The NASA F-15 Intelligent Flight Control Systems: Generation II

NASA Technical Reports Server (NTRS)

Buschbacher, Mark; Bosworth, John

2006-01-01

The Second Generation (Gen II) control system for the F-15 Intelligent Flight Control System (IFCS) program implements direct adaptive neural networks to demonstrate robust tolerance to faults and failures. The direct adaptive tracking controller integrates learning neural networks (NNs) with a dynamic inversion control law. The term direct adaptive is used because the error between the reference model and the aircraft response is being compensated or directly adapted to minimize error without regard to knowing the cause of the error. No parameter estimation is needed for this direct adaptive control system. In the Gen II design, the feedback errors are regulated with a proportional-plus-integral (PI) compensator. This basic compensator is augmented with an online NN that changes the system gains via an error-based adaptation law to improve aircraft performance at all times, including normal flight, system failures, mispredicted behavior, or changes in behavior resulting from damage.

Spatially Compact Neural Clusters in the Dorsal Striatum Encode Locomotion Relevant Information.

PubMed

Barbera, Giovanni; Liang, Bo; Zhang, Lifeng; Gerfen, Charles R; Culurciello, Eugenio; Chen, Rong; Li, Yun; Lin, Da-Ting

2016-10-05

An influential striatal model postulates that neural activities in the striatal direct and indirect pathways promote and inhibit movement, respectively. Normal behavior requires coordinated activity in the direct pathway to facilitate intended locomotion and indirect pathway to inhibit unwanted locomotion. In this striatal model, neuronal population activity is assumed to encode locomotion relevant information. Here, we propose a novel encoding mechanism for the dorsal striatum. We identified spatially compact neural clusters in both the direct and indirect pathways. Detailed characterization revealed similar cluster organization between the direct and indirect pathways, and cluster activities from both pathways were correlated with mouse locomotion velocities. Using machine-learning algorithms, cluster activities could be used to decode locomotion relevant behavioral states and locomotion velocity. We propose that neural clusters in the dorsal striatum encode locomotion relevant information and that coordinated activities of direct and indirect pathway neural clusters are required for normal striatal controlled behavior. VIDEO ABSTRACT. Published by Elsevier Inc.

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.

Functional recordings from awake, behaving rodents through a microchannel based regenerative neural interface

NASA Astrophysics Data System (ADS)

Gore, Russell K.; Choi, Yoonsu; Bellamkonda, Ravi; English, Arthur

2015-02-01

Objective. Neural interface technologies could provide controlling connections between the nervous system and external technologies, such as limb prosthetics. The recording of efferent, motor potentials is a critical requirement for a peripheral neural interface, as these signals represent the user-generated neural output intended to drive external devices. Our objective was to evaluate structural and functional neural regeneration through a microchannel neural interface and to characterize potentials recorded from electrodes placed within the microchannels in awake and behaving animals. Approach. Female rats were implanted with muscle EMG electrodes and, following unilateral sciatic nerve transection, the cut nerve was repaired either across a microchannel neural interface or with end-to-end surgical repair. During a 13 week recovery period, direct muscle responses to nerve stimulation proximal to the transection were monitored weekly. In two rats repaired with the neural interface, four wire electrodes were embedded in the microchannels and recordings were obtained within microchannels during proximal stimulation experiments and treadmill locomotion. Main results. In these proof-of-principle experiments, we found that axons from cut nerves were capable of functional reinnervation of distal muscle targets, whether regenerating through a microchannel device or after direct end-to-end repair. Discrete stimulation-evoked and volitional potentials were recorded within interface microchannels in a small group of awake and behaving animals and their firing patterns correlated directly with intramuscular recordings during locomotion. Of 38 potentials extracted, 19 were identified as motor axons reinnervating tibialis anterior or soleus muscles using spike triggered averaging. Significance. These results are evidence for motor axon regeneration through microchannels and are the first report of in vivo recordings from regenerated motor axons within microchannels in a small group of awake and behaving animals. These unique findings provide preliminary evidence that efferent, volitional motor potentials can be recorded from the microchannel-based peripheral neural interface; a critical requirement for any neural interface intended to facilitate direct neural control of external technologies.

A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment.

PubMed

Li, Yongcheng; Sun, Rong; Wang, Yuechao; Li, Hongyi; Zheng, Xiongfei

2016-01-01

We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning). Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle) to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot's performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks.

A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment

PubMed Central

Wang, Yuechao; Li, Hongyi; Zheng, Xiongfei

2016-01-01

We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning). Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle) to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot’s performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks. PMID:27806074

Robust Neural Sliding Mode Control of Robot Manipulators

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nguyen Tran Hiep; Pham Thuong Cat

2009-03-05

This paper proposes a robust neural sliding mode control method for robot tracking problem to overcome the noises and large uncertainties in robot dynamics. The Lyapunov direct method has been used to prove the stability of the overall system. Simulation results are given to illustrate the applicability of the proposed method.

Direct adaptive control of wind energy conversion systems using Gaussian networks.

PubMed

Mayosky, M A; Cancelo, I E

1999-01-01

Grid connected wind energy conversion systems (WECS) present interesting control demands, due to the intrinsic nonlinear characteristics of windmills and electric generators. In this paper a direct adaptive control strategy for WECS control is proposed. It is based on the combination of two control actions: a radial basis zfunction network-based adaptive controller, which drives the tracking error to zero with user specified dynamics, and a supervisory controller, based on crude bounds of the system's nonlinearities. The supervisory controller fires when the finite neural-network approximation properties cannot be guaranteed. The form of the supervisor control and the adaptation law for the neural controller are derived from a Lyapunov analysis of stability. The results are applied to a typical turbine/generator pair, showing the feasibility of the proposed solution.

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.

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.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Luo, Shaohua; Department of Mechanical Engineering, Chongqing Aerospace Polytechnic, Chongqing, 400021; Wu, Songli

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 themore » 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.« less

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.

Advances in neuroprosthetic learning and control.

PubMed

Carmena, Jose M

2013-01-01

Significant progress has occurred in the field of brain-machine interfaces (BMI) since the first demonstrations with rodents, monkeys, and humans controlling different prosthetic devices directly with neural activity. This technology holds great potential to aid large numbers of people with neurological disorders. However, despite this initial enthusiasm and the plethora of available robotic technologies, existing neural interfaces cannot as yet master the control of prosthetic, paralyzed, or otherwise disabled limbs. Here I briefly discuss recent advances from our laboratory into the neural basis of BMIs that should lead to better prosthetic control and clinically viable solutions, as well as new insights into the neurobiology of action.

Advances in Neuroprosthetic Learning and Control

PubMed Central

Carmena, Jose M.

2013-01-01

Significant progress has occurred in the field of brain–machine interfaces (BMI) since the first demonstrations with rodents, monkeys, and humans controlling different prosthetic devices directly with neural activity. This technology holds great potential to aid large numbers of people with neurological disorders. However, despite this initial enthusiasm and the plethora of available robotic technologies, existing neural interfaces cannot as yet master the control of prosthetic, paralyzed, or otherwise disabled limbs. Here I briefly discuss recent advances from our laboratory into the neural basis of BMIs that should lead to better prosthetic control and clinically viable solutions, as well as new insights into the neurobiology of action. PMID:23700383

Microneurography as a tool in clinical neurophysiology to investigate peripheral neural traffic in humans.

PubMed

Mano, Tadaaki; Iwase, Satoshi; Toma, Shinobu

2006-11-01

Microneurography is a method using metal microelectrodes to investigate directly identified neural traffic in myelinated as well as unmyelinated efferent and afferent nerves leading to and coming from muscle and skin in human peripheral nerves in situ. The present paper reviews how this technique has been used in clinical neurophysiology to elucidate the neural mechanisms of autonomic regulation, motor control and sensory functions in humans under physiological and pathological conditions. Microneurography is particularly important to investigate efferent and afferent neural traffic in unmyelinated C fibers. The recording of efferent discharges in postganglionic sympathetic C efferent fibers innervating muscle and skin (muscle sympathetic nerve activity; MSNA and skin sympathetic nerve activity; SSNA) provides direct information about neural control of autonomic effector organs including blood vessels and sweat glands. Sympathetic microneurography has become a potent tool to reveal neural functions and dysfunctions concerning blood pressure control and thermoregulation. This recording has been used not only in wake conditions but also in sleep to investigate changes in sympathetic neural traffic during sleep and sleep-related events such as sleep apnea. The same recording was also successfully carried out by astronauts during spaceflight. Recordings of afferent discharges from muscle mechanoreceptors have been used to understand the mechanisms of motor control. Muscle spindle afferent information is particularly important for the control of fine precise movements. It may also play important roles to predict behavior outcomes during learning of a motor task. Recordings of discharges in myelinated afferent fibers from skin mechanoreceptors have provided not only objective information about mechanoreceptive cutaneous sensation but also the roles of these signals in fine motor control. Unmyelinated mechanoreceptive afferent discharges from hairy skin seem to be important to convey cutaneous sensation to the central structures related to emotion. Recordings of afferent discharges in thin myelinated and unmyelinated fibers from nociceptors in muscle and skin have been used to provide information concerning pain. Recordings of afferent discharges of different types of cutaneous C-nociceptors identified by marking method have become an important tool to reveal the neural mechanisms of cutaneous sensations such as an itch. No direct microneurographic evidence has been so far proved regarding the effects of sympathoexcitation on sensitization of muscle and skin sensory receptors at least in healthy humans.

Containment control of networked autonomous underwater vehicles: A predictor-based neural DSC design.

PubMed

Peng, Zhouhua; Wang, Dan; Wang, Wei; Liu, Lu

2015-11-01

This paper investigates the containment control problem of networked autonomous underwater vehicles in the presence of model uncertainty and unknown ocean disturbances. A predictor-based neural dynamic surface control design method is presented to develop the distributed adaptive containment controllers, under which the trajectories of follower vehicles nearly converge to the dynamic convex hull spanned by multiple reference trajectories over a directed network. Prediction errors, rather than tracking errors, are used to update the neural adaptation laws, which are independent of the tracking error dynamics, resulting in two time-scales to govern the entire system. The stability property of the closed-loop network is established via Lyapunov analysis, and transient property is quantified in terms of L2 norms of the derivatives of neural weights, which are shown to be smaller than the classical neural dynamic surface control approach. Comparative studies are given to show the substantial improvements of the proposed new method. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Estimation of Locomotion States of a Rat by Neural Signals from the Motor Cortices Based on a Linear Correlation Model

NASA Astrophysics Data System (ADS)

Fukayama, Osamu; Taniguchi, Noriyuki; Suzuki, Takafumi; Mabuchi, Kunihiko

We are developing a brain-machine interface (BMI) called “RatCar," a small vehicle controlled by the neural signals of a rat's brain. An unconfined adult rat with a set of bundled neural electrodes in the brain rides on the vehicle. Each bundle consists of four tungsten wires isolated with parylene polymer. These bundles were implanted in the primary motor and premotor cortices in both hemispheres of the brain. In this paper, methods and results for estimating locomotion speed and directional changes are described. Neural signals were recorded as the rat moved in a straight line and as it changed direction in a curve. Spike-like waveforms were then detected and classified into several clusters to calculate a firing rate for each neuron. The actual locomotion velocity and directional changes of the rat were recorded concurrently. Finally, the locomotion states were correlated with the neural firing rates using a simple linear model. As a result, the abstract estimation of the locomotion velocity and directional changes were achieved.

The role of vision, speed, and attention in overcoming directional biases during arm movements.

PubMed

Dounskaia, Natalia; Goble, Jacob A

2011-03-01

Previous research has revealed directional biases (preferences to select movements in specific directions) during horizontal arm movements with the use of a free-stroke drawing task. The biases were interpreted as a result of a tendency to generate motion at either the shoulder or elbow (leading joint) and move the other (subordinate) joint predominantly passively to avoid neural effort for control of interaction torque. Here, we examined influence of vision, movement speed, and attention on the directional biases. Participants performed the free-stroke drawing task, producing center-out strokes in randomly selected directions. Movements were performed with and without vision and at comfortable and fast pace. A secondary, cognitive task was used to distract attention. Preferred directions remained the same in all conditions. Bias strength mildly increased without vision, especially during fast movements. Striking increases in bias strength were caused by the secondary task, pointing to additional cognitive load associated with selection of movements in the non-preferred directions. Further analyses demonstrated that the tendency to minimize active interference with interaction torque at the subordinate joint matched directional biases in all conditions. This match supports the explanation of directional biases as a result of a tendency to minimize neural effort for interaction torque control. The cognitive load may enhance this tendency in two ways, directly, by reducing neural capacity for interaction torque control, and indirectly, by decreasing capacity of working memory that stores visited directions. The obtained results suggest strong directional biases during daily activities because natural arm movements usually subserve cognitive tasks.

Neural Networks for Flight Control

NASA Technical Reports Server (NTRS)

Jorgensen, Charles C.

1996-01-01

Neural networks are being developed at NASA Ames Research Center to permit real-time adaptive control of time varying nonlinear systems, enhance the fault-tolerance of mission hardware, and permit online system reconfiguration. In general, the problem of controlling time varying nonlinear systems with unknown structures has not been solved. Adaptive neural control techniques show considerable promise and are being applied to technical challenges including automated docking of spacecraft, dynamic balancing of the space station centrifuge, online reconfiguration of damaged aircraft, and reducing cost of new air and spacecraft designs. Our experiences have shown that neural network algorithms solved certain problems that conventional control methods have been unable to effectively address. These include damage mitigation in nonlinear reconfiguration flight control, early performance estimation of new aircraft designs, compensation for damaged planetary mission hardware by using redundant manipulator capability, and space sensor platform stabilization. This presentation explored these developments in the context of neural network control theory. The discussion began with an overview of why neural control has proven attractive for NASA application domains. The more important issues in control system development were then discussed with references to significant technical advances in the literature. Examples of how these methods have been applied were given, followed by projections of emerging application needs and directions.

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.

Neural control of arm movements reveals a tendency to use gravity to simplify joint coordination rather than to decrease muscle effort.

PubMed

Wang, Wanyue; Dounskaia, Natalia

2016-12-17

How gravity influences neural control of arm movements remains under debate. We tested three alternative interpretations suggested by previous research: (1) that muscular control includes two components, tonic which compensates for gravity and phasic which produces the movement; (2) that there is a tendency to exploit gravity to reduce muscle effort; and (3) that there is a tendency to use a trailing pattern of joint control during which either the shoulder or elbow is rotated actively and the other joint rotates predominantly passively, and to exploit gravity for control of the passively rotated joint. A free-stroke drawing task was performed that required production of center-out strokes within a circle while selecting stroke directions randomly. The circle was positioned in the horizontal, sagittal, and frontal plane. The arm joints freely rotated in space. In each plane, the distribution of the strokes across directions was non-uniform. Directional histograms were built and their peaks were used to identify preferred movement directions. The directional preferences were especially pronounced in the two vertical planes. The upward directions were most preferred. To test the three interpretations, we used a kinetic analysis that determined the role of gravitational torque in the production of movement in the preferred directions. The results supported the third interpretation and provided evidence against the first and second interpretation. The trailing pattern has been associated with reduced neural effort for joint coordination, and therefore, we conclude that the major tendency with respect to gravity is to exploit it for simplification of joint coordination. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

Stronger Neural Modulation by Visual Motion Intensity in Autism Spectrum Disorders

PubMed Central

Peiker, Ina; Schneider, Till R.; Milne, Elizabeth; Schöttle, Daniel; Vogeley, Kai; Münchau, Alexander; Schunke, Odette; Siegel, Markus; Engel, Andreas K.; David, Nicole

2015-01-01

Theories of autism spectrum disorders (ASD) have focused on altered perceptual integration of sensory features as a possible core deficit. Yet, there is little understanding of the neuronal processing of elementary sensory features in ASD. For typically developed individuals, we previously established a direct link between frequency-specific neural activity and the intensity of a specific sensory feature: Gamma-band activity in the visual cortex increased approximately linearly with the strength of visual motion. Using magnetoencephalography (MEG), we investigated whether in individuals with ASD neural activity reflect the coherence, and thus intensity, of visual motion in a similar fashion. Thirteen adult participants with ASD and 14 control participants performed a motion direction discrimination task with increasing levels of motion coherence. A polynomial regression analysis revealed that gamma-band power increased significantly stronger with motion coherence in ASD compared to controls, suggesting excessive visual activation with increasing stimulus intensity originating from motion-responsive visual areas V3, V6 and hMT/V5. Enhanced neural responses with increasing stimulus intensity suggest an enhanced response gain in ASD. Response gain is controlled by excitatory-inhibitory interactions, which also drive high-frequency oscillations in the gamma-band. Thus, our data suggest that a disturbed excitatory-inhibitory balance underlies enhanced neural responses to coherent motion in ASD. PMID:26147342

Reconfigurable visible nanophotonic switch for optogenetic applications (Conference Presentation)

NASA Astrophysics Data System (ADS)

Mohanty, Aseema; Li, Qian; Tadayon, Mohammad Amin; Bhatt, Gaurang R.; Cardenas, Jaime; Miller, Steven A.; Kepecs, Adam; Lipson, Michal

2017-02-01

High spatiotemporal resolution deep-brain optical excitation for optogenetics would enable activation of specific neural populations and in-depth study of neural circuits. Conventionally, a single fiber is used to flood light into a large area of the brain with limited resolution. The scalability of silicon photonics could enable neural excitation over large areas with single-cell resolution similar to electrical probes. However, active control of these optical circuits has yet to be demonstrated for optogenetics. Here we demonstrate the first active integrated optical switch for neural excitation at 473 nm, enabling control of multiple beams for deep-brain neural stimulation. Using a silicon nitride waveguide platform, we develop a cascaded Mach-Zehnder interferometer (MZI) network located outside the brain to direct light to 8 different grating emitters located at the tip of the neural probe. We use integrated platinum microheaters to induce a local thermo-optic phase shift in the MZI to control the switch output. We measure an ON/OFF extinction ratio of >8dB for a single switch and a switching speed of 20 microseconds. We characterize the optical output of the switch by imaging its excitation of fluorescent dye. Finally, we demonstrate in vivo single-neuron optical activation from different grating emitters using a fully packaged device inserted into a mouse brain. Directly activated neurons showed robust spike firing activities with low first-spike latency and small jitter. Active switching on a nanophotonic platform is necessary for eventually controlling highly-multiplexed reconfigurable optical circuits, enabling high-resolution optical stimulation in deep-brain regions.

Analog neural network control method proposed for use in a backup satellite control mode

DOE Office of Scientific and Technical Information (OSTI.GOV)

Frigo, J.R.; Tilden, M.W.

1998-03-01

The authors propose to use an analog neural network controller implemented in hardware, independent of the active control system, for use in a satellite backup control mode. The controller uses coarse sun sensor inputs. The field of view of the sensors activate the neural controller, creating an analog dead band with respect to the direction of the sun on each axis. This network controls the orientation of the vehicle toward the sunlight to ensure adequate power for the system. The attitude of the spacecraft is stabilized with respect to the ambient magnetic field on orbit. This paper develops a modelmore » of the controller using real-time coarse sun sensor data and a dynamic model of a prototype system based on a satellite system. The simulation results and the feasibility of this control method for use in a satellite backup control mode are discussed.« less

Adaptive Neural Output Feedback Control for Nonstrict-Feedback Stochastic Nonlinear Systems With Unknown Backlash-Like Hysteresis and Unknown Control Directions.

PubMed

Yu, Zhaoxu; Li, Shugang; Yu, Zhaosheng; Li, Fangfei

2018-04-01

This paper investigates the problem of output feedback adaptive stabilization for a class of nonstrict-feedback stochastic nonlinear systems with both unknown backlashlike hysteresis and unknown control directions. A new linear state transformation is applied to the original system, and then, control design for the new system becomes feasible. By combining the neural network's (NN's) parameterization, variable separation technique, and Nussbaum gain function method, an input-driven observer-based adaptive NN control scheme, which involves only one parameter to be updated, is developed for such systems. All closed-loop signals are bounded in probability and the error signals remain semiglobally bounded in the fourth moment (or mean square). Finally, the effectiveness and the applicability of the proposed control design are verified by two simulation examples.

Adaptive NN control for discrete-time pure-feedback systems with unknown control direction under amplitude and rate actuator constraints.

PubMed

Chen, Weisheng

2009-07-01

This paper focuses on the problem of adaptive neural network tracking control for a class of discrete-time pure-feedback systems with unknown control direction under amplitude and rate actuator constraints. Two novel state-feedback and output-feedback dynamic control laws are established where the function tanh(.) is employed to solve the saturation constraint problem. Implicit function theorem and mean value theorem are exploited to deal with non-affine variables that are used as actual control. Radial basis function neural networks are used to approximate the desired input function. Discrete Nussbaum gain is used to estimate the unknown sign of control gain. The uniform boundedness of all closed-loop signals is guaranteed. The tracking error is proved to converge to a small residual set around the origin. A simulation example is provided to illustrate the effectiveness of control schemes proposed in this paper.

Unscented Kalman Filter for Brain-Machine Interfaces

PubMed Central

Li, Zheng; O'Doherty, Joseph E.; Hanson, Timothy L.; Lebedev, Mikhail A.; Henriquez, Craig S.; Nicolelis, Miguel A. L.

2009-01-01

Brain machine interfaces (BMIs) are devices that convert neural signals into commands to directly control artificial actuators, such as limb prostheses. Previous real-time methods applied to decoding behavioral commands from the activity of populations of neurons have generally relied upon linear models of neural tuning and were limited in the way they used the abundant statistical information contained in the movement profiles of motor tasks. Here, we propose an n-th order unscented Kalman filter which implements two key features: (1) use of a non-linear (quadratic) model of neural tuning which describes neural activity significantly better than commonly-used linear tuning models, and (2) augmentation of the movement state variables with a history of n-1 recent states, which improves prediction of the desired command even before incorporating neural activity information and allows the tuning model to capture relationships between neural activity and movement at multiple time offsets simultaneously. This new filter was tested in BMI experiments in which rhesus monkeys used their cortical activity, recorded through chronically implanted multielectrode arrays, to directly control computer cursors. The 10th order unscented Kalman filter outperformed the standard Kalman filter and the Wiener filter in both off-line reconstruction of movement trajectories and real-time, closed-loop BMI operation. PMID:19603074

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.

A Tensor-Product-Kernel Framework for Multiscale Neural Activity Decoding and Control

PubMed Central

Li, Lin; Brockmeier, Austin J.; Choi, John S.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.

2014-01-01

Brain machine interfaces (BMIs) have attracted intense attention as a promising technology for directly interfacing computers or prostheses with the brain's motor and sensory areas, thereby bypassing the body. The availability of multiscale neural recordings including spike trains and local field potentials (LFPs) brings potential opportunities to enhance computational modeling by enriching the characterization of the neural system state. However, heterogeneity on data type (spike timing versus continuous amplitude signals) and spatiotemporal scale complicates the model integration of multiscale neural activity. In this paper, we propose a tensor-product-kernel-based framework to integrate the multiscale activity and exploit the complementary information available in multiscale neural activity. This provides a common mathematical framework for incorporating signals from different domains. The approach is applied to the problem of neural decoding and control. For neural decoding, the framework is able to identify the nonlinear functional relationship between the multiscale neural responses and the stimuli using general purpose kernel adaptive filtering. In a sensory stimulation experiment, the tensor-product-kernel decoder outperforms decoders that use only a single neural data type. In addition, an adaptive inverse controller for delivering electrical microstimulation patterns that utilizes the tensor-product kernel achieves promising results in emulating the responses to natural stimulation. PMID:24829569

Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine interfaces.

PubMed

Benyamini, Miri; Zacksenhouse, Miriam

2015-01-01

Recent experiments with brain-machine-interfaces (BMIs) indicate that the extent of neural modulations increased abruptly upon starting to operate the interface, and especially after the monkey stopped moving its hand. In contrast, neural modulations that are correlated with the kinematics of the movement remained relatively unchanged. Here we demonstrate that similar changes are produced by simulated neurons that encode the relevant signals generated by an optimal feedback controller during simulated BMI experiments. The optimal feedback controller relies on state estimation that integrates both visual and proprioceptive feedback with prior estimations from an internal model. The processing required for optimal state estimation and control were conducted in the state-space, and neural recording was simulated by modeling two populations of neurons that encode either only the estimated state or also the control signal. Spike counts were generated as realizations of doubly stochastic Poisson processes with linear tuning curves. The model successfully reconstructs the main features of the kinematics and neural activity during regular reaching movements. Most importantly, the activity of the simulated neurons successfully reproduces the observed changes in neural modulations upon switching to brain control. Further theoretical analysis and simulations indicate that increasing the process noise during normal reaching movement results in similar changes in neural modulations. Thus, we conclude that the observed changes in neural modulations during BMI experiments can be attributed to increasing process noise associated with the imperfect BMI filter, and, more directly, to the resulting increase in the variance of the encoded signals associated with state estimation and the required control signal.

Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine interfaces

PubMed Central

Benyamini, Miri; Zacksenhouse, Miriam

2015-01-01

Recent experiments with brain-machine-interfaces (BMIs) indicate that the extent of neural modulations increased abruptly upon starting to operate the interface, and especially after the monkey stopped moving its hand. In contrast, neural modulations that are correlated with the kinematics of the movement remained relatively unchanged. Here we demonstrate that similar changes are produced by simulated neurons that encode the relevant signals generated by an optimal feedback controller during simulated BMI experiments. The optimal feedback controller relies on state estimation that integrates both visual and proprioceptive feedback with prior estimations from an internal model. The processing required for optimal state estimation and control were conducted in the state-space, and neural recording was simulated by modeling two populations of neurons that encode either only the estimated state or also the control signal. Spike counts were generated as realizations of doubly stochastic Poisson processes with linear tuning curves. The model successfully reconstructs the main features of the kinematics and neural activity during regular reaching movements. Most importantly, the activity of the simulated neurons successfully reproduces the observed changes in neural modulations upon switching to brain control. Further theoretical analysis and simulations indicate that increasing the process noise during normal reaching movement results in similar changes in neural modulations. Thus, we conclude that the observed changes in neural modulations during BMI experiments can be attributed to increasing process noise associated with the imperfect BMI filter, and, more directly, to the resulting increase in the variance of the encoded signals associated with state estimation and the required control signal. PMID:26042002

Neural Correlates of Aggressive Behavior in Real Time: a Review of fMRI Studies of Laboratory Reactive Aggression

PubMed Central

Keedy, Sarah; Berman, Mitchell E.; Lee, Royce; Coccaro, Emil F.

2017-01-01

Purpose of review Aggressive behavior has adaptive value in many natural environments; however, it places substantial burden and costs on human society. For this reason, there has long been interest in understanding the neurobiological basis of aggression. This interest, and the flourishing of neuroimaging research in general, has spurred the development of a large and growing scientific literature on the topic. As a result, a neural circuit model of aggressive behavior has emerged that implicates interconnected brain regions that are involved in emotional reactivity, emotion regulation, and cognitive control. Recent findings Recently, behavioral paradigms that simulate provocative interactions have been adapted to neuroimaging protocols, providing an opportunity to directly probe the involvement of neural circuits in an aggressive interaction. Here we review neuroimaging studies of simulated aggressive interactions in research volunteers. We focus on studies that use a well-validated laboratory paradigm for reactive physical aggression and examine the neural correlates of provocation, retaliation, and evaluating punishment of an opponent. Summary Overall, the studies reviewed support the involvement of neural circuits that support emotional reactivity, emotion regulation, and cognitive control in aggressive behavior. Based on a synthesis of this literature, future research directions are discussed. PMID:29607288

Safe Direct Current Stimulator design for reduced power consumption and increased reliability.

PubMed

Fridman, Gene

2017-07-01

Current state of the art neural prosthetics, such as cochlear implants, spinal cord stimulators, and deep brain stimulators use implantable pulse generators (IPGs) to excite neural activity. Inhibition of neural firing is typically indirect and requires excitation of neurons that then have inhibitory projections downstream. Safe Direct Current Stimulator (SDCS) technology is designed to convert electronic pulses delivered to electrodes embedded within an implantable device to ionic direct current (iDC) at the output of the device. iDC from the device can then control neural extracellular potential with the intent of being able to not only excite, but also inhibit and sensitize neurons, thereby greatly expanding the possible applications of neuromodulation therapies and neural interface mechanisms. While the potential applications and proof of concept of this device have been the focus of previous work, the published descriptions of this technology leave significant room for power and reliability optimization. We describe and model a novel device construction designed to reduce power consumption by a factor of 12 and to improve its reliability by a factor of 8.

Operant conditioning of a multiple degree-of-freedom brain-machine interface in a primate model of amputation.

PubMed

Balasubramanian, Karthikeyan; Southerland, Joshua; Vaidya, Mukta; Qian, Kai; Eleryan, Ahmed; Fagg, Andrew H; Sluzky, Marc; Oweiss, Karim; Hatsopoulos, Nicholas

2013-01-01

Operant conditioning with biofeedback has been shown to be an effective method to modify neural activity to generate goal-directed actions in a brain-machine interface. It is particularly useful when neural activity cannot be mathematically mapped to motor actions of the actual body such as in the case of amputation. Here, we implement an operant conditioning approach with visual feedback in which an amputated monkey is trained to control a multiple degree-of-freedom robot to perform a reach-to-grasp behavior. A key innovation is that each controlled dimension represents a behaviorally relevant synergy among a set of joint degrees-of-freedom. We present a number of behavioral metrics by which to assess improvements in BMI control with exposure to the system. The use of non-human primates with chronic amputation is arguably the most clinically-relevant model of human amputation that could have direct implications for developing a neural prosthesis to treat humans with missing upper limbs.

A fast new algorithm for a robot neurocontroller using inverse QR decomposition

DOE Office of Scientific and Technical Information (OSTI.GOV)

Morris, A.S.; Khemaissia, S.

2000-01-01

A new adaptive neural network controller for robots is presented. The controller is based on direct adaptive techniques. Unlike many neural network controllers in the literature, inverse dynamical model evaluation is not required. A numerically robust, computationally efficient processing scheme for neutral network weight estimation is described, namely, the inverse QR decomposition (INVQR). The inverse QR decomposition and a weighted recursive least-squares (WRLS) method for neural network weight estimation is derived using Cholesky factorization of the data matrix. The algorithm that performs the efficient INVQR of the underlying space-time data matrix may be implemented in parallel on a triangular array.more » Furthermore, its systolic architecture is well suited for VLSI implementation. Another important benefit is well suited for VLSI implementation. Another important benefit of the INVQR decomposition is that it solves directly for the time-recursive least-squares filter vector, while avoiding the sequential back-substitution step required by the QR decomposition approaches.« less

Modulating inhibitory control with direct current stimulation of the superior medial frontal cortex.

PubMed

Hsu, Tzu-Yu; Tseng, Lin-Yuan; Yu, Jia-Xin; Kuo, Wen-Jui; Hung, Daisy L; Tzeng, Ovid J L; Walsh, Vincent; Muggleton, Neil G; Juan, Chi-Hung

2011-06-15

The executive control of voluntary action involves not only choosing from a range of possible actions but also the inhibition of responses as circumstances demand. Recent studies have demonstrated that many clinical populations, such as people with attention-deficit hyperactivity disorder, exhibit difficulties in inhibitory control. One prefrontal area that has been particularly associated with inhibitory control is the pre-supplementary motor area (Pre-SMA). Here we applied non-invasive transcranial direct current stimulation (tDCS) over Pre-SMA to test its role in this behavior. tDCS allows for current to be applied in two directions to selectively excite or suppress the neural activity of Pre-SMA. Our results showed that anodal tDCS improved efficiency of inhibitory control. Conversely, cathodal tDCS showed a tendency towards impaired inhibitory control. To our knowledge, this is the first demonstration of non-invasive intervention tDCS altering subjects' inhibitory control. These results further our understanding of the neural bases of inhibitory control and suggest a possible therapeutic intervention method for clinical populations. Copyright © 2011 Elsevier Inc. All rights reserved.

Neural tissue engineering: Bioresponsive nanoscaffolds using engineered self-assembling peptides.

PubMed

Koss, K M; Unsworth, L D

2016-10-15

Rescuing or repairing neural tissues is of utmost importance to the patient's quality of life after an injury. To remedy this, many novel biomaterials are being developed that are, ideally, non-invasive and directly facilitate neural wound healing. As such, this review surveys the recent approaches and applications of self-assembling peptides and peptide amphiphiles, for building multi-faceted nanoscaffolds for direct application to neural injury. Specifically, methods enabling cellular interactions with the nanoscaffold and controlling the release of bioactive molecules from the nanoscaffold for the express purpose of directing endogenous cells in damaged or diseased neural tissues is presented. An extensive overview of recently derived self-assembling peptide-based materials and their use as neural nanoscaffolds is presented. In addition, an overview of potential bioactive peptides and ligands that could be used to direct behaviour of endogenous cells are categorized with their biological effects. Finally, a number of neurotrophic and anti-inflammatory drugs are described and discussed. Smaller therapeutic molecules are emphasized, as they are thought to be able to have less potential effect on the overall peptide self-assembly mechanism. Options for potential nanoscaffolds and drug delivery systems are suggested. Self-assembling nanoscaffolds have many inherent properties making them amenable to tissue engineering applications: ease of synthesis, ease of customization with bioactive moieties, and amenable for in situ nanoscaffold formation. The combination of the existing knowledge on bioactive motifs for neural engineering and the self-assembling propensity of peptides is discussed in specific reference to neural tissue engineering. Copyright © 2016. Published by Elsevier Ltd.

Lifelong Bilingualism Maintains Neural Efficiency for Cognitive Control in Aging

PubMed Central

Gold, Brian T.; Kim, Chobok; Johnson, Nathan F.; Kryscio, Richard J.; Smith, Charles D.

2013-01-01

Recent behavioral data have shown that lifelong bilingualism can maintain youthful cognitive control abilities in aging. Here, we provide the first direct evidence of a neural basis for the bilingual cognitive control boost in aging. Two experiments were conducted, using a perceptual task switching paradigm, and including a total of 110 participants. In Experiment 1, older adult bilinguals showed better perceptual switching performance than their monolingual peers. In Experiment 2, younger and older adult monolinguals and bilinguals completed the same perceptual task switching experiment while fMRI was performed. Typical age-related performance reductions and fMRI activation increases were observed. However, like younger adults, bilingual older adults outperformed their monolingual peers while displaying decreased activation in left lateral frontal cortex and cingulate cortex. Critically, this attenuation of age-related over-recruitment associated with bilingualism was directly correlated with better task switching performance. In addition, the lower BOLD response in frontal regions accounted for 82% of the variance in the bilingual task switching reaction time advantage. These results suggest that lifelong bilingualism offsets age-related declines in the neural efficiency for cognitive control processes. PMID:23303919

Neural Modeling of Fuzzy Controllers for Maximum Power Point Tracking in Photovoltaic Energy Systems

NASA Astrophysics Data System (ADS)

Lopez-Guede, Jose Manuel; Ramos-Hernanz, Josean; Altın, Necmi; Ozdemir, Saban; Kurt, Erol; Azkune, Gorka

2018-06-01

One field in which electronic materials have an important role is energy generation, especially within the scope of photovoltaic energy. This paper deals with one of the most relevant enabling technologies within that scope, i.e, the algorithms for maximum power point tracking implemented in the direct current to direct current converters and its modeling through artificial neural networks (ANNs). More specifically, as a proof of concept, we have addressed the problem of modeling a fuzzy logic controller that has shown its performance in previous works, and more specifically the dimensionless duty cycle signal that controls a quadratic boost converter. We achieved a very accurate model since the obtained medium squared error is 3.47 × 10-6, the maximum error is 16.32 × 10-3 and the regression coefficient R is 0.99992, all for the test dataset. This neural implementation has obvious advantages such as a higher fault tolerance and a simpler implementation, dispensing with all the complex elements needed to run a fuzzy controller (fuzzifier, defuzzifier, inference engine and knowledge base) because, ultimately, ANNs are sums and products.

Regulatory analysis of the mouse Hoxb3 gene: multiple elements work in concert to direct temporal and spatial patterns of expression.

PubMed

Kwan, C T; Tsang, S L; Krumlauf, R; Sham, M H

2001-04-01

The expression pattern of the mouse Hoxb3 gene is exceptionally complex and dynamic compared with that of other members of the Hoxb cluster. There are multiple types of transcripts for Hoxb3 gene, and the anterior boundaries of its expression vary at different stages of development. Two enhancers flanking Hoxb3 on the 3' and 5' sides regulate Hoxb2 and Hoxb4, respectively, and these control regions define the two ends of a 28-kb interval in and around the Hoxb3 locus. To assay the regulatory potential of DNA fragments in this interval we have used transgenic analysis with a lacZ reporter gene to locate cis-elements for directing the dynamic patterns of Hoxb3 expression. Our detailed analysis has identified four new and widely spaced cis-acting regulatory regions that can together account for major aspects of the Hoxb3 expression pattern. Elements Ib, IIIa, and IVb control gene expression in neural and mesodermal tissues; element Va controls mesoderm-specific gene expression. The most anterior neural expression domain of Hoxb3 is controlled by an r5 enhancer (element IVa); element IIIa directs reporter expression in the anterior spinal cord and hindbrain up to r6, and the region A enhancer (in element I) mediates posterior neural expression. Hence, the regulation of segmental expression of Hoxb3 in the hindbrain is different from that of Hoxa3, as two separate enhancer elements contribute to expression in r5 and r6. The mesoderm-specific element (Va) directs reporter expression to prevertebra C1 at 12.5 dpc, which is the anterior limit of paraxial mesoderm expression for Hoxb3. When tested in combinations, these cis-elements appear to work as modules in an additive manner to recapitulate the major endogenous expression patterns of Hoxb3 during embryogenesis. Together our study shows that multiple control elements direct reporter gene expression in diverse tissue-, temporal-, and spatially restricted subset of the endogenous Hoxb3 expression domains and work in concert to control the neural and mesodermal patterns of expression. Copyright 2001 Academic Press.

