effectiveness and robustness of the proposed control.

  10. Command Filter-Based Adaptive Neural Tracking Controller Design for Uncertain Switched Nonlinear Output-Constrained Systems.

    PubMed

    Niu, Ben; Liu, Yanjun; Zong, Guangdeng; Han, Zhaoyu; Fu, Jun

    2017-01-16

    In this paper, a new adaptive approximation-based tracking controller design approach is developed for a class of uncertain nonlinear switched lower-triangular systems with an output constraint using neural networks (NNs). By introducing a novel barrier Lyapunov function (BLF), the constrained switched system is first transformed into a new system without any constraint, which means the control objectives of the both systems are equivalent. Then command filter technique is applied to solve the so-called "explosion of complexity" problem in traditional backstepping procedure, and radial basis function NNs are directly employed to model the unknown nonlinear functions. The designed controller ensures that all the closed-loop variables are ultimately boundedness, while the output limit is not transgressed and the output tracking error can be reduced arbitrarily small. Furthermore, the use of an asymmetric BLF is also explored to handle the case of asymmetric output constraint as a generalization result. Finally, the control performance of the presented control schemes is illustrated via two examples.

  11. Adaptive prognosis of lithium-ion batteries based on the combination of particle filters and radial basis function neural networks

    NASA Astrophysics Data System (ADS)

    Sbarufatti, Claudio; Corbetta, Matteo; Giglio, Marco; Cadini, Francesco

    2017-03-01

    Lithium-Ion rechargeable batteries are widespread power sources with applications to consumer electronics, electrical vehicles, unmanned aerial and spatial vehicles, etc. The failure to supply the required power levels may lead to severe safety and economical consequences. Thus, in view of the implementation of adequate maintenance strategies, the development of diagnostic and prognostic tools for monitoring the state of health of the batteries and predicting their remaining useful life is becoming a crucial task. Here, we propose a method for predicting the end of discharge of Li-Ion batteries, which stems from the combination of particle filters with radial basis function neural networks. The major innovation lies in the fact that the radial basis function model is adaptively trained on-line, i.e., its parameters are identified in real time by the particle filter as new observations of the battery terminal voltage become available. By doing so, the prognostic algorithm achieves the flexibility needed to provide sound end-of-discharge time predictions as the charge-discharge cycles progress, even in presence of anomalous behaviors due to failures or unforeseen operating conditions. The method is demonstrated with reference to actual Li-Ion battery discharge data contained in the prognostics data repository of the NASA Ames Research Center database.

  12. Adaptational changes in the neural control of cardiorespiratory function in a confined environment: The CNEC#3 experiment

    NASA Astrophysics Data System (ADS)

    Pagani, M.; Iellamo, F.; Lucini, D.; Pizzinelli, P.; Castrucci, F.; Peruzzi, G.; Malliani, A.

    The goal of the study was to characterize the changes in neurovegetative control of the circulation, attending the presumed physiological and psychological stress originated by the isolation and confinement typical of the living condition of space stations, as simulated in a ground based unit, using time and frequency domain analysis. As a secondary goal we sought to verify the implementation of real time data acquisition, for off line spectral analisys of R-R interval, systolic arterial pressure (by Finapres) and respiration (by PVF2 piezoelectric sensors). We addressed the cardiorespiratory and neurovegetative responses to standardized, simple Stressors (active standing, dynamic and static handgrip) on the EXEMSI 92 crew, before, during and after the isolation period. On average the appropriate excitatory responses (to stand, dynamic and static handgrip) were elicited also in isolation and confinement. Active standing and small masses muscular exercises are easy to be performed in a confined and isolated environment and provide a valuable tool for investigating the adaptational changes in neural control mechanisms. The possibility there exists of using this time and frequency domain approach to monitor the level of performance and well being of the space crew in (quasi) real time.

  13. Application of Reinforcement Learning Algorithms for the Adaptive Computation of the Smoothing Parameter for Probabilistic Neural Network.

    PubMed

    Kusy, Maciej; Zajdel, Roman

    2015-09-01

    In this paper, we propose new methods for the choice and adaptation of the smoothing parameter of the probabilistic neural network (PNN). These methods are based on three reinforcement learning algorithms: Q(0)-learning, Q(λ)-learning, and stateless Q-learning. We regard three types of PNN classifiers: the model that uses single smoothing parameter for the whole network, the model that utilizes single smoothing parameter for each data attribute, and the model that possesses the matrix of smoothing parameters different for each data variable and data class. Reinforcement learning is applied as the method of finding such a value of the smoothing parameter, which ensures the maximization of the prediction ability. PNN models with smoothing parameters computed according to the proposed algorithms are tested on eight databases by calculating the test error with the use of the cross validation procedure. The results are compared with state-of-the-art methods for PNN training published in the literature up to date and, additionally, with PNN whose sigma is determined by means of the conjugate gradient approach. The results demonstrate that the proposed approaches can be used as alternative PNN training procedures.

  14. Neural adaptation and perceptual learning using a portable real-time cochlear implant simulator in natural environments.

    PubMed

    Smalt, Christopher J; Talavage, Thomas M; Pisoni, David B; Svirsky, Mario A

    2011-01-01

    A portable real-time speech processor that implements an acoustic simulation model of a cochlear implant (CI) has been developed on the Apple iPhone / iPod Touch to permit testing and experimentation under extended exposure in real-world environments. This simulator allows for both a variable number of noise band channels and electrode insertion depth. Utilizing this portable CI simulator, we tested perceptual learning in normal hearing listeners by measuring word and sentence comprehension behaviorally before and after 2 weeks of exposure. To evaluate changes in neural activation related to adaptation to transformed speech, fMRI was also conducted. Differences in brain activation after training occurred in the inferior frontal gyrus and areas related to language processing. A 15-20% improvement in word and sentence comprehension of cochlear implant simulated speech was also observed. These results demonstrate the effectiveness of a portable CI simulator as a research tool and provide new information about the physiological changes that accompany perceptual learning of degraded auditory input.

  15. Adapting Critical Incident Stress Management to the Schools: A Multi-Agency Approach

    ERIC Educational Resources Information Center

    Tortorici, Joanne; Johnson, Luna Kendall

    2004-01-01

    The need for school-appropriate applications of critical incident stress management (CISM) is noted in the literature on school crisis response. This paper presents preliminary data suggesting a School Crisis Response Team's (SCRT) usage and team needs. The SCRT was dispatched for a variety of critical incidents. Self-dispatch, followed by…

  16. Adaptation.

    PubMed

    Broom, Donald M

    2006-01-01

    The term adaptation is used in biology in three different ways. It may refer to changes which occur at the cell and organ level, or at the individual level, or at the level of gene action and evolutionary processes. Adaptation by cells, especially nerve cells helps in: communication within the body, the distinguishing of stimuli, the avoidance of overload and the conservation of energy. The time course and complexity of these mechanisms varies. Adaptive characters of organisms, including adaptive behaviours, increase fitness so this adaptation is evolutionary. The major part of this paper concerns adaptation by individuals and its relationships to welfare. In complex animals, feed forward control is widely used. Individuals predict problems and adapt by acting before the environmental effect is substantial. Much of adaptation involves brain control and animals have a set of needs, located in the brain and acting largely via motivational mechanisms, to regulate life. Needs may be for resources but are also for actions and stimuli which are part of the mechanism which has evolved to obtain the resources. Hence pigs do not just need food but need to be able to carry out actions like rooting in earth or manipulating materials which are part of foraging behaviour. The welfare of an individual is its state as regards its attempts to cope with its environment. This state includes various adaptive mechanisms including feelings and those which cope with disease. The part of welfare which is concerned with coping with pathology is health. Disease, which implies some significant effect of pathology, always results in poor welfare. Welfare varies over a range from very good, when adaptation is effective and there are feelings of pleasure or contentment, to very poor. A key point concerning the concept of individual adaptation in relation to welfare is that welfare may be good or poor while adaptation is occurring. Some adaptation is very easy and energetically cheap and

  17. Neural Mechanisms Behind Identification of Leptokurtic Noise and Adaptive Behavioral Response

    PubMed Central

    d'Acremont, Mathieu; Bossaerts, Peter

    2016-01-01

    Large-scale human interaction through, for example, financial markets causes ceaseless random changes in outcome variability, producing frequent and salient outliers that render the outcome distribution more peaked than the Gaussian distribution, and with longer tails. Here, we study how humans cope with this evolutionary novel leptokurtic noise, focusing on the neurobiological mechanisms that allow the brain, 1) to recognize the outliers as noise and 2) to regulate the control necessary for adaptive response. We used functional magnetic resonance imaging, while participants tracked a target whose movements were affected by leptokurtic noise. After initial overreaction and insufficient subsequent correction, participants improved performance significantly. Yet, persistently long reaction times pointed to continued need for vigilance and control. We ran a contrasting treatment where outliers reflected permanent moves of the target, as in traditional mean-shift paradigms. Importantly, outliers were equally frequent and salient. There, control was superior and reaction time was faster. We present a novel reinforcement learning model that fits observed choices better than the Bayes-optimal model. Only anterior insula discriminated between the 2 types of outliers. In both treatments, outliers initially activated an extensive bottom-up attention and belief network, followed by sustained engagement of the fronto-parietal control network. PMID:26850528

  18. Time course of muscular, neural and tendinous adaptations to 23 day unilateral lower-limb suspension in young men.

    PubMed

    de Boer, Maarten D; Maganaris, Constantinos N; Seynnes, Olivier R; Rennie, Michael J; Narici, Marco V

    2007-09-15

    Muscles and tendons are highly adaptive to changes in chronic loading, though little is known about the adaptative time course. We tested the hypothesis that, in response to unilateral lower limb suspension (ULLS), the magnitude of tendon mechanical adaptations would match or exceed those of skeletal muscle. Seventeen men (1.79 +/- 0.05 m, 76.6 +/- 10.3 kg, 22.3 +/- 3.8 years) underwent ULLS for 23 days (n = 9) or acted as controls (n = 8). Knee extensor (KE) torque, voluntary activation (VA), cross-sectional area (CSA) (by magnetic resonance imaging), vastus lateralis fascicle length (L(f)) and pennation angle (), patellar tendon stiffness and Young's modulus (by ultrasonography) were measured before, during and at the end of ULLS. After 14 and 23 days (i) KE torque decreased by 14.8 +/- 5.5% (P < 0.001) and 21.0 +/- 7.1% (P < 0.001), respectively; (ii) VA did not change; (iii) KE CSA decreased by 5.2 +/- 0.7% (P < 0.001) and 10.0 +/- 2.0% (P < 0.001), respectively; L(f) decreased by 5.9% (n.s.) and 7.7% (P < 0.05), respectively, and by 3.2% (P < 0.05) and 7.6% (P < 0.01); (iv) tendon stiffness decreased by 9.8 +/- 8.2% (P < 0.05) and 29.3 +/- 11.5% (P < 0.005), respectively, and Young's modulus by 9.2 +/- 8.2% (P < 0.05) and 30.1 +/- 11.9% (P < 0.01), respectively, with no changes in the controls. Hence, ULLS induces rapid losses of KE muscle size, architecture and function, but not in neural drive. Significant deterioration in tendon mechanical properties also occurs within 2 weeks, exacerbating in the third week of ULLS. Rehabilitation to limit muscle and tendon deterioration should probably start within 2 weeks of unloading.

  19. MOZ-mediated repression of p16(INK) (4) (a) is critical for the self-renewal of neural and hematopoietic stem cells.

    PubMed

    Perez-Campo, Flor M; Costa, Guilherme; Lie-A-Ling, Michael; Stifani, Stefano; Kouskoff, Valerie; Lacaud, Georges

    2014-06-01

    Although inhibition of p16(INK4a) expression is critical to preserve the proliferative capacity of stem cells, the molecular mechanisms responsible for silencing p16(INK4a) expression remain poorly characterized. Here, we show that the histone acetyltransferase (HAT) monocytic leukemia zinc finger protein (MOZ) controls the proliferation of both hematopoietic and neural stem cells by modulating the transcriptional repression of p16(INK4a) . In the absence of the HAT activity of MOZ, expression of p16(INK4a) is upregulated in progenitor and stem cells, inducing an early entrance into replicative senescence. Genetic deletion of p16(INK4a) reverses the proliferative defect in both Moz(HAT) (-) (/) (-) hematopoietic and neural progenitors. Our results suggest a critical requirement for MOZ HAT activity to silence p16(INK4a) expression and to protect stem cells from early entrance into replicative senescence.

  20. Neural-adaptive control of single-master-multiple-slaves teleoperation for coordinated multiple mobile manipulators with time-varying communication delays and input uncertainties.

    PubMed

    Li, Zhijun; Su, Chun-Yi

    2013-09-01

    In this paper, adaptive neural network control is investigated for single-master-multiple-slaves teleoperation in consideration of time delays and input dead-zone uncertainties for multiple mobile manipulators carrying a common object in a cooperative manner. Firstly, concise dynamics of teleoperation systems consisting of a single master robot, multiple coordinated slave robots, and the object are developed in the task space. To handle asymmetric time-varying delays in communication channels and unknown asymmetric input dead zones, the nonlinear dynamics of the teleoperation system are transformed into two subsystems through feedback linearization: local master or slave dynamics including the unknown input dead zones and delayed dynamics for the purpose of synchronization. Then, a model reference neural network control strategy based on linear matrix inequalities (LMI) and adaptive techniques is proposed. The developed control approach ensures that the defined tracking errors converge to zero whereas the coordination internal force errors remain bounded and can be made arbitrarily small. Throughout this paper, stability analysis is performed via explicit Lyapunov techniques under specific LMI conditions. The proposed adaptive neural network control scheme is robust against motion disturbances, parametric uncertainties, time-varying delays, and input dead zones, which is validated by simulation studies.

  1. Reduced Nrf2 expression mediates the decline in neural stem cell function during a critical middle-age period.

    PubMed

    Corenblum, Mandi J; Ray, Sneha; Remley, Quentin W; Long, Min; Harder, Bryan; Zhang, Donna D; Barnes, Carol A; Madhavan, Lalitha

    2016-08-01

    Although it is known that the regenerative function of neural stem/progenitor cells (NSPCs) declines with age, causal mechanisms underlying this phenomenon are not understood. Here, we systematically analyze subventricular zone (SVZ) NSPCs, in various groups of rats across the aging spectrum, using in vitro and in vivo histological and behavioral techniques. These studies indicate that although NSPC function continuously declines with advancing age, there is a critical time period during middle age (13-15 months) when a striking reduction in NSPC survival and regeneration (proliferation and neuronal differentiation) occurs. The studies also indicate that this specific temporal pattern of NSPC deterioration is functionally relevant at a behavioral level and correlates with the decreasing expression of the redox-sensitive transcription factor, Nrf2, in the NSPCs. When Nrf2 expression was suppressed in 'young' NSPCs, using short interfering RNAs, the survival and regeneration of the NSPCs was significantly compromised and mirrored 'old' NSPCs. Conversely, Nrf2 overexpression in 'old' NSPCs rendered them similar to 'young' NSPCs, and they showed increased survival and regeneration. Furthermore, examination of newborn Nrf2 knockout (Nrf2 -/-) mice revealed a lower number of SVZ NSPCs in these animals, when compared to wild-type controls. In addition, the proliferative and neurogenic potential of the NSPCs was also compromised in the Nrf2-/- mice. These results identify a novel regulatory role for Nrf2 in NSPC function during aging and have important implications for developing NSPC-based strategies to support healthy aging and to treat age-related neurodegenerative disorders.

  2. Defining the critical material attributes of lactose monohydrate in carrier based dry powder inhaler formulations using artificial neural networks.

    PubMed

    Kinnunen, Hanne; Hebbink, Gerald; Peters, Harry; Shur, Jagdeep; Price, Robert

    2014-08-01

    The study aimed to establish a function-based relationship between the physical and bulk properties of pre-blended mixtures of fine and coarse lactose grades with the in vitro performance of an adhesive active pharmaceutical ingredient (API). Different grades of micronised and milled lactose (Lactohale (LH) LH300, LH230, LH210 and Sorbolac 400) were pre-blended with coarse grades of lactose (LH100, LH206 and Respitose SV010) at concentrations of 2.5, 5, 10 and 20 wt.%. The bulk and rheological properties and particle size distributions were characterised. The pre-blends were formulated with micronised budesonide and in vitro performance in a Cyclohaler device tested using a next-generation impactor (NGI) at 90 l/min. Correlations between the lactose properties and in vitro performance were established using linear regression and artificial neural network (ANN) analyses. The addition of milled and micronised lactose fines with the coarse lactose had a significant influence on physical and rheological properties of the bulk lactose. Formulations of the different pre-blends with budesonide directly influenced in vitro performance attributes including fine particle fraction, mass median aerodynamic diameter and pre-separator deposition. While linear regression suggested a number of physical and bulk properties may influence in vitro performance, ANN analysis suggested the critical parameters in describing in vitro deposition patterns were the relative concentrations of lactose fines % < 4.5 μm and % < 15 μm. These data suggest that, for an adhesive API, the proportion of fine particles below % < 4.5 μm and % < 15 μm could be used in rational dry powder inhaler formulation design.

  3. Cooperation, competition and the emergence of criticality in communities of adaptive systems

    NASA Astrophysics Data System (ADS)

    Hidalgo, Jorge; Grilli, Jacopo; Suweis, Samir; Maritan, Amos; Muñoz, Miguel A.

    2016-03-01

    The hypothesis that living systems can benefit from operating at the vicinity of critical points has gained momentum in recent years. Criticality may confer an optimal balance between too ordered and exceedingly noisy states. Here we present a model, based on information theory and statistical mechanics, illustrating how and why a community of agents aimed at understanding and communicating with each other converges to a globally coherent state in which all individuals are close to an internal critical state, i.e. at the borderline between order and disorder. We study—both analytically and computationally—the circumstances under which criticality is the best possible outcome of the dynamical process, confirming the convergence to critical points under very generic conditions. Finally, we analyze the effect of cooperation (agents trying to enhance not only their fitness, but also that of other individuals) and competition (agents trying to improve their own fitness and to diminish those of competitors) within our setting. The conclusion is that, while competition fosters criticality, cooperation hinders it and can lead to more ordered or more disordered consensual outcomes.

  4. Critically Adaptive Pedagogical Relations: The Relevance for Educational Policy and Practice

    ERIC Educational Resources Information Center

    Griffiths, Morwenna

    2013-01-01

    In this article Morwenna Griffiths argues that teacher education policies should be predicated on a proper and full understanding of pedagogical relations as contingent, responsive, and adaptive over the course of a career. Griffiths uses the example of the recent report on teacher education in Scotland, by Graham Donaldson, to argue that for all…

  5. The role of cognitive appraisal in adaptation to traumatic stress in adults with serious mental illness: a critical review.

    PubMed

    Sherrer, Margaret V

    2011-07-01

    A compelling body of literature suggests that negative appraisal may be associated with adverse reactions to traumatic stress. However, very few studies have examined how appraisal influences posttraumatic adaptation in people with serious mental illness (SMI) despite evidence of disproportionately high prevalence rates of trauma exposure and Posttraumatic Stress Disorder (PTSD) in this population. The purpose of this article is to provide a critical analysis of the theoretical and empirical literature on cognitive appraisal and psychological adaptation to traumatic stress with a specific focus on individuals diagnosed with SMI. It will be argued that appraisal is a key correlate that may partially account for higher rates of PTSD in people diagnosed with major mood and schizophrenia-spectrum disorders, meriting special consideration for future research.

  6. Translation, adaptation, and validation of the behavioral pain scale and the critical-care pain observational tools in Taiwan

    PubMed Central

    Hsiung, Nai-Huan; Yang, Yen; Lee, Ming Shinn; Dalal, Koustuv; Smith, Graeme D

    2016-01-01

    This study describes the cultural adaptation and testing of the behavioral pain scale (BPS) and the critical-care pain observation tools (CPOT) for pain assessment in Taiwan. The cross-cultural adaptation followed the steps of translation, including forward translation, back-translation, evaluation of the translations by a committee of experts, adjustments, and then piloting of the prefinal versions of the BPS and the CPOT. A content validity index was used to assess content validities of the BPS and the CPOT, with 0.80 preset as the level that would be regarded as acceptable. The principal investigator then made adjustments when the content validity index was <0.80. The pilot test was performed with a sample of ten purposively selected patients by 2 medical staff from a medical care center in Taiwan. The BPS and the CPOT are adequate instruments for the assessment of pain levels in patients who cannot communicate due to sedation and ventilation treatments. PMID:27695360

  7. Adaptive variability in the duration of critical windows of plasticity: Implications for the programming of obesity.

    PubMed

    Wells, Jonathan C K

    2014-08-05

    Developmental plasticity underlies widespread associations between early-life exposures and many components of adult phenotype, including the risk of chronic diseases. Humans take almost two decades to reach reproductive maturity, and yet the 'critical windows' of physiological sensitivity that confer developmental plasticity tend to close during fetal life or infancy. While several explanations for lengthy human maturation have been offered, the brevity of physiological plasticity has received less attention. I argue that offspring plasticity is only viable within the niche of maternal care, and that as this protection is withdrawn, the offspring is obliged to canalize many developmental traits in order to minimize environmental disruptions. The schedule of maternal care may therefore shape the duration of critical windows, and since the duration of this care is subject to parent-offspring conflict, the resolution of this conflict may shape the duration of critical windows. This perspective may help understand (i) why windows close at different times for different traits, and (ii) why the duration of critical windows may vary across human populations. The issue is explored in relation to population differences in the association between infant weight gain and later body composition. The occupation of more stable environments by western populations may have favoured earlier closure of the critical window during which growth in lean mass is sensitive to nutritional intake. This may paradoxically have elevated the risk of obesity following rapid infant weight gain in such populations.

  8. Adapting, Not Adopting: Barriers Affecting Teaching for Critical Thinking at Two Rwandan Universities

    ERIC Educational Resources Information Center

    Schendel, Rebecca

    2016-01-01

    A recent study of student learning at three of Rwanda's most prestigious public universities has suggested that Rwandan students are not improving in their critical thinking ability during their time at university. This article reports on a series of faculty-level case studies, which were conducted at two of the participating institutions in order…

  9. Neural Networks and other Techniques for Fault Identification and Isolation of Aircraft Systems

    DTIC Science & Technology

    2003-06-01

    in this paper is based on the use of neural networks (NNs) as on-line learning non-linear approximators. The performances of two different neural...Fault identification, isolation. and accommodation have become critical issues in the overall performance of advanced aircraft systems. Neural ... Networks have shown to be a very attractive alternative to classic adaptation methods for identification and control of non-linear dynamic systems. The

  10. The neural code for taste in the nucleus of the solitary tract of the rat: effects of adaptation.

    PubMed

    Di Lorenzo, P M; Lemon, C H

    2000-01-10

    Adaptation of the tongue to NaCl, HCl, quinine or sucrose was used as a tool to study the stability and organization of response profiles in the nucleus of the solitary tract (NTS). Taste responses in the NTS were recorded in anesthetized rats before and after adaptation of the tongue to NaCl, HCl, sucrose or quinine. Results showed that the magnitude of response to test stimuli following adaptation was a function of the context, i.e., adaptation condition, in which the stimuli were presented. Over half of all taste responses were either attenuated or enhanced following the adaptation procedure: NaCl adaptation produced the most widespread, non-stimulus-selective cross-adaptation and sucrose adaptation produced the least frequent cross-adaptation and the most frequent enhancement of taste responses. Adaptation to quinine cross-adapted to sucrose and adaptation to HCl cross-adapted to quinine in over half of the units tested. The adaptation procedure sometimes unmasked taste responses where none were present beforehand and sometimes altered taste responses to test stimuli even though the adapting stimulus did not itself produce a response. These effects demonstrated a form of context-dependency of taste responsiveness in the NTS and further suggest a broad potentiality in the sensitivity of NTS units across taste stimuli. Across unit patterns of response remained distinct from each other under all adaptation conditions. Discriminability of these patterns may provide a neurophysiological basis for residual psychophysical abilities following adaptation.

  11. Adapt

    NASA Astrophysics Data System (ADS)

    Bargatze, L. F.

    2015-12-01

    Active Data Archive Product Tracking (ADAPT) is a collection of software routines that permits one to generate XML metadata files to describe and register data products in support of the NASA Heliophysics Virtual Observatory VxO effort. ADAPT is also a philosophy. The ADAPT concept is to use any and all available metadata associated with scientific data to produce XML metadata descriptions in a consistent, uniform, and organized fashion to provide blanket access to the full complement of data stored on a targeted data server. In this poster, we present an application of ADAPT to describe all of the data products that are stored by using the Common Data File (CDF) format served out by the CDAWEB and SPDF data servers hosted at the NASA Goddard Space Flight Center. These data servers are the primary repositories for NASA Heliophysics data. For this purpose, the ADAPT routines have been used to generate data resource descriptions by using an XML schema named Space Physics Archive, Search, and Extract (SPASE). SPASE is the designated standard for documenting Heliophysics data products, as adopted by the Heliophysics Data and Model Consortium. The set of SPASE XML resource descriptions produced by ADAPT includes high-level descriptions of numerical data products, display data products, or catalogs and also includes low-level "Granule" descriptions. A SPASE Granule is effectively a universal access metadata resource; a Granule associates an individual data file (e.g. a CDF file) with a "parent" high-level data resource description, assigns a resource identifier to the file, and lists the corresponding assess URL(s). The CDAWEB and SPDF file systems were queried to provide the input required by the ADAPT software to create an initial set of SPASE metadata resource descriptions. Then, the CDAWEB and SPDF data repositories were queried subsequently on a nightly basis and the CDF file lists were checked for any changes such as the occurrence of new, modified, or deleted

  12. Mapping the Dynamic Network Interactions Underpinning Cognition: A cTBS-fMRI Study of the Flexible Adaptive Neural System for Semantics

    PubMed Central

    Jung, JeYoung; Lambon Ralph, Matthew A.

    2016-01-01

    Higher cognitive function reflects the interaction of a network of multiple brain regions. Previous investigations have plotted out these networks using functional or structural connectivity approaches. While these map the topography of the regions involved, they do not explore the key aspect of this neuroscience principle—namely that the regions interact in a dynamic fashion. Here, we achieved this aim with respect to semantic memory. Although converging evidence implicates the anterior temporal lobes (ATLs), bilaterally, as a crucial component in semantic representation, the underlying neural interplay between the ATLs remains unclear. By combining continuous theta-burst stimulation (cTBS) with functional magnetic resonance imaging (fMRI), we perturbed the left ventrolateral ATL (vATL) and investigated acute changes in neural activity and effective connectivity of the semantic system. cTBS resulted in decreased activity at the target region and compensatory, increased activity at the contralateral vATL. In addition, there were task-specific increases in effective connectivity between the vATLs, reflecting an increased facilitatory intrinsic connectivity from the right to left vATL. Our results suggest that semantic representation is founded on a flexible, adaptive bilateral neural system and reveals an adaptive plasticity-based mechanism that might support functional recovery after unilateral damage in neurological patients. PMID:27242027

  13. Designing the Architecture of Hierachical Neural Networks Model Attention, Learning and Goal-Oriented Behavior

    DTIC Science & Technology

    1993-12-31

    Adaptive Robot Control," Journal of Adaptive Control and Signal Processing. (Accepted. Expected to Appear 9/90) [25] Nevo , I., Guez, A., Ahmed, F...Anesthesia Critical Care and Cardio-Pulmonary Medicine, Netherland, October 1990. [26] Ahmed, F., Nevo I., Guez, A., "Anesthesiologist’s Adaptive...Based Adaptive Pole Placement for Neurocontrollers," Neural Networks, Vol.4, 1991, pp. 319-335. [33] Nevo , I., Guez, A., Ahmed, F., Roth, J.,"Vital

  14. The association of physical activity to neural adaptability during visuo-spatial processing in healthy elderly adults: A multiscale entropy analysis.

    PubMed

    Wang, Chun-Hao; Tsai, Chia-Liang; Tseng, Philip; Yang, Albert C; Lo, Men-Tzung; Peng, Chung-Kang; Wang, Hsin-Yi; Muggleton, Neil G; Juan, Chi-Hung; Liang, Wei-Kuang

    2014-10-29

    Physical activity has been shown to benefit brain and cognition in late adulthood. However, this effect is still unexplored in terms of brain signal complexity, which reflects the level of neural adaptability and efficiency during cognitive processing that cannot be acquired via averaged neuroelectric signals. Here we employed multiscale entropy analysis (MSE) of electroencephalography (EEG), a new approach that conveys important information related to the temporal dynamics of brain signal complexity across multiple time scales, to reveal the association of physical activity with neural adaptability and efficiency in elderly adults. A between-subjects design that included 24 participants (aged 66.63±1.31years; female=12) with high physical activity and 24 age- and gender-matched low physical activity participants (aged 67.29±1.20years) was conducted to examine differences related to physical activity in performance and MSE of EEG signals during a visuo-spatial cognition task. We observed that physically active elderly adults had better accuracy on both visuo-spatial attention and working memory conditions relative to their sedentary counterparts. Additionally, these physically active elderly adults displayed greater MSE values at larger time scales at the Fz electrode in both attention and memory conditions. The results suggest that physical activity may be beneficial for adaptability of brain systems in tasks involving visuo-spatial information. MSE thus might be a promising approach to test the effects of the benefits of exercise on cognition.

  15. A new method based on Adaptive Discrete Wavelet Entropy Energy and Neural Network Classifier (ADWEENN) for recognition of urine cells from microscopic images independent of rotation and scaling.

    PubMed

    Avci, Derya; Leblebicioglu, Mehmet Kemal; Poyraz, Mustafa; Dogantekin, Esin

    2014-02-01

    So far, analysis and classification of urine cells number has become an important topic for medical diagnosis of some diseases. Therefore, in this study, we suggest a new technique based on Adaptive Discrete Wavelet Entropy Energy and Neural Network Classifier (ADWEENN) for Recognition of Urine Cells from Microscopic Images Independent of Rotation and Scaling. Some digital image processing methods such as noise reduction, contrast enhancement, segmentation, and morphological process are used for feature extraction stage of this ADWEENN in this study. Nowadays, the image processing and pattern recognition topics have come into prominence. The image processing concludes operation and design of systems that recognize patterns in data sets. In the past years, very difficulty in classification of microscopic images was the deficiency of enough methods to characterize. Lately, it is seen that, multi-resolution image analysis methods such as Gabor filters, discrete wavelet decompositions are superior to other classic methods for analysis of these microscopic images. In this study, the structure of the ADWEENN method composes of four stages. These are preprocessing stage, feature extraction stage, classification stage and testing stage. The Discrete Wavelet Transform (DWT) and adaptive wavelet entropy and energy is used for adaptive feature extraction in feature extraction stage to strengthen the premium features of the Artificial Neural Network (ANN) classifier in this study. Efficiency of the developed ADWEENN method was tested showing that an avarage of 97.58% recognition succes was obtained.