Motor control in a Drosophila taste circuit

PubMed Central

Gordon, Michael D.; Scott, Kristin

2009-01-01

Tastes elicit innate behaviors critical for directing animals to ingest nutritious substances and reject toxic compounds, but the neural basis of these behaviors is not understood. Here, we use a neural silencing screen to identify neurons required for a simple Drosophila taste behavior, and characterize a neural population that controls a specific subprogram of this behavior. By silencing and activating subsets of the defined cell population, we identify the neurons involved in the taste behavior as a pair of motor neurons located in the subesophageal ganglion (SOG). The motor neurons are activated by sugar stimulation of gustatory neurons and inhibited by bitter compounds; however, experiments utilizing split-GFP detect no direct connections between the motor neurons and primary sensory neurons, indicating that further study will be necessary to elucidate the circuitry bridging these populations. Combined, these results provide a general strategy and a valuable starting point for future taste circuit analysis. PMID:19217375

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

Eye Velocity Gain Fields in MSTd During Optokinetic Stimulation

PubMed Central

Brostek, Lukas; Büttner, Ulrich; Mustari, Michael J.; Glasauer, Stefan

2015-01-01

Lesion studies argue for an involvement of cortical area dorsal medial superior temporal area (MSTd) in the control of optokinetic response (OKR) eye movements to planar visual stimulation. Neural recordings during OKR suggested that MSTd neurons directly encode stimulus velocity. On the other hand, studies using radial visual flow together with voluntary smooth pursuit eye movements showed that visual motion responses were modulated by eye movement-related signals. Here, we investigated neural responses in MSTd during continuous optokinetic stimulation using an information-theoretic approach for characterizing neural tuning with high resolution. We show that the majority of MSTd neurons exhibit gain-field-like tuning functions rather than directly encoding one variable. Neural responses showed a large diversity of tuning to combinations of retinal and extraretinal input. Eye velocity-related activity was observed prior to the actual eye movements, reflecting an efference copy. The observed tuning functions resembled those emerging in a network model trained to perform summation of 2 population-coded signals. Together, our findings support the hypothesis that MSTd implements the visuomotor transformation from retinal to head-centered stimulus velocity signals for the control of OKR. PMID:24557636

Neural-network-directed alignment of optical systems using the laser-beam spatial filter as an example

NASA Technical Reports Server (NTRS)

Decker, Arthur J.; Krasowski, Michael J.; Weiland, Kenneth E.

1993-01-01

This report describes an effort at NASA Lewis Research Center to use artificial neural networks to automate the alignment and control of optical measurement systems. Specifically, it addresses the use of commercially available neural network software and hardware to direct alignments of the common laser-beam-smoothing spatial filter. The report presents a general approach for designing alignment records and combining these into training sets to teach optical alignment functions to neural networks and discusses the use of these training sets to train several types of neural networks. Neural network configurations used include the adaptive resonance network, the back-propagation-trained network, and the counter-propagation network. This work shows that neural networks can be used to produce robust sequencers. These sequencers can learn by example to execute the step-by-step procedures of optical alignment and also can learn adaptively to correct for environmentally induced misalignment. The long-range objective is to use neural networks to automate the alignment and operation of optical measurement systems in remote, harsh, or dangerous aerospace environments. This work also shows that when neural networks are trained by a human operator, training sets should be recorded, training should be executed, and testing should be done in a manner that does not depend on intellectual judgments of the human operator.

Identification and control of plasma vertical position using neural network in Damavand tokamak.

PubMed

Rasouli, H; Rasouli, C; Koohi, A

2013-02-01

In this work, a nonlinear model is introduced to determine the vertical position of the plasma column in Damavand tokamak. Using this model as a simulator, a nonlinear neural network controller has been designed. In the first stage, the electronic drive and sensory circuits of Damavand tokamak are modified. These circuits can control the vertical position of the plasma column inside the vacuum vessel. Since the vertical position of plasma is an unstable parameter, a direct closed loop system identification algorithm is performed. In the second stage, a nonlinear model is identified for plasma vertical position, based on the multilayer perceptron (MLP) neural network (NN) structure. Estimation of simulator parameters has been performed by back-propagation error algorithm using Levenberg-Marquardt gradient descent optimization technique. The model is verified through simulation of the whole closed loop system using both simulator and actual plant in similar conditions. As the final stage, a MLP neural network controller is designed for simulator model. In the last step, online training is performed to tune the controller parameters. Simulation results justify using of the NN controller for the actual plant.

Control Reallocation Strategies for Damage Adaptation in Transport Class Aircraft

NASA Technical Reports Server (NTRS)

Gundy-Burlet, Karen; Krishnakumar, K.; Limes, Greg; Bryant, Don

2003-01-01

This paper examines the feasibility, potential benefits and implementation issues associated with retrofitting a neural-adaptive flight control system (NFCS) to existing transport aircraft, including both cable/hydraulic and fly-by-wire configurations. NFCS uses a neural network based direct adaptive control approach for applying alternate sources of control authority in the presence of damage or failures in order to achieve desired flight control performance. Neural networks are used to provide consistent handling qualities across flight conditions, adapt to changes in aircraft dynamics and to make the controller easy to apply when implemented on different aircraft. Full-motion piloted simulation studies were performed on two different transport models: the Boeing 747-400 and the Boeing C-17. Subjects included NASA, Air Force and commercial airline pilots. Results demonstrate the potential for improving handing qualities and significantly increased survivability rates under various simulated failure conditions.

Neural Control of a Tracking Task via Attention-Gated Reinforcement Learning for Brain-Machine Interfaces.

PubMed

Wang, Yiwen; Wang, Fang; Xu, Kai; Zhang, Qiaosheng; Zhang, Shaomin; Zheng, Xiaoxiang

2015-05-01

Reinforcement learning (RL)-based brain machine interfaces (BMIs) enable the user to learn from the environment through interactions to complete the task without desired signals, which is promising for clinical applications. Previous studies exploited Q-learning techniques to discriminate neural states into simple directional actions providing the trial initial timing. However, the movements in BMI applications can be quite complicated, and the action timing explicitly shows the intention when to move. The rich actions and the corresponding neural states form a large state-action space, imposing generalization difficulty on Q-learning. In this paper, we propose to adopt attention-gated reinforcement learning (AGREL) as a new learning scheme for BMIs to adaptively decode high-dimensional neural activities into seven distinct movements (directional moves, holdings and resting) due to the efficient weight-updating. We apply AGREL on neural data recorded from M1 of a monkey to directly predict a seven-action set in a time sequence to reconstruct the trajectory of a center-out task. Compared to Q-learning techniques, AGREL could improve the target acquisition rate to 90.16% in average with faster convergence and more stability to follow neural activity over multiple days, indicating the potential to achieve better online decoding performance for more complicated BMI tasks.

Online Recorded Data-Based Composite Neural Control of Strict-Feedback Systems With Application to Hypersonic Flight Dynamics.

PubMed

Xu, Bin; Yang, Daipeng; Shi, Zhongke; Pan, Yongping; Chen, Badong; Sun, Fuchun

2017-09-25

This paper investigates the online recorded data-based composite neural control of uncertain strict-feedback systems using the backstepping framework. In each step of the virtual control design, neural network (NN) is employed for uncertainty approximation. In previous works, most designs are directly toward system stability ignoring the fact how the NN is working as an approximator. In this paper, to enhance the learning ability, a novel prediction error signal is constructed to provide additional correction information for NN weight update using online recorded data. In this way, the neural approximation precision is highly improved, and the convergence speed can be faster. Furthermore, the sliding mode differentiator is employed to approximate the derivative of the virtual control signal, and thus, the complex analysis of the backstepping design can be avoided. The closed-loop stability is rigorously established, and the boundedness of the tracking error can be guaranteed. Through simulation of hypersonic flight dynamics, the proposed approach exhibits better tracking performance.

Addressing the Movement of a Freescale Robotic Car Using Neural Network

NASA Astrophysics Data System (ADS)

Horváth, Dušan; Cuninka, Peter

2016-12-01

This article deals with the management of a Freescale small robotic car along the predefined guide line. Controlling of the direction of movement of the robot is performed by neural networks, and scales (memory) of neurons are calculated by Hebbian learning from the truth tables as learning with a teacher. Reflexive infrared sensors serves as inputs. The results are experiments, which are used to compare two methods of mobile robot control - tracking lines.

Photopolymerized materials and patterning for improved performance of neural prosthetics

NASA Astrophysics Data System (ADS)

Tuft, Bradley William

Neural prosthetics are used to replace or substantially augment remaining motor and sensory functions of neural pathways that were lost or damaged due to physical trauma, disease, or genetics. However, due to poor spatial signal resolution, neural prostheses fail to recapitulate the intimate, precise interactions inherent to neural networks. Designing materials and interfaces that direct de novo nerve growth to spatially specific stimulating elements is, therefore, a promising method to enhance signal specificity and performance of prostheses such as the successful cochlear implant (CI) and the developing retinal implant. In this work, the spatial and temporal reaction control inherent to photopolymerization was used to develop methods to generate micro and nanopatterned materials that direct neurite growth from prosthesis relevant neurons. In particular, neurite growth and directionality has been investigated in response to physical, mechanical, and chemical cues on photopolymerized surfaces. Spiral ganglion neurons (SGNs) serve as the primary neuronal model as they are the principal target for CI stimulation. The objective of the research is to rationally design materials that spatially direct neurite growth and to translate fundamental understanding of nerve cell-material interactions into methods of nerve regeneration that improve neural prosthetic performance. A rapid, single-step photopolymerization method was developed to fabricate micro and nanopatterned physical cues on methacrylate surfaces by selectively blocking light with photomasks. Feature height is readily tuned by modulating parameters of the photopolymerizaiton including initiator concentration and species, light intensity, separation distance from the photomask, and radiation exposure time. Alignment of neural elements increases significantly with increasing feature amplitude and constant periodicity, as well as with decreasing periodicity and constant amplitude. SGN neurite alignment strongly correlates with the maximum feature slope. Neurite alignment is compared on unpatterned, unidirectional, and multidirectional photopolymerized micropatterns. The effect of substrate rigidity on neurite alignment to physical cues was determined by maintaining equivalent pattern microfeatures, afforded by the reaction control of photopolymerization, while concomitantly altering the composition of several copolymer platforms to tune matrix stiffness. For each platform, neurite alignment to unidirectional patterns increases with increasing substrate rigidity. Interestingly, SGN neurites respond to material stiffness cues that are orders of magnitude higher (GPa) than what is typically ascribed to neural environments (kPa). Finally, neurite behavior at bioactive borders of various adhesion modulating molecules was evaluated on micropatterned materials to determine which cues took precedence in establishing neurite directionality. At low microfeatures aspect ratios, neurites align to the pattern direction but are then caused to turn and repel from or turn and align to bioactive borders. Conversely, physical cues dominate neurite path-finding as pattern feature slope increases, i.e. aspect ratio of sloping photopolymerized features increases, causing neurites to readily cross bioactive borders. The photopolymerization method developed in this work to generate micro and nanopatterned materials serves as an additional surface engineering tool that enables investigation of cell-material interactions including directed de novo neurite growth. The results of this interdisciplinary effort contribute substantially to polymer neural regeneration technology and will lead to development of advanced biomaterials that improve neural prosthetic tissue integration and performance by spatially directing nerve growth.

Histone modifications controlling native and induced neural stem cell identity.

PubMed

Broccoli, Vania; Colasante, Gaia; Sessa, Alessandro; Rubio, Alicia

2015-10-01

During development, neural progenitor cells (NPCs) that are capable of self-renewing maintain a proliferative cellular pool while generating all differentiated neural cell components. Although the genetic network of transcription factors (TFs) required for neural specification has been well characterized, the unique set of histone modifications that accompanies this process has only recently started to be investigated. In vitro neural differentiation of pluripotent stem cells is emerging as a powerful system to examine epigenetic programs. Deciphering the histone code and how it shapes the chromatin environment will reveal the intimate link between epigenetic changes and mechanisms for neural fate determination in the developing nervous system. Furthermore, it will offer a molecular framework for a stringent comparison between native and induced neural stem cells (iNSCs) generated by direct neural cell conversion. Copyright © 2015 Elsevier Ltd. All rights reserved.

A neural-network approach to robotic control

NASA Technical Reports Server (NTRS)

Graham, D. P. W.; Deleuterio, G. M. T.

1993-01-01

An artificial neural-network paradigm for the control of robotic systems is presented. The approach is based on the Cerebellar Model Articulation Controller created by James Albus and incorporates several extensions. First, recognizing the essential structure of multibody equations of motion, two parallel modules are used that directly reflect the dynamical characteristics of multibody systems. Second, the architecture of the proposed network is imbued with a self-organizational capability which improves efficiency and accuracy. Also, the networks can be arranged in hierarchical fashion with each subsequent network providing finer and finer resolution.

On the asynchronously continuous control of mobile robot movement by motor cortical spiking activity.

PubMed

Xu, Zhiming; So, Rosa Q; Toe, Kyaw Kyar; Ang, Kai Keng; Guan, Cuntai

2014-01-01

This paper presents an asynchronously intracortical brain-computer interface (BCI) which allows the subject to continuously drive a mobile robot. This system has a great implication for disabled patients to move around. By carefully designing a multiclass support vector machine (SVM), the subject's self-paced instantaneous movement intents are continuously decoded to control the mobile robot. In particular, we studied the stability of the neural representation of the movement directions. Experimental results on the nonhuman primate showed that the overt movement directions were stably represented in ensemble of recorded units, and our SVM classifier could successfully decode such movements continuously along the desired movement path. However, the neural representation of the stop state for the self-paced control was not stably represented and could drift.

Cooperative learning neural network output feedback control of uncertain nonlinear multi-agent systems under directed topologies

NASA Astrophysics Data System (ADS)

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

2017-09-01

Without assuming that the communication topologies among the neural network (NN) weights are to be undirected and the states of each agent are measurable, the cooperative learning NN output feedback control is addressed for uncertain nonlinear multi-agent systems with identical structures in strict-feedback form. By establishing directed communication topologies among NN weights to share their learned knowledge, NNs with cooperative learning laws are employed to identify the uncertainties. By designing NN-based κ-filter observers to estimate the unmeasurable states, a new cooperative learning output feedback control scheme is proposed to guarantee that the system outputs can track nonidentical reference signals with bounded tracking errors. A simulation example is given to demonstrate the effectiveness of the theoretical results.

Non-causal spike filtering improves decoding of movement intention for intracortical BCIs

PubMed Central

Masse, Nicolas Y.; Jarosiewicz, Beata; Simeral, John D.; Bacher, Daniel; Stavisky, Sergey D.; Cash, Sydney S.; Oakley, Erin M.; Berhanu, Etsub; Eskandar, Emad; Friehs, Gerhard; Hochberg, Leigh R.; Donoghue, John P.

2014-01-01

Background Multiple types of neural signals are available for controlling assistive devices through brain-computer interfaces (BCIs). Intracortically-recorded spiking neural signals are attractive for BCIs because they can in principle provide greater fidelity of encoded information compared to electrocorticographic (ECoG) signals and electroencephalograms (EEGs). Recent reports show that the information content of these spiking neural signals can be reliably extracted simply by causally band-pass filtering the recorded extracellular voltage signals and then applying a spike detection threshold, without relying on “sorting” action potentials. New method We show that replacing the causal filter with an equivalent non-causal filter increases the information content extracted from the extracellular spiking signal and improves decoding of intended movement direction. This method can be used for real-time BCI applications by using a 4 ms lag between recording and filtering neural signals. Results Across 18 sessions from two people with tetraplegia enrolled in the BrainGate2 pilot clinical trial, we found that threshold crossing events extracted using this non-causal filtering method were significantly more informative of each participant’s intended cursor kinematics compared to threshold crossing events derived from causally filtered signals. This new method decreased the mean angular error between the intended and decoded cursor direction by 9.7° for participant S3, who was implanted 5.4 years prior to this study, and by 3.5° for participant T2, who was implanted 3 months prior to this study. Conclusions Non-causally filtering neural signals prior to extracting threshold crossing events may be a simple yet effective way to condition intracortically recorded neural activity for direct control of external devices through BCIs. PMID:25128256

Neural Basis of Stimulus-Angle-Dependent Motor Control of Wind-Elicited Walking Behavior in the Cricket Gryllus bimaculatus

PubMed Central

Oe, Momoko; Ogawa, Hiroto

2013-01-01

Crickets exhibit oriented walking behavior in response to air-current stimuli. Because crickets move in the opposite direction from the stimulus source, this behavior is considered to represent ‘escape behavior’ from an approaching predator. However, details of the stimulus-angle-dependent control of locomotion during the immediate phase, and the neural basis underlying the directional motor control of this behavior remain unclear. In this study, we used a spherical-treadmill system to measure locomotory parameters including trajectory, turn angle and velocity during the immediate phase of responses to air-puff stimuli applied from various angles. Both walking direction and turn angle were correlated with stimulus angle, but their relationships followed different rules. A shorter stimulus also induced directionally-controlled walking, but reduced the yaw rotation in stimulus-angle-dependent turning. These results suggest that neural control of the turn angle requires different sensory information than that required for oriented walking. Hemi-severance of the ventral nerve cords containing descending axons from the cephalic to the prothoracic ganglion abolished stimulus-angle-dependent control, indicating that this control required descending signals from the brain. Furthermore, we selectively ablated identified ascending giant interneurons (GIs) in vivo to examine their functional roles in wind-elicited walking. Ablation of GI8-1 diminished control of the turn angle and decreased walking distance in the initial response. Meanwhile, GI9-1b ablation had no discernible effect on stimulus-angle-dependent control or walking distance, but delayed the reaction time. These results suggest that the ascending signals conveyed by GI8-1 are required for turn-angle control and maintenance of walking behavior, and that GI9-1b is responsible for rapid initiation of walking. It is possible that individual types of GIs separately supply the sensory signals required to control wind-elicited walking. PMID:24244644

A two-dimensional neuropsychology of defense: fear/anxiety and defensive distance.

PubMed

McNaughton, Neil; Corr, Philip J

2004-05-01

We present in this paper a picture of the neural systems controlling defense that updates and simplifies Gray's "Neuropsychology of Anxiety". It is based on two behavioural dimensions: 'defensive distance' as defined by the Blanchards and 'defensive direction'. Defensive direction is a categorical dimension with avoidance of threat corresponding to fear and approach to threat corresponding to anxiety. These two psychological dimensions are mapped to underlying neural dimensions. Defensive distance is mapped to neural level, with the shortest defensive distances involving the lowest neural level (periaqueductal grey) and the largest defensive distances the highest neural level (prefrontal cortex). Defensive direction is mapped to separate parallel streams that run across these levels. A significant departure from prior models is the proposal that both fear and anxiety are represented at all levels. The theory is presented in a simplified form that does not incorporate the interactions that must occur between non-adjacent levels of the system. It also requires expansion to include the dimension of escapability of threat. Our current development and these proposed future extensions do not change the core concepts originally proposed by Gray and, we argue, demonstrate their enduring value.

Reward-Modulated Hebbian Plasticity as Leverage for Partially Embodied Control in Compliant Robotics

PubMed Central

Burms, Jeroen; Caluwaerts, Ken; Dambre, Joni

2015-01-01

In embodied computation (or morphological computation), part of the complexity of motor control is offloaded to the body dynamics. We demonstrate that a simple Hebbian-like learning rule can be used to train systems with (partial) embodiment, and can be extended outside of the scope of traditional neural networks. To this end, we apply the learning rule to optimize the connection weights of recurrent neural networks with different topologies and for various tasks. We then apply this learning rule to a simulated compliant tensegrity robot by optimizing static feedback controllers that directly exploit the dynamics of the robot body. This leads to partially embodied controllers, i.e., hybrid controllers that naturally integrate the computations that are performed by the robot body into a neural network architecture. Our results demonstrate the universal applicability of reward-modulated Hebbian learning. Furthermore, they demonstrate the robustness of systems trained with the learning rule. This study strengthens our belief that compliant robots should or can be seen as computational units, instead of dumb hardware that needs a complex controller. This link between compliant robotics and neural networks is also the main reason for our search for simple universal learning rules for both neural networks and robotics. PMID:26347645

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.

Two neural network algorithms for designing optimal terminal controllers with open final time

NASA Technical Reports Server (NTRS)

Plumer, Edward S.

1992-01-01

Multilayer neural networks, trained by the backpropagation through time algorithm (BPTT), have been used successfully as state-feedback controllers for nonlinear terminal control problems. Current BPTT techniques, however, are not able to deal systematically with open final-time situations such as minimum-time problems. Two approaches which extend BPTT to open final-time problems are presented. In the first, a neural network learns a mapping from initial-state to time-to-go. In the second, the optimal number of steps for each trial run is found using a line-search. Both methods are derived using Lagrange multiplier techniques. This theoretical framework is used to demonstrate that the derived algorithms are direct extensions of forward/backward sweep methods used in N-stage optimal control. The two algorithms are tested on a Zermelo problem and the resulting trajectories compare favorably to optimal control results.

Altered Synchronizations among Neural Networks in Geriatric Depression

PubMed Central

Wang, Lihong; Chou, Ying-Hui; Potter, Guy G.; Steffens, David C.

2015-01-01

Although major depression has been considered as a manifestation of discoordinated activity between affective and cognitive neural networks, only a few studies have examined the relationships among neural networks directly. Because of the known disconnection theory, geriatric depression could be a useful model in studying the interactions among different networks. In the present study, using independent component analysis to identify intrinsically connected neural networks, we investigated the alterations in synchronizations among neural networks in geriatric depression to better understand the underlying neural mechanisms. Resting-state fMRI data was collected from thirty-two patients with geriatric depression and thirty-two age-matched never-depressed controls. We compared the resting-state activities between the two groups in the default-mode, central executive, attention, salience, and affective networks as well as correlations among these networks. The depression group showed stronger activity than the controls in an affective network, specifically within the orbitofrontal region. However, unlike the never-depressed controls, geriatric depression group lacked synchronized/antisynchronized activity between the affective network and the other networks. Those depressed patients with lower executive function has greater synchronization between the salience network with the executive and affective networks. Our results demonstrate the effectiveness of the between-network analyses in examining neural models for geriatric depression. PMID:26180795

Altered Synchronizations among Neural Networks in Geriatric Depression.

PubMed

Wang, Lihong; Chou, Ying-Hui; Potter, Guy G; Steffens, David C

2015-01-01

Although major depression has been considered as a manifestation of discoordinated activity between affective and cognitive neural networks, only a few studies have examined the relationships among neural networks directly. Because of the known disconnection theory, geriatric depression could be a useful model in studying the interactions among different networks. In the present study, using independent component analysis to identify intrinsically connected neural networks, we investigated the alterations in synchronizations among neural networks in geriatric depression to better understand the underlying neural mechanisms. Resting-state fMRI data was collected from thirty-two patients with geriatric depression and thirty-two age-matched never-depressed controls. We compared the resting-state activities between the two groups in the default-mode, central executive, attention, salience, and affective networks as well as correlations among these networks. The depression group showed stronger activity than the controls in an affective network, specifically within the orbitofrontal region. However, unlike the never-depressed controls, geriatric depression group lacked synchronized/antisynchronized activity between the affective network and the other networks. Those depressed patients with lower executive function has greater synchronization between the salience network with the executive and affective networks. Our results demonstrate the effectiveness of the between-network analyses in examining neural models for geriatric depression.

Reach and grasp by people with tetraplegia using a neurally controlled robotic arm

PubMed Central

Hochberg, Leigh R.; Bacher, Daniel; Jarosiewicz, Beata; Masse, Nicolas Y.; Simeral, John D.; Vogel, Joern; Haddadin, Sami; Liu, Jie; Cash, Sydney S.; van der Smagt, Patrick; Donoghue, John P.

2012-01-01

Paralysis following spinal cord injury (SCI), brainstem stroke, amyotrophic lateral sclerosis (ALS) and other disorders can disconnect the brain from the body, eliminating the ability to carry out volitional movements. A neural interface system (NIS)1–5 could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices. We have previously shown that people with longstanding tetraplegia can use an NIS to move and click a computer cursor and to control physical devices6–8. Able-bodied monkeys have used an NIS to control a robotic arm9, but it is unknown whether people with profound upper extremity paralysis or limb loss could use cortical neuronal ensemble signals to direct useful arm actions. Here, we demonstrate the ability of two people with long-standing tetraplegia to use NIS-based control of a robotic arm to perform three-dimensional reach and grasp movements. Participants controlled the arm over a broad space without explicit training, using signals decoded from a small, local population of motor cortex (MI) neurons recorded from a 96-channel microelectrode array. One of the study participants, implanted with the sensor five years earlier, also used a robotic arm to drink coffee from a bottle. While robotic reach and grasp actions were not as fast or accurate as those of an able-bodied person, our results demonstrate the feasibility for people with tetraplegia, years after CNS injury, to recreate useful multidimensional control of complex devices directly from a small sample of neural signals. PMID:22596161

Distracted and down: neural mechanisms of affective interference in subclinical depression

PubMed Central

Andrews-Hanna, Jessica R.; Spielberg, Jeffrey M.; Warren, Stacie L.; Sutton, Bradley P.; Miller, Gregory A.; Heller, Wendy; Banich, Marie T.

2015-01-01

Previous studies have shown that depressed individuals have difficulty directing attention away from negative distractors, a phenomenon known as affective interference. However, findings are mixed regarding the neural mechanisms and network dynamics of affective interference. The present study addressed these issues by comparing neural activation during emotion-word and color-word Stroop tasks in participants with varying levels of (primarily subclinical) depression. Depressive symptoms predicted increased activation to negative distractors in areas of dorsal anterior cingulate cortex (dACC) and posterior cingulate cortex (PCC), regions implicated in cognitive control and internally directed attention, respectively. Increased dACC activity was also observed in the group-average response to incongruent distractors, suggesting that dACC activity during affective interference is related to overtaxed cognitive control. In contrast, regions of PCC were deactivated across the group in response to incongruent distractors, suggesting that PCC activity during affective interference represents task-independent processing. A psychophysiological interaction emerged in which higher depression predicted more positively correlated activity between dACC and PCC during affective interference, i.e. greater connectivity between cognitive control and internal-attention systems. These findings suggest that, when individuals high in depression are confronted by negative material, increased attention to internal thoughts and difficulty shifting resources to the external world interfere with goal-directed behavior. PMID:25062838

Improved head direction command classification using an optimised Bayesian neural network.

PubMed

Nguyen, Son T; Nguyen, Hung T; Taylor, Philip B; Middleton, James

2006-01-01

Assistive technologies have recently emerged to improve the quality of life of severely disabled people by enhancing their independence in daily activities. Since many of those individuals have limited or non-existing control from the neck downward, alternative hands-free input modalities have become very important for these people to access assistive devices. In hands-free control, head movement has been proved to be a very effective user interface as it can provide a comfortable, reliable and natural way to access the device. Recently, neural networks have been shown to be useful not only for real-time pattern recognition but also for creating user-adaptive models. Since multi-layer perceptron neural networks trained using standard back-propagation may cause poor generalisation, the Bayesian technique has been proposed to improve the generalisation and robustness of these networks. This paper describes the use of Bayesian neural networks in developing a hands-free wheelchair control system. The experimental results show that with the optimised architecture, classification Bayesian neural networks can detect head commands of wheelchair users accurately irrespective to their levels of injuries.

Neural Plasticity following Abacus Training in Humans: A Review and Future Directions

PubMed Central

Li, Yongxin; Chen, Feiyan; Huang, Wenhua

2016-01-01

The human brain has an enormous capacity to adapt to a broad variety of environmental demands. Previous studies in the field of abacus training have shown that this training can induce specific changes in the brain. However, the neural mechanism underlying these changes remains elusive. Here, we reviewed the behavioral and imaging findings of comparisons between abacus experts and average control subjects and focused on changes in activation patterns and changes in brain structure. Finally, we noted the limitations and the future directions of this field. We concluded that although current studies have provided us with information about the mechanisms of abacus training, more research on abacus training is needed to understand its neural impact. PMID:26881089

The extraction of neural information from the surface EMG for the control of upper-limb prostheses: emerging avenues and challenges.

PubMed

Farina, Dario; Jiang, Ning; Rehbaum, Hubertus; Holobar, Aleš; Graimann, Bernhard; Dietl, Hans; Aszmann, Oskar C

2014-07-01

Despite not recording directly from neural cells, the surface electromyogram (EMG) signal contains information on the neural drive to muscles, i.e., the spike trains of motor neurons. Using this property, myoelectric control consists of the recording of EMG signals for extracting control signals to command external devices, such as hand prostheses. In commercial control systems, the intensity of muscle activity is extracted from the EMG and used for single degrees of freedom activation (direct control). Over the past 60 years, academic research has progressed to more sophisticated approaches but, surprisingly, none of these academic achievements has been implemented in commercial systems so far. We provide an overview of both commercial and academic myoelectric control systems and we analyze their performance with respect to the characteristics of the ideal myocontroller. Classic and relatively novel academic methods are described, including techniques for simultaneous and proportional control of multiple degrees of freedom and the use of individual motor neuron spike trains for direct control. The conclusion is that the gap between industry and academia is due to the relatively small functional improvement in daily situations that academic systems offer, despite the promising laboratory results, at the expense of a substantial reduction in robustness. None of the systems so far proposed in the literature fulfills all the important criteria needed for widespread acceptance by the patients, i.e. intuitive, closed-loop, adaptive, and robust real-time ( 200 ms delay) control, minimal number of recording electrodes with low sensitivity to repositioning, minimal training, limited complexity and low consumption. Nonetheless, in recent years, important efforts have been invested in matching these criteria, with relevant steps forwards.

Closed-Loop and Activity-Guided Optogenetic Control

PubMed Central

Grosenick, Logan; Marshel, James H.; Deisseroth, Karl

2016-01-01

Advances in optical manipulation and observation of neural activity have set the stage for widespread implementation of closed-loop and activity-guided optical control of neural circuit dynamics. Closing the loop optogenetically (i.e., basing optogenetic stimulation on simultaneously observed dynamics in a principled way) is a powerful strategy for causal investigation of neural circuitry. In particular, observing and feeding back the effects of circuit interventions on physiologically relevant timescales is valuable for directly testing whether inferred models of dynamics, connectivity, and causation are accurate in vivo. Here we highlight technical and theoretical foundations as well as recent advances and opportunities in this area, and we review in detail the known caveats and limitations of optogenetic experimentation in the context of addressing these challenges with closed-loop optogenetic control in behaving animals. PMID:25856490

Kcnip1 a Ca²⁺-dependent transcriptional repressor regulates the size of the neural plate in Xenopus.

PubMed

Néant, Isabelle; Mellström, Britt; Gonzalez, Paz; Naranjo, Jose R; Moreau, Marc; Leclerc, Catherine

2015-09-01

In amphibian embryos, our previous work has demonstrated that calcium transients occurring in the dorsal ectoderm at the onset of gastrulation are necessary and sufficient to engage the ectodermal cells into a neural fate by inducing neural specific genes. Some of these genes are direct targets of calcium. Here we search for a direct transcriptional mechanism by which calcium signals are acting. The only known mechanism responsible for a direct action of calcium on gene transcription involves an EF-hand Ca²⁺ binding protein which belongs to a group of four proteins (Kcnip1 to 4). Kcnip protein can act in a Ca²⁺-dependent manner as a transcriptional repressor by binding to a specific DNA sequence, the Downstream Regulatory Element (DRE) site. In Xenopus, among the four kcnips, we show that only kcnip1 is timely and spatially present in the presumptive neural territories and is able to bind DRE sites in a Ca²⁺-dependent manner. The loss of function of kcnip1 results in the expansion of the neural plate through an increased proliferation of neural progenitors. Later on, this leads to an impairment in the development of anterior neural structures. We propose that, in the embryo, at the onset of neurogenesis Kcnip1 is the Ca²⁺-dependent transcriptional repressor that controls the size of the neural plate. This article is part of a Special Issue entitled: 13th European Symposium on Calcium. Copyright © 2014. Published by Elsevier B.V.

Ca(2+) coding and decoding strategies for the specification of neural and renal precursor cells during development.

PubMed

Moreau, Marc; Néant, Isabelle; Webb, Sarah E; Miller, Andrew L; Riou, Jean-François; Leclerc, Catherine

2016-03-01

During embryogenesis, a rise in intracellular Ca(2+) is known to be a widespread trigger for directing stem cells towards a specific tissue fate, but the precise Ca(2+) signalling mechanisms involved in achieving these pleiotropic effects are still poorly understood. In this review, we compare the Ca(2+) signalling events that appear to be one of the first steps in initiating and regulating both neural determination (neural induction) and kidney development (nephrogenesis). We have highlighted the necessary and sufficient role played by Ca(2+) influx and by Ca(2+) transients in the determination and differentiation of pools of neural or renal precursors. We have identified new Ca(2+) target genes involved in neural induction and we showed that the same Ca(2+) early target genes studied are not restricted to neural tissue but are also present in other tissues, principally in the pronephros. In this review, we also described a mechanism whereby the transcriptional control of gene expression during neurogenesis and nephrogenesis might be directly controlled by Ca(2+) signalling. This mechanism involves members of the Kcnip family such that a change in their binding properties to specific DNA sites is a result of Ca(2+) binding to EF-hand motifs. The different functions of Ca(2+) signalling during these two events illustrate the versatility of Ca(2+) as a second messenger. Copyright © 2015 Elsevier Ltd. All rights reserved.

A New Module in Neural Differentiation Control: Two MicroRNAs Upregulated by Retinoic Acid, miR-9 and -103, Target the Differentiation Inhibitor ID2

PubMed Central

Savino, Mauro; Laneve, Pietro; Caffarelli, Elisa; Nasi, Sergio

2012-01-01

The transcription factor ID2 is an important repressor of neural differentiation strongly implicated in nervous system cancers. MicroRNAs (miRNAs) are increasingly involved in differentiation control and cancer development. Here we show that two miRNAs upregulated on differentiation of neuroblastoma cells – miR-9 and miR-103 – restrain ID2 expression by directly targeting the coding sequence and 3′ untranslated region of the ID2 encoding messenger RNA, respectively. Notably, the two miRNAs show an inverse correlation with ID2 during neuroblastoma cell differentiation induced by retinoic acid. Overexpression of miR-9 and miR-103 in neuroblastoma cells reduces proliferation and promotes differentiation, as it was shown to occur upon ID2 inhibition. Conversely, an ID2 mutant that cannot be targeted by either miRNA prevents retinoic acid-induced differentiation more efficient than wild-type ID2. These findings reveal a new regulatory module involving two microRNAs upregulated during neural differentiation that directly target expression of the key differentiation inhibitor ID2, suggesting that its alteration may be involved in neural cancer development. PMID:22848373

A new module in neural differentiation control: two microRNAs upregulated by retinoic acid, miR-9 and -103, target the differentiation inhibitor ID2.

PubMed

Annibali, Daniela; Gioia, Ubaldo; Savino, Mauro; Laneve, Pietro; Caffarelli, Elisa; Nasi, Sergio

2012-01-01

The transcription factor ID2 is an important repressor of neural differentiation strongly implicated in nervous system cancers. MicroRNAs (miRNAs) are increasingly involved in differentiation control and cancer development. Here we show that two miRNAs upregulated on differentiation of neuroblastoma cells--miR-9 and miR-103--restrain ID2 expression by directly targeting the coding sequence and 3' untranslated region of the ID2 encoding messenger RNA, respectively. Notably, the two miRNAs show an inverse correlation with ID2 during neuroblastoma cell differentiation induced by retinoic acid. Overexpression of miR-9 and miR-103 in neuroblastoma cells reduces proliferation and promotes differentiation, as it was shown to occur upon ID2 inhibition. Conversely, an ID2 mutant that cannot be targeted by either miRNA prevents retinoic acid-induced differentiation more efficient than wild-type ID2. These findings reveal a new regulatory module involving two microRNAs upregulated during neural differentiation that directly target expression of the key differentiation inhibitor ID2, suggesting that its alteration may be involved in neural cancer development.

A continually online-trained neural network controller for brushless DC motor drives

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rubaai, A.; Kotaru, R.; Kankam, M.D.

2000-04-01

In this paper, a high-performance controller with simultaneous online identification and control is designed for brushless dc motor drives. The dynamics of the motor/load are modeled online, and controlled using two different neural network based identification and control schemes, as the system is in operation. In the first scheme, an attempt is made to control the rotor angular speed, utilizing a single three-hidden-layer network. The second scheme attempts to control the stator currents, using a predetermined control law as a function of the estimated states. This schemes incorporates three multilayered feedforward neural networks that are online trained, using the Levenburg-Marquadtmore » training algorithm. The control of the direct and quadrature components of the stator current successfully tracked a wide variety of trajectories after relatively short online training periods. The control strategy adapts to the uncertainties of the motor/load dynamics and, in addition, learns their inherent nonlinearities. Simulation results illustrated that a neurocontroller used in conjunction with adaptive control schemes can result in a flexible control device which may be utilized in a wide range of environments.« less

From wheels to wings with evolutionary spiking circuits.

PubMed

Floreano, Dario; Zufferey, Jean-Christophe; Nicoud, Jean-Daniel

2005-01-01

We give an overview of the EPFL indoor flying project, whose goal is to evolve neural controllers for autonomous, adaptive, indoor micro-flyers. Indoor flight is still a challenge because it requires miniaturization, energy efficiency, and control of nonlinear flight dynamics. This ongoing project consists of developing a flying, vision-based micro-robot, a bio-inspired controller composed of adaptive spiking neurons directly mapped into digital microcontrollers, and a method to evolve such a neural controller without human intervention. This article describes the motivation and methodology used to reach our goal as well as the results of a number of preliminary experiments on vision-based wheeled and flying robots.

Intra-day signal instabilities affect decoding performance in an intracortical neural interface system.

PubMed

Perge, János A; Homer, Mark L; Malik, Wasim Q; Cash, Sydney; Eskandar, Emad; Friehs, Gerhard; Donoghue, John P; Hochberg, Leigh R

2013-06-01

Motor neural interface systems (NIS) aim to convert neural signals into motor prosthetic or assistive device control, allowing people with paralysis to regain movement or control over their immediate environment. Effector or prosthetic control can degrade if the relationship between recorded neural signals and intended motor behavior changes. Therefore, characterizing both biological and technological sources of signal variability is important for a reliable NIS. To address the frequency and causes of neural signal variability in a spike-based NIS, we analyzed within-day fluctuations in spiking activity and action potential amplitude recorded with silicon microelectrode arrays implanted in the motor cortex of three people with tetraplegia (BrainGate pilot clinical trial, IDE). 84% of the recorded units showed a statistically significant change in apparent firing rate (3.8 ± 8.71 Hz or 49% of the mean rate) across several-minute epochs of tasks performed on a single session, and 74% of the units showed a significant change in spike amplitude (3.7 ± 6.5 µV or 5.5% of mean spike amplitude). 40% of the recording sessions showed a significant correlation in the occurrence of amplitude changes across electrodes, suggesting array micro-movement. Despite the relatively frequent amplitude changes, only 15% of the observed within-day rate changes originated from recording artifacts such as spike amplitude change or electrical noise, while 85% of the rate changes most likely emerged from physiological mechanisms. Computer simulations confirmed that systematic rate changes of individual neurons could produce a directional 'bias' in the decoded neural cursor movements. Instability in apparent neuronal spike rates indeed yielded a directional bias in 56% of all performance assessments in participant cursor control (n = 2 participants, 108 and 20 assessments over two years), resulting in suboptimal performance in these sessions. We anticipate that signal acquisition and decoding methods that can adapt to the reported instabilities will further improve the performance of intracortically-based NISs.

Neural Crossroads in the Hematopoietic Stem Cell Niche.