  16. Increased Intraregional Synchronized Neural Activity in Adult Brain After Prolonged Adaptation to High-Altitude Hypoxia: A Resting-State fMRI Study.

    PubMed

    Chen, Ji; Fan, Cunxiu; Li, Jinqiang; Han, Qiaoqing; Lin, Jianzhong; Yang, Tianhe; Zhang, Jiaxing

    2016-03-01

    The human brain is intrinsically plastic such that its functional architecture can be reorganized in response to environmental pressures and physiological changes. However, it remains unclear whether a compensatory modification of spontaneous neural activity occurs in adult brain during prolonged high-altitude (HA) adaptation. In this study, we obtained resting-state functional magnetic resonance (MR) images in 16 adults who have immigrated to Qinghai-Tibet Plateau (2300-4400 m) for 2 years and in 16 age-matched sea level (SL) controls. A validated regional homogeneity (Reho) method was employed to investigate the local synchronization of resting-state functional magnetic resonance imaging (fMRI) signals. Seed connectivity analysis was carried out subsequently. Cognitive and physiological assessments were made and correlated with the image metrics. Compared with SL controls, global mean Reho was significantly increased in HA immigrants as well as a regional increase in the right inferolateral sensorimotor cortex. Furthermore, mean z-Reho value extracted within the inferolateral sensorimotor area showed trend-level significant inverse correlation with memory search reaction time in HA immigrants. These observations, for the first time, provide evidence of adult brain resilience of spontaneous neural activity after long-term HA exposure without inherited and developmental effects. Resting-state fMRI could yield valuable information for central mechanisms underlying respiratory and cognitive compensations in adults during prolonged environmentally hypoxic adaptation, paving the way for future HA-adaptive training.

  17. Critical phenomena at a first-order phase transition in a lattice of glow lamps: Experimental findings and analogy to neural activity

    NASA Astrophysics Data System (ADS)

    Minati, Ludovico; de Candia, Antonio; Scarpetta, Silvia

    2016-07-01

    Networks of non-linear electronic oscillators have shown potential as physical models of neural dynamics. However, two properties of brain activity, namely, criticality and metastability, remain under-investigated with this approach. Here, we present a simple circuit that exhibits both phenomena. The apparatus consists of a two-dimensional square lattice of capacitively coupled glow (neon) lamps. The dynamics of lamp breakdown (flash) events are controlled by a DC voltage globally connected to all nodes via fixed resistors. Depending on this parameter, two phases having distinct event rate and degree of spatiotemporal order are observed. The transition between them is hysteretic, thus a first-order one, and it is possible to enter a metastability region, wherein, approaching a spinodal point, critical phenomena emerge. Avalanches of events occur according to power-law distributions having exponents ≈3/2 for size and ≈2 for duration, and fractal structure is evident as power-law scaling of the Fano factor. These critical exponents overlap observations in biological neural networks; hence, this circuit may have value as building block to realize corresponding physical models.

  18. The critical size is set at a single-cell level by growth rate to attain homeostasis and adaptation.

    PubMed

    Ferrezuelo, Francisco; Colomina, Neus; Palmisano, Alida; Garí, Eloi; Gallego, Carme; Csikász-Nagy, Attila; Aldea, Martí

    2012-01-01

    Budding yeast cells are assumed to trigger Start and enter the cell cycle only after they attain a critical size set by external conditions. However, arguing against deterministic models of cell size control, cell volume at Start displays great individual variability even under constant conditions. Here we show that cell size at Start is robustly set at a single-cell level by the volume growth rate in G1, which explains the observed variability. We find that this growth-rate-dependent sizer is intimately hardwired into the Start network and the Ydj1 chaperone is key for setting cell size as a function of the individual growth rate. Mathematical modelling and experimental data indicate that a growth-rate-dependent sizer is sufficient to ensure size homeostasis and, as a remarkable advantage over a rigid sizer mechanism, it reduces noise in G1 length and provides an immediate solution for size adaptation to external conditions at a population level.

  19. Adaptive critic designs for discrete-time zero-sum games with application to H(infinity) control.

    PubMed

    Al-Tamimi, Asma; Abu-Khalaf, Murad; Lewis, Frank L

    2007-02-01

    In this correspondence, adaptive critic approximate dynamic programming designs are derived to solve the discrete-time zero-sum game in which the state and action spaces are continuous. This results in a forward-in-time reinforcement learning algorithm that converges to the Nash equilibrium of the corresponding zero-sum game. The results in this correspondence can be thought of as a way to solve the Riccati equation of the well-known discrete-time H(infinity) optimal control problem forward in time. Two schemes are presented, namely: 1) a heuristic dynamic programming and 2) a dual-heuristic dynamic programming, to solve for the value function and the costate of the game, respectively. An H(infinity) autopilot design for an F-16 aircraft is presented to illustrate the results.

  20. Physiologically-Based Vision Modeling Applications and Gradient Descent-Based Parameter Adaptation of Pulse Coupled Neural Networks

    DTIC Science & Technology

    1997-06-01

    Transactions on Systems, Man and Cybernetics, vol.SMC- 13, no.5, p. 815-26 (1983). 14. Crick , F. and C. Kosch. "The problem of consciousness." Mind and Brain...cortical maps via synchronization," Parallel Pro- cessing in Neural Systems and Computers, p. xv+626, 101-4 (1990). 25. Francis , Gregory and Stephen

  1. Application of Self Adaptive Unsupervised Neural Networks for Processing of VLF-LF signals to detect Seismic-Ionospheric Precursor Phenomena.

    NASA Astrophysics Data System (ADS)

    Skeberis, C.; Xenos, T. D.; Hadjileontiadis, L.; Contadakis, M. E.; Arabelos, D.

    2012-04-01

    This paper investigates the development and application of artificial neural networks (ANN) based on Predictive Modular Neural Networks (PREMONNs) to provide a self adaptive unsupervised method for detecting disturbances that can be attributed to seismic-ionospheric precursor phenomena using VLF radio signals. As such, the neural network is applied to bring forth and adaptively discriminate different characteristics in the received signals, in real time, in order to provide data segments of interest that can be correlated to subsequent seismic phenomena. PREMONNs have been developed for time series prediction and through that for source switching detection in a time series; they are constituted by two modules. The first tier is a module consisting of a dynamic array of neural networks following the data stream in order to predict the next value of a time series whereas the second is a decision one utilizing a Bayes probability equation to decide on source switching. That module is responsible for electing and appropriately training the closest fitting NN or switching to a new NN if a source switch is apparent. For the purpose of this paper, VLF signals transmitted by a number of European VLF transmitters are monitored for over a year in Thessaloniki (40.69N 22.78E) and the data from December 2010 to December 2011 are used. The received signals are sampled and stored for off line processing. The receiver was developed by Elettronika Srl, and is part of the International Network for Frontier Research on Earthquake Precursors (INFREP). Signals received from the 20.27KHz ICV station in Tavolara, Italy (Lat 40.923,Lon. 9.731) were used. The received VLF signal was normalized and then processed using the Empirical Mode Decomposition Method (EMD). The resulting data are used to train the unsupervised ANN and the performance of the developed network is then evaluated. The efficacy of different layouts of the PREMONN is evaluated and the application of a self

  2. Evolvable Neural Software System

    NASA Technical Reports Server (NTRS)

    Curtis, Steven A.

    2009-01-01

    The Evolvable Neural Software System (ENSS) is composed of sets of Neural Basis Functions (NBFs), which can be totally autonomously created and removed according to the changing needs and requirements of the software system. The resulting structure is both hierarchical and self-similar in that a given set of NBFs may have a ruler NBF, which in turn communicates with other sets of NBFs. These sets of NBFs may function as nodes to a ruler node, which are also NBF constructs. In this manner, the synthetic neural system can exhibit the complexity, three-dimensional connectivity, and adaptability of biological neural systems. An added advantage of ENSS over a natural neural system is its ability to modify its core genetic code in response to environmental changes as reflected in needs and requirements. The neural system is fully adaptive and evolvable and is trainable before release. It continues to rewire itself while on the job. The NBF is a unique, bilevel intelligence neural system composed of a higher-level heuristic neural system (HNS) and a lower-level, autonomic neural system (ANS). Taken together, the HNS and the ANS give each NBF the complete capabilities of a biological neural system to match sensory inputs to actions. Another feature of the NBF is the Evolvable Neural Interface (ENI), which links the HNS and ANS. The ENI solves the interface problem between these two systems by actively adapting and evolving from a primitive initial state (a Neural Thread) to a complicated, operational ENI and successfully adapting to a training sequence of sensory input. This simulates the adaptation of a biological neural system in a developmental phase. Within the greater multi-NBF and multi-node ENSS, self-similar ENI s provide the basis for inter-NBF and inter-node connectivity.

  3. A self-adaptive genetic algorithm-artificial neural network algorithm with leave-one-out cross validation for descriptor selection in QSAR study.

    PubMed

    Wu, Jingheng; Mei, Juan; Wen, Sixiang; Liao, Siyan; Chen, Jincan; Shen, Yong

    2010-07-30

    Based on the quantitative structure-activity relationships (QSARs) models developed by artificial neural networks (ANNs), genetic algorithm (GA) was used in the variable-selection approach with molecule descriptors and helped to improve the back-propagation training algorithm as well. The cross validation techniques of leave-one-out investigated the validity of the generated ANN model and preferable variable combinations derived in the GAs. A self-adaptive GA-ANN model was successfully established by using a new estimate function for avoiding over-fitting phenomenon in ANN training. Compared with the variables selected in two recent QSAR studies that were based on stepwise multiple linear regression (MLR) models, the variables selected in self-adaptive GA-ANN model are superior in constructing ANN model, as they revealed a higher cross validation (CV) coefficient (Q(2)) and a lower root mean square deviation both in the established model and biological activity prediction. The introduced methods for validation, including leave-multiple-out, Y-randomization, and external validation, proved the superiority of the established GA-ANN models over MLR models in both stability and predictive power. Self-adaptive GA-ANN showed us a prospect of improving QSAR model.

  4. Adaptive Neural Network-Based Event-Triggered Control of Single-Input Single-Output Nonlinear Discrete-Time Systems.

    PubMed

    Sahoo, Avimanyu; Xu, Hao; Jagannathan, Sarangapani

    2016-01-01

    This paper presents a novel adaptive neural network (NN) control of single-input and single-output uncertain nonlinear discrete-time systems under event sampled NN inputs. In this control scheme, the feedback signals are transmitted, and the NN weights are tuned in an aperiodic manner at the event sampled instants. After reviewing the NN approximation property with event sampled inputs, an adaptive state estimator (SE), consisting of linearly parameterized NNs, is utilized to approximate the unknown system dynamics in an event sampled context. The SE is viewed as a model and its approximated dynamics and the state vector, during any two events, are utilized for the event-triggered controller design. An adaptive event-trigger condition is derived by using both the estimated NN weights and a dead-zone operator to determine the event sampling instants. This condition both facilitates the NN approximation and reduces the transmission of feedback signals. The ultimate boundedness of both the NN weight estimation error and the system state vector is demonstrated through the Lyapunov approach. As expected, during an initial online learning phase, events are observed more frequently. Over time with the convergence of the NN weights, the inter-event times increase, thereby lowering the number of triggered events. These claims are illustrated through the simulation results.

  5. Suppression of Nestin reveals a critical role for p38-EGFR pathway in neural progenitor cell proliferation

    PubMed Central

    Hu, Wentao; Lu, Hong; Wang, Shang; Yin, Wenhan; Liu, Xujie; Dong, Lin; Chiu, Richard; Shen, Li; Lu, Wen-Jing; Lan, Feng

    2016-01-01

    The expression of intermediate filament Nestin is necessary for the neural progenitor cells (NPCs) to maintain stemness, but the underlying cellular and molecular mechanism remains unclear. In this study, we demonstrated that Nestin is required for the self-renew of NPCs through activating MAPK and EGFR pathways. Knockdown of Nestin by shRNA inhibited cell cycle progression and proliferation in mouse NPCs. Moreover, suppression of Nestin reduced expression of the epidermal growth factor receptor (EGFR) in NPCs and inhibited the mitogenic effects of EGF on these cells. Treatment of NPCs with p38-MAPK inhibitor PD169316 reversed cell cycle arrest caused by the knockdown of Nestin. Our findings indicate that Nestin promotes NPC proliferation via p38-MAPK and EGFR pathways, and reveals the necessity of these pathways in NPCs self-renewal. PMID:27894083

  6. A critical review of the allocentric spatial representation and its neural underpinnings: toward a network-based perspective

    PubMed Central

    Ekstrom, Arne D.; Arnold, Aiden E. G. F.; Iaria, Giuseppe

    2014-01-01

    While the widely studied allocentric spatial representation holds a special status in neuroscience research, its exact nature and neural underpinnings continue to be the topic of debate, particularly in humans. Here, based on a review of human behavioral research, we argue that allocentric representations do not provide the kind of map-like, metric representation one might expect based on past theoretical work. Instead, we suggest that almost all tasks used in past studies involve a combination of egocentric and allocentric representation, complicating both the investigation of the cognitive basis of an allocentric representation and the task of identifying a brain region specifically dedicated to it. Indeed, as we discuss in detail, past studies suggest numerous brain regions important to allocentric spatial memory in addition to the hippocampus, including parahippocampal, retrosplenial, and prefrontal cortices. We thus argue that although allocentric computations will often require the hippocampus, particularly those involving extracting details across temporally specific routes, the hippocampus is not necessary for all allocentric computations. We instead suggest that a non-aggregate network process involving multiple interacting brain areas, including hippocampus and extra-hippocampal areas such as parahippocampal, retrosplenial, prefrontal, and parietal cortices, better characterizes the neural basis of spatial representation during navigation. According to this model, an allocentric representation does not emerge from the computations of a single brain region (i.e., hippocampus) nor is it readily decomposable into additive computations performed by separate brain regions. Instead, an allocentric representation emerges from computations partially shared across numerous interacting brain regions. We discuss our non-aggregate network model in light of existing data and provide several key predictions for future experiments. PMID:25346679

  7. Adaptive neural network tracking control for a class of switched stochastic pure-feedback nonlinear systems with backlash-like hysteresis

    NASA Astrophysics Data System (ADS)

    Niu, Ben; Qin, Tian; Fan, Xiaodong

    2016-10-01

    In this paper, an adaptive neural network tracking control approach is proposed for a class of switched stochastic pure-feedback nonlinear systems with backlash-like hysteresis. In the design procedure, an affine variable is constructed, which avoids the use of the mean value theorem, and the additional first-order low-pass filter is employed to deal with the problem of explosion of complexity. Then, a common Lyapunov function and a state feedback controller are explicitly obtained for all subsystems. It is proved that the proposed controller that guarantees all signals in the closed-loop system are semi-globally uniformly ultimately bounded and the tracking error remains an adjustable neighbourhood of the origin. Finally, simulation results show the effectiveness of the presented control design approach.

  8. Distributed Adaptive Neural Network Output Tracking of Leader-Following High-Order Stochastic Nonlinear Multiagent Systems With Unknown Dead-Zone Input.