PubMed

Agarwala, Sobhika; Tamplin, Owen J

2018-05-29

The hematopoietic stem cell (HSC) niche supports steady-state hematopoiesis and responds to changing needs during stress and disease. The nervous system is an important regulator of the niche, and its influence is established early in development when stem cells are specified. Most research has focused on direct innervation of the niche, however recent findings show there are different modes of neural control, including globally by the central nervous system (CNS) and hormone release, locally by neural crest-derived mesenchymal stem cells, and intrinsically by hematopoietic cells that express neural receptors and neurotransmitters. Dysregulation between neural and hematopoietic systems can contribute to disease, however new therapeutic opportunities may be found among neuroregulator drugs repurposed to support hematopoiesis. Copyright © 2018 Elsevier Ltd. All rights reserved.

Drug release control and system understanding of sucrose esters matrix tablets by artificial neural networks.

PubMed

Chansanroj, Krisanin; Petrović, Jelena; Ibrić, Svetlana; Betz, Gabriele

2011-10-09

Artificial neural networks (ANNs) were applied for system understanding and prediction of drug release properties from direct compacted matrix tablets using sucrose esters (SEs) as matrix-forming agents for controlled release of a highly water soluble drug, metoprolol tartrate. Complexity of the system was presented through the effects of SE concentration and tablet porosity at various hydrophilic-lipophilic balance (HLB) values of SEs ranging from 0 to 16. Both effects contributed to release behaviors especially in the system containing hydrophilic SEs where swelling phenomena occurred. A self-organizing map neural network (SOM) was applied for visualizing interrelation among the variables and multilayer perceptron neural networks (MLPs) were employed to generalize the system and predict the drug release properties based on HLB value and concentration of SEs and tablet properties, i.e., tablet porosity, volume and tensile strength. Accurate prediction was obtained after systematically optimizing network performance based on learning algorithm of MLP. Drug release was mainly attributed to the effects of SEs, tablet volume and tensile strength in multi-dimensional interrelation whereas tablet porosity gave a small impact. Ability of system generalization and accurate prediction of the drug release properties proves the validity of SOM and MLPs for the formulation modeling of direct compacted matrix tablets containing controlled release agents of different material properties. Copyright © 2011 Elsevier B.V. All rights reserved.

Neural Activation During Mental Rotation in Complete Androgen Insensitivity Syndrome: The Influence of Sex Hormones and Sex Chromosomes.

PubMed

van Hemmen, Judy; Veltman, Dick J; Hoekzema, Elseline; Cohen-Kettenis, Peggy T; Dessens, Arianne B; Bakker, Julie

2016-03-01

Sex hormones, androgens in particular, are hypothesized to play a key role in the sexual differentiation of the human brain. However, possible direct effects of the sex chromosomes, that is, XX or XY, have not been well studied in humans. Individuals with complete androgen insensitivity syndrome (CAIS), who have a 46,XY karyotype but a female phenotype due to a complete androgen resistance, enable us to study the separate effects of gonadal hormones versus sex chromosomes on neural sex differences. Therefore, in the present study, we compared 46,XY men (n = 30) and 46,XX women (n = 29) to 46,XY individuals with CAIS (n = 21) on a mental rotation task using functional magnetic resonance imaging. Previously reported sex differences in neural activation during mental rotation were replicated in the control groups, with control men showing more activation in the inferior parietal lobe than control women. Individuals with CAIS showed a female-like neural activation pattern in the parietal lobe, indicating feminization of the brain in CAIS. Furthermore, this first neuroimaging study in individuals with CAIS provides evidence that sex differences in regional brain function during mental rotation are most likely not directly driven by genetic sex, but rather reflect gonadal hormone exposure. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Abnormal neural activities of directional brain networks in patients with long-term bilateral hearing loss.

PubMed

Xu, Long-Chun; Zhang, Gang; Zou, Yue; Zhang, Min-Feng; Zhang, Dong-Sheng; Ma, Hua; Zhao, Wen-Bo; Zhang, Guang-Yu

2017-10-13

The objective of the study is to provide some implications for rehabilitation of hearing impairment by investigating changes of neural activities of directional brain networks in patients with long-term bilateral hearing loss. Firstly, we implemented neuropsychological tests of 21 subjects (11 patients with long-term bilateral hearing loss, and 10 subjects with normal hearing), and these tests revealed significant differences between the deaf group and the controls. Then we constructed the individual specific virtual brain based on functional magnetic resonance data of participants by utilizing effective connectivity and multivariate regression methods. We exerted the stimulating signal to the primary auditory cortices of the virtual brain and observed the brain region activations. We found that patients with long-term bilateral hearing loss presented weaker brain region activations in the auditory and language networks, but enhanced neural activities in the default mode network as compared with normally hearing subjects. Especially, the right cerebral hemisphere presented more changes than the left. Additionally, weaker neural activities in the primary auditor cortices were also strongly associated with poorer cognitive performance. Finally, causal analysis revealed several interactional circuits among activated brain regions, and these interregional causal interactions implied that abnormal neural activities of the directional brain networks in the deaf patients impacted cognitive function.

Understanding Return on Investment for Data Center Consolidation

DTIC Science & Technology

2013-09-01

Channel over Ethernet FDCCI Federal Data Center Consolidation Initiative GAO Government Accountability Office GDA Government Directed Actions GIG ...to judge how each stakeholder group will benefit from it. Such measures as lower risk, greater control, better economies of scale, better utilization...NMS product by Kratos Networks called Neural Star to manage the Global Information Grid ( GIG ) (Kratos, 2013). DISA uses Neural Star as the primary

Neural Mechanisms of Interference Control Underlie the Relationship between Fluid Intelligence and Working Memory Span

ERIC Educational Resources Information Center

Burgess, Gregory C.; Gray, Jeremy R.; Conway, Andrew R. A.; Braver, Todd S.

2011-01-01

Fluid intelligence (gF) and working memory (WM) span predict success in demanding cognitive situations. Recent studies show that much of the variance in gF and WM span is shared, suggesting common neural mechanisms. This study provides a direct investigation of the degree to which shared variance in gF and WM span can be explained by neural…

Neural dynamics underlying emotional transmissions between individuals

PubMed Central

Levit-Binnun, Nava; Hendler, Talma; Lerner, Yulia

2017-01-01

Abstract Emotional experiences are frequently shaped by the emotional responses of co-present others. Research has shown that people constantly monitor and adapt to the incoming social–emotional signals, even without face-to-face interaction. And yet, the neural processes underlying such emotional transmissions have not been directly studied. Here, we investigated how the human brain processes emotional cues which arrive from another, co-attending individual. We presented continuous emotional feedback to participants who viewed a movie in the scanner. Participants in the social group (but not in the control group) believed that the feedback was coming from another person who was co-viewing the same movie. We found that social–emotional feedback significantly affected the neural dynamics both in the core affect and in the medial pre-frontal regions. Specifically, the response time-courses in those regions exhibited increased similarity across recipients and increased neural alignment with the timeline of the feedback in the social compared with control group. Taken in conjunction with previous research, this study suggests that emotional cues from others shape the neural dynamics across the whole neural continuum of emotional processing in the brain. Moreover, it demonstrates that interpersonal neural alignment can serve as a neural mechanism through which affective information is conveyed between individuals. PMID:28575520

Adaptive Control Strategies for Flexible Robotic Arm

NASA Technical Reports Server (NTRS)

Bialasiewicz, Jan T.

1996-01-01

The control problem of a flexible robotic arm has been investigated. The control strategies that have been developed have a wide application in approaching the general control problem of flexible space structures. The following control strategies have been developed and evaluated: neural self-tuning control algorithm, neural-network-based fuzzy logic control algorithm, and adaptive pole assignment algorithm. All of the above algorithms have been tested through computer simulation. In addition, the hardware implementation of a computer control system that controls the tip position of a flexible arm clamped on a rigid hub mounted directly on the vertical shaft of a dc motor, has been developed. An adaptive pole assignment algorithm has been applied to suppress vibrations of the described physical model of flexible robotic arm and has been successfully tested using this testbed.

Electronic bypass of spinal lesions: activation of lower motor neurons directly driven by cortical neural signals.

PubMed

Li, Yan; Alam, Monzurul; Guo, Shanshan; Ting, K H; He, Jufang

2014-07-03

Lower motor neurons in the spinal cord lose supraspinal inputs after complete spinal cord injury, leading to a loss of volitional control below the injury site. Extensive locomotor training with spinal cord stimulation can restore locomotion function after spinal cord injury in humans and animals. However, this locomotion is non-voluntary, meaning that subjects cannot control stimulation via their natural "intent". A recent study demonstrated an advanced system that triggers a stimulator using forelimb stepping electromyographic patterns to restore quadrupedal walking in rats with spinal cord transection. However, this indirect source of "intent" may mean that other non-stepping forelimb activities may false-trigger the spinal stimulator and thus produce unwanted hindlimb movements. We hypothesized that there are distinguishable neural activities in the primary motor cortex during treadmill walking, even after low-thoracic spinal transection in adult guinea pigs. We developed an electronic spinal bridge, called "Motolink", which detects these neural patterns and triggers a "spinal" stimulator for hindlimb movement. This hardware can be head-mounted or carried in a backpack. Neural data were processed in real-time and transmitted to a computer for analysis by an embedded processor. Off-line neural spike analysis was conducted to calculate and preset the spike threshold for "Motolink" hardware. We identified correlated activities of primary motor cortex neurons during treadmill walking of guinea pigs with spinal cord transection. These neural activities were used to predict the kinematic states of the animals. The appropriate selection of spike threshold value enabled the "Motolink" system to detect the neural "intent" of walking, which triggered electrical stimulation of the spinal cord and induced stepping-like hindlimb movements. We present a direct cortical "intent"-driven electronic spinal bridge to restore hindlimb locomotion after complete spinal cord injury.

Distracted and down: neural mechanisms of affective interference in subclinical depression.

PubMed

Kaiser, Roselinde H; Andrews-Hanna, Jessica R; Spielberg, Jeffrey M; Warren, Stacie L; Sutton, Bradley P; Miller, Gregory A; Heller, Wendy; Banich, Marie T

2015-05-01

Previous studies have shown that depressed individuals have difficulty directing attention away from negative distractors, a phenomenon known as affective interference. However, findings are mixed regarding the neural mechanisms and network dynamics of affective interference. The present study addressed these issues by comparing neural activation during emotion-word and color-word Stroop tasks in participants with varying levels of (primarily subclinical) depression. Depressive symptoms predicted increased activation to negative distractors in areas of dorsal anterior cingulate cortex (dACC) and posterior cingulate cortex (PCC), regions implicated in cognitive control and internally directed attention, respectively. Increased dACC activity was also observed in the group-average response to incongruent distractors, suggesting that dACC activity during affective interference is related to overtaxed cognitive control. In contrast, regions of PCC were deactivated across the group in response to incongruent distractors, suggesting that PCC activity during affective interference represents task-independent processing. A psychophysiological interaction emerged in which higher depression predicted more positively correlated activity between dACC and PCC during affective interference, i.e. greater connectivity between cognitive control and internal-attention systems. These findings suggest that, when individuals high in depression are confronted by negative material, increased attention to internal thoughts and difficulty shifting resources to the external world interfere with goal-directed behavior. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

A driven active mass damper by using output of a neural oscillator (effects of position control system changes on vibration mitigation performance)

NASA Astrophysics Data System (ADS)

Hongu, J.; Iba, D.; Sasaki, T.; Nakamura, M.; Moriwaki, I.

2015-03-01

In this paper, a design method for a PD controller, which is a part of a new active mass damper system using a neural oscillator for high-rise buildings, is proposed. The new system mimicking the motion of bipedal mammals is a quite simple system, which has the neural oscillator synchronizing with the acceleration response of the structure. The travel distance and direction of the auxiliary mass of the active mass damper is decided by the output of the neural oscillator, and then, the auxiliary mass is transferred to the decided location by using the PD controller. Therefore, the performance of the PD controller must be evaluated by the vibration energy absorbing efficiency by the system. In order to bring the actual path driven by the PD controller in closer alignment with the ideal path, which is assumed to be a sinusoidal wave under resonance, firstly, the path of the auxiliary mass driven by the PD controller is analytically derived, and the inner product between the vector of ideal and analytical path is evaluated. And then, the PD gain is decided by the maximum value of the inner product. Finally, numerical simulations confirm the validity of the proposed design method of the PD controller.

A Unified Approach to Adaptive Neural Control for Nonlinear Discrete-Time Systems With Nonlinear Dead-Zone Input.

PubMed

Liu, Yan-Jun; Gao, Ying; Tong, Shaocheng; Chen, C L Philip

2016-01-01

In this paper, an effective adaptive control approach is constructed to stabilize a class of nonlinear discrete-time systems, which contain unknown functions, unknown dead-zone input, and unknown control direction. Different from linear dead zone, the dead zone, in this paper, is a kind of nonlinear dead zone. To overcome the noncausal problem, which leads to the control scheme infeasible, the systems can be transformed into a m -step-ahead predictor. Due to nonlinear dead-zone appearance, the transformed predictor still contains the nonaffine function. In addition, it is assumed that the gain function of dead-zone input and the control direction are unknown. These conditions bring about the difficulties and the complicacy in the controller design. Thus, the implicit function theorem is applied to deal with nonaffine dead-zone appearance, the problem caused by the unknown control direction can be resolved through applying the discrete Nussbaum gain, and the neural networks are used to approximate the unknown function. Based on the Lyapunov theory, all the signals of the resulting closed-loop system are proved to be semiglobal uniformly ultimately bounded. Moreover, the tracking error is proved to be regulated to a small neighborhood around zero. The feasibility of the proposed approach is demonstrated by a simulation example.

Shared motion signals for human perceptual decisions and oculomotor actions

NASA Technical Reports Server (NTRS)

Stone, Leland S.; Krauzlis, Richard J.

2003-01-01

A fundamental question in primate neurobiology is to understand to what extent motor behaviors are driven by shared neural signals that also support conscious perception or by independent subconscious neural signals dedicated to motor control. Although it has clearly been established that cortical areas involved in processing visual motion support both perception and smooth pursuit eye movements, it remains unknown whether the same or different sets of neurons within these structures perform these two functions. Examination of the trial-by-trial variation in human perceptual and pursuit responses during a simultaneous psychophysical and oculomotor task reveals that the direction signals for pursuit and perception are not only similar on average but also co-vary on a trial-by-trial basis, even when performance is at or near chance and the decisions are determined largely by neural noise. We conclude that the neural signal encoding the direction of target motion that drives steady-state pursuit and supports concurrent perceptual judgments emanates from a shared ensemble of cortical neurons.

Direct heuristic dynamic programming for damping oscillations in a large power system.

PubMed

Lu, Chao; Si, Jennie; Xie, Xiaorong

2008-08-01

This paper applies a neural-network-based approximate dynamic programming method, namely, the direct heuristic dynamic programming (direct HDP), to a large power system stability control problem. The direct HDP is a learning- and approximation-based approach to addressing nonlinear coordinated control under uncertainty. One of the major design parameters, the controller learning objective function, is formulated to directly account for network-wide low-frequency oscillation with the presence of nonlinearity, uncertainty, and coupling effect among system components. Results include a novel learning control structure based on the direct HDP with applications to two power system problems. The first case involves static var compensator supplementary damping control, which is used to provide a comprehensive evaluation of the learning control performance. The second case aims at addressing a difficult complex system challenge by providing a new solution to a large interconnected power network oscillation damping control problem that frequently occurs in the China Southern Power Grid.

Exploring the role of neural mirroring in children with autism spectrum disorder.

PubMed

Ruysschaert, Lieselot; Warreyn, Petra; Wiersema, Jan R; Oostra, Ann; Roeyers, Herbert

2014-04-01

Investigating the underlying neural mechanisms of autism spectrum disorder (ASD) has recently been influenced by the discovery of mirror neurons. These neurons, active during both observation and execution of actions, are thought to play a crucial role in imitation and other social-communicative skills that are often impaired in ASD. In the current electroencephalographic study, we investigated mu suppression, indicating neural mirroring in children with ASD between the ages of 24 and 48 months and age-matched typically developing children, during observation of goal-directed actions and non-goal-directed mimicked hand movements, as well as during action execution. Results revealed no significant group differences with significant central mu suppression in the ASD children and control children during both execution and observation of goal-directed actions and during observation of hand movements. Furthermore, no significant correlations between mu suppression on one hand and quality of imitation, age, and social communication questionnaire scores on the other hand were found. These findings challenge the "broken mirror" hypothesis of ASD, suggesting that impaired neural mirroring is not a distinctive feature of ASD. © 2014 International Society for Autism Research, Wiley Periodicals, Inc.

Towards autonomous neuroprosthetic control using Hebbian reinforcement learning.

PubMed

Mahmoudi, Babak; Pohlmeyer, Eric A; Prins, Noeline W; Geng, Shijia; Sanchez, Justin C

2013-12-01

Our goal was to design an adaptive neuroprosthetic controller that could learn the mapping from neural states to prosthetic actions and automatically adjust adaptation using only a binary evaluative feedback as a measure of desirability/undesirability of performance. Hebbian reinforcement learning (HRL) in a connectionist network was used for the design of the adaptive controller. The method combines the efficiency of supervised learning with the generality of reinforcement learning. The convergence properties of this approach were studied using both closed-loop control simulations and open-loop simulations that used primate neural data from robot-assisted reaching tasks. The HRL controller was able to perform classification and regression tasks using its episodic and sequential learning modes, respectively. In our experiments, the HRL controller quickly achieved convergence to an effective control policy, followed by robust performance. The controller also automatically stopped adapting the parameters after converging to a satisfactory control policy. Additionally, when the input neural vector was reorganized, the controller resumed adaptation to maintain performance. By estimating an evaluative feedback directly from the user, the HRL control algorithm may provide an efficient method for autonomous adaptation of neuroprosthetic systems. This method may enable the user to teach the controller the desired behavior using only a simple feedback signal.

Serial, Covert, Shifts of Attention during Visual Search are Reflected by the Frontal Eye Fields and Correlated with Population Oscillations

PubMed Central

Buschman, Timothy J.; Miller, Earl K.

2009-01-01

Attention regulates the flood of sensory information into a manageable stream, and so understanding how attention is controlled is central to understanding cognition. Competing theories suggest visual search involves serial and/or parallel allocation of attention, but there is little direct, neural, evidence for either mechanism. Two monkeys were trained to covertly search an array for a target stimulus under visual search (endogenous) and pop-out (exogenous) conditions. Here we present neural evidence in the frontal eye fields (FEF) for serial, covert shifts of attention during search but not pop-out. Furthermore, attention shifts reflected in FEF spiking activity were correlated with 18–34 Hz oscillations in the local field potential, suggesting a ‘clocking’ signal. This provides direct neural evidence that primates can spontaneously adopt a serial search strategy and that these serial covert shifts of attention are directed by the FEF. It also suggests that neuron population oscillations may regulate the timing of cognitive processing. PMID:19679077

Engagement of neural circuits underlying 2D spatial navigation in a rodent virtual reality system.

PubMed

Aronov, Dmitriy; Tank, David W

2014-10-22

Virtual reality (VR) enables precise control of an animal's environment and otherwise impossible experimental manipulations. Neural activity in rodents has been studied on virtual 1D tracks. However, 2D navigation imposes additional requirements, such as the processing of head direction and environment boundaries, and it is unknown whether the neural circuits underlying 2D representations can be sufficiently engaged in VR. We implemented a VR setup for rats, including software and large-scale electrophysiology, that supports 2D navigation by allowing rotation and walking in any direction. The entorhinal-hippocampal circuit, including place, head direction, and grid cells, showed 2D activity patterns similar to those in the real world. Furthermore, border cells were observed, and hippocampal remapping was driven by environment shape, suggesting functional processing of virtual boundaries. These results illustrate that 2D spatial representations can be engaged by visual and rotational vestibular stimuli alone and suggest a novel VR tool for studying rat navigation.

Robust adaptive controller design for a class of uncertain nonlinear systems using online T-S fuzzy-neural modeling approach.

PubMed

Chien, Yi-Hsing; Wang, Wei-Yen; Leu, Yih-Guang; Lee, Tsu-Tian

2011-04-01

This paper proposes a novel method of online modeling and control via the Takagi-Sugeno (T-S) fuzzy-neural model for a class of uncertain nonlinear systems with some kinds of outputs. Although studies about adaptive T-S fuzzy-neural controllers have been made on some nonaffine nonlinear systems, little is known about the more complicated uncertain nonlinear systems. Because the nonlinear functions of the systems are uncertain, traditional T-S fuzzy control methods can model and control them only with great difficulty, if at all. Instead of modeling these uncertain functions directly, we propose that a T-S fuzzy-neural model approximates a so-called virtual linearized system (VLS) of the system, which includes modeling errors and external disturbances. We also propose an online identification algorithm for the VLS and put significant emphasis on robust tracking controller design using an adaptive scheme for the uncertain systems. Moreover, the stability of the closed-loop systems is proven by using strictly positive real Lyapunov theory. The proposed overall scheme guarantees that the outputs of the closed-loop systems asymptotically track the desired output trajectories. To illustrate the effectiveness and applicability of the proposed method, simulation results are given in this paper.

Laser direct writing of combinatorial libraries of idealized cellular constructs: Biomedical applications

NASA Astrophysics Data System (ADS)

Schiele, Nathan R.; Koppes, Ryan A.; Corr, David T.; Ellison, Karen S.; Thompson, Deanna M.; Ligon, Lee A.; Lippert, Thomas K. M.; Chrisey, Douglas B.

2009-03-01

The ability to control cell placement and to produce idealized cellular constructs is essential for understanding and controlling intercellular processes and ultimately for producing engineered tissue replacements. We have utilized a novel intra-cavity variable aperture excimer laser operated at 193 nm to reproducibly direct write mammalian cells with micrometer resolution to form a combinatorial array of idealized cellular constructs. We deposited patterns of human dermal fibroblasts, mouse myoblasts, rat neural stem cells, human breast cancer cells, and bovine pulmonary artery endothelial cells to study aspects of collagen network formation, breast cancer progression, and neural stem cell proliferation, respectively. Mammalian cells were deposited by matrix assisted pulsed laser evaporation direct write from ribbons comprised of a UV transparent quartz coated with either a thin layer of extracellular matrix or triazene as a dynamic release layer using CAD/CAM control. We demonstrate that through optical imaging and incorporation of a machine vision algorithm, specific cells on the ribbon can be laser deposited in spatial coherence with respect to geometrical arrays and existing cells on the receiving substrate. Having the ability to direct write cells into idealized cellular constructs can help to answer many biomedical questions and advance tissue engineering and cancer research.

Designing in vivo concentration gradients with discrete controlled release: a computational model

NASA Astrophysics Data System (ADS)

Walker, Edgar Y.; Barbour, Dennis L.

2010-08-01

One promising neurorehabilitation therapy involves presenting neurotrophins directly into the brain to induce growth of new neural connections. The precise control of neurotrophin concentration gradients deep within neural tissue that would be necessary for such a therapy is not currently possible, however. Here we evaluate the theoretical potential of a novel method of drug delivery, discrete controlled release (DCR), to control effective neurotrophin concentration gradients in an isotropic region of neocortex. We do so by constructing computational models of neurotrophin concentration profiles resulting from discrete release locations into the cortex and then optimizing their design for uniform concentration gradients. The resulting model indicates that by rationally selecting initial neurotrophin concentrations for drug-releasing electrode coatings in a square 16-electrode array, nearly uniform concentration gradients (i.e. planar concentration profiles) from one edge of the electrode array to the other should be obtainable. DCR therefore represents a promising new method of precisely directing neuronal growth in vivo over a wider spatial profile than would be possible with single release points.

Indirect iterative learning control for a discrete visual servo without a camera-robot model.

PubMed

Jiang, Ping; Bamforth, Leon C A; Feng, Zuren; Baruch, John E F; Chen, YangQuan

2007-08-01

This paper presents a discrete learning controller for vision-guided robot trajectory imitation with no prior knowledge of the camera-robot model. A teacher demonstrates a desired movement in front of a camera, and then, the robot is tasked to replay it by repetitive tracking. The imitation procedure is considered as a discrete tracking control problem in the image plane, with an unknown and time-varying image Jacobian matrix. Instead of updating the control signal directly, as is usually done in iterative learning control (ILC), a series of neural networks are used to approximate the unknown Jacobian matrix around every sample point in the demonstrated trajectory, and the time-varying weights of local neural networks are identified through repetitive tracking, i.e., indirect ILC. This makes repetitive segmented training possible, and a segmented training strategy is presented to retain the training trajectories solely within the effective region for neural network approximation. However, a singularity problem may occur if an unmodified neural-network-based Jacobian estimation is used to calculate the robot end-effector velocity. A new weight modification algorithm is proposed which ensures invertibility of the estimation, thus circumventing the problem. Stability is further discussed, and the relationship between the approximation capability of the neural network and the tracking accuracy is obtained. Simulations and experiments are carried out to illustrate the validity of the proposed controller for trajectory imitation of robot manipulators with unknown time-varying Jacobian matrices.

Cortical activity in the null space: permitting preparation without movement

PubMed Central

Kaufman, Matthew T.; Churchland, Mark M.; Ryu, Stephen I.; Shenoy, Krishna V.

2014-01-01

Neural circuits must perform computations and then selectively output the results to other circuits. Yet synapses do not change radically at millisecond timescales. A key question then is: how is communication between neural circuits controlled? In motor control, brain areas directly involved in driving movement are active well before movement begins. Muscle activity is some readout of neural activity, yet remains largely unchanged during preparation. Here we find that during preparation, while the monkey holds still, changes in motor cortical activity cancel out at the level of these population readouts. Motor cortex can thereby prepare the movement without prematurely causing it. Further, we found evidence that this mechanism also operates in dorsal premotor cortex (PMd), largely accounting for how preparatory activity is attenuated in primary motor cortex (M1). Selective use of “output-null” vs. “output-potent” patterns of activity may thus help control communication to the muscles and between these brain areas. PMID:24487233

Carbon nanotubes in neural interfacing applications

NASA Astrophysics Data System (ADS)

Voge, Christopher M.; Stegemann, Jan P.

2011-02-01

Carbon nanotubes (CNT) are remarkable materials with a simple and inert molecular structure that gives rise to a range of potentially valuable physical and electronic properties, including high aspect ratio, high mechanical strength and excellent electrical conductivity. This review summarizes recent research on the application of CNT-based materials to study and control cells of the nervous system. It includes the use of CNT as cell culture substrates, to create patterned surfaces and to study cell-matrix interactions. It also summarizes recent investigations of CNT toxicity, particularly as related to neural cells. The application of CNT-based materials to directing the differentiation of progenitor and stem cells toward neural lineages is also discussed. The emphasis is on how CNT surface chemistry and nanotopography can be altered, and how such changes can affect neural cell function. This knowledge can be applied to creating improved neural interfaces and devices, as well as providing new approaches to neural tissue engineering and regeneration.

Toward Real Time Neural Net Flight Controllers

NASA Technical Reports Server (NTRS)

Jorgensen, C. C.; Mah, R. W.; Ross, J.; Lu, Henry, Jr. (Technical Monitor)

1994-01-01

NASA Ames Research Center has an ongoing program in neural network control technology targeted toward real time flight demonstrations using a modified F-15 which permits direct inner loop control of actuators, rapid switching between alternative control designs, and substitutable processors. An important part of this program is the ACTIVE flight project which is examining the feasibility of using neural networks in the design, control, and system identification of new aircraft prototypes. This paper discusses two research applications initiated with this objective in mind: utilization of neural networks for wind tunnel aircraft model identification and rapid learning algorithms for on line reconfiguration and control. The first application involves the identification of aerodynamic flight characteristics from analysis of wind tunnel test data. This identification is important in the early stages of aircraft design because complete specification of control architecture's may not be possible even though concept models at varying scales are available for aerodynamic wind tunnel testing. Testing of this type is often a long and expensive process involving measurement of aircraft lift, drag, and moment of inertia at varying angles of attack and control surface configurations. This information in turn can be used in the design of the flight control systems by applying the derived lookup tables to generate piece wise linearized controllers. Thus, reduced costs in tunnel test times and the rapid transfer of wind tunnel insights into prototype controllers becomes an important factor in more efficient generation and testing of new flight systems. NASA Ames Research Center is successfully applying modular neural networks as one way of anticipating small scale aircraft model performances prior to testing, thus reducing the number of in tunnel test hours and potentially, the number of intermediate scaled models required for estimation of surface flow effects.

Neural Substrates of Visual Spatial Coding and Visual Feedback Control for Hand Movements in Allocentric and Target-Directed Tasks

PubMed Central

Thaler, Lore; Goodale, Melvyn A.

2011-01-01

Neuropsychological evidence suggests that different brain areas may be involved in movements that are directed at visual targets (e.g., pointing or reaching), and movements that are based on allocentric visual information (e.g., drawing or copying). Here we used fMRI to investigate the neural correlates of these two types of movements in healthy volunteers. Subjects (n = 14) performed right hand movements in either a target-directed task (moving a cursor to a target dot) or an allocentric task (moving a cursor to reproduce the distance and direction between two distal target dots) with or without visual feedback about their hand movement. Movements were monitored with an MR compatible touch panel. A whole brain analysis revealed that movements in allocentric conditions led to an increase in activity in the fundus of the left intra-parietal sulcus (IPS), in posterior IPS, in bilateral dorsal premotor cortex (PMd), and in the lateral occipital complex (LOC). Visual feedback in both target-directed and allocentric conditions led to an increase in activity in area MT+, superior parietal–occipital cortex (SPOC), and posterior IPS (all bilateral). In addition, we found that visual feedback affected brain activity differently in target-directed as compared to allocentric conditions, particularly in the pre-supplementary motor area, PMd, IPS, and parieto-occipital cortex. Our results, in combination with previous findings, suggest that the LOC is essential for allocentric visual coding and that SPOC is involved in visual feedback control. The differences in brain activity between target-directed and allocentric visual feedback conditions may be related to behavioral differences in visual feedback control. Our results advance the understanding of the visual coordinate frame used by the LOC. In addition, because of the nature of the allocentric task, our results have relevance for the understanding of neural substrates of magnitude estimation and vector coding of movements. PMID:21941474

Temporal pattern processing in songbirds.

PubMed

Comins, Jordan A; Gentner, Timothy Q

2014-10-01

Understanding how the brain perceives, organizes and uses patterned information is directly related to the neurobiology of language. Given the present limitations, such knowledge at the scale of neurons, neural circuits and neural populations can only come from non-human models, focusing on shared capacities that are relevant to language processing. Here we review recent advances in the behavioral and neural basis of temporal pattern processing of natural auditory communication signals in songbirds, focusing on European starlings. We suggest a general inhibitory circuit for contextual modulation that can act to control sensory representations based on patterning rules. Copyright © 2014. Published by Elsevier Ltd.

Lamellipodin and the Scar/WAVE complex cooperate to promote cell migration in vivo

PubMed Central

Law, Ah-Lai; Vehlow, Anne; Kotini, Maria; Dodgson, Lauren; Soong, Daniel; Theveneau, Eric; Bodo, Cristian; Taylor, Eleanor; Navarro, Christel; Perera, Upamali; Michael, Magdalene; Dunn, Graham A.; Bennett, Daimark; Mayor, Roberto

2013-01-01

Cell migration is essential for development, but its deregulation causes metastasis. The Scar/WAVE complex is absolutely required for lamellipodia and is a key effector in cell migration, but its regulation in vivo is enigmatic. Lamellipodin (Lpd) controls lamellipodium formation through an unknown mechanism. Here, we report that Lpd directly binds active Rac, which regulates a direct interaction between Lpd and the Scar/WAVE complex via Abi. Consequently, Lpd controls lamellipodium size, cell migration speed, and persistence via Scar/WAVE in vitro. Moreover, Lpd knockout mice display defective pigmentation because fewer migrating neural crest-derived melanoblasts reach their target during development. Consistently, Lpd regulates mesenchymal neural crest cell migration cell autonomously in Xenopus laevis via the Scar/WAVE complex. Further, Lpd’s Drosophila melanogaster orthologue Pico binds Scar, and both regulate collective epithelial border cell migration. Pico also controls directed cell protrusions of border cell clusters in a Scar-dependent manner. Taken together, Lpd is an essential, evolutionary conserved regulator of the Scar/WAVE complex during cell migration in vivo. PMID:24247431

Examples of Current and Future Uses of Neural-Net Image Processing for Aerospace Applications

NASA Technical Reports Server (NTRS)

Decker, Arthur J.

2004-01-01

Feed forward artificial neural networks are very convenient for performing correlated interpolation of pairs of complex noisy data sets as well as detecting small changes in image data. Image-to-image, image-to-variable and image-to-index applications have been tested at Glenn. Early demonstration applications are summarized including image-directed alignment of optics, tomography, flow-visualization control of wind-tunnel operations and structural-model-trained neural networks. A practical application is reviewed that employs neural-net detection of structural damage from interference fringe patterns. Both sensor-based and optics-only calibration procedures are available for this technique. These accomplishments have generated the knowledge necessary to suggest some other applications for NASA and Government programs. A tomography application is discussed to support Glenn's Icing Research tomography effort. The self-regularizing capability of a neural net is shown to predict the expected performance of the tomography geometry and to augment fast data processing. Other potential applications involve the quantum technologies. It may be possible to use a neural net as an image-to-image controller of an optical tweezers being used for diagnostics of isolated nano structures. The image-to-image transformation properties also offer the potential for simulating quantum computing. Computer resources are detailed for implementing the black box calibration features of the neural nets.

Listening to Brain Microcircuits for Interfacing With External World—Progress in Wireless Implantable Microelectronic Neuroengineering Devices

PubMed Central

Nurmikko, Arto V.; Donoghue, John P.; Hochberg, Leigh R.; Patterson, William R.; Song, Yoon-Kyu; Bull, Christopher W.; Borton, David A.; Laiwalla, Farah; Park, Sunmee; Ming, Yin; Aceros, Juan

2011-01-01

Acquiring neural signals at high spatial and temporal resolution directly from brain microcircuits and decoding their activity to interpret commands and/or prior planning activity, such as motion of an arm or a leg, is a prime goal of modern neurotechnology. Its practical aims include assistive devices for subjects whose normal neural information pathways are not functioning due to physical damage or disease. On the fundamental side, researchers are striving to decipher the code of multiple neural microcircuits which collectively make up nature’s amazing computing machine, the brain. By implanting biocompatible neural sensor probes directly into the brain, in the form of microelectrode arrays, it is now possible to extract information from interacting populations of neural cells with spatial and temporal resolution at the single cell level. With parallel advances in application of statistical and mathematical techniques tools for deciphering the neural code, extracted populations or correlated neurons, significant understanding has been achieved of those brain commands that control, e.g., the motion of an arm in a primate (monkey or a human subject). These developments are accelerating the work on neural prosthetics where brain derived signals may be employed to bypass, e.g., an injured spinal cord. One key element in achieving the goals for practical and versatile neural prostheses is the development of fully implantable wireless microelectronic “brain-interfaces” within the body, a point of special emphasis of this paper. PMID:21654935

The Correlation among Neural Dynamic Processing of Conflict Control, Testosterone and Cortisol Levels in 10-Year-Old Children.

PubMed

Shangguan, Fangfang; Liu, Tongran; Liu, Xiuying; Shi, Jiannong

2017-01-01

Cognitive control is related to goal-directed self-regulation abilities, which is fundamental for human development. Conflict control includes the neural processes of conflict monitoring and conflict resolution. Testosterone and cortisol are essential hormones for the development of cognitive functions. However, there are no studies that have investigated the correlation of these two hormones with conflict control in preadolescents. In this study, we aimed to explore whether testosterone, cortisol, and testosterone/cortisol ratio worked differently for preadolescent's conflict control processes in varied conflict control tasks. Thirty-two 10-year-old children (16 boys and 16 girls) were enrolled. They were instructed to accomplish three conflict control tasks with different conflict dimensions, including the Flanker, Simon, and Stroop tasks, and electrophysiological signals were recorded. Salivary samples were collected from each child. The testosterone and cortisol levels were determined by enzyme-linked immunosorbent assay. The electrophysiological results showed that the incongruent trials induced greater N2/N450 and P3/SP responses than the congruent trials during neural processes of conflict monitoring and conflict resolution in the Flanker and Stroop tasks. The hormonal findings showed that (1) the testosterone/cortisol ratio was correlated with conflict control accuracy and conflict resolution in the Flanker task; (2) the testosterone level was associated with conflict control performance and neural processing of conflict resolution in the Stroop task; (3) the cortisol level was correlated with conflict control performance and neural processing of conflict monitoring in the Simon task. In conclusion, in 10-year-old children, the fewer processes a task needs, the more likely there is an association between the T/C ratios and the behavioral and brain response, and the dual-hormone effects on conflict resolution may be testosterone-driven in the Stroop and Flanker tasks.

A Symbiotic Brain-Machine Interface through Value-Based Decision Making

PubMed Central

Mahmoudi, Babak; Sanchez, Justin C.

2011-01-01

Background In the development of Brain Machine Interfaces (BMIs), there is a great need to enable users to interact with changing environments during the activities of daily life. It is expected that the number and scope of the learning tasks encountered during interaction with the environment as well as the pattern of brain activity will vary over time. These conditions, in addition to neural reorganization, pose a challenge to decoding neural commands for BMIs. We have developed a new BMI framework in which a computational agent symbiotically decoded users' intended actions by utilizing both motor commands and goal information directly from the brain through a continuous Perception-Action-Reward Cycle (PARC). Methodology The control architecture designed was based on Actor-Critic learning, which is a PARC-based reinforcement learning method. Our neurophysiology studies in rat models suggested that Nucleus Accumbens (NAcc) contained a rich representation of goal information in terms of predicting the probability of earning reward and it could be translated into an evaluative feedback for adaptation of the decoder with high precision. Simulated neural control experiments showed that the system was able to maintain high performance in decoding neural motor commands during novel tasks or in the presence of reorganization in the neural input. We then implanted a dual micro-wire array in the primary motor cortex (M1) and the NAcc of rat brain and implemented a full closed-loop system in which robot actions were decoded from the single unit activity in M1 based on an evaluative feedback that was estimated from NAcc. Conclusions Our results suggest that adapting the BMI decoder with an evaluative feedback that is directly extracted from the brain is a possible solution to the problem of operating BMIs in changing environments with dynamic neural signals. During closed-loop control, the agent was able to solve a reaching task by capturing the action and reward interdependency in the brain. PMID:21423797

Neural control of the ciliary muscle.

DOT National Transportation Integrated Search

1963-03-01

The cat ciliary muscle was shown to respond to both parasympathetic and sympathetic nerve impulses in vitro. Sympathetic responses were directionally opposite to parasympathetic responses and are interpreted as active contractions of the radial muscl...

Letting thoughts take wing.

PubMed

Jorgensen, Chuck; Wheeler, Kevin

2002-03-01

Recent developments in neuroelectronics are applied to aviation and airplane flight control instruments. Electromyographic control has been applied to flight simulations using the autopilot interface in order to use gestures to give bank and pitch commands to the autopilot. In other demonstrations, direct rate control was used to perform repeated successful landings and the damage-adaptive capability of inner-loop neural and propulsion-based controls was utilized.

Intra-day signal instabilities affect decoding performance in an intracortical neural interface system

PubMed Central

Perge, János A.; Homer, Mark L.; Malik, Wasim Q.; Cash, Sydney; Eskandar, Emad; Friehs, Gerhard; Donoghue, John P.; Hochberg, Leigh R.

2013-01-01

Objective Motor Neural Interface Systems (NIS) aim to convert neural signals into motor prosthetic or assistive device control, allowing people with paralysis to regain movement or control over their immediate environment. Effector or prosthetic control can degrade if the relationship between recorded neural signals and intended motor behavior changes. Therefore, characterizing both biological and technological sources of signal variability is important for a reliable NIS. Approach To address the frequency and causes of neural signal variability in a spike-based NIS, we analyzed within-day fluctuations in spiking activity and action potential amplitude recorded with silicon microelectrode arrays implanted in the motor cortex of three people with tetraplegia (BrainGate pilot clinical trial, IDE). Main results Eighty-four percent of the recorded units showed a statistically significant change in apparent firing rate (3.8±8.71Hz or 49% of the mean rate) across several-minute epochs of tasks performed on a single session, and seventy-four percent of the units showed a significant change in spike amplitude (3.7±6.5μV or 5.5% of mean spike amplitude). Forty percent of the recording sessions showed a significant correlation in the occurrence of amplitude changes across electrodes, suggesting array micro-movement. Despite the relatively frequent amplitude changes, only 15% of the observed within-day rate changes originated from recording artifacts such as spike amplitude change or electrical noise, while 85% of the rate changes most likely emerged from physiological mechanisms. Computer simulations confirmed that systematic rate changes of individual neurons could produce a directional “bias” in the decoded neural cursor movements. Instability in apparent neuronal spike rates indeed yielded a directional bias in fifty-six percent of all performance assessments in participant cursor control (n=2 participants, 108 and 20 assessments over two years), resulting in suboptimal performance in these sessions. Significance We anticipate that signal acquisition and decoding methods that can adapt to the reported instabilities will further improve the performance of intracortically-based NISs. PMID:23574741

Engineering neural systems for high-level problem solving.

PubMed

Sylvester, Jared; Reggia, James

2016-07-01

There is a long-standing, sometimes contentious debate in AI concerning the relative merits of a symbolic, top-down approach vs. a neural, bottom-up approach to engineering intelligent machine behaviors. While neurocomputational methods excel at lower-level cognitive tasks (incremental learning for pattern classification, low-level sensorimotor control, fault tolerance and processing of noisy data, etc.), they are largely non-competitive with top-down symbolic methods for tasks involving high-level cognitive problem solving (goal-directed reasoning, metacognition, planning, etc.). Here we take a step towards addressing this limitation by developing a purely neural framework named galis. Our goal in this work is to integrate top-down (non-symbolic) control of a neural network system with more traditional bottom-up neural computations. galis is based on attractor networks that can be "programmed" with temporal sequences of hand-crafted instructions that control problem solving by gating the activity retention of, communication between, and learning done by other neural networks. We demonstrate the effectiveness of this approach by showing that it can be applied successfully to solve sequential card matching problems, using both human performance and a top-down symbolic algorithm as experimental controls. Solving this kind of problem makes use of top-down attention control and the binding together of visual features in ways that are easy for symbolic AI systems but not for neural networks to achieve. Our model can not only be instructed on how to solve card matching problems successfully, but its performance also qualitatively (and sometimes quantitatively) matches the performance of both human subjects that we had perform the same task and the top-down symbolic algorithm that we used as an experimental control. We conclude that the core principles underlying the galis framework provide a promising approach to engineering purely neurocomputational systems for problem-solving tasks that in people require higher-level cognitive functions. Copyright © 2016 Elsevier Ltd. All rights reserved.

Major transcriptome re-organisation and abrupt changes in signalling, cell cycle and chromatin regulation at neural differentiation in vivo.

PubMed

Olivera-Martinez, Isabel; Schurch, Nick; Li, Roman A; Song, Junfang; Halley, Pamela A; Das, Raman M; Burt, Dave W; Barton, Geoffrey J; Storey, Kate G

2014-08-01

Here, we exploit the spatial separation of temporal events of neural differentiation in the elongating chick body axis to provide the first analysis of transcriptome change in progressively more differentiated neural cell populations in vivo. Microarray data, validated against direct RNA sequencing, identified: (1) a gene cohort characteristic of the multi-potent stem zone epiblast, which contains neuro-mesodermal progenitors that progressively generate the spinal cord; (2) a major transcriptome re-organisation as cells then adopt a neural fate; and (3) increasing diversity as neural patterning and neuron production begin. Focussing on the transition from multi-potent to neural state cells, we capture changes in major signalling pathways, uncover novel Wnt and Notch signalling dynamics, and implicate new pathways (mevalonate pathway/steroid biogenesis and TGFβ). This analysis further predicts changes in cellular processes, cell cycle, RNA-processing and protein turnover as cells acquire neural fate. We show that these changes are conserved across species and provide biological evidence for reduced proteasome efficiency and a novel lengthening of S phase. This latter step may provide time for epigenetic events to mediate large-scale transcriptome re-organisation; consistent with this, we uncover simultaneous downregulation of major chromatin modifiers as the neural programme is established. We further demonstrate that transcription of one such gene, HDAC1, is dependent on FGF signalling, making a novel link between signals that control neural differentiation and transcription of a core regulator of chromatin organisation. Our work implicates new signalling pathways and dynamics, cellular processes and epigenetic modifiers in neural differentiation in vivo, identifying multiple new potential cellular and molecular mechanisms that direct differentiation. © 2014. Published by The Company of Biologists Ltd.

Neural Network Based Modeling and Analysis of LP Control Surface Allocation

NASA Technical Reports Server (NTRS)

Langari, Reza; Krishnakumar, Kalmanje; Gundy-Burlet, Karen

2003-01-01

This paper presents an approach to interpretive modeling of LP based control allocation in intelligent flight control. The emphasis is placed on a nonlinear interpretation of the LP allocation process as a static map to support analytical study of the resulting closed loop system, albeit in approximate form. The approach makes use of a bi-layer neural network to capture the essential functioning of the LP allocation process. It is further shown via Lyapunov based analysis that under certain relatively mild conditions the resulting closed loop system is stable. Some preliminary conclusions from a study at Ames are stated and directions for further research are given at the conclusion of the paper.

An Integrated Gait and Balance Analysis System to Define Human Locomotor Control

DTIC Science & Technology

2016-04-29

common in the “real-world”. Furthermore, BCI controllers need some sort of direct link into neural signals and this requires invasive surgery and...L. J., Simon, A. M., Young, A. J., Lipschutz, R. D., Finucane, S. B., Smith, D. G., & Kuiken, T. A. (2013). Robotic leg control with EMG decoding in

An architectural approach to create self organizing control systems for practical autonomous robots

NASA Technical Reports Server (NTRS)

Greiner, Helen

1991-01-01

For practical industrial applications, the development of trainable robots is an important and immediate objective. Therefore, the developing of flexible intelligence directly applicable to training is emphasized. It is generally agreed upon by the AI community that the fusion of expert systems, neural networks, and conventionally programmed modules (e.g., a trajectory generator) is promising in the quest for autonomous robotic intelligence. Autonomous robot development is hindered by integration and architectural problems. Some obstacles towards the construction of more general robot control systems are as follows: (1) Growth problem; (2) Software generation; (3) Interaction with environment; (4) Reliability; and (5) Resource limitation. Neural networks can be successfully applied to some of these problems. However, current implementations of neural networks are hampered by the resource limitation problem and must be trained extensively to produce computationally accurate output. A generalization of conventional neural nets is proposed, and an architecture is offered in an attempt to address the above problems.

Non-neural Muscle Weakness Has Limited Influence on Complexity of Motor Control during Gait

PubMed Central

Goudriaan, Marije; Shuman, Benjamin R.; Steele, Katherine M.; Van den Hauwe, Marleen; Goemans, Nathalie; Molenaers, Guy; Desloovere, Kaat

2018-01-01

Cerebral palsy (CP) and Duchenne muscular dystrophy (DMD) are neuromuscular disorders characterized by muscle weakness. Weakness in CP has neural and non-neural components, whereas in DMD, weakness can be considered as a predominantly non-neural problem. Despite the different underlying causes, weakness is a constraint for the central nervous system when controlling gait. CP demonstrates decreased complexity of motor control during gait from muscle synergy analysis, which is reflected by a higher total variance accounted for by one synergy (tVAF1). However, it remains unclear if weakness directly contributes to higher tVAF1 in CP, or whether altered tVAF1 reflects mainly neural impairments. If muscle weakness directly contributes to higher tVAF1, then tVAF1 should also be increased in DMD. To examine the etiology of increased tVAF1, muscle activity data of gluteus medius, rectus femoris, medial hamstrings, medial gastrocnemius, and tibialis anterior were measured at self-selected walking speed, and strength data from knee extensors, knee flexors, dorsiflexors and plantar flexors, were analyzed in 15 children with CP [median (IQR) age: 8.9 (2.2)], 15 boys with DMD [8.7 (3.1)], and 15 typical developing (TD) children [8.6 (2.7)]. We computed tVAF1 from 10 concatenated steps with non-negative matrix factorization, and compared tVAF1 between the three groups with a Mann-Whiney U-test. Spearman's rank correlation coefficients were used to determine if weakness in specific muscle groups contributed to altered tVAF1. No significant differences in tVAF1 were found between DMD [tVAF1: 0.60 (0.07)] and TD children [0.65 (0.07)], while tVAF1 was significantly higher in CP [(0.74 (0.09)] than in the other groups (both p < 0.005). In CP, weakness in the plantar flexors was related to higher tVAF1 (r = −0.72). In DMD, knee extensor weakness related to increased tVAF1 (r = −0.50). These results suggest that the non-neural weakness in DMD had limited influence on complexity of motor control during gait and that the higher tVAF1 in children with CP is mainly related to neural impairments caused by the brain lesion. PMID:29445330

Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia*

PubMed Central

Kim, Sung-Phil; Simeral, John D; Hochberg, Leigh R; Donoghue, John P; Black, Michael J

2010-01-01

Computer-mediated connections between human motor cortical neurons and assistive devices promise to improve or restore lost function in people with paralysis. Recently, a pilot clinical study of an intracortical neural interface system demonstrated that a tetraplegic human was able to obtain continuous two-dimensional control of a computer cursor using neural activity recorded from his motor cortex. This control, however, was not sufficiently accurate for reliable use in many common computer control tasks. Here, we studied several central design choices for such a system including the kinematic representation for cursor movement, the decoding method that translates neuronal ensemble spiking activity into a control signal and the cursor control task used during training for optimizing the parameters of the decoding method. In two tetraplegic participants, we found that controlling a cursor's velocity resulted in more accurate closed-loop control than controlling its position directly and that cursor velocity control was achieved more rapidly than position control. Control quality was further improved over conventional linear filters by using a probabilistic method, the Kalman filter, to decode human motor cortical activity. Performance assessment based on standard metrics used for the evaluation of a wide range of pointing devices demonstrated significantly improved cursor control with velocity rather than position decoding. PMID:19015583

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.

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.

Restricted Complexity Framework for Nonlinear Adaptive Control in Complex Systems

NASA Astrophysics Data System (ADS)

Williams, Rube B.

2004-02-01

Control law adaptation that includes implicit or explicit adaptive state estimation, can be a fundamental underpinning for the success of intelligent control in complex systems, particularly during subsystem failures, where vital system states and parameters can be impractical or impossible to measure directly. A practical algorithm is proposed for adaptive state filtering and control in nonlinear dynamic systems when the state equations are unknown or are too complex to model analytically. The state equations and inverse plant model are approximated by using neural networks. A framework for a neural network based nonlinear dynamic inversion control law is proposed, as an extrapolation of prior developed restricted complexity methodology used to formulate the adaptive state filter. Examples of adaptive filter performance are presented for an SSME simulation with high pressure turbine failure to support extrapolations to adaptive control problems.

A biologically inspired neural net for trajectory formation and obstacle avoidance.

PubMed

Glasius, R; Komoda, A; Gielen, S C

1996-06-01

In this paper we present a biologically inspired two-layered neural network for trajectory formation and obstacle avoidance. The two topographically ordered neural maps consist of analog neurons having continuous dynamics. The first layer, the sensory map, receives sensory information and builds up an activity pattern which contains the optimal solution (i.e. shortest path without collisions) for any given set of current position, target positions and obstacle positions. Targets and obstacles are allowed to move, in which case the activity pattern in the sensory map will change accordingly. The time evolution of the neural activity in the second layer, the motor map, results in a moving cluster of activity, which can be interpreted as a population vector. Through the feedforward connections between the two layers, input of the sensory map directs the movement of the cluster along the optimal path from the current position of the cluster to the target position. The smooth trajectory is the result of the intrinsic dynamics of the network only. No supervisor is required. The output of the motor map can be used for direct control of an autonomous system in a cluttered environment or for control of the actuators of a biological limb or robot manipulator. The system is able to reach a target even in the presence of an external perturbation. Computer simulations of a point robot and a multi-joint manipulator illustrate the theory.

The impact of visual gaze direction on auditory object tracking.

PubMed

Pomper, Ulrich; Chait, Maria

2017-07-05

Subjective experience suggests that we are able to direct our auditory attention independent of our visual gaze, e.g when shadowing a nearby conversation at a cocktail party. But what are the consequences at the behavioural and neural level? While numerous studies have investigated both auditory attention and visual gaze independently, little is known about their interaction during selective listening. In the present EEG study, we manipulated visual gaze independently of auditory attention while participants detected targets presented from one of three loudspeakers. We observed increased response times when gaze was directed away from the locus of auditory attention. Further, we found an increase in occipital alpha-band power contralateral to the direction of gaze, indicative of a suppression of distracting input. Finally, this condition also led to stronger central theta-band power, which correlated with the observed effect in response times, indicative of differences in top-down processing. Our data suggest that a misalignment between gaze and auditory attention both reduce behavioural performance and modulate underlying neural processes. The involvement of central theta-band and occipital alpha-band effects are in line with compensatory neural mechanisms such as increased cognitive control and the suppression of task irrelevant inputs.

The alcoholic brain: neural bases of impaired reward-based decision-making in alcohol use disorders.

PubMed

Galandra, Caterina; Basso, Gianpaolo; Cappa, Stefano; Canessa, Nicola

2018-03-01

Neuroeconomics is providing insights into the neural bases of decision-making in normal and pathological conditions. In the neuropsychiatric domain, this discipline investigates how abnormal functioning of neural systems associated with reward processing and cognitive control promotes different disorders, and whether such evidence may inform treatments. This endeavor is crucial when studying different types of addiction, which share a core promoting mechanism in the imbalance between impulsive subcortical neural signals associated with immediate pleasurable outcomes and inhibitory signals mediated by a prefrontal reflective system. The resulting impairment in behavioral control represents a hallmark of alcohol use disorders (AUDs), a chronic relapsing disorder characterized by excessive alcohol consumption despite devastating consequences. This review aims to summarize available magnetic resonance imaging (MRI) evidence on reward-related decision-making alterations in AUDs, and to envision possible future research directions. We review functional MRI (fMRI) studies using tasks involving monetary rewards, as well as MRI studies relating decision-making parameters to neurostructural gray- or white-matter metrics. The available data suggest that excessive alcohol exposure affects neural signaling within brain networks underlying adaptive behavioral learning via the implementation of prediction errors. Namely, weaker ventromedial prefrontal cortex activity and altered connectivity between ventral striatum and dorsolateral prefrontal cortex likely underpin a shift from goal-directed to habitual actions which, in turn, might underpin compulsive alcohol consumption and relapsing episodes despite adverse consequences. Overall, these data highlight abnormal fronto-striatal connectivity as a candidate neurobiological marker of impaired choice in AUDs. Further studies are needed, however, to unveil its implications in the multiple facets of decision-making.

Listening to Brain Microcircuits for Interfacing With External World-Progress in Wireless Implantable Microelectronic Neuroengineering Devices: Experimental systems are described for electrical recording in the brain using multiple microelectrodes and short range implantable or wearable broadcasting units.

PubMed

Nurmikko, Arto V; Donoghue, John P; Hochberg, Leigh R; Patterson, William R; Song, Yoon-Kyu; Bull, Christopher W; Borton, David A; Laiwalla, Farah; Park, Sunmee; Ming, Yin; Aceros, Juan

2010-01-01

Acquiring neural signals at high spatial and temporal resolution directly from brain microcircuits and decoding their activity to interpret commands and/or prior planning activity, such as motion of an arm or a leg, is a prime goal of modern neurotechnology. Its practical aims include assistive devices for subjects whose normal neural information pathways are not functioning due to physical damage or disease. On the fundamental side, researchers are striving to decipher the code of multiple neural microcircuits which collectively make up nature's amazing computing machine, the brain. By implanting biocompatible neural sensor probes directly into the brain, in the form of microelectrode arrays, it is now possible to extract information from interacting populations of neural cells with spatial and temporal resolution at the single cell level. With parallel advances in application of statistical and mathematical techniques tools for deciphering the neural code, extracted populations or correlated neurons, significant understanding has been achieved of those brain commands that control, e.g., the motion of an arm in a primate (monkey or a human subject). These developments are accelerating the work on neural prosthetics where brain derived signals may be employed to bypass, e.g., an injured spinal cord. One key element in achieving the goals for practical and versatile neural prostheses is the development of fully implantable wireless microelectronic "brain-interfaces" within the body, a point of special emphasis of this paper.

Individual differences in self-reported self-control predict successful emotion regulation

PubMed Central

Dörfel, Denise; Steimke, Rosa; Trempler, Ima; Magrabi, Amadeus; Ludwig, Vera U.; Schubert, Torsten; Stelzel, Christine; Walter, Henrik

2016-01-01

Both self-control and emotion regulation enable individuals to adapt to external circumstances and social contexts, and both are assumed to rely on the overlapping neural resources. Here, we tested whether high self-reported self-control is related to successful emotion regulation on the behavioral and neural level. One hundred eight participants completed three self-control questionnaires and regulated their negative emotions during functional magnetic resonance imaging using reappraisal (distancing). Trait self-control correlated positively with successful emotion regulation both subjectively and neurally, as indicated by online ratings of negative emotions and functional connectivity strength between the amygdala and prefrontal areas, respectively. This stronger overall connectivity of the left amygdala was related to more successful subjective emotion regulation. Comparing amygdala activity over time showed that high self-controllers successfully maintained down-regulation of the left amygdala over time, while low self-controllers failed to down-regulate towards the end of the experiment. This indicates that high self-controllers are better at maintaining a motivated state supporting emotion regulation over time. Our results support assumptions concerning a close relation of self-control and emotion regulation as two domains of behavioral control. They further indicate that individual differences in functional connectivity between task-related brain areas directly relate to differences in trait self-control. PMID:27013102

A real time neural net estimator of fatigue life

NASA Technical Reports Server (NTRS)

Troudet, T.; Merrill, W.

1990-01-01

A neural net architecture is proposed to estimate, in real-time, the fatigue life of mechanical components, as part of the Intelligent Control System for Reusable Rocket Engines. Arbitrary component loading values were used as input to train a two hidden-layer feedforward neural net to estimate component fatigue damage. The ability of the net to learn, based on a local strain approach, the mapping between load sequence and fatigue damage has been demonstrated for a uniaxial specimen. Because of its demonstrated performance, the neural computation may be extended to complex cases where the loads are biaxial or triaxial, and the geometry of the component is complex (e.g., turbopump blades). The generality of the approach is such that load/damage mappings can be directly extracted from experimental data without requiring any knowledge of the stress/strain profile of the component. In addition, the parallel network architecture allows real-time life calculations even for high frequency vibrations. Owing to its distributed nature, the neural implementation will be robust and reliable, enabling its use in hostile environments such as rocket engines. This neural net estimator of fatigue life is seen as the enabling technology to achieve component life prognosis, and therefore would be an important part of life extending control for reusable rocket engines.

Ultrasonic neuromodulation

NASA Astrophysics Data System (ADS)

Naor, Omer; Krupa, Steve; Shoham, Shy

2016-06-01

Ultrasonic waves can be non-invasively steered and focused into mm-scale regions across the human body and brain, and their application in generating controlled artificial modulation of neuronal activity could therefore potentially have profound implications for neural science and engineering. Ultrasonic neuro-modulation phenomena were experimentally observed and studied for nearly a century, with recent discoveries on direct neural excitation and suppression sparking a new wave of investigations in models ranging from rodents to humans. In this paper we review the physics, engineering and scientific aspects of ultrasonic fields, their control in both space and time, and their effect on neuronal activity, including a survey of both the field’s foundational history and of recent findings. We describe key constraints encountered in this field, as well as key engineering systems developed to surmount them. In closing, the state of the art is discussed, with an emphasis on emerging research and clinical directions.

Evolutionary Wavelet Neural Network ensembles for breast cancer and Parkinson's disease prediction.

PubMed

Khan, Maryam Mahsal; Mendes, Alexandre; Chalup, Stephan K

2018-01-01

Wavelet Neural Networks are a combination of neural networks and wavelets and have been mostly used in the area of time-series prediction and control. Recently, Evolutionary Wavelet Neural Networks have been employed to develop cancer prediction models. The present study proposes to use ensembles of Evolutionary Wavelet Neural Networks. The search for a high quality ensemble is directed by a fitness function that incorporates the accuracy of the classifiers both independently and as part of the ensemble itself. The ensemble approach is tested on three publicly available biomedical benchmark datasets, one on Breast Cancer and two on Parkinson's disease, using a 10-fold cross-validation strategy. Our experimental results show that, for the first dataset, the performance was similar to previous studies reported in literature. On the second dataset, the Evolutionary Wavelet Neural Network ensembles performed better than all previous methods. The third dataset is relatively new and this study is the first to report benchmark results.

Evolutionary Wavelet Neural Network ensembles for breast cancer and Parkinson’s disease prediction

PubMed Central

Mendes, Alexandre; Chalup, Stephan K.

2018-01-01

Wavelet Neural Networks are a combination of neural networks and wavelets and have been mostly used in the area of time-series prediction and control. Recently, Evolutionary Wavelet Neural Networks have been employed to develop cancer prediction models. The present study proposes to use ensembles of Evolutionary Wavelet Neural Networks. The search for a high quality ensemble is directed by a fitness function that incorporates the accuracy of the classifiers both independently and as part of the ensemble itself. The ensemble approach is tested on three publicly available biomedical benchmark datasets, one on Breast Cancer and two on Parkinson’s disease, using a 10-fold cross-validation strategy. Our experimental results show that, for the first dataset, the performance was similar to previous studies reported in literature. On the second dataset, the Evolutionary Wavelet Neural Network ensembles performed better than all previous methods. The third dataset is relatively new and this study is the first to report benchmark results. PMID:29420578

Hardware implementation of CMAC neural network with reduced storage requirement.

PubMed

Ker, J S; Kuo, Y H; Wen, R C; Liu, B D

1997-01-01

The cerebellar model articulation controller (CMAC) neural network has the advantages of fast convergence speed and low computation complexity. However, it suffers from a low storage space utilization rate on weight memory. In this paper, we propose a direct weight address mapping approach, which can reduce the required weight memory size with a utilization rate near 100%. Based on such an address mapping approach, we developed a pipeline architecture to efficiently perform the addressing operations. The proposed direct weight address mapping approach also speeds up the computation for the generation of weight addresses. Besides, a CMAC hardware prototype used for color calibration has been implemented to confirm the proposed approach and architecture.

Adaptive neural output-feedback control for nonstrict-feedback time-delay fractional-order systems with output constraints and actuator nonlinearities.

PubMed

Zouari, Farouk; Ibeas, Asier; Boulkroune, Abdesselem; Cao, Jinde; Mehdi Arefi, Mohammad

2018-06-01

This study addresses the issue of the adaptive output tracking control for a category of uncertain nonstrict-feedback delayed incommensurate fractional-order systems in the presence of nonaffine structures, unmeasured pseudo-states, unknown control directions, unknown actuator nonlinearities and output constraints. Firstly, the mean value theorem and the Gaussian error function are introduced to eliminate the difficulties that arise from the nonaffine structures and the unknown actuator nonlinearities, respectively. Secondly, the immeasurable tracking error variables are suitably estimated by constructing a fractional-order linear observer. Thirdly, the neural network, the Razumikhin Lemma, the variable separation approach, and the smooth Nussbaum-type function are used to deal with the uncertain nonlinear dynamics, the unknown time-varying delays, the nonstrict feedback and the unknown control directions, respectively. Fourthly, asymmetric barrier Lyapunov functions are employed to overcome the violation of the output constraints and to tune online the parameters of the adaptive neural controller. Through rigorous analysis, it is proved that the boundedness of all variables in the closed-loop system and the semi global asymptotic tracking are ensured without transgression of the constraints. The principal contributions of this study can be summarized as follows: (1) based on Caputo's definitions and new lemmas, methods concerning the controllability, observability and stability analysis of integer-order systems are extended to fractional-order ones, (2) the output tracking objective for a relatively large class of uncertain systems is achieved with a simple controller and less tuning parameters. Finally, computer-simulation studies from the robotic field are given to demonstrate the effectiveness of the proposed controller. Copyright © 2018 Elsevier Ltd. All rights reserved.

EEG neural correlates of goal-directed movement intention.

PubMed

Pereira, Joana; Ofner, Patrick; Schwarz, Andreas; Sburlea, Andreea Ioana; Müller-Putz, Gernot R

2017-04-01

Using low-frequency time-domain electroencephalographic (EEG) signals we show, for the same type of upper limb movement, that goal-directed movements have different neural correlates than movements without a particular goal. In a reach-and-touch task, we explored the differences in the movement-related cortical potentials (MRCPs) between goal-directed and non-goal-directed movements. We evaluated if the detection of movement intention was influenced by the goal-directedness of the movement. In a single-trial classification procedure we found that classification accuracies are enhanced if there is a goal-directed movement in mind. Furthermore, by using the classifier patterns and estimating the corresponding brain sources, we show the importance of motor areas and the additional involvement of the posterior parietal lobule in the discrimination between goal-directed movements and non-goal-directed movements. We discuss next the potential contribution of our results on goal-directed movements to a more reliable brain-computer interface (BCI) control that facilitates recovery in spinal-cord injured or stroke end-users. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Intranasal oxytocin enhances neural processing of monetary reward and loss in post-traumatic stress disorder and traumatized controls.

PubMed

Nawijn, Laura; van Zuiden, Mirjam; Koch, Saskia B J; Frijling, Jessie L; Veltman, Dick J; Olff, Miranda

2016-04-01

Anhedonia is a significant clinical problem in post-traumatic stress disorder (PTSD). PTSD patients show reduced motivational approach behavior, which may underlie anhedonic symptoms. Oxytocin administration is known to increase reward sensitivity and approach behavior. We therefore investigated whether oxytocin administration affected neural responses during motivational processing in PTSD patients and trauma-exposed controls. 35 police officers with PTSD (21 males) and 37 trauma-exposed police officers without PTSD (19 males) were included in a within-subjects, randomized, placebo-controlled fMRI study. Neural responses during anticipation of monetary reward and loss were investigated with a monetary incentive delay task (MID) after placebo and oxytocin (40 IU) administration. Oxytocin increased neural responses during reward and loss anticipation in PTSD patients and controls in the striatum, dorsal anterior cingulate cortex and insula, key regions in the reward pathway. Although PTSD patients did not differ from controls in motivational processing under placebo, anhedonia severity in PTSD patients was negatively related to reward responsiveness in the ventral striatum. Furthermore, oxytocin effects on reward processing in the ventral striatum were positively associated with anhedonia. Oxytocin administration increased reward pathway sensitivity during reward and loss anticipation in PTSD patients and trauma-exposed controls. Thus, oxytocin administration may increase motivation for goal-directed approach behavior in PTSD patients and controls, providing evidence for a neurobiological pathway through which oxytocin could potentially increase motivation and reward sensitivity in PTSD patients. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Hybrid Robotic Control System Using Neuroblastoma Cultures

NASA Astrophysics Data System (ADS)

Ferrández, J. M.; Lorente, V.; Cuadra, J. M.; Delapaz, F.; Álvarez-Sánchez, José Ramón; Fernández, E.

The main objective of this work is to analyze the computing capabilities of human neuroblastoma cultured cells and to define connection schemes for controlling a robot behavior. Multielectrode Array (MEA) setups have been designed for direct culturing neural cells over silicon or glass substrates, providing the capability to stimulate and record simultaneously populations of neural cells. This paper describes the process of growing human neuroblastoma cells over MEA substrates and tries to modulate the natural physiologic responses of these cells by tetanic stimulation of the culture. We show that the large neuroblastoma networks developed in cultured MEAs are capable of learning: establishing numerous and dynamic connections, with modifiability induced by external stimuli and we propose an hybrid system for controlling a robot to avoid obstacles.

Neural correlates of semantic associations in patients with schizophrenia.

PubMed

Sass, Katharina; Heim, Stefan; Sachs, Olga; Straube, Benjamin; Schneider, Frank; Habel, Ute; Kircher, Tilo

2014-03-01

Patients with schizophrenia have semantic processing disturbances leading to expressive language deficits (formal thought disorder). The underlying pathology has been related to alterations in the semantic network and its neural correlates. Moreover, crossmodal processing, an important aspect of communication, is impaired in schizophrenia. Here we investigated specific processing abnormalities in patients with schizophrenia with regard to modality and semantic distance in a semantic priming paradigm. Fourteen patients with schizophrenia and fourteen demographically matched controls made visual lexical decisions on successively presented word-pairs (SOA = 350 ms) with direct or indirect relations, unrelated word-pairs, and pseudoword-target stimuli during fMRI measurement. Stimuli were presented in a unimodal (visual) or crossmodal (auditory-visual) fashion. On the neural level, the effect of semantic relation indicated differences (patients > controls) within the right angular gyrus and precuneus. The effect of modality revealed differences (controls > patients) within the left superior frontal, middle temporal, inferior occipital, right angular gyri, and anterior cingulate cortex. Semantic distance (direct vs. indirect) induced distinct activations within the left middle temporal, fusiform gyrus, right precuneus, and thalamus with patients showing fewer differences between direct and indirect word-pairs. The results highlight aberrant priming-related brain responses in patients with schizophrenia. Enhanced activation for patients possibly reflects deficits in semantic processes that might be caused by a delayed and enhanced spread of activation within the semantic network. Modality-specific decreases of activation in patients might be related to impaired perceptual integration. Those deficits could induce and increase the prominent symptoms of schizophrenia like impaired speech processing.

Enhanced robust fractional order proportional-plus-integral controller based on neural network for velocity control of permanent magnet synchronous motor.

PubMed

Zhang, Bitao; Pi, YouGuo

2013-07-01

The traditional integer order proportional-integral-differential (IO-PID) controller is sensitive to the parameter variation or/and external load disturbance of permanent magnet synchronous motor (PMSM). And the fractional order proportional-integral-differential (FO-PID) control scheme based on robustness tuning method is proposed to enhance the robustness. But the robustness focuses on the open-loop gain variation of controlled plant. In this paper, an enhanced robust fractional order proportional-plus-integral (ERFOPI) controller based on neural network is proposed. The control law of the ERFOPI controller is acted on a fractional order implement function (FOIF) of tracking error but not tracking error directly, which, according to theory analysis, can enhance the robust performance of system. Tuning rules and approaches, based on phase margin, crossover frequency specification and robustness rejecting gain variation, are introduced to obtain the parameters of ERFOPI controller. And the neural network algorithm is used to adjust the parameter of FOIF. Simulation and experimental results show that the method proposed in this paper not only achieve favorable tracking performance, but also is robust with regard to external load disturbance and parameter variation. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

A zinc finger protein Zfp521 directs neural differentiation and beyond

PubMed Central

2011-01-01

Neural induction is largely considered a default process, whereas little is known about intrinsic factors that drive neural differentiation. Kamiya and colleagues now demonstrate that a transcription factor, Zfp521, is capable of directing embryonic stem (ES) cells into neural progenitors. They discovered that Zfp521 transcripts were enriched in early neural lineage of ES cell differentiation. Forced expression of Zfp521 turned ES cells into neural progenitors in culture conditions that would normally inhibit neural differentiation. Zfp521 was expressed in mouse embryos during gastrulation. The protein was shown to associate with a co-activator p300 and directly induce expression of early neural genes. Knockdown of the Zfp521 by shRNA halted cells at the epiblast stage and suppressed neural differentiation. Zfp521 is a nuclear protein with 30 Krüppel-like zinc fingers mediating multiple protein-protein interactions, and regulates transcription in diverse tissues and organs. The protein promotes proliferation, delays differentiation and reduces apoptosis. The findings by Kamiya and colleagues that Zfp521 directs and sustains early neural differentiation now opens up a series of studies to investigate roles of Zfp521 in stem cells and brain development of mice and men. PMID:21539723

A Neuroanatomical Model of Prefrontal Inhibitory Modulation of Memory Retrieval

PubMed Central

Depue, Brendan E.

2012-01-01

Memory of past experience is essential for guiding goal-related behavior. Being able to control accessibility of memory through modulation of retrieval enables humans to flexibly adapt to their environment. Understanding the specific neural pathways of how this control is achieved has largely eluded cognitive neuroscience. Accordingly, in the current paper I review literature that examines the overt control over retrieval in order to reduce accessibility. I first introduce three hypotheses of inhibition of retrieval. These hypotheses involve: i) attending to other stimuli as a form of diversionary attention, ii) inhibiting the specific individual neural representation of the memory, and iii) inhibiting the hippocampus and retrieval process more generally to prevent reactivation of the representation. I then analyze literature taken from the White Bear Suppression, Directed Forgetting and Think/No-Think tasks to provide evidence for these hypotheses. Finally, a neuroanatomical model is developed to indicate three pathways from PFC to the hippocampal complex that support inhibition of memory retrieval. Describing these neural pathways increases our understanding of control over memory in general. PMID:22374224

Power maximization of variable-speed variable-pitch wind turbines using passive adaptive neural fault tolerant control

NASA Astrophysics Data System (ADS)

Habibi, Hamed; Rahimi Nohooji, Hamed; Howard, Ian

2017-09-01

Power maximization has always been a practical consideration in wind turbines. The question of how to address optimal power capture, especially when the system dynamics are nonlinear and the actuators are subject to unknown faults, is significant. This paper studies the control methodology for variable-speed variable-pitch wind turbines including the effects of uncertain nonlinear dynamics, system fault uncertainties, and unknown external disturbances. The nonlinear model of the wind turbine is presented, and the problem of maximizing extracted energy is formulated by designing the optimal desired states. With the known system, a model-based nonlinear controller is designed; then, to handle uncertainties, the unknown nonlinearities of the wind turbine are estimated by utilizing radial basis function neural networks. The adaptive neural fault tolerant control is designed passively to be robust on model uncertainties, disturbances including wind speed and model noises, and completely unknown actuator faults including generator torque and pitch actuator torque. The Lyapunov direct method is employed to prove that the closed-loop system is uniformly bounded. Simulation studies are performed to verify the effectiveness of the proposed method.

Motor-Skill Learning in an Insect Inspired Neuro-Computational Control System

PubMed Central

Arena, Eleonora; Arena, Paolo; Strauss, Roland; Patané, Luca

2017-01-01

In nature, insects show impressive adaptation and learning capabilities. The proposed computational model takes inspiration from specific structures of the insect brain: after proposing key hypotheses on the direct involvement of the mushroom bodies (MBs) and on their neural organization, we developed a new architecture for motor learning to be applied in insect-like walking robots. The proposed model is a nonlinear control system based on spiking neurons. MBs are modeled as a nonlinear recurrent spiking neural network (SNN) with novel characteristics, able to memorize time evolutions of key parameters of the neural motor controller, so that existing motor primitives can be improved. The adopted control scheme enables the structure to efficiently cope with goal-oriented behavioral motor tasks. Here, a six-legged structure, showing a steady-state exponentially stable locomotion pattern, is exposed to the need of learning new motor skills: moving through the environment, the structure is able to modulate motor commands and implements an obstacle climbing procedure. Experimental results on a simulated hexapod robot are reported; they are obtained in a dynamic simulation environment and the robot mimicks the structures of Drosophila melanogaster. PMID:28337138

Intelligent control based on fuzzy logic and neural net theory

NASA Technical Reports Server (NTRS)

Lee, Chuen-Chien

1991-01-01

In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment.

The Correlation among Neural Dynamic Processing of Conflict Control, Testosterone and Cortisol Levels in 10-Year-Old Children

PubMed Central

Shangguan, Fangfang; Liu, Tongran; Liu, Xiuying; Shi, Jiannong

2017-01-01

Cognitive control is related to goal-directed self-regulation abilities, which is fundamental for human development. Conflict control includes the neural processes of conflict monitoring and conflict resolution. Testosterone and cortisol are essential hormones for the development of cognitive functions. However, there are no studies that have investigated the correlation of these two hormones with conflict control in preadolescents. In this study, we aimed to explore whether testosterone, cortisol, and testosterone/cortisol ratio worked differently for preadolescent’s conflict control processes in varied conflict control tasks. Thirty-two 10-year-old children (16 boys and 16 girls) were enrolled. They were instructed to accomplish three conflict control tasks with different conflict dimensions, including the Flanker, Simon, and Stroop tasks, and electrophysiological signals were recorded. Salivary samples were collected from each child. The testosterone and cortisol levels were determined by enzyme-linked immunosorbent assay. The electrophysiological results showed that the incongruent trials induced greater N2/N450 and P3/SP responses than the congruent trials during neural processes of conflict monitoring and conflict resolution in the Flanker and Stroop tasks. The hormonal findings showed that (1) the testosterone/cortisol ratio was correlated with conflict control accuracy and conflict resolution in the Flanker task; (2) the testosterone level was associated with conflict control performance and neural processing of conflict resolution in the Stroop task; (3) the cortisol level was correlated with conflict control performance and neural processing of conflict monitoring in the Simon task. In conclusion, in 10-year-old children, the fewer processes a task needs, the more likely there is an association between the T/C ratios and the behavioral and brain response, and the dual-hormone effects on conflict resolution may be testosterone-driven in the Stroop and Flanker tasks. PMID:28690571

Backstepping fuzzy-neural-network control design for hybrid maglev transportation system.

PubMed

Wai, Rong-Jong; Yao, Jing-Xiang; Lee, Jeng-Dao

2015-02-01

This paper focuses on the design of a backstepping fuzzy-neural-network control (BFNNC) for the online levitated balancing and propulsive positioning of a hybrid magnetic levitation (maglev) transportation system. The dynamic model of the hybrid maglev transportation system including levitated hybrid electromagnets to reduce the suspension power loss and the friction force during linear movement and a propulsive linear induction motor based on the concepts of mechanical geometry and motion dynamics is first constructed. The ultimate goal is to design an online fuzzy neural network (FNN) control methodology to cope with the problem of the complicated control transformation and the chattering control effort in backstepping control (BSC) design, and to directly ensure the stability of the controlled system without the requirement of strict constraints, detailed system information, and auxiliary compensated controllers despite the existence of uncertainties. In the proposed BFNNC scheme, an FNN control is utilized to be the major control role by imitating the BSC strategy, and adaptation laws for network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. The effectiveness of the proposed control strategy for the hybrid maglev transportation system is verified by experimental results, and the superiority of the BFNNC scheme is indicated in comparison with the BSC strategy and the backstepping particle-swarm-optimization control system in previous research.