    PubMed

    Hua, Changchun; Zhang, Liuliu; Guan, Xinping

    2017-01-01

    This paper studies the problem of distributed output tracking consensus control for a class of high-order stochastic nonlinear multiagent systems with unknown nonlinear dead-zone under a directed graph topology. The adaptive neural networks are used to approximate the unknown nonlinear functions and a new inequality is used to deal with the completely unknown dead-zone input. Then, we design the controllers based on backstepping method and the dynamic surface control technique. It is strictly proved that the resulting closed-loop system is stable in probability in the sense of semiglobally uniform ultimate boundedness and the tracking errors between the leader and the followers approach to a small residual set based on Lyapunov stability theory. Finally, two simulation examples are presented to show the effectiveness and the advantages of the proposed techniques.

  9. Neural networks for aircraft control

    NASA Technical Reports Server (NTRS)

    Linse, Dennis

    1990-01-01

    Current research in Artificial Neural Networks indicates that networks offer some potential advantages in adaptation and fault tolerance. This research is directed at determining the possible applicability of neural networks to aircraft control. The first application will be to aircraft trim. Neural network node characteristics, network topology and operation, neural network learning and example histories using neighboring optimal control with a neural net are discussed.

  10. Stathmin-like 4 is critical for the maintenance of neural progenitor cells in dorsal midbrain of zebrafish larvae

    PubMed Central

    Lin, Meng-Ju; Lee, Shyh-Jye

    2016-01-01

    A delicate balance between proliferating and differentiating signals is necessary to ensure proper growth and neuronal specification. By studying the developing zebrafish brain, we observed a specific and dynamic expression of a microtubule destabilizer gene, stathmin-like 4 (stmn4), in the dorsal midbrain region. The expression of stmn4 was mutually exclusive to a pan-neuronal marker, elavl3 that indicates its role in regulating neurogenesis. We showed the knockdown or overexpression of stmn4 resulted in premature neuronal differentiation in dorsal midbrain. We also generated stmn4 maternal-zygotic knockout zebrafish by the CRISPR/Cas9 system. Unexpectedly, only less than 10% of stmn4 mutants showed similar phenotypes observed in that of stmn4 morphants. It might be due to the complementation of the increased stmn1b expression observed in stmn4 mutants. In addition, time-lapse recordings revealed the changes in cellular proliferation and differentiation in stmn4 morphants. Stmn4 morphants displayed a longer G2 phase that could be rescued by Cdc25a. Furthermore, the inhibition of Wnt could reduce stmn4 transcripts. These results suggest that the Wnt-mediated Stmn4 homeostasis is crucial for preventing dorsal midbrain from premature differentiation via the G2 phase control during the neural keel stage. PMID:27819330

  11. Neural stem cell transplantation at critical period improves learning and memory through restoring synaptic impairment in Alzheimer's disease mouse model.

    PubMed

    Kim, J A; Ha, S; Shin, K Y; Kim, S; Lee, K J; Chong, Y H; Chang, K-A; Suh, Y-H

    2015-06-18

    Alzheimer's disease (AD) is characterized by neuronal loss in several regions of the brain. Recent studies have suggested that stem cell transplantation could serve as a potential therapeutic strategy to halt or ameliorate the inexorable disease progression. However, the optimal stage of the disease for stem cell transplantation to have a therapeutic effect has yet to be determined. Here, we demonstrated that transplantation of neural stem cells into 12-month-old Tg2576 brains markedly improved both cognitive impairments and neuropathological features by reducing β-amyloid processing and upregulating clearance of β-amyloid, secretion of anti-inflammatory cytokines, endogenous neurogenesis, as well as synapse formation. In contrast, the stem cell transplantation did not recover cognitive dysfunction and β-amyloid neuropathology in Tg2576 mice aged 15 months when the memory loss is manifest. Overall, this study underscores that stem cell therapy at optimal time frame is crucial to obtain maximal therapeutic effects that can restore functional deficits or stop the progression of AD.

  12. Neural Flight Control System

    NASA Technical Reports Server (NTRS)

    Gundy-Burlet, Karen

    2003-01-01

    The Neural Flight Control System (NFCS) was developed to address the need for control systems that can be produced and tested at lower cost, easily adapted to prototype vehicles and for flight systems that can accommodate damaged control surfaces or changes to aircraft stability and control characteristics resulting from failures or accidents. NFCS utilizes on a neural network-based flight control algorithm which automatically compensates for a broad spectrum of unanticipated damage or failures of an aircraft in flight. Pilot stick and rudder pedal inputs are fed into a reference model which produces pitch, roll and yaw rate commands. The reference model frequencies and gains can be set to provide handling quality characteristics suitable for the aircraft of interest. The rate commands are used in conjunction with estimates of the aircraft s stability and control (S&C) derivatives by a simplified Dynamic Inverse controller to produce virtual elevator, aileron and rudder commands. These virtual surface deflection commands are optimally distributed across the aircraft s available control surfaces using linear programming theory. Sensor data is compared with the reference model rate commands to produce an error signal. A Proportional/Integral (PI) error controller "winds up" on the error signal and adds an augmented command to the reference model output with the effect of zeroing the error signal. In order to provide more consistent handling qualities for the pilot, neural networks learn the behavior of the error controller and add in the augmented command before the integrator winds up. In the case of damage sufficient to affect the handling qualities of the aircraft, an Adaptive Critic is utilized to reduce the reference model frequencies and gains to stay within a flyable envelope of the aircraft.

  13. Is neural Darwinism Darwinism?

    PubMed

    van Belle, T

    1997-01-01

    Neural Darwinism is a theory of cognition developed by Gerald Edelman along with George Reeke and Olaf Sporns at Rockefeller University. As its name suggests, neural Darwinism is modeled after biological Darwinism, and its authors assert that the two processes are strongly analogous. both operate on variation in a population, amplifying the more adaptive individuals. However, from a computational perspective, neural Darwinism is quite different from other models of natural selection, such as genetic algorithms. The individuals of neural Darwinism do not replicate, thus robbing the process of the capacity to explore new solutions over time and ultimately reducing it to a random search. Because neural Darwinism does not have the computational power of a truly Darwinian process, it is misleading to label it as such. to illustrate this disparity in adaptive power, one of Edelman's early computer experiments, Darwin I, is revisited, and it is shown that adding replication greatly improves the adaptive power of the system.

  14. Hysteresis compensation of the piezoelectric ceramic actuators-based tip/tilt mirror with a neural network method in adaptive optics

    NASA Astrophysics Data System (ADS)

    Wang, Chongchong; Wang, Yukun; Hu, Lifa; Wang, Shaoxin; Cao, Zhaoliang; Mu, Quanquan; Li, Dayu; Yang, Chengliang; Xuan, Li

    2016-05-01

    The intrinsic hysteresis nonlinearity of the piezo-actuators can severely degrade the positioning accuracy of a tip-tilt mirror (TTM) in an adaptive optics system. This paper focuses on compensating this hysteresis nonlinearity by feed-forward linearization with an inverse hysteresis model. This inverse hysteresis model is based on the classical Presiach model, and the neural network (NN) is used to describe the hysteresis loop. In order to apply it in the real-time adaptive correction, an analytical nonlinear function derived from the NN is introduced to compute the inverse hysteresis model output instead of the time-consuming NN simulation process. Experimental results show that the proposed method effectively linearized the TTM behavior with the static hysteresis nonlinearity of TTM reducing from 15.6% to 1.4%. In addition, the tip-tilt tracking experiments using the integrator with and without hysteresis compensation are conducted. The wavefront tip-tilt aberration rejection ability of the TTM control system is significantly improved with the -3 dB error rejection bandwidth increasing from 46 to 62 Hz.

  15. The Neural Mechanism Exploration of Adaptive Motor Control: Dynamical Economic Cell Allocation in the Primary Motor Cortex.

    PubMed

    Li, Wei; Guo, Yangyang; Fan, Jing; Ma, Chaolin; Ma, Xuan; Chen, Xi; He, Jiping

    2016-06-14

    Adaptive flexibility is of significance for the smooth and efficient movements in goal attainment. However, the underlying work mechanism of the cerebral cortex in adaptive motor control still remains unclear. How does the cerebral cortex organize and coordinate the activity of a large population of cells in the implementation of various motor strategies? To explore this issue, single-unit activities from the M1 region and kinematic data were recorded simultaneously in monkeys performing 3D reach-to-grasp tasks with different perturbations. Varying motor control strategies were employed and achieved in different perturbed tasks, via the dynamic allocation of cells to modulate specific movement parameters. An economic principle was proposed for the first time to describe a basic rule for cell allocation in the primary motor cortex. This principle, defined as the Dynamic Economic Cell Allocation Mechanism (DECAM), guarantees benefit maximization in cell allocation under limited neuronal resources, and avoids committing resources to uneconomic investments for unreliable factors with no or little revenue. That is to say, the cells recruited are always preferentially allocated to those factors with reliable return; otherwise, the cells are dispatched to respond to other factors about task. The findings of this study might partially reveal the working mechanisms underlying the role of the cerebral cortex in adaptive motor control, wherein is also of significance for the design of future intelligent brain-machine interfaces and rehabilitation device.

  16. Verification and Validation of Adaptive and Intelligent Systems with Flight Test Results

    NASA Technical Reports Server (NTRS)

    Burken, John J.; Larson, Richard R.

    2009-01-01

    F-15 IFCS project goals are: a) Demonstrate Control Approaches that can Efficiently Optimize Aircraft Performance in both Normal and Failure Conditions [A] & [B] failures. b) Advance Neural Network-Based Flight Control Technology for New Aerospace Systems Designs with a Pilot in the Loop. Gen II objectives include; a) Implement and Fly a Direct Adaptive Neural Network Based Flight Controller; b) Demonstrate the Ability of the System to Adapt to Simulated System Failures: 1) Suppress Transients Associated with Failure; 2) Re-Establish Sufficient Control and Handling of Vehicle for Safe Recovery. c) Provide Flight Experience for Development of Verification and Validation Processes for Flight Critical Neural Network Software.

  17. A Boolean Function for Neural Induction Reveals a Critical Role of Direct Intercellular Interactions in Patterning the Ectoderm of the Ascidian Embryo

    PubMed Central

    Mochizuki, Atsushi; Satou, Yutaka

    2015-01-01

    A complex system of multiple signaling molecules often produce differential gene expression patterns in animal embryos. In the ascidian embryo, four signaling ligands, Ephrin-A.d (Efna.d), Fgf9/16/20, Admp, and Gdf1/3-r, coordinately induce Otx expression in the neural lineage at the 32-cell stage. However, it has not been determined whether differential inputs of all of these signaling pathways are really necessary. It is possible that differential activation of one of these signaling pathways is sufficient and the remaining signaling pathways are activated in all cells at similar levels. To address this question, we developed a parameter-free method for determining a Boolean function for Otx expression in the present study. We treated activities of signaling pathways as Boolean values, and we also took all possible patterns of signaling gradients into consideration. We successfully determined a Boolean function that explains Otx expression in the animal hemisphere of wild-type and morphant embryos at the 32-cell stage. This Boolean function was not inconsistent with three sensing patterns, which represented whether or not individual cells received sufficient amounts of the signaling molecules. These sensing patterns all indicated that differential expression of Otx in the neural lineage is primarily determined by Efna.d, but not by differential inputs of Fgf9/16/20, Admp, and Gdf1/3-r signaling. To confirm this hypothesis experimentally, we simultaneously knocked-down Admp, Gdf1/3-r, and Fgf9/16/20, and treated this triple morphant with recombinant bFGF and BMP4 proteins, which mimic Fgf9/16/20 and Admp/Gdf1/3-r activity, respectively. Although no differential inputs of Admp, Gdf1/3-r and Fgf9/16/20 signaling were expected under this experimental condition, Otx was expressed specifically in the neural lineage. Thus, direct cell–cell interactions through Efna.d play a critical role in patterning the ectoderm of the early ascidian embryo. PMID:26714026

  18. A Boolean Function for Neural Induction Reveals a Critical Role of Direct Intercellular Interactions in Patterning the Ectoderm of the Ascidian Embryo.

    PubMed

    Ohta, Naoyuki; Waki, Kana; Mochizuki, Atsushi; Satou, Yutaka

    2015-12-01

    A complex system of multiple signaling molecules often produce differential gene expression patterns in animal embryos. In the ascidian embryo, four signaling ligands, Ephrin-A.d (Efna.d), Fgf9/16/20, Admp, and Gdf1/3-r, coordinately induce Otx expression in the neural lineage at the 32-cell stage. However, it has not been determined whether differential inputs of all of these signaling pathways are really necessary. It is possible that differential activation of one of these signaling pathways is sufficient and the remaining signaling pathways are activated in all cells at similar levels. To address this question, we developed a parameter-free method for determining a Boolean function for Otx expression in the present study. We treated activities of signaling pathways as Boolean values, and we also took all possible patterns of signaling gradients into consideration. We successfully determined a Boolean function that explains Otx expression in the animal hemisphere of wild-type and morphant embryos at the 32-cell stage. This Boolean function was not inconsistent with three sensing patterns, which represented whether or not individual cells received sufficient amounts of the signaling molecules. These sensing patterns all indicated that differential expression of Otx in the neural lineage is primarily determined by Efna.d, but not by differential inputs of Fgf9/16/20, Admp, and Gdf1/3-r signaling. To confirm this hypothesis experimentally, we simultaneously knocked-down Admp, Gdf1/3-r, and Fgf9/16/20, and treated this triple morphant with recombinant bFGF and BMP4 proteins, which mimic Fgf9/16/20 and Admp/Gdf1/3-r activity, respectively. Although no differential inputs of Admp, Gdf1/3-r and Fgf9/16/20 signaling were expected under this experimental condition, Otx was expressed specifically in the neural lineage. Thus, direct cell-cell interactions through Efna.d play a critical role in patterning the ectoderm of the early ascidian embryo.

  19. Multiobjective algebraic synthesis of neural control systems by implicit model following.

    PubMed

    Ferrari, Silvia

    2009-03-01

    The advantages brought about by using classical linear control theory in conjunction with neural approximators have long been recognized in the literature. In particular, using linear controllers to obtain the starting neural control design has been shown to be a key step for the successful development and implementation of adaptive-critic neural controllers. Despite their adaptive capabilities, neural controllers are often criticized for not providing the same performance and stability guarantees as classical linear designs. Therefore, this paper develops an algebraic synthesis procedure for designing dynamic output-feedback neural controllers that are closed-loop stable and meet the same performance objectives as any classical linear design. The performance synthesis problem is addressed by deriving implicit model-following algebraic relationships between model matrices, obtained from the classical design, and the neural control parameters. Additional linear matrix inequalities (LMIs) conditions for closed-loop exponential stability of the neural controller are derived using existing integral quadratic constraints (IQCs) for operators with repeated slope-restricted nonlinearities. The approach is demonstrated by designing a recurrent neural network controller for a highly maneuverable tailfin-controlled missile that meets multiple design objectives, including pole placement for transient tuning, H(infinity) and H(2) performance in the presence of parameter uncertainty, and command-input tracking.

  20. Vascular Endothelial Growth Factor-A Is a Survival Factor for Retinal Neurons and a Critical Neuroprotectant during the Adaptive Response to Ischemic Injury

    PubMed Central

    Nishijima, Kazuaki; Ng, Yin-Shan; Zhong, Lichun; Bradley, John; Schubert, William; Jo, Nobuo; Akita, Jo; Samuelsson, Steven J.; Robinson, Gregory S.; Adamis, Anthony P.; Shima, David T.