Direct brain recordings reveal impaired neural function in infants with single-suture craniosynostosis: a future modality for guiding management?

PubMed

Hashim, Peter W; Brooks, Eric D; Persing, John A; Reuman, Hannah; Naples, Adam; Travieso, Roberto; Terner, Jordan; Steinbacher, Derek; Landi, Nicole; Mayes, Linda; McPartland, James C

2015-01-01

Patients with single-suture craniosynostosis (SSC) are at an elevated risk for long-term learning disabilities. Such adverse outcomes indicate that the early development of neural processing in SSC may be abnormal. At present, however, the precise functional derangements of the developing brain remain largely unknown. Event-related potentials (ERPs) are a form of noninvasive neuroimaging that provide direct measurements of cortical activity and have shown value in predicting long-term cognitive functioning. The current study used ERPs to examine auditory processing in infants with SSC to help clarify the developmental onset of delays in this population. Fifteen infants with untreated SSC and 23 typically developing controls were evaluated. ERPs were recorded during the presentation of speech sounds. Analyses focused on the P150 and N450 components of auditory processing. Infants with SSC demonstrated attenuated P150 amplitudes relative to typically developing controls. No differences in the N450 component were identified between untreated SSC and controls. Infants with untreated SSC demonstrate abnormal speech sound processing. Atypicalities are detectable as early as 6 months of age and may represent precursors to long-term language delay. Electrophysiological assessments provide a precise examination of neural processing in SSC and hold potential as a future modality to examine the effects of surgical treatment on brain development.

Approaches to Neural Tissue Engineering Using Scaffolds for Drug Delivery

PubMed Central

Willerth, Stephanie M.; Sakiyama-Elbert, Shelly E.

2007-01-01

This review seeks to give an overview of the current approaches to drug delivery from scaffolds for neural tissue engineering applications. The challenges presented by attempting to replicate the three types of nervous tissue (brain, spinal cord, and peripheral nerve) are summarized. Potential scaffold materials (both synthetic and natural) and target drugs are discussed with the benefits and drawbacks given. Finally, common methods of drug delivery, including degradable/diffusion-based delivery systems, affinity-based delivery systems, immobilized drug delivery systems, and electrically controlled drug delivery systems, are examined and critiqued. Based on the current body of work, suggestions for future directions of research in the field of neural tissue engineering are presented. PMID:17482308

Optogenetic dissection of neural circuits underlying emotional valence and motivated behaviors

PubMed Central

Nieh, Edward H.; Kim, Sung-Yon; Namburi, Praneeth; Tye, Kay M.

2014-01-01

The neural circuits underlying emotional valence and motivated behaviors are several synapses away from both defined sensory inputs and quantifiable motor outputs. Electrophysiology has provided us with a suitable means for observing neural activity during behavior, but methods for controlling activity for the purpose of studying motivated behaviors have been inadequate: electrical stimulation lacks cellular specificity and pharmacological manipulation lacks temporal resolution. The recent emergence of optogenetic tools provides a new means for establishing causal relationships between neural activity and behavior. Optogenetics, the use of genetically-encodable light-activated proteins, permits the modulation of specific neural circuit elements with millisecond precision. The ability to control individual cell types, and even projections between distal regions, allows us to investigate functional connectivity in a causal manner. The greatest consequence of controlling neural activity with finer precision has been the characterization of individual neural circuits within anatomical brain regions as defined functional units. Within the mesolimbic dopamine system, optogenetics has helped separate subsets of dopamine neurons with distinct functions for reward, aversion and salience processing, elucidated GABA neuronal effects on behavior, and characterized connectivity with forebrain and cortical structures. Within the striatum, optogenetics has confirmed the opposing relationship between direct and indirect pathway medium spiny neurons (MSNs), in addition to characterizing the inhibition of MSNs by cholinergic interneurons. Within the hypothalamus, optogenetics has helped overcome the heterogeneity in neuronal cell-type and revealed distinct circuits mediating aggression and feeding. Within the amygdala, optogenetics has allowed the study of intra-amygdala microcircuitry as well as interconnections with distal regions involved in fear and anxiety. In this review, we will present the body of optogenetic studies that has significantly enhanced our understanding of emotional valence and motivated behaviors. PMID:23142759

Reduced neural activity of the prefrontal cognitive control circuitry during response inhibition to negative words in people with schizophrenia

PubMed Central

Vercammen, Ans; Morris, Richard; Green, Melissa J.; Lenroot, Rhoshel; Kulkarni, Jayashri; Carr, Vaughan J.; Weickert, Cynthia Shannon; Weickert, Thomas W.

2012-01-01

Background Schizophrenia is characterized by deficits in executive control and impairments in emotion processing. This study assessed the nature and extent of potential alterations in the neural substrates supporting the interaction between cognitive control mechanisms and emotion attribution processes in people with schizophrenia. Methods Functional magnetic resonance imaging was performed during a verbal emotional go/no-go task. People with schizophrenia and healthy controls responded to word stimuli of a prespecified emotional valence (positive, negative or neutral) while inhibiting responses to stimuli of a different valence. Results We enrolled 20 people with schizophrenia and 23 controls in the study. Healthy controls activated an extensive dorsal prefrontal–parietal network while inhibiting responses to negative words compared to neutral words, but showed deactivation of the midcingulate cortex while inhibiting responses to positive words compared to neutral words. People with schizophrenia failed to activate this network during response inhibition to negative words, whereas during response inhibition to positive words they did not deactivate the cingulate, but showed increased responsivity in the frontal cortex. Limitations Sample heterogeneity is characteristic of studies of schizophrenia and may have contributed to more variable neural responses in the patient sample despite the care taken to control for potentially confounding variables. Conclusion Our results showed that schizophrenia is associated with aberrant modulation of neural responses during the interaction between cognitive control and emotion processing. Failure of the frontal circuitry to regulate goal-directed behaviour based on emotion attributions may contribute to deficits in psychosocial functioning in daily life. PMID:22617625

Application of dynamic recurrent neural networks in nonlinear system identification

NASA Astrophysics Data System (ADS)

Du, Yun; Wu, Xueli; Sun, Huiqin; Zhang, Suying; Tian, Qiang

2006-11-01

An adaptive identification method of simple dynamic recurrent neural network (SRNN) for nonlinear dynamic systems is presented in this paper. This method based on the theory that by using the inner-states feed-back of dynamic network to describe the nonlinear kinetic characteristics of system can reflect the dynamic characteristics more directly, deduces the recursive prediction error (RPE) learning algorithm of SRNN, and improves the algorithm by studying topological structure on recursion layer without the weight values. The simulation results indicate that this kind of neural network can be used in real-time control, due to its less weight values, simpler learning algorithm, higher identification speed, and higher precision of model. It solves the problems of intricate in training algorithm and slow rate in convergence caused by the complicate topological structure in usual dynamic recurrent neural network.

Limit-cycle-based control of the myogenic wingbeat rhythm in the fruit fly Drosophila

PubMed Central

Bartussek, Jan; Mutlu, A. Kadir; Zapotocky, Martin; Fry, Steven N.

2013-01-01

In many animals, rhythmic motor activity is governed by neural limit cycle oscillations under the control of sensory feedback. In the fruit fly Drosophila melanogaster, the wingbeat rhythm is generated myogenically by stretch-activated muscles and hence independently from direct neural input. In this study, we explored if generation and cycle-by-cycle control of Drosophila's wingbeat are functionally separated, or if the steering muscles instead couple into the myogenic rhythm as a weak forcing of a limit cycle oscillator. We behaviourally tested tethered flying flies for characteristic properties of limit cycle oscillators. To this end, we mechanically stimulated the fly's ‘gyroscopic’ organs, the halteres, and determined the phase relationship between the wing motion and stimulus. The flies synchronized with the stimulus for specific ranges of stimulus amplitude and frequency, revealing the characteristic Arnol'd tongues of a forced limit cycle oscillator. Rapid periodic modulation of the wingbeat frequency prior to locking demonstrates the involvement of the fast steering muscles in the observed control of the wingbeat frequency. We propose that the mechanical forcing of a myogenic limit cycle oscillator permits flies to avoid the comparatively slow control based on a neural central pattern generator. PMID:23282849

On the Control of Social Approach-Avoidance Behavior: Neural and Endocrine Mechanisms.

PubMed

Kaldewaij, Reinoud; Koch, Saskia B J; Volman, Inge; Toni, Ivan; Roelofs, Karin

The ability to control our automatic action tendencies is crucial for adequate social interactions. Emotional events trigger automatic approach and avoidance tendencies. Although these actions may be generally adaptive, the capacity to override these emotional reactions may be key to flexible behavior during social interaction. The present chapter provides a review of the neuroendocrine mechanisms underlying this ability and their relation to social psychopathologies. Aberrant social behavior, such as observed in social anxiety or psychopathy, is marked by abnormalities in approach-avoidance tendencies and the ability to control them. Key neural regions involved in the regulation of approach-avoidance behavior are the amygdala, widely implicated in automatic emotional processing, and the anterior prefrontal cortex, which exerts control over the amygdala. Hormones, especially testosterone and cortisol, have been shown to affect approach-avoidance behavior and the associated neural mechanisms. The present chapter also discusses ways to directly influence social approach and avoidance behavior and will end with a research agenda to further advance this important research field. Control over approach-avoidance tendencies may serve as an exemplar of emotional action regulation and might have a great value in understanding the underlying mechanisms of the development of affective disorders.

New aspects in pathogenesis of konzo: neural cell damage directly caused by linamarin contained in cassava (Manihot esculenta Crantz).

PubMed

Sreeja, V G; Nagahara, N; Li, Q; Minami, M

2003-08-01

Epidemic spastic paraparesis (konzo) found in tropical and subtropical countries is known to be caused by long-term intake of cassava (Manihot esculenta Crantz), which contains a cyanoglucoside linamarin (alpha-hydroxyisobutyronitrile-beta-d-glucopyranoside). It has been reported that linamarin is enzymatically converted to cyanide by bacteria in the intestine, and this is absorbed into the blood and then damages neural cells. However, unmetabolized linamarin was found in the urine after oral administration of cassava; thus, we hypothesized that konzo could be caused by direct toxicity of the unmetabolized linamarin that was transferred to the brain and could be transported into neural cells via a glucose transporter. In the present study it was confirmed that linamarin directly damaged neural culture pheochromocytoma cell (PC) 12 cells; 0.10 mm-linamarin caused cell death at 13.31 (SD 2.07) %, which was significantly different from that of control group (3.18 (SD 0.92) %, P=0.0004). Additional 10 microM-cytochalasin B, an inhibitor of a glucose transporter, prevented cell death: the percentage of dead cells significantly decreased to 6.06 (SD 1.98), P=0.0088). Furthermore, glucose also prevented cell death. These present results strongly suggest that linamarin competes with cytochalasin B and glucose for binding to a glucose transporter and enters into cells via glucose transporter.

Projective synchronization of nonidentical fractional-order neural networks based on sliding mode controller.

PubMed

Ding, Zhixia; Shen, Yi

2016-04-01

This paper investigates global projective synchronization of nonidentical fractional-order neural networks (FNNs) based on sliding mode control technique. We firstly construct a fractional-order integral sliding surface. Then, according to the sliding mode control theory, we design a sliding mode controller to guarantee the occurrence of the sliding motion. Based on fractional Lyapunov direct methods, system trajectories are driven to the proposed sliding surface and remain on it evermore, and some novel criteria are obtained to realize global projective synchronization of nonidentical FNNs. As the special cases, some sufficient conditions are given to ensure projective synchronization of identical FNNs, complete synchronization of nonidentical FNNs and anti-synchronization of nonidentical FNNs. Finally, one numerical example is given to demonstrate the effectiveness of the obtained results. Copyright © 2016 Elsevier Ltd. All rights reserved.

Predicate calculus for an architecture of multiple neural networks

NASA Astrophysics Data System (ADS)

Consoli, Robert H.

1990-08-01

Future projects with neural networks will require multiple individual network components. Current efforts along these lines are ad hoc. This paper relates the neural network to a classical device and derives a multi-part architecture from that model. Further it provides a Predicate Calculus variant for describing the location and nature of the trainings and suggests Resolution Refutation as a method for determining the performance of the system as well as the location of needed trainings for specific proofs. 2. THE NEURAL NETWORK AND A CLASSICAL DEVICE Recently investigators have been making reports about architectures of multiple neural networksL234. These efforts are appearing at an early stage in neural network investigations they are characterized by architectures suggested directly by the problem space. Touretzky and Hinton suggest an architecture for processing logical statements1 the design of this architecture arises from the syntax of a restricted class of logical expressions and exhibits syntactic limitations. In similar fashion a multiple neural netword arises out of a control problem2 from the sequence learning problem3 and from the domain of machine learning. 4 But a general theory of multiple neural devices is missing. More general attempts to relate single or multiple neural networks to classical computing devices are not common although an attempt is made to relate single neural devices to a Turing machines and Sun et a!. develop a multiple neural architecture that performs pattern classification.

Individual differences in self-reported self-control predict successful emotion regulation.

PubMed

Paschke, Lena M; Dörfel, Denise; Steimke, Rosa; Trempler, Ima; Magrabi, Amadeus; Ludwig, Vera U; Schubert, Torsten; Stelzel, Christine; Walter, Henrik

2016-08-01

Both self-control and emotion regulation enable individuals to adapt to external circumstances and social contexts, and both are assumed to rely on the overlapping neural resources. Here, we tested whether high self-reported self-control is related to successful emotion regulation on the behavioral and neural level. One hundred eight participants completed three self-control questionnaires and regulated their negative emotions during functional magnetic resonance imaging using reappraisal (distancing). Trait self-control correlated positively with successful emotion regulation both subjectively and neurally, as indicated by online ratings of negative emotions and functional connectivity strength between the amygdala and prefrontal areas, respectively. This stronger overall connectivity of the left amygdala was related to more successful subjective emotion regulation. Comparing amygdala activity over time showed that high self-controllers successfully maintained down-regulation of the left amygdala over time, while low self-controllers failed to down-regulate towards the end of the experiment. This indicates that high self-controllers are better at maintaining a motivated state supporting emotion regulation over time. Our results support assumptions concerning a close relation of self-control and emotion regulation as two domains of behavioral control. They further indicate that individual differences in functional connectivity between task-related brain areas directly relate to differences in trait self-control. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Distributed task-specific processing of somatosensory feedback for voluntary motor control

PubMed Central

Omrani, Mohsen; Murnaghan, Chantelle D; Pruszynski, J Andrew; Scott, Stephen H

2016-01-01

Corrective responses to limb disturbances are surprisingly complex, but the neural basis of these goal-directed responses is poorly understood. Here we show that somatosensory feedback is transmitted to many sensory and motor cortical regions within 25 ms of a mechanical disturbance applied to the monkey’s arm. When limb feedback was salient to an ongoing motor action (task engagement), neurons in parietal area 5 immediately (~25 ms) increased their response to limb disturbances, whereas neurons in other regions did not alter their response until 15 to 40 ms later. In contrast, initiation of a motor action elicited by a limb disturbance (target selection) altered neural responses in primary motor cortex ~65 ms after the limb disturbance, and then in dorsal premotor cortex, with no effect in parietal regions until 150 ms post-perturbation. Our findings highlight broad parietofrontal circuits that provide the neural substrate for goal-directed corrections, an essential aspect of highly skilled motor behaviors. DOI: http://dx.doi.org/10.7554/eLife.13141.001 PMID:27077949

Neural net target-tracking system using structured laser patterns

NASA Astrophysics Data System (ADS)

Cho, Jae-Wan; Lee, Yong-Bum; Lee, Nam-Ho; Park, Soon-Yong; Lee, Jongmin; Choi, Gapchu; Baek, Sunghyun; Park, Dong-Sun

1996-06-01

In this paper, we describe a robot endeffector tracking system using sensory information from recently-announced structured pattern laser diodes, which can generate images with several different types of structured pattern. The neural network approach is employed to recognize the robot endeffector covering the situation of three types of motion: translation, scaling and rotation. Features for the neural network to detect the position of the endeffector are extracted from the preprocessed images. Artificial neural networks are used to store models and to match with unknown input features recognizing the position of the robot endeffector. Since a minimal number of samples are used for different directions of the robot endeffector in the system, an artificial neural network with the generalization capability can be utilized for unknown input features. A feedforward neural network with the generalization capability can be utilized for unknown input features. A feedforward neural network trained with the back propagation learning is used to detect the position of the robot endeffector. Another feedforward neural network module is used to estimate the motion from a sequence of images and to control movements of the robot endeffector. COmbining the tow neural networks for recognizing the robot endeffector and estimating the motion with the preprocessing stage, the whole system keeps tracking of the robot endeffector effectively.

Development of human locomotion.

PubMed

Lacquaniti, Francesco; Ivanenko, Yuri P; Zago, Myrka

2012-10-01

Neural control of locomotion in human adults involves the generation of a small set of basic patterned commands directed to the leg muscles. The commands are generated sequentially in time during each step by neural networks located in the spinal cord, called Central Pattern Generators. This review outlines recent advances in understanding how motor commands are expressed at different stages of human development. Similar commands are found in several other vertebrates, indicating that locomotion development follows common principles of organization of the control networks. Movements show a high degree of flexibility at all stages of development, which is instrumental for learning and exploration of variable interactions with the environment. Copyright © 2012 Elsevier Ltd. All rights reserved.

Thalamocortical integration of instrumental learning and performance and their disintegration in addiction.

PubMed

Balleine, Bernard W; Morris, Richard W; Leung, Beatrice K

2015-12-02

A recent focus of addiction research has been on the effect of drug exposure on the neural processes that mediate the acquisition and performance of goal-directed instrumental actions. Deficits in goal-directed control and a consequent dysregulation of habit learning processes have been described as resulting in compulsive drug seeking. Similarly, considerable research has focussed on the motivational and emotional changes that drugs produce and that result in changes in the incentive processes that modulate goal-directed performance. Although these areas have developed independently, we argue that the effects they described are likely not independent. Here we hypothesize that these changes result from a core deficit in the way the learning and performance factors that support goal-directed action are integrated at a neural level to maintain behavioural control. A dorsal basal ganglia stream mediating goal-directed learning and a ventral stream mediating various performance factors find several points of integration in the cortical basal ganglia system, most notably in the thalamocortical network linking basal ganglia output to a variety of cortical control centres. Recent research in humans and other animals is reviewed suggesting that learning and performance factors are integrated in a network centred on the mediodorsal thalamus and that disintegration in this network may provide the basis for a 'switch' from recreational to dysregulated drug seeking resulting in the well documented changes associated with addiction. Copyright © 2014 Elsevier B.V. All rights reserved.

First Steps Toward Incorporating Image Based Diagnostics Into Particle Accelerator Control Systems Using Convolutional Neural Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Edelen, A. L.; Biedron, S. G.; Milton, S. V.

At present, a variety of image-based diagnostics are used in particle accelerator systems. Often times, these are viewed by a human operator who then makes appropriate adjustments to the machine. Given recent advances in using convolutional neural networks (CNNs) for image processing, it should be possible to use image diagnostics directly in control routines (NN-based or otherwise). This is especially appealing for non-intercepting diagnostics that could run continuously during beam operation. Here, we show results of a first step toward implementing such a controller: our trained CNN can predict multiple simulated downstream beam parameters at the Fermilab Accelerator Science andmore » Technology (FAST) facility's low energy beamline using simulated virtual cathode laser images, gun phases, and solenoid strengths.« less

Baroreflex and neurovascular responses to skeletal muscle mechanoreflex activation in humans: an exercise in integrative physiology.

PubMed

Drew, Rachel C

2017-12-01

Cardiovascular adjustments to exercise resulting in increased blood pressure (BP) and heart rate (HR) occur in response to activation of several neural mechanisms: the exercise pressor reflex, central command, and the arterial baroreflex. Neural inputs from these feedback and feedforward mechanisms integrate in the cardiovascular control centers in the brain stem and modulate sympathetic and parasympathetic neural outflow, resulting in the increased BP and HR observed during exercise. Another specific consequence of the central neural integration of these inputs during exercise is increased sympathetic neural outflow directed to the kidneys, causing renal vasoconstriction, a key reflex mechanism involved in blood flow redistribution during increased skeletal muscle work. Studies in humans have shown that muscle mechanoreflex activation inhibits cardiac vagal outflow, decreasing the sensitivity of baroreflex control of HR. Metabolite sensitization of muscle mechanoreceptors can lead to reduced sensitivity of baroreflex control of HR, with thromboxane being one of the metabolites involved, via greater inhibition of cardiac vagal outflow without affecting baroreflex control of BP or baroreflex resetting. Muscle mechanoreflex activation appears to play a predominant role in causing renal vasoconstriction, both in isolation and in the presence of local metabolites. Limited investigations in older adults and patients with cardiovascular-related disease have provided some insight into how the influence of muscle mechanoreflex activation on baroreflex function and renal vasoconstriction is altered in these populations. However, future research is warranted to better elucidate the specific effect of muscle mechanoreflex activation on baroreflex and neurovascular responses with aging and cardiovascular-related disease. Copyright © 2017 the American Physiological Society.

Further Investigation of Receding Horizion-Based Controllers and Neural Network-Based Systems

NASA Technical Reports Server (NTRS)

Kelkar, Atul G.; Haley, Pamela J. (Technical Monitor)

2000-01-01

This report provides a comprehensive summary of the research work performed over the entire duration of the co-operative research agreement between NASA Langley Research Center and Kansas State University. This summary briefly lists the findings and also suggests possible future directions for the continuation of the subject research in the area of Generalized Predictive Control (GPC) and Network Based Generalized Predictive Control (NGPC).

Controlling ionotropic and metabotropic glutamate receptors with light: principles and potential

PubMed Central

Reiner, Andreas; Levitz, Joshua; Isacoff, Ehud Y.

2014-01-01

Light offers unique advantages for studying and manipulating biomolecules and the cellular processes that they control. Optical control of ionotropic and metabotropic glutamate receptors has garnered significant interest, since these receptors are central to signaling at neuronal synapses and only optical approaches provide the spatial and temporal resolution required to directly probe receptor function in cells and tissue. Following the classical method of glutamate photo-uncaging, recently developed methods have added other forms of remote control, including those with high molecular specificity and genetic targeting. These tools open the door to the direct optical control of synaptic transmission and plasticity, as well as the probing of native receptor function in intact neural circuits. PMID:25573450

A recurrent neural-network-based sensor and actuator fault detection and isolation for nonlinear systems with application to the satellite's attitude control subsystem.

PubMed

Talebi, H A; Khorasani, K; Tafazoli, S

2009-01-01

This paper presents a robust fault detection and isolation (FDI) scheme for a general class of nonlinear systems using a neural-network-based observer strategy. Both actuator and sensor faults are considered. The nonlinear system considered is subject to both state and sensor uncertainties and disturbances. Two recurrent neural networks are employed to identify general unknown actuator and sensor faults, respectively. The neural network weights are updated according to a modified backpropagation scheme. Unlike many previous methods developed in the literature, our proposed FDI scheme does not rely on availability of full state measurements. The stability of the overall FDI scheme in presence of unknown sensor and actuator faults as well as plant and sensor noise and uncertainties is shown by using the Lyapunov's direct method. The stability analysis developed requires no restrictive assumptions on the system and/or the FDI algorithm. Magnetorquer-type actuators and magnetometer-type sensors that are commonly employed in the attitude control subsystem (ACS) of low-Earth orbit (LEO) satellites for attitude determination and control are considered in our case studies. The effectiveness and capabilities of our proposed fault diagnosis strategy are demonstrated and validated through extensive simulation studies.

A circular model for song motor control in Serinus canaria

PubMed Central

Alonso, Rodrigo G.; Trevisan, Marcos A.; Amador, Ana; Goller, Franz; Mindlin, Gabriel B.

2015-01-01

Song production in songbirds is controlled by a network of nuclei distributed across several brain regions, which drives respiratory and vocal motor systems to generate sound. We built a model for birdsong production, whose variables are the average activities of different neural populations within these nuclei of the song system. We focus on the predictions of respiratory patterns of song, because these can be easily measured and therefore provide a validation for the model. We test the hypothesis that it is possible to construct a model in which (1) the activity of an expiratory related (ER) neural population fits the observed pressure patterns used by canaries during singing, and (2) a higher forebrain neural population, HVC, is sparsely active, simultaneously with significant motor instances of the pressure patterns. We show that in order to achieve these two requirements, the ER neural population needs to receive two inputs: a direct one, and its copy after being processed by other areas of the song system. The model is capable of reproducing the measured respiratory patterns and makes specific predictions on the timing of HVC activity during their production. These results suggest that vocal production is controlled by a circular network rather than by a simple top-down architecture. PMID:25904860

Neural signature of behavioural inhibition in women with bulimia nervosa.

PubMed

Skunde, Mandy; Walther, Stephan; Simon, Joe J; Wu, Mudan; Bendszus, Martin; Herzog, Wolfgang; Friederich, Hans-Christoph

2016-08-01

Impaired inhibitory control is considered a behavioural phenotype in patients with bulimia nervosa. However, the underlying neural correlates of impaired general and food-specific behavioural inhibition are largely unknown. Therefore, we investigated brain activation during the performance of behavioural inhibition to general and food-related stimuli in adults with bulimia nervosa. Women with bulimia and healthy control women underwent event-related fMRI while performing a general and a food-specific no-go task. We included 28 women with bulimia nervosa and 29 healthy control women in our study. On a neuronal level, we observed significant group differences in response to general no-go stimuli in women with bulimia nervosa with high symptom severity; compared with healthy controls, the patients showed reduced activation in the right sensorimotor area (postcentral gyrus, precentral gyrus) and right dorsal striatum (caudate nucleus, putamen). The present results are limited to adult women with bulimia nervosa. Furthermore, it remains unclear whether impaired behavioural inhibition in patients with this disorder are a cause or consequence of chronic illness. Our findings suggest that diminished frontostriatal brain activation in patients with bulimia nervosa contribute to the severity of binge eating symptoms. Gaining further insight into the neural mechanisms of behavioural inhibition problems in individuals with this disorder may inform brain-directed treatment approaches and the development of response inhibition training approaches to improve inhibitory control in patients with bulimia nervosa. The present study does not support greater behavioural and neural impairments to food-specific behavioural inhibition in these patients.

Seeing direct and averted gaze activates the approach-avoidance motivational brain systems.

PubMed

Hietanen, Jari K; Leppänen, Jukka M; Peltola, Mikko J; Linna-Aho, Kati; Ruuhiala, Heidi J

2008-01-01

Gaze direction is known to be an important factor in regulating social interaction. Recent evidence suggests that direct and averted gaze can signal the sender's motivational tendencies of approach and avoidance, respectively. We aimed at determining whether seeing another person's direct vs. averted gaze has an influence on the observer's neural approach-avoidance responses. We also examined whether it would make a difference if the participants were looking at the face of a real person or a picture. Measurements of hemispheric asymmetry in the frontal electroencephalographic activity indicated that another person's direct gaze elicited a relative left-sided frontal EEG activation (indicative of a tendency to approach), whereas averted gaze activated right-sided asymmetry (indicative of avoidance). Skin conductance responses were larger to faces than to control objects and to direct relative to averted gaze, indicating that faces, in general, and faces with direct gaze, in particular, elicited more intense autonomic activation and strength of the motivational tendencies than did control stimuli. Gaze direction also influenced subjective ratings of emotional arousal and valence. However, all these effects were observed only when participants were facing a real person, not when looking at a picture of a face. This finding was suggested to be due to the motivational responses to gaze direction being activated in the context of enhanced self-awareness by the presence of another person. The present results, thus, provide direct evidence that eye contact and gaze aversion between two persons influence the neural mechanisms regulating basic motivational-emotional responses and differentially activate the motivational approach-avoidance brain systems.

Integrating robotic action with biologic perception: A brain-machine symbiosis theory

NASA Astrophysics Data System (ADS)

Mahmoudi, Babak

In patients with motor disability the natural cyclic flow of information between the brain and external environment is disrupted by their limb impairment. Brain-Machine Interfaces (BMIs) aim to provide new communication channels between the brain and environment by direct translation of brain's internal states into actions. For enabling the user in a wide range of daily life activities, the challenge is designing neural decoders that autonomously adapt to different tasks, environments, and to changes in the pattern of neural activity. In this dissertation, a novel decoding framework for BMIs is developed in which a computational agent autonomously learns how to translate neural states into action based on maximization of a measure of shared goal between user and the agent. Since the agent and brain share the same goal, a symbiotic relationship between them will evolve therefore this decoding paradigm is called a Brain-Machine Symbiosis (BMS) framework. A decoding agent was implemented within the BMS framework based on the Actor-Critic method of Reinforcement Learning. The rule of the Actor as a neural decoder was to find mapping between the neural representation of motor states in the primary motor cortex (MI) and robot actions in order to solve reaching tasks. The Actor learned the optimal control policy using an evaluative feedback that was estimated by the Critic directly from the user's neural activity of the Nucleus Accumbens (NAcc). Through a series of computational neuroscience studies in a cohort of rats it was demonstrated that NAcc could provide a useful evaluative feedback by predicting the increase or decrease in the probability of earning reward based on the environmental conditions. Using a closed-loop BMI simulator it was demonstrated the Actor-Critic decoding architecture was able to adapt to different tasks as well as changes in the pattern of neural activity. The custom design of a dual micro-wire array enabled simultaneous implantation of MI and NAcc for the development of a full closed-loop system. The Actor-Critic decoding architecture was able to solve the brain-controlled reaching task using a robotic arm by capturing the interdependency between the simultaneous action representation in MI and reward expectation in NAcc.

Adaptive neural network output feedback control for stochastic nonlinear systems with unknown dead-zone and unmodeled dynamics.

PubMed

Tong, Shaocheng; Wang, Tong; Li, Yongming; Zhang, Huaguang

2014-06-01

This paper discusses the problem of adaptive neural network output feedback control for a class of stochastic nonlinear strict-feedback systems. The concerned systems have certain characteristics, such as unknown nonlinear uncertainties, unknown dead-zones, unmodeled dynamics and without the direct measurements of state variables. In this paper, the neural networks (NNs) are employed to approximate the unknown nonlinear uncertainties, and then by representing the dead-zone as a time-varying system with a bounded disturbance. An NN state observer is designed to estimate the unmeasured states. Based on both backstepping design technique and a stochastic small-gain theorem, a robust adaptive NN output feedback control scheme is developed. It is proved that all the variables involved in the closed-loop system are input-state-practically stable in probability, and also have robustness to the unmodeled dynamics. Meanwhile, the observer errors and the output of the system can be regulated to a small neighborhood of the origin by selecting appropriate design parameters. Simulation examples are also provided to illustrate the effectiveness of the proposed approach.

Prenatal Organophosphates Exposure Alternates the Cleavage Plane Orientation of Apical Neural Progenitor in Developing Neocortex

PubMed Central

Chen, Xiao-Ping; Chen, Wei-Feng; Wang, Da-Wei

2014-01-01

Prenatal organophosphate exposure elicits long-term brain cytoarchitecture and cognitive function impairments, but the mechanism underlying the onset and development of neural progenitors remain largely unclear. Using precise positioned brain slices, we observed an alternated cleavage plane bias that emerged in the mitotic neural progenitors of embryonal neocortex with diazinion (DZN) and chlorpyrifos (CPF) pretreatment. In comparison with the control, DZN and CPF treatment induced decrease of vertical orientation, increase of oblique orientation, and increase of horizontal orientation. That is, the cleavage plane orientation bias had been rotated from vertical to horizontal after DZN and CPF treatment. Meanwhile, general morphology and mitotic index of the progenitors were unchanged. Acephate (ACP), another common organophosphate, had no significant effects on the cleavage plane orientation, cell morphology and mitotic index. These results represent direct evidence for the toxicity mechanism in onset multiplication of neural progenitors. PMID:24740262

Flight Test Comparison of Different Adaptive Augmentations for Fault Tolerant Control Laws for a Modified F-15 Aircraft

NASA Technical Reports Server (NTRS)

Burken, John J.; Hanson, Curtis E.; Lee, James A.; Kaneshige, John T.

2009-01-01

This report describes the improvements and enhancements to a neural network based approach for directly adapting to aerodynamic changes resulting from damage or failures. This research is a follow-on effort to flight tests performed on the NASA F-15 aircraft as part of the Intelligent Flight Control System research effort. Previous flight test results demonstrated the potential for performance improvement under destabilizing damage conditions. Little or no improvement was provided under simulated control surface failures, however, and the adaptive system was prone to pilot-induced oscillations. An improved controller was designed to reduce the occurrence of pilot-induced oscillations and increase robustness to failures in general. This report presents an analysis of the neural networks used in the previous flight test, the improved adaptive controller, and the baseline case with no adaptation. Flight test results demonstrate significant improvement in performance by using the new adaptive controller compared with the previous adaptive system and the baseline system for control surface failures.

A cortical-spinal prosthesis for targeted limb movement in paralyzed primate avatars

PubMed Central

Shanechi, Maryam M.; Hu, Rollin C.; Williams, Ziv M.

2014-01-01

Motor paralysis is among the most disabling aspects of injury to the central nervous system. Here we develop and test a target-based cortical-spinal neural prosthesis that employs neural activity recorded from pre-motor neurons to control limb movements in functionally paralyzed primate avatars. Given the complexity by which muscle contractions are naturally controlled, we approach the problem of eliciting goal-directed limb movement in paralyzed animals by focusing on the intended targets of movement rather than their intermediate trajectories. We then match this information in real-time with spinal cord and muscle stimulation parameters that produce free planar limb movements to those intended target locations. We demonstrate that both the decoded activities of pre-motor populations and their adaptive responses can be used, after brief training, to effectively direct an avatar’s limb to distinct targets variably displayed on a screen. These findings advance the future possibility of reconstituting targeted limb movement in paralyzed subjects. PMID:24549394

Engagement of neural circuits underlying 2D spatial navigation in a rodent virtual reality system

PubMed Central

Aronov, Dmitriy; Tank, David W.

2015-01-01

SUMMARY Virtual reality (VR) enables precise control of an animal’s environment and otherwise impossible experimental manipulations. Neural activity in navigating rodents has been studied on virtual linear tracks. However, the spatial navigation system’s engagement in complete two-dimensional environments has not been shown. We describe a VR setup for rats, including control software and a large-scale electrophysiology system, which supports 2D navigation by allowing animals to rotate and walk in any direction. The entorhinal-hippocampal circuit, including place cells, grid cells, head direction cells and border cells, showed 2D activity patterns in VR similar to those in the real world. Hippocampal neurons exhibited various remapping responses to changes in the appearance or the shape of the virtual environment, including a novel form in which a VR-induced cue conflict caused remapping to lock to geometry rather than salient cues. These results suggest a general-purpose tool for novel types of experimental manipulations in navigating rats. PMID:25374363

Macrophage-Derived Extracellular Succinate Licenses Neural Stem Cells to Suppress Chronic Neuroinflammation.

PubMed

Peruzzotti-Jametti, Luca; Bernstock, Joshua D; Vicario, Nunzio; Costa, Ana S H; Kwok, Chee Keong; Leonardi, Tommaso; Booty, Lee M; Bicci, Iacopo; Balzarotti, Beatrice; Volpe, Giulio; Mallucci, Giulia; Manferrari, Giulia; Donegà, Matteo; Iraci, Nunzio; Braga, Alice; Hallenbeck, John M; Murphy, Michael P; Edenhofer, Frank; Frezza, Christian; Pluchino, Stefano

2018-03-01

Neural stem cell (NSC) transplantation can influence immune responses and suppress inflammation in the CNS. Metabolites, such as succinate, modulate the phenotype and function of immune cells, but whether and how NSCs are also activated by such immunometabolites to control immunoreactivity and inflammatory responses is unclear. Here, we show that transplanted somatic and directly induced NSCs ameliorate chronic CNS inflammation by reducing succinate levels in the cerebrospinal fluid, thereby decreasing mononuclear phagocyte (MP) infiltration and secondary CNS damage. Inflammatory MPs release succinate, which activates succinate receptor 1 (SUCNR1)/GPR91 on NSCs, leading them to secrete prostaglandin E2 and scavenge extracellular succinate with consequential anti-inflammatory effects. Thus, our work reveals an unexpected role for the succinate-SUCNR1 axis in somatic and directly induced NSCs, which controls the response of stem cells to inflammatory metabolic signals released by type 1 MPs in the chronically inflamed brain. Crown Copyright © 2018. Published by Elsevier Inc. All rights reserved.

Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia

NASA Astrophysics Data System (ADS)

Kim, Sung-Phil; Simeral, John D.; Hochberg, Leigh R.; Donoghue, John P.; Black, Michael J.

2008-12-01

Computer-mediated connections between human motor cortical neurons and assistive devices promise to improve or restore lost function in people with paralysis. Recently, a pilot clinical study of an intracortical neural interface system demonstrated that a tetraplegic human was able to obtain continuous two-dimensional control of a computer cursor using neural activity recorded from his motor cortex. This control, however, was not sufficiently accurate for reliable use in many common computer control tasks. Here, we studied several central design choices for such a system including the kinematic representation for cursor movement, the decoding method that translates neuronal ensemble spiking activity into a control signal and the cursor control task used during training for optimizing the parameters of the decoding method. In two tetraplegic participants, we found that controlling a cursor's velocity resulted in more accurate closed-loop control than controlling its position directly and that cursor velocity control was achieved more rapidly than position control. Control quality was further improved over conventional linear filters by using a probabilistic method, the Kalman filter, to decode human motor cortical activity. Performance assessment based on standard metrics used for the evaluation of a wide range of pointing devices demonstrated significantly improved cursor control with velocity rather than position decoding. Disclosure. JPD is the Chief Scientific Officer and a director of Cyberkinetics Neurotechnology Systems (CYKN); he holds stock and receives compensation. JDS has been a consultant for CYKN. LRH receives clinical trial support from CYKN.

Adult subependymal neural precursors, but not differentiated cells, undergo rapid cathodal migration in the presence of direct current electric fields.

PubMed

Babona-Pilipos, Robart; Droujinine, Ilia A; Popovic, Milos R; Morshead, Cindi M

2011-01-01

The existence of neural stem and progenitor cells (together termed neural precursor cells) in the adult mammalian brain has sparked great interest in utilizing these cells for regenerative medicine strategies. Endogenous neural precursors within the adult forebrain subependyma can be activated following injury, resulting in their proliferation and migration toward lesion sites where they differentiate into neural cells. The administration of growth factors and immunomodulatory agents following injury augments this activation and has been shown to result in behavioural functional recovery following stroke. With the goal of enhancing neural precursor migration to facilitate the repair process we report that externally applied direct current electric fields induce rapid and directed cathodal migration of pure populations of undifferentiated adult subependyma-derived neural precursors. Using time-lapse imaging microscopy in vitro we performed an extensive single-cell kinematic analysis demonstrating that this galvanotactic phenomenon is a feature of undifferentiated precursors, and not differentiated phenotypes. Moreover, we have shown that the migratory response of the neural precursors is a direct effect of the electric field and not due to chemotactic gradients. We also identified that epidermal growth factor receptor (EGFR) signaling plays a role in the galvanotactic response as blocking EGFR significantly attenuates the migratory behaviour. These findings suggest direct current electric fields may be implemented in endogenous repair paradigms to promote migration and tissue repair following neurotrauma.