    2007-01-01

    Vascular endothelial growth factor-A (VEGF-A) has recently been recognized as an important neuroprotectant in the central nervous system. Given its position as an anti-angiogenic target in the treatment of human diseases, understanding the extent of VEGF’s role in neural cell survival is paramount. Here, we used a model of ischemia-reperfusion injury and found that VEGF-A exposure resulted in a dose-dependent reduction in retinal neuron apoptosis. Although mechanistic studies suggested that VEGF-A-induced volumetric blood flow to the retina may be partially responsible for the neuroprotection, ex vivo retinal culture demonstrated a direct neuroprotective effect for VEGF-A. VEGF receptor-2 (VEGFR2) expression was detected in several neuronal cell layers of the retina, and functional analyses showed that VEGFR2 was involved in retinal neuroprotection. VEGF-A was also shown to be involved in the adaptive response to retinal ischemia. Ischemic preconditioning 24 hours before ischemia-reperfusion injury increased VEGF-A levels and substantially decreased the number of apoptotic retinal cells. The protective effect of ischemic preconditioning was reversed after VEGF-A inhibition. Finally, chronic inhibition of VEGF-A function in normal adult animals led to a significant loss of retinal ganglion cells yet had no observable effect on several vascular parameters. These findings have implications for both neural pathologies and ocular vascular diseases, such as diabetic retinopathy and age-related macular degeneration. PMID:17591953

  1. Cascaded neural networks for sequenced propagation estimation, multiuser detection, and adaptive radio resource control of third-generation wireless networks for multimedia services

    NASA Astrophysics Data System (ADS)

    Hortos, William S.

    1999-03-01

    A hybrid neural network approach is presented to estimate radio propagation characteristics and multiuser interference and to evaluate their combined impact on throughput, latency and information loss in third-generation (3G) wireless networks. The latter three performance parameters influence the quality of service (QoS) for multimedia services under consideration for 3G networks. These networks, based on a hierarchical architecture of overlaying macrocells on top of micro- and picocells, are planned to operate in mobile urban and indoor environments with service demands emanating from circuit-switched, packet-switched and satellite-based traffic sources. Candidate radio interfaces for these networks employ a form of wideband CDMA in 5-MHz and wider-bandwidth channels, with possible asynchronous operation of the mobile subscribers. The proposed neural network (NN) architecture allocates network resources to optimize QoS metrics. Parameters of the radio propagation channel are estimated, followed by control of an adaptive antenna array at the base station to minimize interference, and then joint multiuser detection is performed at the base station receiver. These adaptive processing stages are implemented as a sequence of NN techniques that provide their estimates as inputs to a final- stage Kohonen self-organizing feature map (SOFM). The SOFM optimizes the allocation of available network resources to satisfy QoS requirements for variable-rate voice, data and video services. As the first stage of the sequence, a modified feed-forward multilayer perceptron NN is trained on the pilot signals of the mobile subscribers to estimate the parameters of shadowing, multipath fading and delays on the uplinks. A recurrent NN (RNN) forms the second stage to control base stations' adaptive antenna arrays to minimize intra-cell interference. The third stage is based on a Hopfield NN (HNN), modified to detect multiple users on the uplink radio channels to mitigate multiaccess

  2. Application of adaptive neuro-fuzzy inference system techniques and artificial neural networks to predict solid oxide fuel cell performance in residential microgeneration installation

    NASA Astrophysics Data System (ADS)

    Entchev, Evgueniy; Yang, Libing

    This study applies adaptive neuro-fuzzy inference system (ANFIS) techniques and artificial neural network (ANN) to predict solid oxide fuel cell (SOFC) performance while supplying both heat and power to a residence. A microgeneration 5 kW el SOFC system was installed at the Canadian Centre for Housing Technology (CCHT), integrated with existing mechanical systems and connected in parallel to the grid. SOFC performance data were collected during the winter heating season and used for training of both ANN and ANFIS models. The ANN model was built on back propagation algorithm as for ANFIS model a combination of least squares method and back propagation gradient decent method were developed and applied. Both models were trained with experimental data and used to predict selective SOFC performance parameters such as fuel cell stack current, stack voltage, etc. The study revealed that both ANN and ANFIS models' predictions agreed well with variety of experimental data sets representing steady-state, start-up and shut-down operations of the SOFC system. The initial data set was subjected to detailed sensitivity analysis and statistically insignificant parameters were excluded from the training set. As a result, significant reduction of computational time was achieved without affecting models' accuracy. The study showed that adaptive models can be applied with confidence during the design process and for performance optimization of existing and newly developed solid oxide fuel cell systems. It demonstrated that by using ANN and ANFIS techniques SOFC microgeneration system's performance could be modelled with minimum time demand and with a high degree of accuracy.

  3. Reduced susceptibility to eccentric exercise-induced muscle damage in resistance-trained men is not linked to resistance training-related neural adaptations

    PubMed Central

    Beck, TW; Wages, NP

    2015-01-01

    The purpose of this study was to examine the acute effects of maximal concentric vs. eccentric exercise on the isometric strength of the elbow flexor, as well as the biceps brachii muscle electromyographic (EMG) responses in resistance-trained (RT) vs. untrained (UT) men. Thirteen RT men (age: 24 ± 4 years; height: 180.2 ± 7.7 cm; body weight: 92.2 ± 16.9 kg) and twelve UT men (age: 23 ± 4 years; height: 179.2 ± 5.0 cm; body weight: 81.5 ± 8.6 kg) performed six sets of ten maximal concentric isokinetic (CON) or eccentric isokinetic (ECC) elbow flexion exercise in two separate visits. Before and after the exercise interventions, maximal voluntary contractions (MVCs) were performed for testing isometric strength. In addition, bipolar surface EMG signals were detected from the biceps brachii muscle during the strength testing. Both CON and ECC caused isometric strength to decrease, regardless of the training status. However, ECC caused greater isometric strength decline than CON did for the UT group (p = 0.006), but not for the RT group. Both EMG amplitude and mean frequency significantly decreased and increased, respectively, regardless of the training status and exercise intervention. Resistance-trained men are less susceptible to eccentric exercise-induced muscle damage, but this advantage is not likely linked to the chronic resistance training-induced neural adaptations. PMID:26424922

  4. Power spectrum scale invariance quantifies limbic dysregulation in trait anxious adults using fMRI: adapting methods optimized for characterizing autonomic dysregulation to neural dynamic timeseries.

    PubMed Central

    Tolkunov, Denis; Rubin, Denis; Mujica-Parodi, LR

    2010-01-01

    In a well-regulated control system, excitatory and inhibitory components work closely together with minimum lag; in response to inputs of finite duration, outputs should show rapid rise and, following the input's termination, immediate return to baseline. The efficiency of this response can be quantified using the power spectrum density's scaling parameter β, a measure of self-similarity, applied to the first-derivative of the raw signal. In this study, we adapted power spectrum density methods, previously used to quantify autonomic dysregulation (heart rate variability), to neural time-series obtained via functional MRI. The negative feedback loop we investigated was the limbic system, using affect-valent faces as stimuli. We hypothesized that trait anxiety would be related to efficiency of regulation of limbic responses, as quantified by power law scaling of fMRI time series. Our results supported this hypothesis, showing moderate to strong correlations of β (r = 0.4–0.54) for the amygdala, orbitofrontal cortex, hippocampus, superior temporal gyrus, posterior insula, and anterior cingulate. Strong anticorrelations were also found between the amygdala's β and wake heart rate variability (r = −0.61), suggesting a robust relationship between dysregulated limbic outputs and their autonomic consequences. PMID:20025979

  5. Genetic algorithm-artificial neural network and adaptive neuro-fuzzy inference system modeling of antibacterial activity of annatto dye on Salmonella enteritidis.

    PubMed

    Yolmeh, Mahmoud; Habibi Najafi, Mohammad B; Salehi, Fakhreddin

    2014-01-01

    Annatto is commonly used as a coloring agent in the food industry and has antimicrobial and antioxidant properties. In this study, genetic algorithm-artificial neural network (GA-ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were used to predict the effect of annatto dye on Salmonella enteritidis in mayonnaise. The GA-ANN and ANFIS were fed with 3 inputs of annatto dye concentration (0, 0.1, 0.2 and 0.4%), storage temperature (4 and 25°C) and storage time (1-20 days) for prediction of S. enteritidis population. Both models were trained with experimental data. The results showed that the annatto dye was able to reduce of S. enteritidis and its effect was stronger at 25°C than 4°C. The developed GA-ANN, which included 8 hidden neurons, could predict S. enteritidis population with correlation coefficient of 0.999. The overall agreement between ANFIS predictions and experimental data was also very good (r=0.998). Sensitivity analysis results showed that storage temperature was the most sensitive factor for prediction of S. enteritidis population.

  6. The Neural Correlates of Deficient Error Awareness in Attention-Deficit Hyperactivity Disorder (ADHD)

    ERIC Educational Resources Information Center

    O'Connell, Redmond G.; Bellgrove, Mark A.; Dockree, Paul M.; Lau, Adam; Hester, Robert; Garavan, Hugh; Fitzgerald, Michael; Foxe, John J.; Robertson, Ian H.

    2009-01-01

    The ability to detect and correct errors is critical to adaptive control of behaviour and represents a discrete neuropsychological function. A number of studies have highlighted that attention-deficit hyperactivity disorder (ADHD) is associated with abnormalities in behavioural and neural responsiveness to performance errors. One limitation of…

  7. Critical role of astrocytic interleukin-17 A in post-stroke survival and neuronal differentiation of neural precursor cells in adult mice

    PubMed Central

    Lin, Y; Zhang, J-C; Yao, C-Y; Wu, Y; Abdelgawad, A F; Yao, S-L; Yuan, S-Y

    2016-01-01

    The brain and the immune system interact in complex ways after ischemic stroke, and the long-term effects of immune response associated with stroke remain controversial. As a linkage between innate and adaptive immunity, interleukin-17 A (IL-17 A) secreted from gamma delta (γδ) T cells has detrimental roles in the pathogenesis of acute ischemic stroke. However, to date, the long-term actions of IL-17 A after stroke have not been investigated. Here, we found that IL-17 A showed two distinct peaks of expression in the ischemic hemisphere: the first occurring within 3 days and the second on day 28 after stroke. Our data also showed that astrocyte was the major cellular source of IL-17 A that maintained and augmented subventricular zone (SVZ) neural precursor cells (NPCs) survival, neuronal differentiation, and subsequent synaptogenesis and functional recovery after stroke. IL-17 A also promoted neuronal differentiation in cultured NPCs from the ischemic SVZ. Furthermore, our in vitro data revealed that in primary astrocyte cultures activated astrocytes released IL-17 A via p38 mitogen-activated protein kinase (MAPK). Culture media from reactive astrocytes increased neuronal differentiation of NSCs in vitro. Blockade of IL-17 A with neutralizing antibody prevented this effect. In addition, after screening for multiple signaling pathways, we revealed that the p38 MAPK/calpain 1 signaling pathway was involved in IL-17 A-mediated neurogenesis in vivo and in vitro. Thus, our results reveal a previously uncharacterized property of astrocytic IL-17 A in the maintenance and augment of survival and neuronal differentiation of NPCs, and subsequent synaptogenesis and spontaneous recovery after ischemic stroke. PMID:27336717

  8. Payload Invariant Control via Neural Networks: Development and Experimental Evaluation

    DTIC Science & Technology

    1989-12-01

    control is proposed and experimentally evaluated. An Adaptive Model-Based Neural Network Controller (AMBNNC) uses multilayer perceptron artificial neural ... networks to estimate the payload during high speed manipulator motion. The payload estimate adapts the feedforward compensator to unmodeled system

  9. p21Cip1 plays a critical role in the physiological adaptation to fasting through activation of PPARα

    PubMed Central

    Lopez-Guadamillas, Elena; Fernandez-Marcos, Pablo J.; Pantoja, Cristina; Muñoz-Martin, Maribel; Martínez, Dolores; Gómez-López, Gonzalo; Campos-Olivas, Ramón; Valverde, Angela M.; Serrano, Manuel

    2016-01-01

    Fasting is a physiological stress that elicits well-known metabolic adaptations, however, little is known about the role of stress-responsive tumor suppressors in fasting. Here, we have examined the expression of several tumor suppressors upon fasting in mice. Interestingly, p21 mRNA is uniquely induced in all the tissues tested, particularly in liver and muscle (>10 fold), and this upregulation is independent of p53. Remarkably, in contrast to wild-type mice, p21-null mice become severely morbid after prolonged fasting. The defective adaptation to fasting of p21-null mice is associated to elevated energy expenditure, accelerated depletion of fat stores, and premature activation of protein catabolism in the muscle. Analysis of the liver transcriptome and cell-based assays revealed that the absence of p21 partially impairs the transcriptional program of PPARα, a key regulator of fasting metabolism. Finally, treatment of p21-null mice with a PPARα agonist substantially protects them from their accelerated loss of fat upon fasting. We conclude that p21 plays a relevant role in fasting adaptation through the positive regulation of PPARα. PMID:27721423

  10. Playful and Mindful Interactions in the Recursive Adaptations of the Zone of Proximal Development: A Critical Complexity Science Approach

    ERIC Educational Resources Information Center

    Raia, Federica; Deng, Mario C.

    2011-01-01

    We discuss Konstantinos Alexakos, Jayson Jones and Victor Rodriguez's hermeneutic study of formation and function of kinship-like relationships among inner city male students of color in a college physics classroom. From our Critical Complexity Science framework we first discuss the reading "erlebnisse" of students laughing at and with each other…

  11. The anatomy of the bifurcated neural spine and its occurrence within Tetrapoda.

    PubMed

    Woodruff, D Cary

    2014-09-01

    Vertebral neural spine bifurcation has been historically treated as largely restrictive to sauropodomorph dinosaurs; wherein it is inferred to be an adaptation in response to the increasing weight from the horizontally extended cervical column. Because no extant terrestrial vertebrates have massive, horizontally extended necks, extant forms with large cranial masses were examined for the presence of neural spine bifurcation. Here, I report for the first time on the soft tissue surrounding neural spine bifurcation in a terrestrial quadruped through the dissection of three Ankole-Watusi cattle. With horns weighing up to a combined 90 kg, the Ankole-Watusi is unlike any other breed of cattle in terms of cranial weight and presence of neural spine bifurcation. Using the Ankole-Watusi as a model, it appears that neural spine bifurcation plays a critical role in supporting a large mobile weight adjacent to the girdles. In addition to neural spine bifurcation being recognized within nonavian dinosaurs, this vertebral feature is also documented within many members of temnospondyls, captorhinids, seymouriamorphs, diadectomorphs, Aves, marsupials, artiodactyls, perissodactyls, and Primates, amongst others. This phylogenetic distribution indicates that spine bifurcation is more common than previously thought, and that this vertebral adaptation has contributed throughout the evolutionary history of tetrapods. Neural spine bifurcation should now be recognized as an anatomical component adapted by some vertebrates to deal with massive, horizontal, mobile weights adjacent the girdles.

  12. Long-term, passive exposure to non-traumatic acoustic noise induces neural adaptation in the adult rat medial geniculate body and auditory cortex.

    PubMed

    Lau, Condon; Zhang, Jevin W; McPherson, Bradley; Pienkowski, Martin; Wu, Ed X

    2015-02-15

    Exposure to loud sounds can lead to permanent hearing loss, i.e., the elevation of hearing thresholds. Exposure at more moderate sound pressure levels (SPLs) (non-traumatic and within occupational limits) may not elevate thresholds, but could in the long-term be detrimental to speech intelligibility by altering its spectrotemporal representation in the central auditory system. In support of this, electrophysiological and behavioral changes following long-term, passive (no conditioned learning) exposure at moderate SPLs have recently been observed in adult animals. To assess the potential effects of moderately loud noise on the entire auditory brain, we employed functional magnetic resonance imaging (fMRI) to study noise-exposed adult rats. We find that passive, pulsed broadband noise exposure for two months at 65 dB SPL leads to a decrease of the sound-evoked blood oxygenation level-dependent fMRI signal in the thalamic medial geniculate body (MGB) and in the auditory cortex (AC). This points to the thalamo-cortex as the site of the neural adaptation to the moderately noisy environment. The signal reduction is statistically significant during 10 Hz pulsed acoustic stimulation (MGB: p<0.05, AC: p<10(-4)), but not during 5 Hz stimulation. This indicates that noise exposure has a greater effect on the processing of higher pulse rate sounds. This study has enhanced our understanding of functional changes following exposure by mapping changes across the entire auditory brain. These findings have important implications for speech processing, which depends on accurate processing of sounds with a wide spectrum of pulse rates.

  13. Adaptation and Evaluation of Online Self-learning Modules to Teach Critical Appraisal and Evidence-Based Practice in Nursing: An International Collaboration.

    PubMed

    Gagnon, Johanne; Gagnon, Marie-Pierre; Buteau, Rose-Anne; Azizah, Ginette Mbourou; Jetté, Sylvie; Lampron, Amélie; Simonyan, David; Asua, José; Reviriego, Eva

    2015-07-01

    Healthcare professionals need to update their knowledge and acquire skills to continually inform their practice based on scientific evidence. This study was designed to evaluate online self-learning modules on critical appraisal skills to promote the use of research in clinical practice among nurses from Quebec (Canada) and the Basque Country (Spain). The teaching material was developed in Quebec and adapted to the Basque Country as part of an international collaboration project. A prospective pre-post study was conducted with 36 nurses from Quebec and 47 from the Basque Country. Assessment comprised the administration of questionnaires before and after the course in order to explore the main intervention outcomes: knowledge acquisition and self-learning readiness. Satisfaction was also measured at the end of the course. Two of the three research hypotheses were confirmed: (1) participants significantly improved their overall knowledge score after the educational intervention; and (2) they were, in general, satisfied with the course, giving it a rating of seven out of 10. Participants also reported a greater readiness for self-directed learning after the course, but this result was not significant in Quebec. The study provides unique knowledge on the cultural adaptation of online self-learning modules for teaching nurses about critical appraisal skills and evidence-based practice.

  14. Contemporary approaches to neural circuit manipulation and mapping: focus on reward and addiction

    PubMed Central

    Saunders, Benjamin T.; Richard, Jocelyn M.; Janak, Patricia H.

    2015-01-01

    Tying complex psychological processes to precisely defined neural circuits is a major goal of systems and behavioural neuroscience. This is critical for understanding adaptive behaviour, and also how neural systems are altered in states of psychopathology, such as addiction. Efforts to relate psychological processes relevant to addiction to activity within defined neural circuits have been complicated by neural heterogeneity. Recent advances in technology allow for manipulation and mapping of genetically and anatomically defined neurons, which when used in concert with sophisticated behavioural models, have the potential to provide great insight into neural circuit bases of behaviour. Here we discuss contemporary approaches for understanding reward and addiction, with a focus on midbrain dopamine and cortico-striato-pallidal circuits. PMID:26240425

  15. Monitoring the Performance of a Neuro-Adaptive Controller

    NASA Technical Reports Server (NTRS)

    Schumann, Johann; Gupta, Pramod

    2004-01-01

    Traditional control has proven to be ineffective to deal with catastrophic changes or slow degradation of complex, highly nonlinear systems like aircraft or spacecraft, robotics, or flexible manufacturing systems. Control systems which can adapt toward changes in the plant have been proposed as they offer many advantages (e.g., better performance, controllability of aircraft despite of a damaged wing). In the last few years, use of neural networks in adaptive controllers (neuro-adaptive control) has been studied actively. Neural networks of various architectures have been used successfully for online learning adaptive controllers. In such a typical control architecture, the neural network receives as an input the current deviation between desired and actual plant behavior and, by on-line training, tries to minimize this discrepancy (e.g.; by producing a control augmentation signal). Even though neuro-adaptive controllers offer many advantages, they have not been used in mission- or safety-critical applications, because performance and safety guarantees cannot b e provided at development time-a major prerequisite for safety certification (e.g., by the FAA or NASA). Verification and Validation (V&V) of an adaptive controller requires the development of new analysis techniques which can demonstrate that the control system behaves safely under all operating conditions. Because of the requirement to adapt toward unforeseen changes during operation, i.e., in real time, design-time V&V is not sufficient.

  16. Playful and mindful interactions in the recursive adaptations of the zone of proximal development: a critical complexity science approach

    NASA Astrophysics Data System (ADS)

    Raia, Federica; Deng, Mario C.

    2011-12-01

    We discuss Konstantinos Alexakos, Jayson Jones and Victor Rodriguez's hermeneutic study of formation and function of kinship-like relationships among inner city male students of color in a college physics classroom. From our Critical Complexity Science framework we first discuss the reading erlebnisse of students laughing at and with each other as something that immediately captured our attention in being transformative of the classroom. We continue by exploring their classroom and research experience as an emergent structure modifying their collective as well as their individual experiences. As we analyze both the classroom and the research space as a complex system, we reflect on the instructor/students interactions characterized by an asymmetrical "power" relationship. From our analysis we propose to consider the zone of proximal development as the constantly emerging and transforming person experience ( erlebnisse and erfahrung).

  17. Adaptive shape coding for perceptual decisions in the human brain

    PubMed Central

    Kourtzi, Zoe; Welchman, Andrew E.

    2015-01-01

    In its search for neural codes, the field of visual neuroscience has uncovered neural representations that reflect the structure of stimuli of variable complexity from simple features to object categories. However, accumulating evidence suggests an adaptive neural code that is dynamically shaped by experience to support flexible and efficient perceptual decisions. Here, we review work showing that experience plays a critical role in molding midlevel visual representations for perceptual decisions. Combining behavioral and brain imaging measurements, we demonstrate that learning optimizes feature binding for object recognition in cluttered scenes, and tunes the neural representations of informative image parts to support efficient categorical judgements. Our findings indicate that similar learning mechanisms may mediate long-term optimization through development, tune the visual system to fundamental principles of feature binding, and optimize feature templates for perceptual decisions. PMID:26024511

  18. Developing an Adaptability Training Strategy and Policy for the DoD

    DTIC Science & Technology

    2008-10-01

    One reviewer observed: From a neuroscience perspective, adaptability is a core function of the brain and related neural systems. The nervous system...IDA model, but also of individual predispositions and organizational openness. Though neuroscience recognizes adaptability as a core function of the...Moore at the National Security Agency ( NSA ), who teaches critical thinking there and has published a lengthy paper on the subject.51 NSA offers a

  19. Optical Neural Network Classifier Architectures

    DTIC Science & Technology

    1998-04-01

    We present an adaptive opto-electronic neural network hardware architecture capable of exploiting parallel optics to realize real-time processing and...function neural network based on a previously demonstrated binary-input version. The greyscale-input capability broadens the range of applications for...a reduced feature set of multiwavelet images to improve training times and discrimination capability of the neural network . The design uses a joint

  20. Neural Networks For Robot Control

    DTIC Science & Technology

    2001-04-17

    following: (a) Application of artificial neural networks (multi-layer perceptrons, MLPs) for 2D planar robot arm by using the dynamic backpropagation...methods for the adjustment of parameters; and optimization of the architecture; (b) Application of artificial neural networks in controlling closed...studies in controlling dynamic robot arms by using neural networks in real-time process; (2) Research of optimal architectures used in closed-loop systems in order to compare with adaptive and robust control.

  1. Neural network applications in telecommunications

    NASA Technical Reports Server (NTRS)

    Alspector, Joshua

    1994-01-01

    Neural network capabilities include automatic and organized handling of complex information, quick adaptation to continuously changing environments, nonlinear modeling, and parallel implementation. This viewgraph presentation presents Bellcore work on applications, learning chip computational function, learning system block diagram, neural network equalization, broadband access control, calling-card fraud detection, software reliability prediction, and conclusions.

  2. Critical Climate Controls and Information Needs for the Glen Canyon Adaptive Management Program and Environmental Assessment in the Grand Canyon Region

    NASA Astrophysics Data System (ADS)

    Melis, T. S.; Jain, S.; Topping, D. J.; Pulwarty, R. S.; Eischeid, J. K.

    2005-12-01

    Climatic drivers of episodic to interdecadal variations to the observed changes in the flood magnitude, timing and spatial scales affect the sediment inputs to the Colorado River ecosystem. Since the 1963 closure of Glen Canyon Dam, the dominant sole major supplier of sand to the Colorado River in the upper portion of Grand Canyon is the Paria River, which supplies about 6% of the pre-dam supply of sand at the upstream boundary of Grand Canyon National Park. Sand is delivered by the Paria River during short-duration (< 24 hours), large magnitude (up to 300 cubic meters/second) floods that occur primarily during the warm season (July-October). The planning and decision processes in the Glen Canyon Dam Adaptive Management Program (GCD-AMP) strive to balance numerous, often competing, objectives, such as,water supply, hydropower generation, low flow maintenance, maximizing conservation of the tributary supplied sediment, endangered species recovery, and cultural resources. In this work, we focus on a key concern identified by the AMP, related to the timing and volume of sediment input into Grand Canyon. Adequate sediment inputs into the river ecosystem Canyon combined with active flow management, of the timed in the form of strategically timed bypass releases from Glen Canyon Dam, support the restoration and maintenance of sand bar habitats and instream ecology. Variability in regional precipitation distribution on multiple time scales is diagnosed with emphasis on understanding the relative role of East Pacific tropical storms, North Pacific sea surface temperatures, and subtropical moisture sources. On longer time scales, structured variations in the sediment supply imply a changing baseline for mean ecological and geomorphological conditions in the Canyon, counter to the static view taken in the current environmental impact assessments. Better understanding of the coupled climate-hydrologic variations on multiple time scales is increasingly recognized as critical

  3. Multipotent glia-like stem cells mediate stress adaptation.

    PubMed

    Rubin de Celis, Maria F; Garcia-Martin, Ruben; Wittig, Dierk; Valencia, Gabriela D; Enikolopov, Grigori; Funk, Richard H; Chavakis, Triantafyllos; Bornstein, Stefan R; Androutsellis-Theotokis, Andreas; Ehrhart-Bornstein, Monika

    2015-06-01

    The neural crest-derived adrenal medulla is closely related to the sympathetic nervous system; however, unlike neural tissue, it is characterized by high plasticity which suggests the involvement of stem cells. Here, we show that a defined pool of glia-like nestin-expressing progenitor cells in the adult adrenal medulla contributes to this plasticity. These glia-like cells have features of adrenomedullary sustentacular cells, are multipotent, and are able to differentiate into chromaffin cells and neurons. The adrenal is central to the body's response to stress making its proper adaptation critical to maintaining homeostasis. Our results from stress experiments in vivo show the activation and differentiation of these progenitors into new chromaffin cells. In summary, we demonstrate the involvement of a new glia-like multipotent stem cell population in adrenal tissue adaptation. Our data also suggest the contribution of stem and progenitor cells in the adaptation of neuroendocrine tissue function in general.

  4. From Metaphors to Models: Broadening the Lens of the Hunter Warrior Experiment with a Complex Adaptive System Tool

    DTIC Science & Technology

    1999-01-01

    complexity and complex adaptive systems, self-organizing criticality, cellular automata , and so on, because they all globally share this property...distribution unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT Same...four categories: Artificial Life, Evolution and Complexity, Classification , Heuristic Search and Computation, Neural Networks, Chaos and Fractals. 14

  5. Toll-like receptors are critical for clearance of Brucella and play different roles in development of adaptive immunity following aerosol challenge in mice.

    PubMed

    Pei, Jianwu; Ding, Xicheng; Fan, Yaping; Rice-Ficht, Allison; Ficht, Thomas A

    2012-01-01

    Brucella spp. cause undulant fever in humans and brucellosis in variety of other animals. Both innate and adaptive immunity have been shown to be important in controlling Brucella infection. Toll-like receptors (TLRs) represent a group of pattern recognition receptors (PRRs) that play critical roles in the host innate immune response, as well as development of adaptive immunity. In the current report, we investigated the role of TLR signaling in the clearance of Brucella and development of adaptive immunity in TLR2(-/-), TLR4(-/-), or MyD88(-/-) mice following aerosol exposure to B. melitensis 16 M. Consistent with previous reports, MyD88 is required for efficient clearance of Brucella from all three organs (lung, spleen, and liver). The results reveal Th2-skewed immune responses in TLR2(-/-) mice late in infection and support a TLR2 requirement for efficient clearance of Brucella from the lungs, but not from the spleen or liver. Similarly, TLR4 is required for efficient clearance of Brucella from the lung, but exhibits a minor contribution to clearance from the spleen and no demonstrable contribution to clearance from the liver. Lymphocyte proliferation assays suggest that the TLRs are not involved in the development of cell-mediated memory response to Brucella antigen. Antibody detection reveals that TLR2 and 4 are required to generate early antigen-specific IgG, but not during the late stages of infection. TLR2 and 4 are only transiently required for IgM production and not at all for IgA production. In contrast, MyD88 is essential for antigen specific IgG production late in infection, but is not required for IgM generation over the course of infection. Surprisingly, despite the prominent role for MyD88 in clearance from all tissues, MyD88-knockout mice express significantly higher levels of serum IgA. These results confirm the important role of MyD88 in controlling infection in the spleen while providing evidence of a prominent contribution to protection in

  6. Normalization is a general neural mechanism for context-dependent decision making.

    PubMed

    Louie, Kenway; Khaw, Mel W; Glimcher, Paul W

    2013-04-09

    Understanding the neural code is critical to linking brain and behavior. In sensory systems, divisive normalization seems to be a canonical neural computation, observed in areas ranging from retina to cortex and mediating processes including contrast adaptation, surround suppression, visual attention, and multisensory integration. Recent electrophysiological studies have extended these insights beyond the sensory domain, demonstrating an analogous algorithm for the value signals that guide decision making, but the effects of normalization on choice behavior are unknown. Here, we show that choice models using normalization generate significant (and classically irrational) choice phenomena driven by either the value or number of alternative options. In value-guided choice experiments, both monkey and human choosers show novel context-dependent behavior consistent with normalization. These findings suggest that the neural mechanism of value coding critically influences stochastic choice behavior and provide a generalizable quantitative framework for examining context effects in decision making.

  7. CD25+ natural regulatory T cells are critical in limiting innate and adaptive immunity and resolving disease following respiratory syncytial virus infection.

    PubMed

    Lee, Debbie C P; Harker, James A E; Tregoning, John S; Atabani, Sowsan F; Johansson, Cecilia; Schwarze, Jürgen; Openshaw, Peter J M

    2010-09-01

    Regulatory CD4(+) T cells have been shown to be important in limiting immune responses, but their role in respiratory viral infections has received little attention. Here we observed that following respiratory syncytial virus (RSV) infection, CD4(+) Foxp3(+) CD25(+) natural regulatory T-cell numbers increased in the bronchoalveolar lavage fluid, lung, mediastinal lymph nodes, and spleen. The depletion of CD25(+) natural regulatory T cells prior to RSV infection led to enhanced weight loss with delayed recovery that was surprisingly accompanied by increased numbers of activated natural killer cells in the lung and bronchoalveolar lavage fluid on day 8 postinfection. Increased numbers of neutrophils were also detected within the bronchoalveolar lavage fluid and correlated with elevated levels of myeloperoxidase as well as interleukin-6 (IL-6) and gamma interferon (IFN-gamma). CD25(+) natural regulatory T-cell depletion also led to enhanced numbers of proinflammatory T cells producing IFN-gamma and tumor necrosis factor alpha (TNF-alpha) in the lung. Despite these increases in inflammatory responses and disease severity, the viral load was unaltered. This work highlights a critical role for natural regulatory T cells in regulating the adaptive and innate immune responses during the later stages of lung viral infections.