Autonomous self-configuration of artificial neural networks for data classification or system control

NASA Astrophysics Data System (ADS)

Fink, Wolfgang

2009-05-01

Artificial neural networks (ANNs) are powerful methods for the classification of multi-dimensional data as well as for the control of dynamic systems. In general terms, ANNs consist of neurons that are, e.g., arranged in layers and interconnected by real-valued or binary neural couplings or weights. ANNs try mimicking the processing taking place in biological brains. The classification and generalization capabilities of ANNs are given by the interconnection architecture and the coupling strengths. To perform a certain classification or control task with a particular ANN architecture (i.e., number of neurons, number of layers, etc.), the inter-neuron couplings and their accordant coupling strengths must be determined (1) either by a priori design (i.e., manually) or (2) using training algorithms such as error back-propagation. The more complex the classification or control task, the less obvious it is how to determine an a priori design of an ANN, and, as a consequence, the architecture choice becomes somewhat arbitrary. Furthermore, rather than being able to determine for a given architecture directly the corresponding coupling strengths necessary to perform the classification or control task, these have to be obtained/learned through training of the ANN on test data. We report on the use of a Stochastic Optimization Framework (SOF; Fink, SPIE 2008) for the autonomous self-configuration of Artificial Neural Networks (i.e., the determination of number of hidden layers, number of neurons per hidden layer, interconnections between neurons, and respective coupling strengths) for performing classification or control tasks. This may provide an approach towards cognizant and self-adapting computing architectures and systems.

Embedded System for Prosthetic Control Using Implanted Neuromuscular Interfaces Accessed Via an Osseointegrated Implant.

PubMed

Mastinu, Enzo; Doguet, Pascal; Botquin, Yohan; Hakansson, Bo; Ortiz-Catalan, Max

2017-08-01

Despite the technological progress in robotics achieved in the last decades, prosthetic limbs still lack functionality, reliability, and comfort. Recently, an implanted neuromusculoskeletal interface built upon osseointegration was developed and tested in humans, namely the Osseointegrated Human-Machine Gateway. Here, we present an embedded system to exploit the advantages of this technology. Our artificial limb controller allows for bioelectric signals acquisition, processing, decoding of motor intent, prosthetic control, and sensory feedback. It includes a neurostimulator to provide direct neural feedback based on sensory information. The system was validated using real-time tasks characterization, power consumption evaluation, and myoelectric pattern recognition performance. Functionality was proven in a first pilot patient from whom results of daily usage were obtained. The system was designed to be reliably used in activities of daily living, as well as a research platform to monitor prosthesis usage and training, machine-learning-based control algorithms, and neural stimulation paradigms.

An Inverse Neural Controller Based on the Applicability Domain of RBF Network Models

PubMed Central

Alexandridis, Alex; Stogiannos, Marios; Papaioannou, Nikolaos; Zois, Elias; Sarimveis, Haralambos

2018-01-01

This paper presents a novel methodology of generic nature for controlling nonlinear systems, using inverse radial basis function neural network models, which may combine diverse data originating from various sources. The algorithm starts by applying the particle swarm optimization-based non-symmetric variant of the fuzzy means (PSO-NSFM) algorithm so that an approximation of the inverse system dynamics is obtained. PSO-NSFM offers models of high accuracy combined with small network structures. Next, the applicability domain concept is suitably tailored and embedded into the proposed control structure in order to ensure that extrapolation is avoided in the controller predictions. Finally, an error correction term, estimating the error produced by the unmodeled dynamics and/or unmeasured external disturbances, is included to the control scheme to increase robustness. The resulting controller guarantees bounded input-bounded state (BIBS) stability for the closed loop system when the open loop system is BIBS stable. The proposed methodology is evaluated on two different control problems, namely, the control of an experimental armature-controlled direct current (DC) motor and the stabilization of a highly nonlinear simulated inverted pendulum. For each one of these problems, appropriate case studies are tested, in which a conventional neural controller employing inverse models and a PID controller are also applied. The results reveal the ability of the proposed control scheme to handle and manipulate diverse data through a data fusion approach and illustrate the superiority of the method in terms of faster and less oscillatory responses. PMID:29361781

Response of neural reward regions to food cues in autism spectrum disorders

PubMed Central

2012-01-01

Background One hypothesis for the social deficits that characterize autism spectrum disorders (ASD) is diminished neural reward response to social interaction and attachment. Prior research using established monetary reward paradigms as a test of non-social reward to compare with social reward may involve confounds in the ability of individuals with ASD to utilize symbolic representation of money and the abstraction required to interpret monetary gains. Thus, a useful addition to our understanding of neural reward circuitry in ASD includes a characterization of the neural response to primary rewards. Method We asked 17 children with ASD and 18 children without ASD to abstain from eating for at least four hours before an MRI scan in which they viewed images of high-calorie foods. We assessed the neural reward network for increases in the blood oxygenation level dependent (BOLD) signal in response to the food images Results We found very similar patterns of increased BOLD signal to these images in the two groups; both groups showed increased BOLD signal in the bilateral amygdala, as well as in the nucleus accumbens, orbitofrontal cortex, and insula. Direct group comparisons revealed that the ASD group showed a stronger response to food cues in bilateral insula along the anterior-posterior gradient and in the anterior cingulate cortex than the control group, whereas there were no neural reward regions that showed higher activation for controls than for ASD. Conclusion These results suggest that neural response to primary rewards is not diminished but in fact shows an aberrant enhancement in children with ASD. PMID:22958533

A functional neuroimaging study assessing gender differences in the neural mechanisms underlying the ability to resist impulsive desires.

PubMed

Diekhof, Esther K; Keil, Maria; Obst, Katrin U; Henseler, Ilona; Dechent, Peter; Falkai, Peter; Gruber, Oliver

2012-09-14

There is ample evidence of gender differences in neural processes and behavior. Differences in reward-related behaviors have been linked to either temporary or permanent organizational influences of gonadal hormones on the mesolimbic dopamine system and reward-related activation. Still, little is known about the association between biological gender and the neural underpinnings of the ability to resist reward-related impulses. Here we assessed with functional magnetic resonance imaging which neural processes enable men and women to successfully control their desire for immediate reward when this is required by a higher-order goal (i.e., during a 'desire-reason dilemma'; Diekhof and Gruber, 2010). Thirty-two participants (16 females) were closely matched for age, personality characteristics (e.g., novelty seeking) and behavioral performance in the 'desire-reason task'. On the neural level, men and women showed similarities in the general response of the nucleus accumbens and of the ventral tegmental area to predictors of immediate reward, but they differed in additional brain mechanisms that enabled self-controlled decisions against the preference for immediate reward. Firstly, men exhibited a stronger reduction of activation in the ventral pallidum, putamen, temporal pole and pregenual anterior cingulate cortex during the 'desire-reason dilemma'. Secondly, connectivity analyses revealed a significant change in the direction of the connectivity between anteroventral prefrontal cortex and nucleus accumbens during decisions counteracting the reward-related impulse when comparing men and women. Together, these findings support the view of a sexual dimorphism that manifested in the recruitment of gender-specific neural resources during the successful deployment of self-control. Copyright © 2012 Elsevier B.V. All rights reserved.

Dynamics and Adaptive Control for Stability Recovery of Damaged Aircraft

NASA Technical Reports Server (NTRS)

Nguyen, Nhan; Krishnakumar, Kalmanje; Kaneshige, John; Nespeca, Pascal

2006-01-01

This paper presents a recent study of a damaged generic transport model as part of a NASA research project to investigate adaptive control methods for stability recovery of damaged aircraft operating in off-nominal flight conditions under damage and or failures. Aerodynamic modeling of damage effects is performed using an aerodynamic code to assess changes in the stability and control derivatives of a generic transport aircraft. Certain types of damage such as damage to one of the wings or horizontal stabilizers can cause the aircraft to become asymmetric, thus resulting in a coupling between the longitudinal and lateral motions. Flight dynamics for a general asymmetric aircraft is derived to account for changes in the center of gravity that can compromise the stability of the damaged aircraft. An iterative trim analysis for the translational motion is developed to refine the trim procedure by accounting for the effects of the control surface deflection. A hybrid direct-indirect neural network, adaptive flight control is proposed as an adaptive law for stabilizing the rotational motion of the damaged aircraft. The indirect adaptation is designed to estimate the plant dynamics of the damaged aircraft in conjunction with the direct adaptation that computes the control augmentation. Two approaches are presented 1) an adaptive law derived from the Lyapunov stability theory to ensure that the signals are bounded, and 2) a recursive least-square method for parameter identification. A hardware-in-the-loop simulation is conducted and demonstrates the effectiveness of the direct neural network adaptive flight control in the stability recovery of the damaged aircraft. A preliminary simulation of the hybrid adaptive flight control has been performed and initial data have shown the effectiveness of the proposed hybrid approach. Future work will include further investigations and high-fidelity simulations of the proposed hybrid adaptive Bight control approach.

The role of visual processing in motor learning and control: Insights from electroencephalography.

PubMed

Krigolson, Olav E; Cheng, Darian; Binsted, Gord

2015-05-01

Traditionally our understanding of goal-directed action been derived from either behavioral findings or neuroanatomically derived imaging (i.e., fMRI). While both of these approaches have proven valuable, they lack the ability to determine a direct locus of function while concurrently having the necessary temporal precision needed to understand millisecond scale neural interactions respectively. In this review we summarize some seminal behavioral findings across three broad areas (target perturbation, feed-forward control, and feedback processing) and for each discuss the application of electroencephalography (EEG) to the understanding of the temporal nature of visual cue utilization during movement planning, control, and learning using four existing scalp potentials. Specifically, we examine the appropriateness of using the N100 potential as an indicator of corrective behaviors in response to target perturbation, the N200 as an index of movement planning, the P300 potential as a metric of feed-forward processes, and the feedback-related negativity as an index of motor learning. Although these existing components have potential for insight into cognitive contributions and the timing of the neural processes that contribute to motor control further research is needed to expand the control-related potentials and to develop methods to permit their accurate characterization across a wide range of behavioral tasks. Copyright © 2015 Elsevier B.V. All rights reserved.

Invasive species change detection using artificial neural networks and CASI hyperspectral imagery

USDA-ARS?s Scientific Manuscript database

For monitoring and controlling the extent and intensity of an invasive species, a direct multi-date image classification method was applied in invasive species (saltcedar) change detection in the study area of Lovelock, Nevada. With multi-date Compact Airborne Spectrographic Imager (CASI) hyperspec...

Optogenetic interrogation of neural circuits: technology for probing mammalian brain structures

PubMed Central

Zhang, Feng; Gradinaru, Viviana; Adamantidis, Antoine R; Durand, Remy; Airan, Raag D; de Lecea, Luis; Deisseroth, Karl

2015-01-01

Elucidation of the neural substrates underlying complex animal behaviors depends on precise activity control tools, as well as compatible readout methods. Recent developments in optogenetics have addressed this need, opening up new possibilities for systems neuroscience. Interrogation of even deep neural circuits can be conducted by directly probing the necessity and sufficiency of defined circuit elements with millisecond-scale, cell type-specific optical perturbations, coupled with suitable readouts such as electrophysiology, optical circuit dynamics measures and freely moving behavior in mammals. Here we collect in detail our strategies for delivering microbial opsin genes to deep mammalian brain structures in vivo, along with protocols for integrating the resulting optical control with compatible readouts (electrophysiological, optical and behavioral). The procedures described here, from initial virus preparation to systems-level functional readout, can be completed within 4–5 weeks. Together, these methods may help in providing circuit-level insight into the dynamics underlying complex mammalian behaviors in health and disease. PMID:20203662

Alx4 relays sequential FGF signaling to induce lacrimal gland morphogenesis

PubMed Central

Garg, Ankur; Gotoh, Noriko; Feng, Gen-Sheng; Zhong, Jian; Wang, Fen; Kariminejad, Ariana; Brooks, Steven

2017-01-01

The sequential use of signaling pathways is essential for the guidance of pluripotent progenitors into diverse cell fates. Here, we show that Shp2 exclusively mediates FGF but not PDGF signaling in the neural crest to control lacrimal gland development. In addition to preventing p53-independent apoptosis and promoting the migration of Sox10-expressing neural crests, Shp2 is also required for expression of the homeodomain transcription factor Alx4, which directly controls Fgf10 expression in the periocular mesenchyme that is necessary for lacrimal gland induction. We show that Alx4 binds an Fgf10 intronic element conserved in terrestrial but not aquatic animals, underlying the evolutionary emergence of the lacrimal gland system in response to an airy environment. Inactivation of ALX4/Alx4 causes lacrimal gland aplasia in both human and mouse. These results reveal a key role of Alx4 in mediating FGF-Shp2-FGF signaling in the neural crest for lacrimal gland development. PMID:29028795

Trait Approach and Avoidance Motivation: Lateralized Neural Activity Associated with Executive Function

PubMed Central

Spielberg, Jeffrey M.; Miller, Gregory A.; Engels, Anna S.; Herrington, John D.; Sutton, Bradley P.; Banich, Marie T.; Heller, Wendy

2010-01-01

Motivation and executive function are both necessary for the completion of goal-directed behavior. Research investigating the manner in which these processes interact is beginning to emerge and has implicated middle frontal gyrus (MFG) as a site of interaction for relevant neural mechanisms. However, this research has focused on state motivation, and it has not examined functional lateralization. The present study examined the impact of trait levels of approach and avoidance motivation on neural processes associated with executive function. Functional magnetic resonance imaging was conducted while participants performed a color-word Stroop task. Analyses identified brain regions in which trait approach and avoidance motivation (measured by questionnaires) moderated activation associated with executive control. Approach was hypothesized to be associated with left-lateralized MFG activation, whereas avoidance was hypothesized to be associated with right-lateralized MFG activation. Results supported both hypotheses. Present findings implicate areas of middle frontal gyrus in top-down control to guide behavior in accordance with motivational goals. PMID:20728552

Common and disorder-specific neural responses to emotional faces in generalised anxiety, social anxiety and panic disorders

PubMed Central

Fonzo, Gregory A.; Ramsawh, Holly J.; Flagan, Taru M.; Sullivan, Sarah G.; Letamendi, Andrea; Simmons, Alan N.; Paulus, Martin P.; Stein, Murray B.

2015-01-01

Background Although evidence exists for abnormal brain function across various anxiety disorders, direct comparison of neural function across diagnoses is needed to elicit abnormalities common across disorders and those distinct to a particular diagnosis. Aims To delineate common and distinct abnormalities within generalised anxiety (GAD), panic and social anxiety disorder (SAD) during affective processing. Method Fifty-nine adults (15 with GAD, 15 with panic disorder, 14 with SAD, and 15 healthy controls) underwent functional magnetic resonance imaging while completing a facial emotion matching task with fearful, angry and happy faces. Results Greater differential right amygdala activation to matching fearful v. happy facial expressions related to greater negative affectivity (i.e. trait anxiety) and was heightened across all anxiety disorder groups compared with controls. Collapsing across emotional face types, participants with panic disorder uniquely displayed greater posterior insula activation. Conclusions These preliminary results highlight a common neural basis for clinical anxiety in these diagnoses and also suggest the presence of disorder-specific dysfunction. PMID:25573399

Trait approach and avoidance motivation: lateralized neural activity associated with executive function.

PubMed

Spielberg, Jeffrey M; Miller, Gregory A; Engels, Anna S; Herrington, John D; Sutton, Bradley P; Banich, Marie T; Heller, Wendy

2011-01-01

Motivation and executive function are both necessary for the completion of goal-directed behavior. Research investigating the manner in which these processes interact is beginning to emerge and has implicated middle frontal gyrus (MFG) as a site of interaction for relevant neural mechanisms. However, this research has focused on state motivation, and it has not examined functional lateralization. The present study examined the impact of trait levels of approach and avoidance motivation on neural processes associated with executive function. Functional magnetic resonance imaging was conducted while participants performed a color-word Stroop task. Analyses identified brain regions in which trait approach and avoidance motivation (measured by questionnaires) moderated activation associated with executive control. Approach was hypothesized to be associated with left-lateralized MFG activation, whereas avoidance was hypothesized to be associated with right-lateralized MFG activation. Results supported both hypotheses. Present findings implicate areas of middle frontal gyrus in top-down control to guide behavior in accordance with motivational goals. Copyright © 2010 Elsevier Inc. All rights reserved.

Alx4 relays sequential FGF signaling to induce lacrimal gland morphogenesis.

PubMed

Garg, Ankur; Bansal, Mukesh; Gotoh, Noriko; Feng, Gen-Sheng; Zhong, Jian; Wang, Fen; Kariminejad, Ariana; Brooks, Steven; Zhang, Xin

2017-10-01

The sequential use of signaling pathways is essential for the guidance of pluripotent progenitors into diverse cell fates. Here, we show that Shp2 exclusively mediates FGF but not PDGF signaling in the neural crest to control lacrimal gland development. In addition to preventing p53-independent apoptosis and promoting the migration of Sox10-expressing neural crests, Shp2 is also required for expression of the homeodomain transcription factor Alx4, which directly controls Fgf10 expression in the periocular mesenchyme that is necessary for lacrimal gland induction. We show that Alx4 binds an Fgf10 intronic element conserved in terrestrial but not aquatic animals, underlying the evolutionary emergence of the lacrimal gland system in response to an airy environment. Inactivation of ALX4/Alx4 causes lacrimal gland aplasia in both human and mouse. These results reveal a key role of Alx4 in mediating FGF-Shp2-FGF signaling in the neural crest for lacrimal gland development.

Optogenetic Control of Cells and Circuits

PubMed Central

Miesenböck, Gero

2013-01-01

The absorption of light by bound or diffusible chromophores causes conformational rearrangements in natural and artificial photoreceptor proteins. These rearrangements are coupled to the opening or closing of ion transport pathways, the association or dissociation of binding partners, the enhancement or suppression of catalytic activity, or the transcription or repression of genetic information. Illumination of cells, tissues, or organisms engineered genetically to express photoreceptor proteins can thus be used to perturb biochemical and electrical signaling with exquisite cellular and molecular specificity. First demonstrated in 2002, this principle of optogenetic control has had a profound impact on neuroscience, where it provides a direct and stringent means of probing the organization of neural circuits and of identifying the neural substrates of behavior. The impact of optogenetic control is also beginning to be felt in other areas of cell and organismal biology. PMID:21819234

Functional neural correlates of social approval in schizophrenia

PubMed Central

Lepage, Martin

2016-01-01

Social approval is a reward that uses abstract social reinforcers to guide interpersonal interactions. Few studies have specifically explored social reward processing and its related neural substrates in schizophrenia. Fifteen patients with schizophrenia and fifteen healthy controls participated in a two-part study to explore the functional neural correlates of social approval. In the first session, participants were led to believe their personality would be assessed based on their results from various questionnaires and an interview. Participants were then presented with the results of their supposed evaluation in the scanner, while engaging in a relevant fMRI social approval task. Subjects provided subjective reports of pleasure associated with receiving self-directed positive or negative feedback. Higher activation of the right parietal lobe was found in controls compared with individuals with schizophrenia. Both groups rated traits from the high social reward condition as more pleasurable than the low social reward condition, while intergroup differences emerged in the low social reward condition. Positive correlations were found in patients only between subjective ratings of positive feedback and right insula activation, and a relevant behavioural measure. Evidence suggests potential neural substrates underlying the cognitive representation of social reputation in schizophrenia. PMID:26516171

Cdk1 Activates Pre-mitotic Nuclear Envelope Dynein Recruitment and Apical Nuclear Migration in Neural Stem Cells.

PubMed

Baffet, Alexandre D; Hu, Daniel J; Vallee, Richard B

2015-06-22

Dynein recruitment to the nuclear envelope is required for pre-mitotic nucleus-centrosome interactions in nonneuronal cells and for apical nuclear migration in neural stem cells. In each case, dynein is recruited to the nuclear envelope (NE) specifically during G2 via two nuclear pore-mediated mechanisms involving RanBP2-BicD2 and Nup133-CENP-F. The mechanisms responsible for cell-cycle control of this behavior are unknown. We now find that Cdk1 serves as a direct master controller for NE dynein recruitment in neural stem cells and HeLa cells. Cdk1 phosphorylates conserved sites within RanBP2 and activates BicD2 binding and early dynein recruitment. Late recruitment is triggered by a Cdk1-induced export of CENP-F from the nucleus. Forced NE targeting of BicD2 overrides Cdk1 inhibition, fully rescuing dynein recruitment and nuclear migration in neural stem cells. These results reveal how NE dynein recruitment is cell-cycle regulated and identify the trigger mechanism for apical nuclear migration in the brain. Copyright © 2015 Elsevier Inc. All rights reserved.

A chronometric functional sub-network in the thalamo-cortical system regulates the flow of neural information necessary for conscious cognitive processes.

PubMed

León-Domínguez, Umberto; Vela-Bueno, Antonio; Froufé-Torres, Manuel; León-Carrión, Jose

2013-06-01

The thalamo-cortical system has been defined as a neural network associated with consciousness. While there seems to be wide agreement that the thalamo-cortical system directly intervenes in vigilance and arousal, a divergence of opinion persists regarding its intervention in the control of other cognitive processes necessary for consciousness. In the present manuscript, we provide a review of recent scientific findings on the thalamo-cortical system and its role in the control and regulation of the flow of neural information necessary for conscious cognitive processes. We suggest that the axis formed by the medial prefrontal cortex and different thalamic nuclei (reticular nucleus, intralaminar nucleus, and midline nucleus), represents a core component for consciousness. This axis regulates different cerebral structures which allow basic cognitive processes like attention, arousal and memory to emerge. In order to produce a synchronized coherent response, neural communication between cerebral structures must have exact timing (chronometry). Thus, a chronometric functional sub-network within the thalamo-cortical system keeps us in an optimal and continuous functional state, allowing high-order cognitive processes, essential to awareness and qualia, to take place. Copyright © 2013 Elsevier Ltd. All rights reserved.

Neural Mechanisms of Interference Control Underlie the Relationship Between Fluid Intelligence and Working Memory Span

PubMed Central

Burgess, Gregory C.; Gray, Jeremy R.; Conway, Andrew R. A.; Braver, Todd S.

2014-01-01

Fluid intelligence (gF) and working memory (WM) span predict success in demanding cognitive situations. Recent studies show that much of the variance in gF and WM span is shared, suggesting common neural mechanisms. This study provides a direct investigation of the degree to which shared variance in gF and WM span can be explained by neural mechanisms of interference control. We measured performance and fMRI activity in 102 participants during the n-back WM task, focusing on the selective activation effects associated with high-interference lure trials. Brain activity on these trials was correlated with gF, WM span, and task performance in core brain regions linked to WM and executive control, including bilateral dorsolateral PFC (middle frontal gyrus, BA9) and parietal cortex (inferior parietal cortex; BA 40/7). Interference-related performance and interference-related activity accounted for a significant proportion of the shared variance in gF and WM span. Path analyses indicate that interference control activity may affect gF through a common set of processes that also influence WM span. These results suggest that individual differences in interference control mechanisms are important for understanding the relationship between gF and WM span. PMID:21787103

Divergent neural responses to narrative speech in disorders of consciousness.

PubMed

Iotzov, Ivan; Fidali, Brian C; Petroni, Agustin; Conte, Mary M; Schiff, Nicholas D; Parra, Lucas C

2017-11-01

Clinical assessment of auditory attention in patients with disorders of consciousness is often limited by motor impairment. Here, we employ intersubject correlations among electroencephalography responses to naturalistic speech in order to assay auditory attention among patients and healthy controls. Electroencephalographic data were recorded from 20 subjects with disorders of consciousness and 14 healthy controls during of two narrative audio stimuli, presented both forwards and time-reversed. Intersubject correlation of evoked electroencephalography signals were calculated, comparing responses of both groups to those of the healthy control subjects. This analysis was performed blinded and subsequently compared to the diagnostic status of each patient based on the Coma Recovery Scale-Revised. Subjects with disorders of consciousness exhibit significantly lower intersubject correlation than healthy controls during narrative speech. Additionally, while healthy subjects had higher intersubject correlation values in forwards versus backwards presentation, neural responses did not vary significantly with the direction of playback in subjects with disorders of consciousness. Increased intersubject correlation values in the backward speech condition were noted with improving disorder of consciousness diagnosis, both in cross-sectional analysis and in a subset of patients with longitudinal data. Intersubject correlation of neural responses to narrative speech audition differentiates healthy controls from patients and appears to index clinical diagnoses in disorders of consciousness.

The Role of Direct Current Electric Field-Guided Stem Cell Migration in Neural Regeneration.

PubMed

Yao, Li; Li, Yongchao

2016-06-01

Effective directional axonal growth and neural cell migration are crucial in the neural regeneration of the central nervous system (CNS). Endogenous currents have been detected in many developing nervous systems. Experiments have demonstrated that applied direct current (DC) electric fields (EFs) can guide axonal growth in vitro, and attempts have been made to enhance the regrowth of damaged spinal cord axons using DC EFs in in vivo experiments. Recent work has revealed that the migration of stem cells and stem cell-derived neural cells can be guided by DC EFs. These studies have raised the possibility that endogenous and applied DC EFs can be used to direct neural tissue regeneration. Although the mechanism of EF-directed axonal growth and cell migration has not been fully understood, studies have shown that the polarization of cell membrane proteins and the activation of intracellular signaling molecules are involved in the process. The application of EFs is a promising biotechnology for regeneration of the CNS.

Speaker gaze increases information coupling between infant and adult brains.

PubMed

Leong, Victoria; Byrne, Elizabeth; Clackson, Kaili; Georgieva, Stanimira; Lam, Sarah; Wass, Sam

2017-12-12

When infants and adults communicate, they exchange social signals of availability and communicative intention such as eye gaze. Previous research indicates that when communication is successful, close temporal dependencies arise between adult speakers' and listeners' neural activity. However, it is not known whether similar neural contingencies exist within adult-infant dyads. Here, we used dual-electroencephalography to assess whether direct gaze increases neural coupling between adults and infants during screen-based and live interactions. In experiment 1 ( n = 17), infants viewed videos of an adult who was singing nursery rhymes with ( i ) direct gaze (looking forward), ( ii ) indirect gaze (head and eyes averted by 20°), or ( iii ) direct-oblique gaze (head averted but eyes orientated forward). In experiment 2 ( n = 19), infants viewed the same adult in a live context, singing with direct or indirect gaze. Gaze-related changes in adult-infant neural network connectivity were measured using partial directed coherence. Across both experiments, the adult had a significant (Granger) causal influence on infants' neural activity, which was stronger during direct and direct-oblique gaze relative to indirect gaze. During live interactions, infants also influenced the adult more during direct than indirect gaze. Further, infants vocalized more frequently during live direct gaze, and individual infants who vocalized longer also elicited stronger synchronization from the adult. These results demonstrate that direct gaze strengthens bidirectional adult-infant neural connectivity during communication. Thus, ostensive social signals could act to bring brains into mutual temporal alignment, creating a joint-networked state that is structured to facilitate information transfer during early communication and learning. Copyright © 2017 the Author(s). Published by PNAS.

Speaker gaze increases information coupling between infant and adult brains

PubMed Central

Leong, Victoria; Byrne, Elizabeth; Clackson, Kaili; Georgieva, Stanimira; Lam, Sarah

2017-01-01

When infants and adults communicate, they exchange social signals of availability and communicative intention such as eye gaze. Previous research indicates that when communication is successful, close temporal dependencies arise between adult speakers’ and listeners’ neural activity. However, it is not known whether similar neural contingencies exist within adult–infant dyads. Here, we used dual-electroencephalography to assess whether direct gaze increases neural coupling between adults and infants during screen-based and live interactions. In experiment 1 (n = 17), infants viewed videos of an adult who was singing nursery rhymes with (i) direct gaze (looking forward), (ii) indirect gaze (head and eyes averted by 20°), or (iii) direct-oblique gaze (head averted but eyes orientated forward). In experiment 2 (n = 19), infants viewed the same adult in a live context, singing with direct or indirect gaze. Gaze-related changes in adult–infant neural network connectivity were measured using partial directed coherence. Across both experiments, the adult had a significant (Granger) causal influence on infants’ neural activity, which was stronger during direct and direct-oblique gaze relative to indirect gaze. During live interactions, infants also influenced the adult more during direct than indirect gaze. Further, infants vocalized more frequently during live direct gaze, and individual infants who vocalized longer also elicited stronger synchronization from the adult. These results demonstrate that direct gaze strengthens bidirectional adult–infant neural connectivity during communication. Thus, ostensive social signals could act to bring brains into mutual temporal alignment, creating a joint-networked state that is structured to facilitate information transfer during early communication and learning. PMID:29183980

Generation of diverse neural cell types through direct conversion

PubMed Central

Petersen, Gayle F; Strappe, Padraig M

2016-01-01

A characteristic of neurological disorders is the loss of critical populations of cells that the body is unable to replace, thus there has been much interest in identifying methods of generating clinically relevant numbers of cells to replace those that have been damaged or lost. The process of neural direct conversion, in which cells of one lineage are converted into cells of a neural lineage without first inducing pluripotency, shows great potential, with evidence of the generation of a range of functional neural cell types both in vitro and in vivo, through viral and non-viral delivery of exogenous factors, as well as chemical induction methods. Induced neural cells have been proposed as an attractive alternative to neural cells derived from embryonic or induced pluripotent stem cells, with prospective roles in the investigation of neurological disorders, including neurodegenerative disease modelling, drug screening, and cellular replacement for regenerative medicine applications, however further investigations into improving the efficacy and safety of these methods need to be performed before neural direct conversion becomes a clinically viable option. In this review, we describe the generation of diverse neural cell types via direct conversion of somatic cells, with comparison against stem cell-based approaches, as well as discussion of their potential research and clinical applications. PMID:26981169

Neural control of finger movement via intracortical brain-machine interface

NASA Astrophysics Data System (ADS)

Irwin, Z. T.; Schroeder, K. E.; Vu, P. P.; Bullard, A. J.; Tat, D. M.; Nu, C. S.; Vaskov, A.; Nason, S. R.; Thompson, D. E.; Bentley, J. N.; Patil, P. G.; Chestek, C. A.

2017-12-01

Objective. Intracortical brain-machine interfaces (BMIs) are a promising source of prosthesis control signals for individuals with severe motor disabilities. Previous BMI studies have primarily focused on predicting and controlling whole-arm movements; precise control of hand kinematics, however, has not been fully demonstrated. Here, we investigate the continuous decoding of precise finger movements in rhesus macaques. Approach. In order to elicit precise and repeatable finger movements, we have developed a novel behavioral task paradigm which requires the subject to acquire virtual fingertip position targets. In the physical control condition, four rhesus macaques performed this task by moving all four fingers together in order to acquire a single target. This movement was equivalent to controlling the aperture of a power grasp. During this task performance, we recorded neural spikes from intracortical electrode arrays in primary motor cortex. Main results. Using a standard Kalman filter, we could reconstruct continuous finger movement offline with an average correlation of ρ  =  0.78 between actual and predicted position across four rhesus macaques. For two of the monkeys, this movement prediction was performed in real-time to enable direct brain control of the virtual hand. Compared to physical control, neural control performance was slightly degraded; however, the monkeys were still able to successfully perform the task with an average target acquisition rate of 83.1%. The monkeys’ ability to arbitrarily specify fingertip position was also quantified using an information throughput metric. During brain control task performance, the monkeys achieved an average 1.01 bits s-1 throughput, similar to that achieved in previous studies which decoded upper-arm movements to control computer cursors using a standard Kalman filter. Significance. This is, to our knowledge, the first demonstration of brain control of finger-level fine motor skills. We believe that these results represent an important step towards full and dexterous control of neural prosthetic devices.

Controlling ionotropic and metabotropic glutamate receptors with light: principles and potential.

PubMed

Reiner, Andreas; Levitz, Joshua; Isacoff, Ehud Y

2015-02-01

Light offers unique advantages for studying and manipulating biomolecules and the cellular processes that they control. Optical control of ionotropic and metabotropic glutamate receptors has garnered significant interest, since these receptors are central to signaling at neuronal synapses and only optical approaches provide the spatial and temporal resolution required to directly probe receptor function in cells and tissue. Following the classical method of glutamate photo-uncaging, recently developed methods have added other forms of remote control, including those with high molecular specificity and genetic targeting. These tools open the door to the direct optical control of synaptic transmission and plasticity, as well as the probing of native receptor function in intact neural circuits. Copyright © 2014 Elsevier Ltd. All rights reserved.

Decoding Trajectories from Posterior Parietal Cortex Ensembles

PubMed Central

Mulliken, Grant H.; Musallam, Sam; Andersen, Richard A.

2009-01-01

High-level cognitive signals in the posterior parietal cortex (PPC) have previously been used to decode the intended endpoint of a reach, providing the first evidence that PPC can be used for direct control of a neural prosthesis (Musallam et al., 2004). Here we expand on this work by showing that PPC neural activity can be harnessed to estimate not only the endpoint but also to continuously control the trajectory of an end effector. Specifically, we trained two monkeys to use a joystick to guide a cursor on a computer screen to peripheral target locations while maintaining central ocular fixation. We found that we could accurately reconstruct the trajectory of the cursor using a relatively small ensemble of simultaneously recorded PPC neurons. Using a goal-based Kalman filter that incorporates target information into the state-space, we showed that the decoded estimate of cursor position could be significantly improved. Finally, we tested whether we could decode trajectories during closed-loop brain control sessions, in which the real-time position of the cursor was determined solely by a monkey’s neural activity in PPC. The monkey learned to perform brain control trajectories at 80% success rate(for 8 targets) after just 4–5 sessions. This improvement in behavioral performance was accompanied by a corresponding enhancement in neural tuning properties (i.e., increased tuning depth and coverage of encoding parameter space) as well as an increase in off-line decoding performance of the PPC ensemble. PMID:19036985

The C. elegans male exercises directional control during mating through cholinergic regulation of sex-shared command interneurons.

PubMed

Sherlekar, Amrita L; Janssen, Abbey; Siehr, Meagan S; Koo, Pamela K; Caflisch, Laura; Boggess, May; Lints, Robyn

2013-01-01

Mating behaviors in simple invertebrate model organisms represent tractable paradigms for understanding the neural bases of sex-specific behaviors, decision-making and sensorimotor integration. However, there are few examples where such neural circuits have been defined at high resolution or interrogated. Here we exploit the simplicity of the nematode Caenorhabditis elegans to define the neural circuits underlying the male's decision to initiate mating in response to contact with a mate. Mate contact is sensed by male-specific sensilla of the tail, the rays, which subsequently induce and guide a contact-based search of the hermaphrodite's surface for the vulva (the vulva search). Atypically, search locomotion has a backward directional bias so its implementation requires overcoming an intrinsic bias for forward movement, set by activity of the sex-shared locomotory system. Using optogenetics, cell-specific ablation- and mutant behavioral analyses, we show that the male makes this shift by manipulating the activity of command cells within this sex-shared locomotory system. The rays control the command interneurons through the male-specific, decision-making interneuron PVY and its auxiliary cell PVX. Unlike many sex-shared pathways, PVY/PVX regulate the command cells via cholinergic, rather than glutamatergic transmission, a feature that likely contributes to response specificity and coordinates directional movement with other cholinergic-dependent motor behaviors of the mating sequence. PVY/PVX preferentially activate the backward, and not forward, command cells because of a bias in synaptic inputs and the distribution of key cholinergic receptors (encoded by the genes acr-18, acr-16 and unc-29) in favor of the backward command cells. Our interrogation of male neural circuits reveals that a sex-specific response to the opposite sex is conferred by a male-specific pathway that renders subordinate, sex-shared motor programs responsive to mate cues. Circuit modifications of these types may make prominent contributions to natural variations in behavior that ultimately bring about speciation.

The C. elegans Male Exercises Directional Control during Mating through Cholinergic Regulation of Sex-Shared Command Interneurons

PubMed Central

Sherlekar, Amrita L.; Janssen, Abbey; Siehr, Meagan S.; Koo, Pamela K.; Caflisch, Laura; Boggess, May; Lints, Robyn

2013-01-01

Background Mating behaviors in simple invertebrate model organisms represent tractable paradigms for understanding the neural bases of sex-specific behaviors, decision-making and sensorimotor integration. However, there are few examples where such neural circuits have been defined at high resolution or interrogated. Methodology/Principal Findings Here we exploit the simplicity of the nematode Caenorhabditis elegans to define the neural circuits underlying the male’s decision to initiate mating in response to contact with a mate. Mate contact is sensed by male-specific sensilla of the tail, the rays, which subsequently induce and guide a contact-based search of the hermaphrodite’s surface for the vulva (the vulva search). Atypically, search locomotion has a backward directional bias so its implementation requires overcoming an intrinsic bias for forward movement, set by activity of the sex-shared locomotory system. Using optogenetics, cell-specific ablation- and mutant behavioral analyses, we show that the male makes this shift by manipulating the activity of command cells within this sex-shared locomotory system. The rays control the command interneurons through the male-specific, decision-making interneuron PVY and its auxiliary cell PVX. Unlike many sex-shared pathways, PVY/PVX regulate the command cells via cholinergic, rather than glutamatergic transmission, a feature that likely contributes to response specificity and coordinates directional movement with other cholinergic-dependent motor behaviors of the mating sequence. PVY/PVX preferentially activate the backward, and not forward, command cells because of a bias in synaptic inputs and the distribution of key cholinergic receptors (encoded by the genes acr-18, acr-16 and unc-29) in favor of the backward command cells. Conclusion/Significance Our interrogation of male neural circuits reveals that a sex-specific response to the opposite sex is conferred by a male-specific pathway that renders subordinate, sex-shared motor programs responsive to mate cues. Circuit modifications of these types may make prominent contributions to natural variations in behavior that ultimately bring about speciation. PMID:23577128

Vortioxetine reduces BOLD signal during performance of the N-back working memory task: a randomised neuroimaging trial in remitted depressed patients and healthy controls.

PubMed

Smith, J; Browning, M; Conen, S; Smallman, R; Buchbjerg, J; Larsen, K G; Olsen, C K; Christensen, S R; Dawson, G R; Deakin, J F; Hawkins, P; Morris, R; Goodwin, G; Harmer, C J

2018-05-01

Cognitive dysfunction is common in depression during both acute episodes and remission. Vortioxetine is a novel multimodal antidepressant that has improved cognitive function including executive function in depressed patients in randomised placebo-controlled clinical trials. However, it is unclear whether vortioxetine is able to target directly the neural circuitry implicated in the cognitive deficits in depression. Remitted depressed (n=48) and healthy volunteers (n=48) were randomised to receive 14 days treatment with 20 mg vortioxetine or placebo in a double-blind design. The effects of treatment on functional magnetic resonance imaging responses during an N-back working memory task were assessed at baseline and at the end of treatment. Neuropsychological measures of executive function, speed and information processing, attention and learning and memory were examined with the Trail Making Test (TMT), Rey Auditory Learning Test and Digit Symbol Substitution Test before and after treatment; subjective cognitive function was assessed using the Perceived Deficits Questionnaire (PDQ). Compared with placebo, vortioxetine reduced activation in the right dorsolateral prefrontal cortex and left hippocampus during the N-back task compared with placebo. Vortioxetine also increased TMT-A performance and self-reported cognitive function on the PDQ. These effects were seen across both subject groups. Vortioxetine modulates neural responses across a circuit subserving working memory in a direction opposite to the changes described in depression, when performance is maintained. This study provides evidence that vortioxetine has direct effects on the neural circuitry supporting cognitive function that can be dissociated from its effects on the mood symptoms of depression.

Directional constraint of endpoint force emerges from hindlimb anatomy.

PubMed

Bunderson, Nathan E; McKay, J Lucas; Ting, Lena H; Burkholder, Thomas J

2010-06-15

Postural control requires the coordination of force production at the limb endpoints to apply an appropriate force to the body. Subjected to horizontal plane perturbations, quadruped limbs stereotypically produce force constrained along a line that passes near the center of mass. This phenomenon, referred to as the force constraint strategy, may reflect mechanical constraints on the limb or body, a specific neural control strategy or an interaction among neural controls and mechanical constraints. We used a neuromuscular model of the cat hindlimb to test the hypothesis that the anatomical constraints restrict the mechanical action of individual muscles during stance and constrain the response to perturbations to a line independent of perturbation direction. In a linearized neuromuscular model of the cat hindlimb, muscle lengthening directions were highly conserved across 10,000 different muscle activation patterns, each of which produced an identical, stance-like endpoint force. These lengthening directions were closely aligned with the sagittal plane and reveal an anatomical structure for directionally constrained force responses. Each of the 10,000 activation patterns was predicted to produce stable stance based on Lyapunov stability analysis. In forward simulations of the nonlinear, seven degree of freedom model under the action of 200 random muscle activation patterns, displacement of the endpoint from its equilibrium position produced restoring forces, which were also biased toward the sagittal plane. The single exception was an activation pattern based on minimum muscle stress optimization, which produced destabilizing force responses in some perturbation directions. The sagittal force constraint increased during simulations as the system shifted from an inertial response during the acceleration phase to a viscoelastic response as peak velocity was obtained. These results qualitatively match similar experimental observations and suggest that the force constraint phenomenon may result from the anatomical arrangement of the limb.

Directional constraint of endpoint force emerges from hindlimb anatomy

PubMed Central

Bunderson, Nathan E.; McKay, J. Lucas; Ting, Lena H.; Burkholder, Thomas J.

2010-01-01

Postural control requires the coordination of force production at the limb endpoints to apply an appropriate force to the body. Subjected to horizontal plane perturbations, quadruped limbs stereotypically produce force constrained along a line that passes near the center of mass. This phenomenon, referred to as the force constraint strategy, may reflect mechanical constraints on the limb or body, a specific neural control strategy or an interaction among neural controls and mechanical constraints. We used a neuromuscular model of the cat hindlimb to test the hypothesis that the anatomical constraints restrict the mechanical action of individual muscles during stance and constrain the response to perturbations to a line independent of perturbation direction. In a linearized neuromuscular model of the cat hindlimb, muscle lengthening directions were highly conserved across 10,000 different muscle activation patterns, each of which produced an identical, stance-like endpoint force. These lengthening directions were closely aligned with the sagittal plane and reveal an anatomical structure for directionally constrained force responses. Each of the 10,000 activation patterns was predicted to produce stable stance based on Lyapunov stability analysis. In forward simulations of the nonlinear, seven degree of freedom model under the action of 200 random muscle activation patterns, displacement of the endpoint from its equilibrium position produced restoring forces, which were also biased toward the sagittal plane. The single exception was an activation pattern based on minimum muscle stress optimization, which produced destabilizing force responses in some perturbation directions. The sagittal force constraint increased during simulations as the system shifted from an inertial response during the acceleration phase to a viscoelastic response as peak velocity was obtained. These results qualitatively match similar experimental observations and suggest that the force constraint phenomenon may result from the anatomical arrangement of the limb. PMID:20511528

Visual Neuroscience: Unique Neural System for Flight Stabilization in Hummingbirds.

PubMed

Ibbotson, M R

2017-01-23

The pretectal visual motion processing area in the hummingbird brain is unlike that in other birds: instead of emphasizing detection of horizontal movements, it codes for motion in all directions through 360°, possibly offering precise visual stability control during hovering. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Neural Basis of Cognitive Control: Response Selection and Inhibition

ERIC Educational Resources Information Center

Goghari, Vina M.; MacDonald, Angus W., III

2009-01-01

The functional neuroanatomy of tasks that recruit different forms of response selection and inhibition has to our knowledge, never been directly addressed in a single fMRI study using similar stimulus-response paradigms where differences between scanning time and sequence, stimuli, and experimenter instructions were minimized. Twelve right-handed…

Toward Neural Control of Prosthetic Devices

DTIC Science & Technology

2007-05-21

A direct comparison of eye-centered and limb -centered reference frames for reach planning in the dorsal aspect of the premotor cortex. Journal of... Neuroprosthetics : In search of the sixth sense. Nature News Feature, 442:125; Is this the bionic man? Nature Editorial 442:109, 2006). 8) Churchland MM, Yu BM, Ryu

Neural signature of behavioural inhibition in women with bulimia nervosa

PubMed Central

Skunde, Mandy; Walther, Stephan; Simon, Joe J.; Wu, Mudan; Bendszus, Martin; Herzog, Wolfgang; Friederich, Hans-Christoph

2016-01-01

Background Impaired inhibitory control is considered a behavioural phenotype in patients with bulimia nervosa. However, the underlying neural correlates of impaired general and food-specific behavioural inhibition are largely unknown. Therefore, we investigated brain activation during the performance of behavioural inhibition to general and food-related stimuli in adults with bulimia nervosa. Methods Women with bulimia and healthy control women underwent event-related fMRI while performing a general and a food-specific no-go task. Results We included 28 women with bulimia nervosa and 29 healthy control women in our study. On a neuronal level, we observed significant group differences in response to general no-go stimuli in women with bulimia nervosa with high symptom severity; compared with healthy controls, the patients showed reduced activation in the right sensorimotor area (postcentral gyrus, precentral gyrus) and right dorsal striatum (caudate nucleus, putamen). Limitations The present results are limited to adult women with bulimia nervosa. Furthermore, it remains unclear whether impaired behavioural inhibition in patients with this disorder are a cause or consequence of chronic illness. Conclusion Our findings suggest that diminished frontostriatal brain activation in patients with bulimia nervosa contribute to the severity of binge eating symptoms. Gaining further insight into the neural mechanisms of behavioural inhibition problems in individuals with this disorder may inform brain-directed treatment approaches and the development of response inhibition training approaches to improve inhibitory control in patients with bulimia nervosa. The present study does not support greater behavioural and neural impairments to food-specific behavioural inhibition in these patients. PMID:27575858

Neural correlates of recognition memory of social information in people with schizophrenia

PubMed Central

Harvey, Philippe-Olivier; Lepage, Martin

2014-01-01

Background Social dysfunction is a hallmark characteristic of schizophrenia. Part of it may stem from an inability to efficiently encode social information into memory and retrieve it later. This study focused on whether patients with schizophrenia show a memory boost for socially relevant information and engage the same neural network as controls when processing social stimuli that were previously encoded into memory. Methods Patients with schizophrenia and healthy controls performed a social and nonsocial picture recognition memory task while being scanned. We calculated memory performance using d′. Our main analysis focused on brain activity associated with recognition memory of social and nonsocial pictures. Results Our study included 28 patients with schizophrenia and 26 controls. Healthy controls demonstrated a memory boost for socially relevant information. In contrast, patients with schizophrenia failed to show enhanced recognition sensitivity for social pictures. At the neural level, patients did not engage the dorsomedial prefrontal cortex (DMPFC) as much as controls while recognizing social pictures. Limitations Our study did not include direct measures of self-referential processing. All but 3 patients were taking antipsychotic medications, which may have altered both the behavioural performance during the picture recognition memory task and brain activity. Conclusion Impaired social memory in patients with schizophrenia may be associated with altered DMPFC activity. A reduction of DMPFC activity may reflect less involvement of self-referential processes during memory retrieval. Our functional MRI results contribute to a better mapping of the neural disturbances associated with social memory impairment in patients with schizophrenia and may facilitate the development of innovative treatments, such as transcranial magnetic stimulation. PMID:24119792

Neural correlates of recognition memory of social information in people with schizophrenia.

PubMed

Harvey, Philippe-Olivier; Lepage, Martin

2014-03-01

Social dysfunction is a hallmark characteristic of schizophrenia. Part of it may stem from an inability to efficiently encode social information into memory and retrieve it later. This study focused on whether patients with schizophrenia show a memory boost for socially relevant information and engage the same neural network as controls when processing social stimuli that were previously encoded into memory. Patients with schizophrenia and healthy controls performed a social and nonsocial picture recognition memory task while being scanned. We calculated memory performance using d'. Our main analysis focused on brain activity associated with recognition memory of social and nonsocial pictures. Our study included 28 patients with schizophrenia and 26 controls. Healthy controls demonstrated a memory boost for socially relevant information. In contrast, patients with schizophrenia failed to show enhanced recognition sensitivity for social pictures. At the neural level, patients did not engage the dorsomedial prefrontal cortex (DMPFC) as much as controls while recognizing social pictures. Our study did not include direct measures of self-referential processing. All but 3 patients were taking antipsychotic medications, which may have altered both the behavioural performance during the picture recognition memory task and brain activity. Impaired social memory in patients with schizophrenia may be associated with altered DMPFC activity. A reduction of DMPFC activity may reflect less involvement of self-referential processes during memory retrieval. Our functional MRI results contribute to a better mapping of the neural disturbances associated with social memory impairment in patients with schizophrenia and may facilitate the development of innovative treatments, such as transcranial magnetic stimulation.

A Generic Framework for Real-Time Multi-Channel Neuronal Signal Analysis, Telemetry Control, and Sub-Millisecond Latency Feedback Generation

PubMed Central

Zrenner, Christoph; Eytan, Danny; Wallach, Avner; Thier, Peter; Marom, Shimon

2010-01-01

Distinct modules of the neural circuitry interact with each other and (through the motor-sensory loop) with the environment, forming a complex dynamic system. Neuro-prosthetic devices seeking to modulate or restore CNS function need to interact with the information flow at the level of neural modules electrically, bi-directionally and in real-time. A set of freely available generic tools is presented that allow computationally demanding multi-channel short-latency bi-directional interactions to be realized in in vivo and in vitro preparations using standard PC data acquisition and processing hardware and software (Mathworks Matlab and Simulink). A commercially available 60-channel extracellular multi-electrode recording and stimulation set-up connected to an ex vivo developing cortical neuronal culture is used as a model system to validate the method. We demonstrate how complex high-bandwidth (>10 MBit/s) neural recording data can be analyzed in real-time while simultaneously generating specific complex electrical stimulation feedback with deterministically timed responses at sub-millisecond resolution. PMID:21060803

Biomechanics and energetics of walking in powered ankle exoskeletons using myoelectric control versus mechanically intrinsic control.

PubMed

Koller, Jeffrey R; Remy, C David; Ferris, Daniel P

2018-05-25

Controllers for assistive robotic devices can be divided into two main categories: controllers using neural signals and controllers using mechanically intrinsic signals. Both approaches are prevalent in research devices, but a direct comparison between the two could provide insight into their relative advantages and disadvantages. We studied subjects walking with robotic ankle exoskeletons using two different control modes: dynamic gain proportional myoelectric control based on soleus muscle activity (neural signal), and timing-based mechanically intrinsic control based on gait events (mechanically intrinsic signal). We hypothesized that subjects would have different measures of metabolic work rate between the two controllers as we predicted subjects would use each controller in a unique manner due to one being dependent on muscle recruitment and the other not. The two controllers had the same average actuation signal as we used the control signals from walking with the myoelectric controller to shape the mechanically intrinsic control signal. The difference being the myoelectric controller allowed step-to-step variation in the actuation signals controlled by the user's soleus muscle recruitment while the timing-based controller had the same actuation signal with each step regardless of muscle recruitment. We observed no statistically significant difference in metabolic work rate between the two controllers. Subjects walked with 11% less soleus activity during mid and late stance and significantly less peak soleus recruitment when using the timing-based controller than when using the myoelectric controller. While walking with the myoelectric controller, subjects walked with significantly higher average positive and negative total ankle power compared to walking with the timing-based controller. We interpret the reduced ankle power and muscle activity with the timing-based controller relative to the myoelectric controller to result from greater slacking effects. Subjects were able to be less engaged on a muscle level when using a controller driven by mechanically intrinsic signals than when using a controller driven by neural signals, but this had no affect on their metabolic work rate. These results suggest that the type of controller (neural vs. mechanical) is likely to affect how individuals use robotic exoskeletons for therapeutic rehabilitation or human performance augmentation.

Differential neural responses to food images in women with bulimia versus anorexia nervosa.

PubMed

Brooks, Samantha J; O'Daly, Owen G; Uher, Rudolf; Friederich, Hans-Christoph; Giampietro, Vincent; Brammer, Michael; Williams, Steven C R; Schiöth, Helgi B; Treasure, Janet; Campbell, Iain C

2011-01-01

Previous fMRI studies show that women with eating disorders (ED) have differential neural activation to viewing food images. However, despite clinical differences in their responses to food, differential neural activation to thinking about eating food, between women with anorexia nervosa (AN) and bulimia nervosa (BN) is not known. We compare 50 women (8 with BN, 18 with AN and 24 age-matched healthy controls [HC]) while they view food images during functional Magnetic Resonance Imaging (fMRI). In response to food (vs non-food) images, women with BN showed greater neural activation in the visual cortex, right dorsolateral prefrontal cortex, right insular cortex and precentral gyrus, women with AN showed greater activation in the right dorsolateral prefrontal cortex, cerebellum and right precuneus. HC women activated the cerebellum, right insular cortex, right medial temporal lobe and left caudate. Direct comparisons revealed that compared to HC, the BN group showed relative deactivation in the bilateral superior temporal gyrus/insula, and visual cortex, and compared to AN had relative deactivation in the parietal lobe and dorsal posterior cingulate cortex, but greater activation in the caudate, superior temporal gyrus, right insula and supplementary motor area. Women with AN and BN activate top-down cognitive control in response to food images, yet women with BN have increased activation in reward and somatosensory regions, which might impinge on cognitive control over food consumption and binge eating.

State-Dependent Decoding Algorithms Improve the Performance of a Bidirectional BMI in Anesthetized Rats.

PubMed

De Feo, Vito; Boi, Fabio; Safaai, Houman; Onken, Arno; Panzeri, Stefano; Vato, Alessandro

2017-01-01

Brain-machine interfaces (BMIs) promise to improve the quality of life of patients suffering from sensory and motor disabilities by creating a direct communication channel between the brain and the external world. Yet, their performance is currently limited by the relatively small amount of information that can be decoded from neural activity recorded form the brain. We have recently proposed that such decoding performance may be improved when using state-dependent decoding algorithms that predict and discount the large component of the trial-to-trial variability of neural activity which is due to the dependence of neural responses on the network's current internal state. Here we tested this idea by using a bidirectional BMI to investigate the gain in performance arising from using a state-dependent decoding algorithm. This BMI, implemented in anesthetized rats, controlled the movement of a dynamical system using neural activity decoded from motor cortex and fed back to the brain the dynamical system's position by electrically microstimulating somatosensory cortex. We found that using state-dependent algorithms that tracked the dynamics of ongoing activity led to an increase in the amount of information extracted form neural activity by 22%, with a consequently increase in all of the indices measuring the BMI's performance in controlling the dynamical system. This suggests that state-dependent decoding algorithms may be used to enhance BMIs at moderate computational cost.

Iterative free-energy optimization for recurrent neural networks (INFERNO).

PubMed

Pitti, Alexandre; Gaussier, Philippe; Quoy, Mathias

2017-01-01

The intra-parietal lobe coupled with the Basal Ganglia forms a working memory that demonstrates strong planning capabilities for generating robust yet flexible neuronal sequences. Neurocomputational models however, often fails to control long range neural synchrony in recurrent spiking networks due to spontaneous activity. As a novel framework based on the free-energy principle, we propose to see the problem of spikes' synchrony as an optimization problem of the neurons sub-threshold activity for the generation of long neuronal chains. Using a stochastic gradient descent, a reinforcement signal (presumably dopaminergic) evaluates the quality of one input vector to move the recurrent neural network to a desired activity; depending on the error made, this input vector is strengthened to hill-climb the gradient or elicited to search for another solution. This vector can be learned then by one associative memory as a model of the basal-ganglia to control the recurrent neural network. Experiments on habit learning and on sequence retrieving demonstrate the capabilities of the dual system to generate very long and precise spatio-temporal sequences, above two hundred iterations. Its features are applied then to the sequential planning of arm movements. In line with neurobiological theories, we discuss its relevance for modeling the cortico-basal working memory to initiate flexible goal-directed neuronal chains of causation and its relation to novel architectures such as Deep Networks, Neural Turing Machines and the Free-Energy Principle.

Neural recording and modulation technologies

NASA Astrophysics Data System (ADS)

Chen, Ritchie; Canales, Andres; Anikeeva, Polina

2017-01-01

In the mammalian nervous system, billions of neurons connected by quadrillions of synapses exchange electrical, chemical and mechanical signals. Disruptions to this network manifest as neurological or psychiatric conditions. Despite decades of neuroscience research, our ability to treat or even to understand these conditions is limited by the capability of tools to probe the signalling complexity of the nervous system. Although orders of magnitude smaller and computationally faster than neurons, conventional substrate-bound electronics do not recapitulate the chemical and mechanical properties of neural tissue. This mismatch results in a foreign-body response and the encapsulation of devices by glial scars, suggesting that the design of an interface between the nervous system and a synthetic sensor requires additional materials innovation. Advances in genetic tools for manipulating neural activity have fuelled the demand for devices that are capable of simultaneously recording and controlling individual neurons at unprecedented scales. Recently, flexible organic electronics and bio- and nanomaterials have been developed for multifunctional and minimally invasive probes for long-term interaction with the nervous system. In this Review, we discuss the design lessons from the quarter-century-old field of neural engineering, highlight recent materials-driven progress in neural probes and look at emergent directions inspired by the principles of neural transduction.

Oxytocin improves behavioural and neural deficits in inferring others' social emotions in autism.

PubMed

Aoki, Yuta; Yahata, Noriaki; Watanabe, Takamitsu; Takano, Yosuke; Kawakubo, Yuki; Kuwabara, Hitoshi; Iwashiro, Norichika; Natsubori, Tatsunobu; Inoue, Hideyuki; Suga, Motomu; Takao, Hidemasa; Sasaki, Hiroki; Gonoi, Wataru; Kunimatsu, Akira; Kasai, Kiyoto; Yamasue, Hidenori

2014-11-01

Recent studies have suggested oxytocin's therapeutic effects on deficits in social communication and interaction in autism spectrum disorder through improvement of emotion recognition with direct emotional cues, such as facial expression and voice prosody. Although difficulty in understanding of others' social emotions and beliefs under conditions without direct emotional cues also plays an important role in autism spectrum disorder, no study has examined the potential effect of oxytocin on this difficulty. Here, we sequentially conducted both a case-control study and a clinical trial to investigate the potential effects of oxytocin on this difficulty at behavioural and neural levels measured using functional magnetic resonance imaging during a psychological task. This task was modified from the Sally-Anne Task, a well-known first-order false belief task. The task was optimized for investigation of the abilities to infer another person's social emotions and beliefs distinctively so as to test the hypothesis that oxytocin improves deficit in inferring others' social emotions rather than beliefs, under conditions without direct emotional cues. In the case-control study, 17 males with autism spectrum disorder showed significant behavioural deficits in inferring others' social emotions (P = 0.018) but not in inferring others' beliefs (P = 0.064) compared with 17 typically developing demographically-matched male participants. They also showed significantly less activity in the right anterior insula and posterior superior temporal sulcus during inferring others' social emotions, and in the dorsomedial prefrontal cortex during inferring others' beliefs compared with the typically developing participants (P < 0.001 and cluster size > 10 voxels). Then, to investigate potential effects of oxytocin on these behavioural and neural deficits, we conducted a double-blind placebo-controlled crossover within-subject trial for single-dose intranasal administration of 24 IU oxytocin in an independent group of 20 males with autism spectrum disorder. Behaviourally, oxytocin significantly increased the correct rate in inferring others' social emotions (P = 0.043, one-tail). At the neural level, the peptide significantly enhanced the originally-diminished brain activity in the right anterior insula during inferring others' social emotions (P = 0.004), but not in the dorsomedial prefrontal cortex during inferring others' beliefs (P = 0.858). The present findings suggest that oxytocin enhances the ability to understand others' social emotions that have also required second-order false belief rather than first-order false beliefs under conditions without direct emotional cues in autism spectrum disorder at both the behaviour and neural levels. © The Author (2014). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Neural Excitability and Joint Laxity in Chronic Ankle Instability, Coper, and Control Groups.

PubMed

Bowker, Samantha; Terada, Masafumi; Thomas, Abbey C; Pietrosimone, Brian G; Hiller, Claire E; Gribble, Phillip A

2016-04-01

Neuromuscular and mechanical deficiencies are commonly studied in participants with chronic ankle instability (CAI). Few investigators have attempted to comprehensively consider sensorimotor and mechanical differences among people with CAI, copers who did not present with prolonged dysfunctions after an initial ankle sprain, and a healthy control group. To determine if differences exist in spinal reflex excitability and ankle laxity among participants with CAI, copers, and healthy controls. Case-control study. Research laboratory. Thirty-seven participants with CAI, 30 participants categorized as copers, and 26 healthy control participants. We assessed spinal reflex excitability of the soleus using the Hoffmann reflex protocol. Participants' ankle laxity was measured with an instrumented ankle arthrometer. The maximum Hoffmann reflex : maximal muscle response ratio was calculated. Ankle laxity was measured as the total displacement in the anterior-posterior directions (mm) and total rotation in the inversion and eversion directions (°). Spinal reflex excitability was diminished in participants with CAI compared with copers and control participants (P = .01). No differences were observed among any of the groups for ankle laxity. Changes in the spinal reflex excitability of the soleus that likely affect ankle stability were seen only in the CAI group, yet no mechanical differences were noted across the groups. These findings support the importance of finding effective ways to increase spinal reflex excitability for the purpose of treating neural excitability dysfunction in patients with CAI.

Defining biotypes for depression and anxiety based on large-scale circuit dysfunction: A theoretical review of the evidence and future directions for clinical translation

PubMed Central

Williams, Leanne M

2016-01-01

Complex emotional, cognitive and self-reflective functions rely on the activation and connectivity of large-scale neural circuits. These circuits offer a relevant scale of focus for conceptualizing a taxonomy for depression and anxiety based on specific profiles (or biotypes) of neural circuit dysfunction. Here, the theoretical review first outlined the current consensus as to what constitutes the organization of large-scale circuits in the human brain identified using parcellation and meta-analysis. The focus is on neural circuits implicated in resting reflection (“default mode”), detection of “salience”, affective processing (“threat” and “reward”), “attention” and “cognitive control”. Next, the current evidence regarding which type of dysfunctions in these circuits characterize depression and anxiety disorders was reviewed, with an emphasis on published meta-analyses and reviews of circuit dysfunctions that have been identified in at least two well-powered case:control studies. Grounded in the review of these topics, a conceptual framework is proposed for considering neural circuit-defined “biotypes”. In this framework, biotypes are defined by profiles of extent of dysfunction on each large-scale circuit. The clinical implications of a biotype approach for guiding classification and treatment of depression and anxiety is considered. Future research directions will develop the validity and clinical utility of a neural circuit biotype model that spans diagnostic categories and helps to translate neuroscience into clinical practice in the real world. PMID:27653321

Fuzzy-neural control of an aircraft tracking camera platform

NASA Technical Reports Server (NTRS)

Mcgrath, Dennis

1994-01-01

A fuzzy-neural control system simulation was developed for the control of a camera platform used to observe aircraft on final approach to an aircraft carrier. The fuzzy-neural approach to control combines the structure of a fuzzy knowledge base with a supervised neural network's ability to adapt and improve. The performance characteristics of this hybrid system were compared to those of a fuzzy system and a neural network system developed independently to determine if the fusion of these two technologies offers any advantage over the use of one or the other. The results of this study indicate that the fuzzy-neural approach to control offers some advantages over either fuzzy or neural control alone.

A real time neural net estimator of fatigue life

NASA Technical Reports Server (NTRS)

Troudet, T.; Merrill, W.

1990-01-01

A neural network architecture is proposed to estimate, in real-time, the fatigue life of mechanical components, as part of the intelligent Control System for Reusable Rocket Engines. Arbitrary component loading values were used as input to train a two hidden-layer feedforward neural net to estimate component fatigue damage. The ability of the net to learn, based on a local strain approach, the mapping between load sequence and fatigue damage has been demonstrated for a uniaxial specimen. Because of its demonstrated performance, the neural computation may be extended to complex cases where the loads are biaxial or triaxial, and the geometry of the component is complex (e.g., turbopumps blades). The generality of the approach is such that load/damage mappings can be directly extracted from experimental data without requiring any knowledge of the stress/strain profile of the component. In addition, the parallel network architecture allows real-time life calculations even for high-frequency vibrations. Owing to its distributed nature, the neural implementation will be robust and reliable, enabling its use in hostile environments such as rocket engines.

Analysis of Al2O3—parylene C bilayer coatings and impact of microelectrode topography on long term stability of implantable neural arrays

NASA Astrophysics Data System (ADS)

Caldwell, Ryan; Mandal, Himadri; Sharma, Rohit; Solzbacher, Florian; Tathireddy, Prashant; Rieth, Loren

2017-08-01

Objective. Performance of many dielectric coatings for neural electrodes degrades over time, contributing to loss of neural signals and evoked percepts. Studies using planar test substrates have found that a novel bilayer coating of atomic-layer deposited (ALD) Al2O3 and parylene C is a promising candidate for neural electrode applications, exhibiting superior stability to parylene C alone. However, initial results from bilayer encapsulation testing on non-planar devices have been less positive. Our aim was to evaluate ALD Al2O3-parylene C coatings using novel test paradigms, to rigorously evaluate dielectric coatings for neural electrode applications by incorporating neural electrode topography into test structure design. Approach. Five test devices incorporated three distinct topographical features common to neural electrodes, derived from the utah electrode array (UEA). Devices with bilayer (52 nm Al2O3  +  6 µm parylene C) were evaluated against parylene C controls (N  ⩾  6 per device type). Devices were aged in phosphate buffered saline at 67 °C for up to 311 d, and monitored through: (1) leakage current to evaluate encapsulation lifetimes (>1 nA during 5VDC bias indicated failure), and (2) wideband (1-105 Hz) impedance. Main results. Mean-times-to-failure (MTTFs) ranged from 12 to 506 d for bilayer-coated devices, versus 10 to  >2310 d for controls. Statistical testing (log-rank test, α  =  0.05) of failure rates gave mixed results but favored the control condition. After failure, impedance loss for bilayer devices continued for months and manifested across the entire spectrum, whereas the effect was self-limiting after several days, and restricted to frequencies  <100 Hz for controls. These results correlated well with observations of UEAs encapsulated with bilayer and control films. Significance. We observed encapsulation failure modes and behaviors comparable to neural electrode performance which were undetected in studies with planar test devices. We found the impact of parylene C defects to be exacerbated by ALD Al2O3, and conclude that inferior bilayer performance arises from degradation of ALD Al2O3 when directly exposed to saline. This is an important consideration, given that neural electrodes with bilayer coatings are expected to have ALD Al2O3 exposed at dielectric boundaries that delineate electrode sites. Process improvements and use of different inorganic coatings to decrease dissolution in physiological fluids may improve performance. Testing frameworks which take neural electrode complexities into account will be well suited to reliably evaluate such encapsulation schemes.

Cognitive control predicts use of model-based reinforcement learning.

PubMed

Otto, A Ross; Skatova, Anya; Madlon-Kay, Seth; Daw, Nathaniel D

2015-02-01

Accounts of decision-making and its neural substrates have long posited the operation of separate, competing valuation systems in the control of choice behavior. Recent theoretical and experimental work suggest that this classic distinction between behaviorally and neurally dissociable systems for habitual and goal-directed (or more generally, automatic and controlled) choice may arise from two computational strategies for reinforcement learning (RL), called model-free and model-based RL, but the cognitive or computational processes by which one system may dominate over the other in the control of behavior is a matter of ongoing investigation. To elucidate this question, we leverage the theoretical framework of cognitive control, demonstrating that individual differences in utilization of goal-related contextual information--in the service of overcoming habitual, stimulus-driven responses--in established cognitive control paradigms predict model-based behavior in a separate, sequential choice task. The behavioral correspondence between cognitive control and model-based RL compellingly suggests that a common set of processes may underpin the two behaviors. In particular, computational mechanisms originally proposed to underlie controlled behavior may be applicable to understanding the interactions between model-based and model-free choice behavior.

Neural response in obsessive-compulsive washers depends on individual fit of triggers

PubMed Central

Baioui, Ali; Pilgramm, Juliane; Merz, Christian J.; Walter, Bertram; Vaitl, Dieter; Stark, Rudolf

2013-01-01

Background: Patients with obsessive-compulsive disorder (OCD) have highly idiosyncratic triggers. To fully understand which role this idiosyncrasy plays in the neurobiological mechanisms behind OCD, it is necessary to elucidate the impact of individualization regarding the applied investigation methods. This functional magnetic resonance imaging (fMRI) study explores the neural correlates of contamination/washing-related OCD with a highly individualized symptom provocation paradigm. Additionally, it is the first study to directly compare individualized and standardized symptom provocation. Methods: Nineteen patients with washing compulsions created individual OCD hierarchies, which later served as instructions to photograph their own individualized stimulus sets. The patients and 19 case-by-case matched healthy controls participated in a symptom provocation fMRI experiment with individualized and standardized stimulus sets created for each patient. Results: OCD patients compared to healthy controls displayed stronger activation in the basal ganglia (nucleus accumbens, nucleus caudatus, pallidum) for individualized symptom provocation. Using standardized symptom provocation, this group comparison led to stronger activation in the nucleus caudatus. The direct comparison of between-group effects for both symptom provocation approaches revealed stronger activation of the orbitofronto-striatal network for individualized symptom provocation. Conclusions: The present study provides insight into the differential impact of individualized and standardized symptom provocation on the orbitofronto-striatal network of OCD washers. Behavioral and neural responses imply a higher symptom-specificity of individualized symptom provocation. PMID:23630478

Neural basis of postural focus effect on concurrent postural and motor tasks: phase-locked electroencephalogram responses.

PubMed

Huang, Cheng-Ya; Zhao, Chen-Guang; Hwang, Ing-Shiou

2014-11-01

Dual-task performance is strongly affected by the direction of attentional focus. This study investigated neural control of a postural-suprapostural procedure when postural focus strategy varied. Twelve adults concurrently conducted force-matching and maintained stabilometer stance with visual feedback on ankle movement (visual internal focus, VIF) and on stabilometer movement (visual external focus, VEF). Force-matching error, dynamics of ankle and stabilometer movements, and event-related potentials (ERPs) were registered. Postural control with VEF caused superior force-matching performance, more complex ankle movement, and stronger kinematic coupling between the ankle and stabilometer movements than postural control with VIF. The postural focus strategy also altered ERP temporal-spatial patterns. Postural control with VEF resulted in later N1 with less negativity around the bilateral fronto-central and contralateral sensorimotor areas, earlier P2 deflection with more positivity around the bilateral fronto-central and ipsilateral temporal areas, and late movement-related potential commencing in the left frontal-central area, as compared with postural control with VIF. The time-frequency distribution of the ERP principal component revealed phase-locked neural oscillations in the delta (1-4Hz), theta (4-7Hz), and beta (13-35Hz) rhythms. The delta and theta rhythms were more pronounced prior to the timing of P2 positive deflection, and beta rebound was greater after the completion of force-matching in VEF condition than VIF condition. This study is the first to reveal the neural correlation of postural focusing effect on a postural-suprapostural task. Postural control with VEF takes advantage of efficient task-switching to facilitate autonomous postural response, in agreement with the "constrained-action" hypothesis. Copyright © 2014 Elsevier B.V. All rights reserved.

Role of cranial neural crest cells in visceral arch muscle positioning and morphogenesis in the Mexican axolotl, Ambystoma mexicanum.

PubMed

Ericsson, Rolf; Cerny, Robert; Falck, Pierre; Olsson, Lennart

2004-10-01

The role of cranial neural crest cells in the formation of visceral arch musculature was investigated in the Mexican axolotl, Ambystoma mexicanum. DiI (1,1'-dioctadecyl-3,3,3',3'-tetramethylindocarbocyanine, perchlorate) labeling and green fluorescent protein (GFP) mRNA injections combined with unilateral transplantations of neural folds showed that neural crest cells contribute to the connective tissues but not the myofibers of developing visceral arch muscles in the mandibular, hyoid, and branchial arches. Extirpations of individual cranial neural crest streams demonstrated that neural crest cells are necessary for correct morphogenesis of visceral arch muscles. These do, however, initially develop in their proper positions also in the absence of cranial neural crest. Visceral arch muscles forming in the absence of neural crest cells start to differentiate at their origins but fail to extend toward their insertions and may have a frayed appearance. Our data indicate that visceral arch muscle positioning is controlled by factors that do not have a neural crest origin. We suggest that the cranial neural crest-derived connective tissues provide directional guidance important for the proper extension of the cranial muscles and the subsequent attachment to the insertion on the correct cartilage. In a comparative context, our data from the Mexican axolotl support the view that the cranial neural crest plays a fundamental role in the development of not only the skeleton of the vertebrate head but also in the morphogenesis of the cranial muscles and that this might be a primitive feature of cranial development in vertebrates. 2004 Wiley-Liss, Inc.

Parsing Heterogeneity in the Brain Connectivity of Depressed and Healthy Adults During Positive Mood.

PubMed

Price, Rebecca B; Lane, Stephanie; Gates, Kathleen; Kraynak, Thomas E; Horner, Michelle S; Thase, Michael E; Siegle, Greg J

2017-02-15

There is well-known heterogeneity in affective mechanisms in depression that may extend to positive affect. We used data-driven parsing of neural connectivity to reveal subgroups present across depressed and healthy individuals during positive processing, informing targets for mechanistic intervention. Ninety-two individuals (68 depressed patients, 24 never-depressed control subjects) completed a sustained positive mood induction during functional magnetic resonance imaging. Directed functional connectivity paths within a depression-relevant network were characterized using Group Iterative Multiple Model Estimation (GIMME), a method shown to accurately recover the direction and presence of connectivity paths in individual participants. During model selection, individuals were clustered using community detection on neural connectivity estimates. Subgroups were externally tested across multiple levels of analysis. Two connectivity-based subgroups emerged: subgroup A, characterized by weaker connectivity overall, and subgroup B, exhibiting hyperconnectivity (relative to subgroup A), particularly among ventral affective regions. Subgroup predicted diagnostic status (subgroup B contained 81% of patients; 50% of control subjects; χ 2 = 8.6, p = .003) and default mode network connectivity during a separate resting-state task. Among patients, subgroup B members had higher self-reported symptoms, lower sustained positive mood during the induction, and higher negative bias on a reaction-time task. Symptom-based depression subgroups did not predict these external variables. Neural connectivity-based categorization travels with diagnostic category and is clinically predictive, but not clinically deterministic. Both patients and control subjects showed heterogeneous, and overlapping, profiles. The larger and more severely affected patient subgroup was characterized by ventrally driven hyperconnectivity during positive processing. Data-driven parsing suggests heterogeneous substrates of depression and possible resilience in control subjects in spite of biological overlap. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Gust prediction via artificial hair sensor array and neural network

NASA Astrophysics Data System (ADS)

Pankonien, Alexander M.; Thapa Magar, Kaman S.; Beblo, Richard V.; Reich, Gregory W.

2017-04-01

Gust Load Alleviation (GLA) is an important aspect of flight dynamics and control that reduces structural loadings and enhances ride quality. In conventional GLA systems, the structural response to aerodynamic excitation informs the control scheme. A phase lag, imposed by inertia, between the excitation and the measurement inherently limits the effectiveness of these systems. Hence, direct measurement of the aerodynamic loading can eliminate this lag, providing valuable information for effective GLA system design. Distributed arrays of Artificial Hair Sensors (AHS) are ideal for surface flow measurements that can be used to predict other necessary parameters such as aerodynamic forces, moments, and turbulence. In previous work, the spatially distributed surface flow velocities obtained from an array of artificial hair sensors using a Single-State (or feedforward) Neural Network were found to be effective in estimating the steady aerodynamic parameters such as air speed, angle of attack, lift and moment coefficient. This paper extends the investigation of the same configuration to unsteady force and moment estimation, which is important for active GLA control design. Implementing a Recurrent Neural Network that includes previous-timestep sensor information, the hair sensor array is shown to be capable of capturing gust disturbances with a wide range of periods, reducing predictive error in lift and moment by 68% and 52% respectively. The L2 norms of the first layer of the weight matrices were compared showing a 23% emphasis on prior versus current information. The Recurrent architecture also improves robustness, exhibiting only a 30% increase in predictive error when undertrained as compared to a 170% increase by the Single-State NN. This diverse, localized information can thus be directly implemented into a control scheme that alleviates the gusts without waiting for a structural response or requiring user-intensive sensor calibration.

Application of a neural network for reflectance spectrum classification

NASA Astrophysics Data System (ADS)

Yang, Gefei; Gartley, Michael

2017-05-01

Traditional reflectance spectrum classification algorithms are based on comparing spectrum across the electromagnetic spectrum anywhere from the ultra-violet to the thermal infrared regions. These methods analyze reflectance on a pixel by pixel basis. Inspired by high performance that Convolution Neural Networks (CNN) have demonstrated in image classification, we applied a neural network to analyze directional reflectance pattern images. By using the bidirectional reflectance distribution function (BRDF) data, we can reformulate the 4-dimensional into 2 dimensions, namely incident direction × reflected direction × channels. Meanwhile, RIT's micro-DIRSIG model is utilized to simulate additional training samples for improving the robustness of the neural networks training. Unlike traditional classification by using hand-designed feature extraction with a trainable classifier, neural networks create several layers to learn a feature hierarchy from pixels to classifier and all layers are trained jointly. Hence, the our approach of utilizing the angular features are different to traditional methods utilizing spatial features. Although training processing typically has a large computational cost, simple classifiers work well when subsequently using neural network generated features. Currently, most popular neural networks such as VGG, GoogLeNet and AlexNet are trained based on RGB spatial image data. Our approach aims to build a directional reflectance spectrum based neural network to help us to understand from another perspective. At the end of this paper, we compare the difference among several classifiers and analyze the trade-off among neural networks parameters.