  8. Adaptive fuzzy system for 3-D vision

    NASA Technical Reports Server (NTRS)

    Mitra, Sunanda

    1993-01-01

    An adaptive fuzzy system using the concept of the Adaptive Resonance Theory (ART) type neural network architecture and incorporating fuzzy c-means (FCM) system equations for reclassification of cluster centers was developed. The Adaptive Fuzzy Leader Clustering (AFLC) architecture is a hybrid neural-fuzzy system which learns on-line in a stable and efficient manner. The system uses a control structure similar to that found in the Adaptive Resonance Theory (ART-1) network to identify the cluster centers initially. The initial classification of an input takes place in a two stage process; a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from Fuzzy c-Means (FCM) system equations for the centroids and the membership values. The operational characteristics of AFLC and the critical parameters involved in its operation are discussed. The performance of the AFLC algorithm is presented through application of the algorithm to the Anderson Iris data, and laser-luminescent fingerprint image data. The AFLC algorithm successfully classifies features extracted from real data, discrete or continuous, indicating the potential strength of this new clustering algorithm in analyzing complex data sets. The hybrid neuro-fuzzy AFLC algorithm will enhance analysis of a number of difficult recognition and control problems involved with Tethered Satellite Systems and on-orbit space shuttle attitude controller.

  9. Gradient liquid chromatographic retention time prediction for suspect screening applications: A critical assessment of a generalised artificial neural network-based approach across 10 multi-residue reversed-phase analytical methods.

    PubMed

    Barron, Leon P; McEneff, Gillian L

    2016-01-15

    For the first time, the performance of a generalised artificial neural network (ANN) approach for the prediction of 2492 chromatographic retention times (tR) is presented for a total of 1117 chemically diverse compounds present in a range of complex matrices and across 10 gradient reversed-phase liquid chromatography-(high resolution) mass spectrometry methods. Probabilistic, generalised regression, radial basis function as well as 2- and 3-layer multilayer perceptron-type neural networks were investigated to determine the most robust and accurate model for this purpose. Multi-layer perceptrons most frequently yielded the best correlations in 8 out of 10 methods. Averaged correlations of predicted versus measured tR across all methods were R(2)=0.918, 0.924 and 0.898 for the training, verification and test sets respectively. Predictions of blind test compounds (n=8-84 cases) resulted in an average absolute accuracy of 1.02±0.54min for all methods. Within this variation, absolute accuracy was observed to marginally improve for shorter runtimes, but was found to be relatively consistent with respect to analyte retention ranges (~5%). Finally, optimised and replicated network dependency on molecular descriptor data is presented and critically discussed across all methods. Overall, ANNs were considered especially suitable for suspects screening applications and could potentially be utilised in bracketed-type analyses in combination with high resolution mass spectrometry.

  10. Neural dynamics based on the recognition of neural fingerprints

    PubMed Central

    Carrillo-Medina, José Luis; Latorre, Roberto

    2015-01-01

    Experimental evidence has revealed the existence of characteristic spiking features in different neural signals, e.g., individual neural signatures identifying the emitter or functional signatures characterizing specific tasks. These neural fingerprints may play a critical role in neural information processing, since they allow receptors to discriminate or contextualize incoming stimuli. This could be a powerful strategy for neural systems that greatly enhances the encoding and processing capacity of these networks. Nevertheless, the study of information processing based on the identification of specific neural fingerprints has attracted little attention. In this work, we study (i) the emerging collective dynamics of a network of neurons that communicate with each other by exchange of neural fingerprints and (ii) the influence of the network topology on the self-organizing properties within the network. Complex collective dynamics emerge in the network in the presence of stimuli. Predefined inputs, i.e., specific neural fingerprints, are detected and encoded into coexisting patterns of activity that propagate throughout the network with different spatial organization. The patterns evoked by a stimulus can survive after the stimulation is over, which provides memory mechanisms to the network. The results presented in this paper suggest that neural information processing based on neural fingerprints can be a plausible, flexible, and powerful strategy. PMID:25852531

  11. Homeostatic Scaling of Excitability in Recurrent Neural Networks

    PubMed Central

    Remme, Michiel W. H.; Wadman, Wytse J.

    2012-01-01

    Neurons adjust their intrinsic excitability when experiencing a persistent change in synaptic drive. This process can prevent neural activity from moving into either a quiescent state or a saturated state in the face of ongoing plasticity, and is thought to promote stability of the network in which neurons reside. However, most neurons are embedded in recurrent networks, which require a delicate balance between excitation and inhibition to maintain network stability. This balance could be disrupted when neurons independently adjust their intrinsic excitability. Here, we study the functioning of activity-dependent homeostatic scaling of intrinsic excitability (HSE) in a recurrent neural network. Using both simulations of a recurrent network consisting of excitatory and inhibitory neurons that implement HSE, and a mean-field description of adapting excitatory and inhibitory populations, we show that the stability of such adapting networks critically depends on the relationship between the adaptation time scales of both neuron populations. In a stable adapting network, HSE can keep all neurons functioning within their dynamic range, while the network is undergoing several (patho)physiologically relevant types of plasticity, such as persistent changes in external drive, changes in connection strengths, or the loss of inhibitory cells from the network. However, HSE cannot prevent the unstable network dynamics that result when, due to such plasticity, recurrent excitation in the network becomes too strong compared to feedback inhibition. This suggests that keeping a neural network in a stable and functional state requires the coordination of distinct homeostatic mechanisms that operate not only by adjusting neural excitability, but also by controlling network connectivity. PMID:22570604

  12. Toward energy efficient neural interfaces.

    PubMed

    Peng, Chung-Ching; Xiao, Zhiming; Bashirullah, Rizwan

    2009-11-01

    This letter presents progress toward an energy efficient neural data acquisition transponder for brain-computer interfaces. The transponder utilizes a four-channel time-multiplexed analog front-end and an energy efficient short-range backscattering RF link to transmit digitized wireless data. In addition, a low-complexity autonomous and adaptive digital neural signal processor is proposed to minimize wireless bandwidth and overall power dissipation.

  13. Engineered neural tissue with aligned, differentiated adipose-derived stem cells promotes peripheral nerve regeneration across a critical sized defect in rat sciatic nerve.

    PubMed

    Georgiou, Melanie; Golding, Jon P; Loughlin, Alison J; Kingham, Paul J; Phillips, James B

    2015-01-01

    Adipose-derived stem cells were isolated from rats and differentiated to a Schwann cell-like phenotype in vitro. The differentiated cells (dADSCs) underwent self-alignment in a tethered type-1 collagen gel, followed by stabilisation to generate engineered neural tissue (EngNT-dADSC). The pro-regenerative phenotype of dADSCs was enhanced by this process, and the columns of aligned dADSCs in the aligned collagen matrix supported and guided neurite extension in vitro. EngNT-dADSC sheets were rolled to form peripheral nerve repair constructs that were implanted within NeuraWrap conduits to bridge a 15 mm gap in rat sciatic nerve. After 8 weeks regeneration was assessed using immunofluorescence imaging and transmission electron microscopy and compared to empty conduit and nerve graft controls. The proportion of axons detected in the distal stump was 3.5 fold greater in constructs containing EngNT-dADSC than empty tube controls. Our novel combination of technologies that can organise autologous therapeutic cells within an artificial tissue construct provides a promising new cellular biomaterial for peripheral nerve repair.

  14. Regulated segregation of kinase Dyrk1A during asymmetric neural stem cell division is critical for EGFR-mediated biased signaling.

    PubMed

    Ferron, Sacri R; Pozo, Natividad; Laguna, Ariadna; Aranda, Sergi; Porlan, Eva; Moreno, Mireia; Fillat, Cristina; de la Luna, Susana; Sánchez, Pilar; Arbonés, María L; Fariñas, Isabel

    2010-09-03

    Stem cell division can result in two sibling cells exhibiting differential mitogenic and self-renewing potential. Here, we present evidence that the dual-specificity kinase Dyrk1A is part of a molecular pathway involved in the regulation of biased epidermal growth factor receptor (EGFR) signaling in the progeny of dividing neural stem cells (NSC) of the adult subependymal zone (SEZ). We show that EGFR asymmetry requires regulated sorting and that a normal Dyrk1a dosage is required to sustain EGFR in the two daughters of a symmetrically dividing progenitor. Dyrk1A is symmetrically or asymmetrically distributed during mitosis, and biochemical analyses indicate that it prevents endocytosis-mediated degradation of EGFR by a mechanism that requires phosphorylation of the EGFR signaling modulator Sprouty2. Finally, Dyrk1a heterozygous NSCs exhibit defects in self-renewal, EGF-dependent cell-fate decisions, and long-term persistence in vivo, suggesting that symmetrical divisions play a role in the maintenance of the SEZ reservoir.

  15. Spike history neural response model.

    PubMed

    Kameneva, Tatiana; Abramian, Miganoosh; Zarelli, Daniele; Nĕsić, Dragan; Burkitt, Anthony N; Meffin, Hamish; Grayden, David B

    2015-06-01

    There is a potential for improved efficacy of neural stimulation if stimulation levels can be modified dynamically based on the responses of neural tissue in real time. A neural model is developed that describes the response of neurons to electrical stimulation and that is suitable for feedback control neuroprosthetic stimulation. Experimental data from NZ white rabbit retinae is used with a data-driven technique to model neural dynamics. The linear-nonlinear approach is adapted to incorporate spike history and to predict the neural response of ganglion cells to electrical stimulation. To validate the fitness of the model, the penalty term is calculated based on the time difference between each simulated spike and the closest spike in time in the experimentally recorded train. The proposed model is able to robustly predict experimentally observed spike trains.

  16. The brain adapts to dishonesty.

    PubMed

    Garrett, Neil; Lazzaro, Stephanie C; Ariely, Dan; Sharot, Tali

    2016-12-01

    Dishonesty is an integral part of our social world, influencing domains ranging from finance and politics to personal relationships. Anecdotally, digressions from a moral code are often described as a series of small breaches that grow over time. Here we provide empirical evidence for a gradual escalation of self-serving dishonesty and reveal a neural mechanism supporting it. Behaviorally, we show that the extent to which participants engage in self-serving dishonesty increases with repetition. Using functional MRI, we show that signal reduction in the amygdala is sensitive to the history of dishonest behavior, consistent with adaptation. Critically, the extent of reduced amygdala sensitivity to dishonesty on a present decision relative to the previous one predicts the magnitude of escalation of self-serving dishonesty on the next decision. The findings uncover a biological mechanism that supports a 'slippery slope': what begins as small acts of dishonesty can escalate into larger transgressions.

  17. Neural tube defects.

    PubMed

    Greene, Nicholas D E; Copp, Andrew J

    2014-01-01

    Neural tube defects (NTDs), including spina bifida and anencephaly, are severe birth defects of the central nervous system that originate during embryonic development when the neural tube fails to close completely. Human NTDs are multifactorial, with contributions from both genetic and environmental factors. The genetic basis is not yet well understood, but several nongenetic risk factors have been identified as have possibilities for prevention by maternal folic acid supplementation. Mechanisms underlying neural tube closure and NTDs may be informed by experimental models, which have revealed numerous genes whose abnormal function causes NTDs and have provided details of critical cellular and morphological events whose regulation is essential for closure. Such models also provide an opportunity to investigate potential risk factors and to develop novel preventive therapies.

  18. Visual adaptation of the perception of “life”: animacy is a basic perceptual dimension of faces

    PubMed Central

    Koldewyn, Kami; Hanus, Patricia; Balas, Benjamin

    2014-01-01

    One critical component of understanding another’s mind is the perception of “life” in a face. However, little is known about the cognitive and neural mechanisms underlying this perception of animacy. Here, using a visual adaptation paradigm, we ask whether face animacy is (1) a basic dimension of face perception and (2) supported by a common neural mechanism across distinct face categories defined by age and species. Observers rated the perceived animacy of adult human faces before and after adaptation to (1) adult faces, (2) child faces, and (3) dog faces. When testing the perception of animacy in human faces, we found significant adaptation to both adult and child faces, but not dog faces. We did, however, find significant adaptation when morphed dog images and dog adaptors were used. Thus, animacy perception in faces appears to be a basic dimension of face perception that is species-specific, but not constrained by age categories. PMID:24323739

  19. A novel process monitoring and control system based on a neural manufacturing concept

    SciTech Connect

    Fu, Chi Yung; Petrich, L.; Law, B.

    1993-09-14

    This paper describes our work to produce ``smart`` equipment using a neural network to provide intelligence for process monitoring, adaptive control, metrology, and equipment diagnostics. This novel system will improve both quality and yield for critical thin films used in semiconductors, superconductors, high-density magnetic and optical storage, and advanced displays, all of which are critical to maintaining our leadership in the multibillion-dollar electronic and computer industries. The equipment will have on-line, real-time diagnostics capabilities to detect component problems early in order to minimize maintenance and downtime.

  20. Contextual behavior and neural circuits

    PubMed Central

    Lee, Inah; Lee, Choong-Hee

    2013-01-01

    Animals including humans engage in goal-directed behavior flexibly in response to items and their background, which is called contextual behavior in this review. Although the concept of context has long been studied, there are differences among researchers in defining and experimenting with the concept. The current review aims to provide a categorical framework within which not only the neural mechanisms of contextual information processing but also the contextual behavior can be studied in more concrete ways. For this purpose, we categorize contextual behavior into three subcategories as follows by considering the types of interactions among context, item, and response: contextual response selection, contextual item selection, and contextual item–response selection. Contextual response selection refers to the animal emitting different types of responses to the same item depending on the context in the background. Contextual item selection occurs when there are multiple items that need to be chosen in a contextual manner. Finally, when multiple items and multiple contexts are involved, contextual item–response selection takes place whereby the animal either chooses an item or inhibits such a response depending on item–context paired association. The literature suggests that the rhinal cortical regions and the hippocampal formation play key roles in mnemonically categorizing and recognizing contextual representations and the associated items. In addition, it appears that the fronto-striatal cortical loops in connection with the contextual information-processing areas critically control the flexible deployment of adaptive action sets and motor responses for maximizing goals. We suggest that contextual information processing should be investigated in experimental settings where contextual stimuli and resulting behaviors are clearly defined and measurable, considering the dynamic top-down and bottom-up interactions among the neural systems for contextual behavior

  1. Towards an Irritable Bowel Syndrome Control System Based on Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Podolski, Ina; Rettberg, Achim

    To solve health problems with medical applications that use complex algorithms is a trend nowadays. It could also be a chance to help patients with critical problems caused from nerve irritations to overcome them and provide a better living situation. In this paper a system for monitoring and controlling the nerves from the intestine is described on a theoretical basis. The presented system could be applied to the irritable bowel syndrome. For control a neural network is used. The advantages for using a neural network for the control of irritable bowel syndrome are the adaptation and learning. These two aspects are important because the syndrome behavior varies from patient to patient and have also concerning the time a lot of variations with respect to each patient. The developed neural network is implemented and can be simulated. Therefore, it can be shown how the network monitor and control the nerves for individual input parameters.

  2. Neural Network Research: A Personal Perspective,

    DTIC Science & Technology

    1988-03-01

    These vision preprocessor and ART autonomous classifier examples are just two of the many neural network architectures now being developed by...computational theories with natural realizations as real-time adaptive neural network architectures with promising properties for tackling some of the

  3. Genotypic Variation in Growth and Physiological Response to Drought Stress and Re-Watering Reveals the Critical Role of Recovery in Drought Adaptation in Maize Seedlings.

    PubMed

    Chen, Daoqian; Wang, Shiwen; Cao, Beibei; Cao, Dan; Leng, Guohui; Li, Hongbing; Yin, Lina; Shan, Lun; Deng, Xiping

    2015-01-01

    Non-irrigated crops in temperate climates and irrigated crops in arid climates are subjected to continuous cycles of water stress and re-watering. Thus, fast and efficient recovery from water stress may be among the key determinants of plant drought adaptation. The present study was designed to comparatively analyze the roles of drought resistance and drought recovery in drought adaptation and to investigate the physiological basis of genotypic variation in drought adaptation in maize (Zea mays) seedlings. As the seedlings behavior in growth associate with yield under drought, it could partly reflect the potential of drought adaptability. Growth and physiological responses to progressive drought stress and recovery were observed in seedlings of 10 maize lines. The results showed that drought adaptability is closely related to drought recovery (r = 0.714(**)), but not to drought resistance (r = 0.332). Drought induced decreases in leaf water content, water potential, osmotic potential, gas exchange parameters, chlorophyll content, Fv/Fm and nitrogen content, and increased H2O2 accumulation and lipid peroxidation. After recovery, most of these physiological parameters rapidly returned to normal levels. The physiological responses varied between lines. Further correlation analysis indicated that the physiological bases of drought resistance and drought recovery are definitely different, and that maintaining higher chlorophyll content (r = 0.874(***)) and Fv/Fm (r = 0.626(*)) under drought stress contributes to drought recovery. Our results suggest that both drought resistance and recovery are key determinants of plant drought adaptation, and that drought recovery may play a more important role than previously thought. In addition, leaf water potential, chlorophyll content and Fv/Fm could be used as efficient reference indicators in the selection of drought-adaptive genotypes.

  4. Neural Networks and other Techniques for Fault Identification and Isolation of Aircraft Systems

    NASA Technical Reports Server (NTRS)

    Innocenti, M.; Napolitano, M.

    2003-01-01

    Fault identification, isolation, and accomodation have become critical issues in the overall performance of advanced aircraft systems. Neural Networks have shown to be a very attractive alternative to classic adaptation methods for identification and control of non-linear dynamic systems. The purpose of this paper is to show the improvements in neural network applications achievable through the use of learning algorithms more efficient than the classic Back-Propagation, and through the implementation of the neural schemes in parallel hardware. The results of the analysis of a scheme for Sensor Failure, Detection, Identification and Accommodation (SFDIA) using experimental flight data of a research aircraft model are presented. Conventional approaches to the problem are based on observers and Kalman Filters while more recent methods are based on neural approximators. The work described in this paper is based on the use of neural networks (NNs) as on-line learning non-linear approximators. The performances of two different neural architectures were compared. The first architecture is based on a Multi Layer Perceptron (MLP) NN trained with the Extended Back Propagation algorithm (EBPA). The second architecture is based on a Radial Basis Function (RBF) NN trained with the Extended-MRAN (EMRAN) algorithms. In addition, alternative methods for communications links fault detection and accomodation are presented, relative to multiple unmanned aircraft applications.

  5. Statistical context shapes stimulus-specific adaptation in human auditory cortex

    PubMed Central

    Henry, Molly J.; Fromboluti, Elisa Kim; McAuley, J. Devin

    2015-01-01

    Stimulus-specific adaptation is the phenomenon whereby neural response magnitude decreases with repeated stimulation. Inconsistencies between recent nonhuman animal recordings and computational modeling suggest dynamic influences on stimulus-specific adaptation. The present human electroencephalography (EEG) study investigates the potential role of statistical context in dynamically modulating stimulus-specific adaptation by examining the auditory cortex-generated N1 and P2 components. As in previous studies of stimulus-specific adaptation, listeners were presented with oddball sequences in which the presentation of a repeated tone was infrequently interrupted by rare spectral changes taking on three different magnitudes. Critically, the statistical context varied with respect to the probability of small versus large spectral changes within oddball sequences (half of the time a small change was most probable; in the other half a large change was most probable). We observed larger N1 and P2 amplitudes (i.e., release from adaptation) for all spectral changes in the small-change compared with the large-change statistical context. The increase in response magnitude also held for responses to tones presented with high probability, indicating that statistical adaptation can overrule stimulus probability per se in its influence on neural responses. Computational modeling showed that the degree of coadaptation in auditory cortex changed depending on the statistical context, which in turn affected stimulus-specific adaptation. Thus the present data demonstrate that stimulus-specific adaptation in human auditory cortex critically depends on statistical context. Finally, the present results challenge the implicit assumption of stationarity of neural response magnitudes that governs the practice of isolating established deviant-detection responses such as the mismatch negativity. PMID:25652920

  6. [Application of artificial neural networks in infectious diseases].

    PubMed

    Xu, Jun-fang; Zhou, Xiao-nong

    2011-02-28

    With the development of information technology, artificial neural networks has been applied to many research fields. Due to the special features such as nonlinearity, self-adaptation, and parallel processing, artificial neural networks are applied in medicine and biology. This review summarizes the application of artificial neural networks in the relative factors, prediction and diagnosis of infectious diseases in recent years.

  7. Using Reinforcement Learning to Provide Stable Brain-Machine Interface Control Despite Neural Input Reorganization

    PubMed Central

    Pohlmeyer, Eric A.; Mahmoudi, Babak; Geng, Shijia; Prins, Noeline W.; Sanchez, Justin C.

    2014-01-01

    Brain-machine interface (BMI) systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder’s neural input space (e.g. neurons appearing or being lost amongst electrode recordings). These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI) to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled. PMID:24498055

  8. Being Critical of Criticality in the Brain

    PubMed Central

    Beggs, John M.; Timme, Nicholas

    2012-01-01

    Relatively recent work has reported that networks of neurons can produce avalanches of activity whose sizes follow a power law distribution. This suggests that these networks may be operating near a critical point, poised between a phase where activity rapidly dies out and a phase where activity is amplified over time. The hypothesis that the electrical activity of neural networks in the brain is critical is potentially important, as many simulations suggest that information processing functions would be optimized at the critical point. This hypothesis, however, is still controversial. Here we will explain the concept of criticality and review the substantial objections to the criticality hypothesis raised by skeptics. Points and counter points are presented in dialog form. PMID:22701101

  9. Another look at neural multigrid

    SciTech Connect

    Baeker, M.

    1997-04-01

    We present a new multigrid method called neural multigrid which is based on joining multigrid ideas with concepts from neural nets. The main idea is to use the Greenbaum criterion as a cost functional for the neural net. The algorithm is able to learn efficient interpolation operators in the case of the ordered Laplace equation with only a very small critical slowing down and with a surprisingly small amount of work comparable to that of a Conjugate Gradient solver. In the case of the two-dimensional Laplace equation with SU(2) gauge fields at {beta}=0 the learning exhibits critical slowing down with an exponent of about z {approx} 0.4. The algorithm is able to find quite good interpolation operators in this case as well. Thereby it is proven that a practical true multigrid algorithm exists even for a gauge theory. An improved algorithm using dynamical blocks that will hopefully overcome the critical slowing down completely is sketched.

  10. Prism Adaptation in Schizophrenia

    ERIC Educational Resources Information Center

    Bigelow, Nirav O.; Turner, Beth M.; Andreasen, Nancy C.; Paulsen, Jane S.; O'Leary, Daniel S.; Ho, Beng-Choon

    2006-01-01

    The prism adaptation test examines procedural learning (PL) in which performance facilitation occurs with practice on tasks without the need for conscious awareness. Dynamic interactions between frontostriatal cortices, basal ganglia, and the cerebellum have been shown to play key roles in PL. Disruptions within these neural networks have also…

  11. Determining critical points in organizational learning modes

    NASA Astrophysics Data System (ADS)

    Hamner, Marvine P.

    2002-07-01

    It has been postulated that organizations can be categorized into one of three perspectives that represent the mind-set of managers within organizations with respect to their organization and organizational learning. These are the normative, the developmental and the capability perspectives. Each of these reflects variations among organizational features such as the source of organizational learning, the timeframe for organizational learning and the relationship between organizational learning and organizational culture. However, much like the dynamics experienced by teams, i.e. various stages such as forming, norming, storming and performing, organizations can move through various learning stages, i.e. the three 'perspectives,' often stopping and restarting at different points in their cycles. This means that the three perspectives can be simply viewed as different modes of organizational learning. All organizations operate within one of the three perspectives all the time. And, the perspective through which the organization is best viewed at any point in time changes over time. Because organizations are complex, adaptive systems these modes can be mathematically represented using the output from a neural network model of complex, adaptive systems. This paper briefly describes the organizational science, the neural network model, and the mathematics required to determine critical points in these modes.

  12. Neural Components Underlying Behavioral Flexibility in Human Reversal Learning

    PubMed Central

    Monterosso, John; Jentsch, J. David; Bilder, Robert M.; Poldrack, Russell A.

    2010-01-01

    The ability to flexibly respond to changes in the environment is critical for adaptive behavior. Reversal learning (RL) procedures test adaptive response updating when contingencies are altered. We used functional magnetic resonance imaging to examine brain areas that support specific RL components. We compared neural responses to RL and initial learning (acquisition) to isolate reversal-related brain activation independent of cognitive control processes invoked during initial feedback-based learning. Lateral orbitofrontal cortex (OFC) was more activated during reversal than acquisition, suggesting its relevance for reformation of established stimulus–response associations. In addition, the dorsal anterior cingulate (dACC) and right inferior frontal gyrus (rIFG) correlated with change in postreversal accuracy. Because optimal RL likely requires suppression of a prior learned response, we hypothesized that similar regions serve both response inhibition (RI) and inhibition of learned associations during reversal. However, reversal-specific responding and stopping (requiring RI and assessed via the stop-signal task) revealed distinct frontal regions. Although RI-related regions do not appear to support inhibition of prepotent learned associations, a subset of these regions, dACC and rIFG, guide actions consistent with current reward contingencies. These regions and lateral OFC represent distinct neural components that support behavioral flexibility important for adaptive learning. PMID:19915091

  13. A density driven mesh generator guided by a neural network

    SciTech Connect

    Lowther, D.A.; Dyck, D.N. )

    1993-03-01

    A neural network guided mesh generator is described. The mesh generator used density information provided by the neural network to determine the size and placement of elements. This system is coupled with an adaptive meshing and solving process and is shown to have major computational benefits compared with adaptation alone.

  14. Creative critical-thinking strategies.

    PubMed

    Chubinski, S

    1996-01-01

    Are you looking for strategies to teach critical thinking? The author presents a variety of quick, creative strategies to facilitate teaching critical-thinking skills. These strategies engage students in their learning and are adaptable to any nursing course.

  15. Evolvable synthetic neural system

    NASA Technical Reports Server (NTRS)

    Curtis, Steven A. (Inventor)

    2009-01-01

    An evolvable synthetic neural system includes an evolvable neural interface operably coupled to at least one neural basis function. Each neural basis function includes an evolvable neural interface operably coupled to a heuristic neural system to perform high-level functions and an autonomic neural system to perform low-level functions. In some embodiments, the evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy.

  16. Neural learning of constrained nonlinear transformations

    NASA Technical Reports Server (NTRS)

    Barhen, Jacob; Gulati, Sandeep; Zak, Michail

    1989-01-01

    Two issues that are fundamental to developing autonomous intelligent robots, namely, rudimentary learning capability and dexterous manipulation, are examined. A powerful neural learning formalism is introduced for addressing a large class of nonlinear mapping problems, including redundant manipulator inverse kinematics, commonly encountered during the design of real-time adaptive control mechanisms. Artificial neural networks with terminal attractor dynamics are used. The rapid network convergence resulting from the infinite local stability of these attractors allows the development of fast neural learning algorithms. Approaches to manipulator inverse kinematics are reviewed, the neurodynamics model is discussed, and the neural learning algorithm is presented.

  17. Neural Networks

    SciTech Connect

    Smith, Patrick I.

    2003-09-23

    Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neural networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing

  18. Improving the Resilience of Major Ports and Critical Supply Chains to Extreme Coastal Flooding: a Combined Artificial Neural Network and Hydrodynamic Simulation Approach to Predicting Tidal Surge Inundation of Port Infrastructure and Impact on Operations.

    NASA Astrophysics Data System (ADS)

    French, J.

    2015-12-01

    Ports are vital to the global economy, but assessments of global exposure to flood risk have generally focused on major concentrations of population or asset values. Few studies have examined the impact of extreme inundation events on port operation and critical supply chains. Extreme water levels and recurrence intervals have conventionally been estimated via analysis of historic water level maxima, and these vary widely depending on the statistical assumptions made. This information is supplemented by near-term forecasts from operational surge-tide models, which give continuous water levels but at considerable computational cost. As part of a NERC Infrastructure and Risk project, we have investigated the impact of North Sea tidal surges on the Port of Immingham, eastern, UK. This handles the largest volume of bulk cargo in the UK and flows of coal and biomass that are critically important for national energy security. The port was partly flooded during a major tidal surge in 2013. This event highlighted the need for improved local forecasts of surge timing in relation to high water, with a better indication of flood depth and duration. We address this problem using a combination of data-driven and numerical hydrodynamic models. An Artificial Neural Network (ANN) is first used to predict the surge component of water level from meteorological data. The input vector comprises time-series of local wind (easterly and northerly wind stress) and pressure, as well as regional pressure and pressure gradients from stations between the Shetland Islands and the Humber estuary. The ANN achieves rms errors of around 0.1 m and can generate short-range (~ 3 to 12 hour) forecasts given real-time input data feeds. It can also synthesize water level events for a wider range of tidal and meteorological forcing combinations than contained in the observational records. These are used to force Telemac2D numerical floodplain simulations using a LiDAR digital elevation model of the port

  19. Genetic Dissection of Neural Circuits

    PubMed Central

    Luo, Liqun; Callaway, Edward M.; Svoboda, Karel

    2009-01-01

    Understanding the principles of information processing in neural circuits requires systematic characterization of the participating cell types and their connections, and the ability to measure and perturb their activity. Genetic approaches promise to bring experimental access to complex neural systems, including genetic stalwarts such as the fly and mouse, but also to nongenetic systems such as primates. Together with anatomical and physiological methods, cell-type-specific expression of protein markers and sensors and transducers will be critical to construct circuit diagrams and to measure the activity of genetically defined neurons. Inactivation and activation of genetically defined cell types will establish causal relationships between activity in specific groups of neurons, circuit function, and animal behavior. Genetic analysis thus promises to reveal the logic of the neural circuits in complex brains that guide behaviors. Here we review progress in the genetic analysis of neural circuits and discuss directions for future research and development. PMID:18341986

  20. An Approach to V&V of Embedded Adaptive Systems

    NASA Technical Reports Server (NTRS)

    Liu, Yan; Yerramalla, Sampath; Fuller, Edgar; Cukic, Bojan; Gururajan, Srikaruth

    2004-01-01

    Rigorous Verification and Validation (V&V) techniques are essential