A video based feedback system for control of an active commutator during behavioral physiology.

PubMed

Roh, Mootaek; McHugh, Thomas J; Lee, Kyungmin

2015-10-12

To investigate the relationship between neural function and behavior it is necessary to record neuronal activity in the brains of freely behaving animals, a technique that typically involves tethering to a data acquisition system. Optimally this approach allows animals to behave without any interference of movement or task performance. Currently many laboratories in the cognitive and behavioral neuroscience fields employ commercial motorized commutator systems using torque sensors to detect tether movement induced by the trajectory behaviors of animals. In this study we describe a novel motorized commutator system which is automatically controlled by video tracking. To obtain accurate head direction data two light emitting diodes were used and video image noise was minimized by physical light source manipulation. The system calculates the rotation of the animal across a single trial by processing head direction data and the software, which calibrates the motor rotation angle, subsequently generates voltage pulses to actively untwist the tether. This system successfully provides a tether twist-free environment for animals performing behavioral tasks and simultaneous neural activity recording. To the best of our knowledge, it is the first to utilize video tracking generated head direction to detect tether twisting and compensate with a motorized commutator system. Our automatic commutator control system promises an affordable and accessible method to improve behavioral neurophysiology experiments, particularly in mice.

Differentiating neural systems mediating the acquisition versus expression of goal-directed and habitual behavioral control

PubMed Central

Liljeholm, Mimi; Dunne, Simon; O'Doherty, John P.

2015-01-01

Considerable behavioral data indicates that operant actions can become habitual, as evidenced by insensitivity to changes in the action-outcome contingency and in subjective outcome values. Notably, although several studies have investigated the neural substrates of habits, none has clearly differentiated the areas of the human brain that support habit formation from those that implement habitual control. We scanned participants with fMRI as they learned and performed an operant task in which the conditional structure of the environment encouraged either goal-directed encoding of the consequences of actions, or a habit-like mapping of actions to antecedent cues. Participants were also scanned during a subsequent assessment of insensitivity to outcome devaluation. We identified dissociable roles of the cerebellum and ventral striatum, across learning and test performance, in behavioral insensitivity to outcome devaluation. We also show that the inferior parietal lobule – an area previously implicated in several aspects of goal-directed action selection, including the attribution of intent and awareness of agency – predicts sensitivity to outcome devaluation. Finally, we reveal a potential functional homology between the human subgenual cortex and rodent infralimbic cortex in the implementation of habitual control. In summary, our findings suggest a broad systems division, at the cortical and subcortical levels, between brain areas mediating the encoding and expression of action-outcome and stimulus-response associations. PMID:25892332

Microengineered embryonic stem cells niche to induce neural differentiation.

PubMed

Joshi, Ramila; Tavana, Hossein

2015-08-01

A major challenge in therapeutic use of embryonic stem cells (ESCs) for treating neurodegenerative diseases is creating a niche in vitro for controlled neural-specific differentiation of ESCs. We employ a niche microengineering approach to derive neural cells from ESCs by mimicking embryonic development in terms of direct intercellular interactions. Using a polymeric aqueous two-phase system (ATPS) microprinting technology, murine ESCs (mESCs) are precisely localized over a monolayer of supporting stromal cells to allow formation of individual mESC colonies. Polyethylene glycol (PEG) and dextran (DEX) are dissolved in culture media to form two immiscible aqueous solutions. A robotic liquid handler is used to print a nanoliter-volume drop of the denser DEX phase solution containing mESCs onto a confluent layer of supporting PA6 stromal cells submerged in the aqueous PEG phase. mESCs proliferate into isolated colonies of uniform size. For the first time, a comprehensive protein expression analysis of individual mESC colonies is performed over a two-week culture period to track temporal progression of cells from a pluripotent stage to specific neural cells. Starting from day 4, the expression of nestin, neural cell adhesion molecule (NCAM), and beta-III tubulin shows a significant increase but then levels off after the first week of culture. The expression of specific neural cell markers glial fibrillary acidic protein (GFAP), 2',3'-cyclic-nucleotide 3'-phosphodiesterase (CNPase), and tyrosine hydroxylase (TH) is elevated during the second week of culture. This microengineering approach to control ESCs differentiation niche combined with the time-course protein expression analysis of individual differentiating colonies facilitates understanding of evolution of specific neural cells from ESCs and identifying underlying molecular markers.

Reduced neural baroreflex sensitivity is related to enhanced endothelial function in patients with end-stage liver disease.

PubMed

Sárközi, Adrienn; Cseh, Domonkos; Gerlei, Zsuzsanna; Kollai, Márk

2018-02-01

Reduced baroreflex sensitivity (BRS) is a frequent complication in end-stage liver disease, but the underlying mechanism is unknown. We investigated the mechanical and neural components of BRS. Increased nitric oxide (NO) production has been reported in end-stage liver failure. Based on earlier experiments, we hypothesised that enhanced endothelial function might affect baroreflex function. Therefore, we explored the relation between endothelial function and the components of BRS. We enrolled 24 patients and 23 controls. BRS was determined by the spontaneous sequence method. Mechanical component was characterised by the distensibility coefficient (DC) of common carotid artery. Neural component was estimated as the ratio of integrated BRS and DC. Endothelial function was quantified by flow-mediated dilation (FMD) of the brachial artery. Integrated BRS was reduced in patients [7.00 (5.80-9.25) vs. 11.1 (8.50-14.80) ms/mmHg]. The mechanical component was not different in the two groups, whereas neural component showed significant reduction in patients (3.54 ± 1.20 vs. 4.48 ± 1.43 ms/10 -3 ). FMD was higher in patients (9.81 ± 3.77 vs. 5.59 ± 1.36%). FMD and neural BRS were directly related in controls (r = 0.62), but inversely related in patients (r = -0.49). Baroreflex impairment in end-stage liver disease might be explained by deterioration of the neural component, while the mechanical component appears to be preserved. Endothelial NO may enhance BRS in health; however, central endothelial overproduction of NO likely contributes to the reduction of neural component of BRS in patients awaiting liver transplantation.

RIVER LEVEL ESTIMATION USING ARTIFICIAL NEURAL NETWORK FOR URBAN SMALL RIVER IN TIDAL REACH

NASA Astrophysics Data System (ADS)

Takasaki, Tadakatsu; Kawamura, Akira; Amaguchi, Hideo

Prediction of water level in small rivers is great interest for flood control in an urban area located in the river mouth. The tidal river water level is affected by not only flood discharge but also tide, atmospheric pressure, wind direction and speed. We propose a method of estimating river water level considering these factors using an artificial neural network model for the Kanda River located in the center of Tokyo. The effects by those factors are quantitatively investigated. As for the effects by the atmospheric pressure, river water level rises about 7cm per 5hPa increase of the pressure regardless of river discharge under the conditions of 1m/s wind speed and north wind direction. The accurate rating curve for the tidal river is finally obtained.

Control of magnetic bearing systems via the Chebyshev polynomial-based unified model (CPBUM) neural network.

PubMed

Jeng, J T; Lee, T T

2000-01-01

A Chebyshev polynomial-based unified model (CPBUM) neural network is introduced and applied to control a magnetic bearing systems. First, we show that the CPBUM neural network not only has the same capability of universal approximator, but also has faster learning speed than conventional feedforward/recurrent neural network. It turns out that the CPBUM neural network is more suitable in the design of controller than the conventional feedforward/recurrent neural network. Second, we propose the inverse system method, based on the CPBUM neural networks, to control a magnetic bearing system. The proposed controller has two structures; namely, off-line and on-line learning structures. We derive a new learning algorithm for each proposed structure. The experimental results show that the proposed neural network architecture provides a greater flexibility and better performance in controlling magnetic bearing systems.

Selective Attention to Emotion in the Aging Brain

PubMed Central

Samanez-Larkin, Gregory R.; Robertson, Elaine R.; Mikels, Joseph A.; Carstensen, Laura L.; Gotlib, Ian H.

2009-01-01

A growing body of research suggests that the ability to regulate emotion remains stable or improves across the adult life span. Socioemotional selectivity theory maintains that this pattern of findings reflects the prioritization of emotional goals. Given that goal-directed behavior requires attentional control, the present study was designed to investigate age differences in selective attention to emotional lexical stimuli under conditions of emotional interference. Both neural and behavioral measures were obtained during an experiment in which participants completed a flanker task that required them to make categorical judgments about emotional and non-emotional stimuli. Older adults showed interference in both the behavioral and neural measures on control trials, but not on emotion trials. Although older adults typically show relatively high levels of interference and reduced cognitive control during non-emotional tasks, they appear to be able successfully to reduce interference during emotional tasks. PMID:19739908

Hierarchical control of procedural and declarative category-learning systems

PubMed Central

Turner, Benjamin O.; Crossley, Matthew J.; Ashby, F. Gregory

2017-01-01

Substantial evidence suggests that human category learning is governed by the interaction of multiple qualitatively distinct neural systems. In this view, procedural memory is used to learn stimulus-response associations, and declarative memory is used to apply explicit rules and test hypotheses about category membership. However, much less is known about the interaction between these systems: how is control passed between systems as they interact to influence motor resources? Here, we used fMRI to elucidate the neural correlates of switching between procedural and declarative categorization systems. We identified a key region of the cerebellum (left Crus I) whose activity was bidirectionally modulated depending on switch direction. We also identified regions of the default mode network (DMN) that were selectively connected to left Crus I during switching. We propose that the cerebellum—in coordination with the DMN—serves a critical role in passing control between procedural and declarative memory systems. PMID:28213114

Neural Correlates of Lyrical Improvisation: An fMRI Study of Freestyle Rap

PubMed Central

Liu, Siyuan; Chow, Ho Ming; Xu, Yisheng; Erkkinen, Michael G.; Swett, Katherine E.; Eagle, Michael W.; Rizik-Baer, Daniel A.; Braun, Allen R.

2012-01-01

The neural correlates of creativity are poorly understood. Freestyle rap provides a unique opportunity to study spontaneous lyrical improvisation, a multidimensional form of creativity at the interface of music and language. Here we use functional magnetic resonance imaging to characterize this process. Task contrast analyses indicate that improvised performance is characterized by dissociated activity in medial and dorsolateral prefrontal cortices, providing a context in which stimulus-independent behaviors may unfold in the absence of conscious monitoring and volitional control. Connectivity analyses reveal widespread improvisation-related correlations between medial prefrontal, cingulate motor, perisylvian cortices and amygdala, suggesting the emergence of a network linking motivation, language, affect and movement. Lyrical improvisation appears to be characterized by altered relationships between regions coupling intention and action, in which conventional executive control may be bypassed and motor control directed by cingulate motor mechanisms. These functional reorganizations may facilitate the initial improvisatory phase of creative behavior. PMID:23155479

Neural correlates of lyrical improvisation: an FMRI study of freestyle rap.

PubMed

Liu, Siyuan; Chow, Ho Ming; Xu, Yisheng; Erkkinen, Michael G; Swett, Katherine E; Eagle, Michael W; Rizik-Baer, Daniel A; Braun, Allen R

2012-01-01

The neural correlates of creativity are poorly understood. Freestyle rap provides a unique opportunity to study spontaneous lyrical improvisation, a multidimensional form of creativity at the interface of music and language. Here we use functional magnetic resonance imaging to characterize this process. Task contrast analyses indicate that improvised performance is characterized by dissociated activity in medial and dorsolateral prefrontal cortices, providing a context in which stimulus-independent behaviors may unfold in the absence of conscious monitoring and volitional control. Connectivity analyses reveal widespread improvisation-related correlations between medial prefrontal, cingulate motor, perisylvian cortices and amygdala, suggesting the emergence of a network linking motivation, language, affect and movement. Lyrical improvisation appears to be characterized by altered relationships between regions coupling intention and action, in which conventional executive control may be bypassed and motor control directed by cingulate motor mechanisms. These functional reorganizations may facilitate the initial improvisatory phase of creative behavior.

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.

Selective attention to emotion in the aging brain.

PubMed

Samanez-Larkin, Gregory R; Robertson, Elaine R; Mikels, Joseph A; Carstensen, Laura L; Gotlib, Ian H

2009-09-01

A growing body of research suggests that the ability to regulate emotion remains stable or improves across the adult life span. Socioemotional selectivity theory maintains that this pattern of findings reflects the prioritization of emotional goals. Given that goal-directed behavior requires attentional control, the present study was designed to investigate age differences in selective attention to emotional lexical stimuli under conditions of emotional interference. Both neural and behavioral measures were obtained during an experiment in which participants completed a flanker task that required them to make categorical judgments about emotional and nonemotional stimuli. Older adults showed interference in both the behavioral and neural measures on control trials but not on emotion trials. Although older adults typically show relatively high levels of interference and reduced cognitive control during nonemotional tasks, they appear to be able to successfully reduce interference during emotional tasks. (c) 2009 APA, all rights reserved.

Glove-talk II - a neural-network interface which maps gestures to parallel formant speech synthesizer controls.

PubMed

Fels, S S; Hinton, G E

1997-01-01

Glove-Talk II is a system which translates hand gestures to speech through an adaptive interface. Hand gestures are mapped continuously to ten control parameters of a parallel formant speech synthesizer. The mapping allows the hand to act as an artificial vocal tract that produces speech in real time. This gives an unlimited vocabulary in addition to direct control of fundamental frequency and volume. Currently, the best version of Glove-Talk II uses several input devices, a parallel formant speech synthesizer, and three neural networks. The gesture-to-speech task is divided into vowel and consonant production by using a gating network to weight the outputs of a vowel and a consonant neural network. The gating network and the consonant network are trained with examples from the user. The vowel network implements a fixed user-defined relationship between hand position and vowel sound and does not require any training examples from the user. Volume, fundamental frequency, and stop consonants are produced with a fixed mapping from the input devices. With Glove-Talk II, the subject can speak slowly but with far more natural sounding pitch variations than a text-to-speech synthesizer.

The dissociable neural dynamics of cognitive conflict and emotional conflict control: An ERP study.

PubMed

Xue, Song; Li, Yu; Kong, Xia; He, Qiaolin; Liu, Jia; Qiu, Jiang

2016-04-21

This study investigated differences in the neural time-course of cognitive conflict and emotional conflict control, using event-related potentials (ERPs). Although imaging studies have provided some evidence that distinct, dissociable neural systems underlie emotional and nonemotional conflict resolution, no ERP study has directly compared these two types of conflict. Therefore, the present study used a modified face-word Stroop task to explore the electrophysiological correlates of cognitive and emotional conflict control. The behavioral data showed that the difference in response time of congruency (incongruent condition minus the congruent condition) was larger in the cognitive conflict task than in the emotional conflict task, which indicated that cognitive conflict was stronger than the emotional conflict in the present tasks. Analysis of the ERP data revealed a main effect of task type on N2, which may be associated with top-down attention. The N450 results showed an interaction between cognitive and emotional conflict, which might be related to conflict detection. In addition, we found the incongruent condition elicited a larger SP than the congruent condition, which might be related to conflict resolution. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A Brain for Speech. Evolutionary Continuity in Primate and Human Auditory-Vocal Processing

PubMed Central

Aboitiz, Francisco

2018-01-01

In this review article, I propose a continuous evolution from the auditory-vocal apparatus and its mechanisms of neural control in non-human primates, to the peripheral organs and the neural control of human speech. Although there is an overall conservatism both in peripheral systems and in central neural circuits, a few changes were critical for the expansion of vocal plasticity and the elaboration of proto-speech in early humans. Two of the most relevant changes were the acquisition of direct cortical control of the vocal fold musculature and the consolidation of an auditory-vocal articulatory circuit, encompassing auditory areas in the temporoparietal junction and prefrontal and motor areas in the frontal cortex. This articulatory loop, also referred to as the phonological loop, enhanced vocal working memory capacity, enabling early humans to learn increasingly complex utterances. The auditory-vocal circuit became progressively coupled to multimodal systems conveying information about objects and events, which gradually led to the acquisition of modern speech. Gestural communication accompanies the development of vocal communication since very early in human evolution, and although both systems co-evolved tightly in the beginning, at some point speech became the main channel of communication. PMID:29636657

Brain-wide mapping of neural activity controlling zebrafish exploratory locomotion

PubMed Central

Dunn, Timothy W; Mu, Yu; Narayan, Sujatha; Randlett, Owen; Naumann, Eva A; Yang, Chao-Tsung; Schier, Alexander F

2016-01-01

In the absence of salient sensory cues to guide behavior, animals must still execute sequences of motor actions in order to forage and explore. How such successive motor actions are coordinated to form global locomotion trajectories is unknown. We mapped the structure of larval zebrafish swim trajectories in homogeneous environments and found that trajectories were characterized by alternating sequences of repeated turns to the left and to the right. Using whole-brain light-sheet imaging, we identified activity relating to the behavior in specific neural populations that we termed the anterior rhombencephalic turning region (ARTR). ARTR perturbations biased swim direction and reduced the dependence of turn direction on turn history, indicating that the ARTR is part of a network generating the temporal correlations in turn direction. We also find suggestive evidence for ARTR mutual inhibition and ARTR projections to premotor neurons. Finally, simulations suggest the observed turn sequences may underlie efficient exploration of local environments. DOI: http://dx.doi.org/10.7554/eLife.12741.001 PMID:27003593

Differential coding of reward and movement information in the dorsomedial striatal direct and indirect pathways.

PubMed

Shin, Jung Hwan; Kim, Dohoung; Jung, Min Whan

2018-01-26

The direct and indirect pathways of the basal ganglia have long been thought to mediate behavioral promotion and inhibition, respectively. However, this classic dichotomous model has been recently challenged. To better understand neural processes underlying reward-based learning and movement control, we recorded from direct (dSPNs) and indirect (iSPNs) pathway spiny projection neurons in the dorsomedial striatum of D1-Cre and D2-Cre mice performing a probabilistic Pavlovian conditioning task. dSPNs tend to increase activity while iSPNs decrease activity as a function of reward value, suggesting the striatum represents value in the relative activity levels of dSPNs versus iSPNs. Lick offset-related activity increase is largely dSPN selective, suggesting dSPN involvement in suppressing ongoing licking behavior. Rapid responses to negative outcome and previous reward-related responses are more frequent among iSPNs than dSPNs, suggesting stronger contributions of iSPNs to outcome-dependent behavioral adjustment. These findings provide new insights into striatal neural circuit operations.

Development of drug-loaded polymer microcapsules for treatment of epilepsy.

PubMed

Chen, Yu; Gu, Qi; Yue, Zhilian; Crook, Jeremy M; Moulton, Simon E; Cook, Mark J; Wallace, Gordon G

2017-09-26

Despite significant progress in developing new drugs for seizure control, epilepsy still affects 1% of the global population and is drug-resistant in more than 30% of cases. To improve the therapeutic efficacy of epilepsy medication, a promising approach is to deliver anti-epilepsy drugs directly to affected brain areas using local drug delivery systems. The drug delivery systems must meet a number of criteria, including high drug loading efficiency, biodegradability, neuro-cytocompatibility and predictable drug release profiles. Here we report the development of fibre- and sphere-based microcapsules that exhibit controllable uniform morphologies and drug release profiles as predicted by mathematical modelling. Importantly, both forms of fabricated microcapsules are compatible with human brain derived neural stem cells and differentiated neurons and neuroglia, indicating clinical compliance for neural implantation and therapeutic drug delivery.

Prefrontal-limbic connectivity during worry in older adults with generalized anxiety disorder.

PubMed

Mohlman, Jan; Eldreth, Dana A; Price, Rebecca B; Staples, Alison M; Hanson, Catherine

2017-04-01

Although generalized anxiety disorder (GAD) is one of the most prevalent anxiety disorders in older adults, very little is known about the neurobiology of worry, the hallmark symptom of GAD in adults over the age of 60. This study investigated the neurobiology and neural circuitry of worry in older GAD patients and controls. Twenty older GAD patients and 16 age-matched controls (mean age = 67.88) were compared on clinical measures and neural activity during worry using functional magnetic resonance imaging. As expected, worry elicited activation in frontal regions, amygdala, and insula within the GAD group, with a similar but less prominent frontal pattern was observed in controls. Effective connectivity analyses revealed a positive directional circuit in the GAD group extending from ventromedial through dorsolateral prefrontal cortices, converging on the amygdala. A less complex circuit was observed in controls with only dorsolateral prefrontal regions converging on the amygdala; however, a separate circuit passing through the orbitofrontal cortex converged on the insula. Results elucidate a different neurobiology of pathological versus normal worry in later life. A limited resource model is implicated wherein worry in GAD competes for the same neural resources (e.g. prefrontal cortical areas) that are involved in the adaptive regulation of emotion through cognitive and behavioral strategies.

Improved discriminability of spatiotemporal neural patterns in rat motor cortical areas as directional choice learning progresses

PubMed Central

Mao, Hongwei; Yuan, Yuan; Si, Jennie

2015-01-01

Animals learn to choose a proper action among alternatives to improve their odds of success in food foraging and other activities critical for survival. Through trial-and-error, they learn correct associations between their choices and external stimuli. While a neural network that underlies such learning process has been identified at a high level, it is still unclear how individual neurons and a neural ensemble adapt as learning progresses. In this study, we monitored the activity of single units in the rat medial and lateral agranular (AGm and AGl, respectively) areas as rats learned to make a left or right side lever press in response to a left or right side light cue. We noticed that rat movement parameters during the performance of the directional choice task quickly became stereotyped during the first 2–3 days or sessions. But learning the directional choice problem took weeks to occur. Accompanying rats' behavioral performance adaptation, we observed neural modulation by directional choice in recorded single units. Our analysis shows that ensemble mean firing rates in the cue-on period did not change significantly as learning progressed, and the ensemble mean rate difference between left and right side choices did not show a clear trend of change either. However, the spatiotemporal firing patterns of the neural ensemble exhibited improved discriminability between the two directional choices through learning. These results suggest a spatiotemporal neural coding scheme in a motor cortical neural ensemble that may be responsible for and contributing to learning the directional choice task. PMID:25798093

Control of octopus arm extension by a peripheral motor program.

PubMed

Sumbre, G; Gutfreund, Y; Fiorito, G; Flash, T; Hochner, B

2001-09-07

For goal-directed arm movements, the nervous system generates a sequence of motor commands that bring the arm toward the target. Control of the octopus arm is especially complex because the arm can be moved in any direction, with a virtually infinite number of degrees of freedom. Here we show that arm extensions can be evoked mechanically or electrically in arms whose connection with the brain has been severed. These extensions show kinematic features that are almost identical to normal behavior, suggesting that the basic motor program for voluntary movement is embedded within the neural circuitry of the arm itself. Such peripheral motor programs represent considerable simplification in the motor control of this highly redundant appendage.

The effect of pulsed electric fields on the electrotactic migration of human neural progenitor cells through the involvement of intracellular calcium signaling.

PubMed

Hayashi, Hisamitsu; Edin, Fredrik; Li, Hao; Liu, Wei; Rask-Andersen, Helge

2016-12-01

Endogenous electric fields (EFs) are required for the physiological control of the central nervous system development. Application of the direct current EFs to neural stem cells has been studied for the possibility of stem cell transplantation as one of the therapies for brain injury. EFs generated within the nervous system are often associated with action potentials and synaptic activity, apparently resulting in a pulsed current in nature. The aim of this study is to investigate the effect of pulsed EF, which can reduce the cytotoxicity, on the migration of human neural progenitor cells (hNPCs). We applied the mono-directional pulsed EF with a strength of 250mV/mm to hNPCs for 6h. The migration distance of the hNPCs exposed to pulsed EF was significantly greater compared with the control not exposed to the EF. Pulsed EFs, however, had less of an effect on the migration of the differentiated hNPCs. There was no significant change in the survival of hNPCs after exposure to the pulsed EF. To investigate the role of Ca 2+ signaling in electrotactic migration of hNPCs, pharmacological inhibition of Ca 2+ channels in the EF-exposed cells revealed that the electrotactic migration of hNPCs exposed to Ca 2+ channel blockers was significantly lower compared to the control group. The findings suggest that the pulsed EF induced migration of hNPCs is partly influenced by intracellular Ca 2+ signaling. Copyright © 2016 Elsevier B.V. All rights reserved.

Microglia modulate hippocampal neural precursor activity in response to exercise and aging.

PubMed

Vukovic, Jana; Colditz, Michael J; Blackmore, Daniel G; Ruitenberg, Marc J; Bartlett, Perry F

2012-05-09

Exercise has been shown to positively augment adult hippocampal neurogenesis; however, the cellular and molecular pathways mediating this effect remain largely unknown. Previous studies have suggested that microglia may have the ability to differentially instruct neurogenesis in the adult brain. Here, we used transgenic Csf1r-GFP mice to investigate whether hippocampal microglia directly influence the activation of neural precursor cells. Our results revealed that an exercise-induced increase in neural precursor cell activity was mediated via endogenous microglia and abolished when these cells were selectively removed from hippocampal cultures. Conversely, microglia from the hippocampi of animals that had exercised were able to activate latent neural precursor cells when added to neurosphere preparations from sedentary mice. We also investigated the role of CX(3)CL1, a chemokine that is known to provide a more neuroprotective microglial phenotype. Intraparenchymal infusion of a blocking antibody against the CX(3)CL1 receptor, CX(3)CR1, but not control IgG, dramatically reduced the neurosphere formation frequency in mice that had exercised. While an increase in soluble CX(3)CL1 was observed following running, reduced levels of this chemokine were found in the aged brain. Lower levels of CX(3)CL1 with advancing age correlated with the natural decline in neural precursor cell activity, a state that could be partially alleviated through removal of microglia. These findings provide the first direct evidence that endogenous microglia can exert a dual and opposing influence on neural precursor cell activity within the hippocampus, and that signaling through the CX(3)CL1-CX(3)CR1 axis critically contributes toward this process.

A Goal Direction Signal in the Human Entorhinal/Subicular Region

PubMed Central

Chadwick, Martin J.; Jolly, Amy E.J.; Amos, Doran P.; Hassabis, Demis; Spiers, Hugo J.

2015-01-01

Summary Navigating to a safe place, such as a home or nest, is a fundamental behavior for all complex animals. Determining the direction to such goals is a crucial first step in navigation. Surprisingly, little is known about how or where in the brain this “goal direction signal” is represented. In mammals, “head-direction cells” are thought to support this process, but despite 30 years of research, no evidence for a goal direction representation has been reported [1, 2]. Here, we used fMRI to record neural activity while participants made goal direction judgments based on a previously learned virtual environment. We applied multivoxel pattern analysis [3–5] to these data and found that the human entorhinal/subicular region contains a neural representation of intended goal direction. Furthermore, the neural pattern expressed for a given goal direction matched the pattern expressed when simply facing that same direction. This suggests the existence of a shared neural representation of both goal and facing direction. We argue that this reflects a mechanism based on head-direction populations that simulate future goal directions during route planning [6]. Our data further revealed that the strength of direction information predicts performance. Finally, we found a dissociation between this geocentric information in the entorhinal/subicular region and egocentric direction information in the precuneus. PMID:25532898

Intention Concepts and Brain-Machine Interfacing

PubMed Central

Thinnes-Elker, Franziska; Iljina, Olga; Apostolides, John Kyle; Kraemer, Felicitas; Schulze-Bonhage, Andreas; Aertsen, Ad; Ball, Tonio

2012-01-01

Intentions, including their temporal properties and semantic content, are receiving increased attention, and neuroscientific studies in humans vary with respect to the topography of intention-related neural responses. This may reflect the fact that the kind of intentions investigated in one study may not be exactly the same kind investigated in the other. Fine-grained intention taxonomies developed in the philosophy of mind may be useful to identify the neural correlates of well-defined types of intentions, as well as to disentangle them from other related mental states, such as mere urges to perform an action. Intention-related neural signals may be exploited by brain-machine interfaces (BMIs) that are currently being developed to restore speech and motor control in paralyzed patients. Such BMI devices record the brain activity of the agent, interpret (“decode”) the agent’s intended action, and send the corresponding execution command to an artificial effector system, e.g., a computer cursor or a robotic arm. In the present paper, we evaluate the potential of intention concepts from philosophy of mind to improve the performance and safety of BMIs based on higher-order, intention-related control signals. To this end, we address the distinction between future-, present-directed, and motor intentions, as well as the organization of intentions in time, specifically to what extent it is sequential or hierarchical. This has consequences as to whether these different types of intentions can be expected to occur simultaneously or not. We further illustrate how it may be useful or even necessary to distinguish types of intentions exposited in philosophy, including yes- vs. no-intentions and oblique vs. direct intentions, to accurately decode the agent’s intentions from neural signals in practical BMI applications. PMID:23162504

A GPU-accelerated cortical neural network model for visually guided robot navigation.

PubMed

Beyeler, Michael; Oros, Nicolas; Dutt, Nikil; Krichmar, Jeffrey L

2015-12-01

Humans and other terrestrial animals use vision to traverse novel cluttered environments with apparent ease. On one hand, although much is known about the behavioral dynamics of steering in humans, it remains unclear how relevant perceptual variables might be represented in the brain. On the other hand, although a wealth of data exists about the neural circuitry that is concerned with the perception of self-motion variables such as the current direction of travel, little research has been devoted to investigating how this neural circuitry may relate to active steering control. Here we present a cortical neural network model for visually guided navigation that has been embodied on a physical robot exploring a real-world environment. The model includes a rate based motion energy model for area V1, and a spiking neural network model for cortical area MT. The model generates a cortical representation of optic flow, determines the position of objects based on motion discontinuities, and combines these signals with the representation of a goal location to produce motor commands that successfully steer the robot around obstacles toward the goal. The model produces robot trajectories that closely match human behavioral data. This study demonstrates how neural signals in a model of cortical area MT might provide sufficient motion information to steer a physical robot on human-like paths around obstacles in a real-world environment, and exemplifies the importance of embodiment, as behavior is deeply coupled not only with the underlying model of brain function, but also with the anatomical constraints of the physical body it controls. Copyright © 2015 Elsevier Ltd. All rights reserved.

Navigating conjugated polymer actuated neural probes in a brain phantom

NASA Astrophysics Data System (ADS)

Daneshvar, Eugene D.; Kipke, Daryl; Smela, Elisabeth

2012-04-01

Neural probe insertion methods have a direct impact on the longevity of the device in the brain. Initial tissue and vascular damage caused by the probe entering the brain triggers a chronic tissue response that is known to attenuate neural recordings and ultimately encapsulate the probes. Smaller devices have been found to evoke reduced inflammatory response. One way to record from undamaged neural networks may be to position the electrode sites away from the probe. To investigate this approach, we are developing probes with controllably movable electrode projections, which would move outside of the zone that is damaged by the insertion of the larger probe. The objective of this study was to test the capability of conjugated polymer bilayer actuators to actuate neural electrode projections from a probe shank into a transparent brain phantom. Parylene neural probe devices, having five electrode projections with actuating segments and with varying widths (50 - 250 μm) and lengths (200 - 1000 μm) were fabricated. The electroactive polymer polypyrrole (PPy) was used to bend or flatten the projections. The devices were inserted into the brain phantom using an electronic microdrive while simultaneously activating the actuators. Deflections were quantified based on video images. The electrode projections were successfully controlled to either remain flat or to actuate out-of-plane and into the brain phantom during insertion. The projection width had a significant effect on their ability to deflect within the phantom, with thinner probes deflecting but not the wider ones. Thus, small integrated conjugated polymer actuators may enable multiple neuro-experiments and applications not possible before.

Physical Exercise Promotes Recovery of Neurological Function after Ischemic Stroke in Rats

PubMed Central

Zheng, Hai-Qing; Zhang, Li-Ying; Luo, Jing; Li, Li-Li; Li, Menglin; Zhang, Qingjie; Hu, Xi-Quan

2014-01-01

Although physical exercise is an effective strategy for treatment of ischemic stroke, the underlying protective mechanisms are still not well understood. It has been recently demonstrated that neural progenitor cells play a vital role in the recovery of neurological function (NF) through differentiation into mature neurons. In the current study, we observed that physical exercise significantly reduced the infarct size and improved damaged neural functional recovery after an ischemic stroke. Furthermore, we found that the treatment not only exhibited a significant increase in the number of neural progenitor cells and neurons but also decreased the apoptotic cells in the peri-infarct region, compared to a control in the absence of exercise. Importantly, the insulin-like growth factor-1 (IGF-1)/Akt signaling pathway was dramatically activated in the peri-infarct region of rats after physical exercise training. Therefore, our findings suggest that physical exercise directly influences the NF recovery process by increasing neural progenitor cell count via activation of the IGF-1/Akt signaling pathway. PMID:24945308

Flexible body control using neural networks

NASA Technical Reports Server (NTRS)

Mccullough, Claire L.

1992-01-01

Progress is reported on the control of Control Structures Interaction suitcase demonstrator (a flexible structure) using neural networks and fuzzy logic. It is concluded that while control by neural nets alone (i.e., allowing the net to design a controller with no human intervention) has yielded less than optimal results, the neural net trained to emulate the existing fuzzy logic controller does produce acceptible system responses for the initial conditions examined. Also, a neural net was found to be very successful in performing the emulation step necessary for the anticipatory fuzzy controller for the CSI suitcase demonstrator. The fuzzy neural hybrid, which exhibits good robustness and noise rejection properties, shows promise as a controller for practical flexible systems, and should be further evaluated.

The 1992 Goddard Conference on Space Applications of Artificial Intelligence

NASA Technical Reports Server (NTRS)

Rash, James L. (Editor)

1992-01-01

The purpose of this conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The papers fall into the following areas: planning and scheduling, control, fault monitoring/diagnosis and recovery, information management, tools, neural networks, and miscellaneous applications.

EDITORIAL: Focus on the neural interface Focus on the neural interface

NASA Astrophysics Data System (ADS)

Durand, Dominique M.

2009-10-01

The possibility of an effective connection between neural tissue and computers has inspired scientists and engineers to develop new ways of controlling and obtaining information from the nervous system. These applications range from `brain hacking' to neural control of artificial limbs with brain signals. Notwithstanding the significant advances in neural prosthetics in the last few decades and the success of some stimulation devices such as cochlear prosthesis, neurotechnology remains below its potential for restoring neural function in patients with nervous system disorders. One of the reasons for this limited impact can be found at the neural interface and close attention to the integration between electrodes and tissue should improve the possibility of successful outcomes. The neural interfaces research community consists of investigators working in areas such as deep brain stimulation, functional neuromuscular/electrical stimulation, auditory prostheses, cortical prostheses, neuromodulation, microelectrode array technology, brain-computer/machine interfaces. Following the success of previous neuroprostheses and neural interfaces workshops, funding (from NIH) was obtained to establish a biennial conference in the area of neural interfaces. The first Neural Interfaces Conference took place in Cleveland, OH in 2008 and several topics from this conference have been selected for publication in this special section of the Journal of Neural Engineering. Three `perspectives' review the areas of neural regeneration (Corredor and Goldberg), cochlear implants (O'Leary et al) and neural prostheses (Anderson). Seven articles focus on various aspects of neural interfacing. One of the most popular of these areas is the field of brain-computer interfaces. Fraser et al, report on a method to generate robust control with simple signal processing algorithms of signals obtained with electrodes implanted in the brain. One problem with implanted electrode arrays, however, is that they can fail to record reliably neural signals for long periods of time. McConnell et al show that by measuring the impedance of the tissue, one can evaluate the extent of the tissue response to the presence of the electrode. Another problem with the neural interface is the mismatch of the mechanical properties between electrode and tissue. Basinger et al use finite element modeling to analyze this mismatch in retinal prostheses and guide the design of new implantable devices. Electrical stimulation has been the method of choice to activate externally the nervous system. However, Zhang et al show that a novel dual hybrid device integrating electrical and optical stimulation can provide an effective interface for simultaneous recording and stimulation. By interfacing an EMG recording system and a movement detection system, Johnson and Fuglevand develop a model capable of predicting muscle activity during movement that could be important for the development of motor prostheses. Sensory restoration is another unsolved problem in neural prostheses. By developing a novel interface between the dorsal root ganglia and electrodes arrays, Gaunt et al show that it is possible to recruit afferent fibers for sensory substitution. Finally, by interfacing directly with muscles, Jung and colleagues show that stimulation of muscles involved in locomotion following spinal cord damage in rats can provide an effective treatment modality for incomplete spinal cord injury. This series of articles clearly shows that the interface is indeed one of the keys to successful therapeutic neural devices. The next Neural Interfaces Conference will take place in Los Angeles, CA in June 2010 and one can expect to see new developments in neural engineering obtained by focusing on the neural interface.

Altered Brain Dynamics in Patients With Type 1 Diabetes During Working Memory Processing.

PubMed

Embury, Christine M; Wiesman, Alex I; Proskovec, Amy L; Heinrichs-Graham, Elizabeth; McDermott, Timothy J; Lord, Grace H; Brau, Kaitlin L; Drincic, Andjela T; Desouza, Cyrus V; Wilson, Tony W

2018-06-01

It is now generally accepted that diabetes increases the risk for cognitive impairment, but the precise mechanisms are poorly understood. A critical problem in linking diabetes to cognitive impairment is that patients often have multiple comorbidities (e.g., obesity, hypertension) that have been independently linked to cognitive deficits. In the study reported here we focused on young adults with and without type 1 diabetes who were virtually free of such comorbidities. The two groups were matched on major health and demographic factors, and all participants completed a verbal working memory task during magnetoencephalographic brain imaging. We hypothesized that patients would have altered neural dynamics in verbal working memory processing and that these differences would directly relate to clinical disease measures. Accordingly, we found that patients had significantly stronger neural responses in the superior parietal cortices during memory encoding and significantly weaker activity in parietal-occipital regions during maintenance compared with control subjects. Moreover, disease duration and glycemic control were both significantly correlated with neural responses in various brain regions. In conclusion, young healthy adults with type 1 diabetes already have aberrant neural processing relative to their peers without diabetes, using compensatory responses to perform the task, and glucose management and duration may play a central role. © 2018 by the American Diabetes Association.

A hybrid artificial neural network as a software sensor for optimal control of a wastewater treatment process.

PubMed

Choi, D J; Park, H

2001-11-01

For control and automation of biological treatment processes, lack of reliable on-line sensors to measure water quality parameters is one of the most important problems to overcome. Many parameters cannot be measured directly with on-line sensors. The accuracy of existing hardware sensors is also not sufficient and maintenance problems such as electrode fouling often cause trouble. This paper deals with the development of software sensor techniques that estimate the target water quality parameter from other parameters using the correlation between water quality parameters. We focus our attention on the preprocessing of noisy data and the selection of the best model feasible to the situation. Problems of existing approaches are also discussed. We propose a hybrid neural network as a software sensor inferring wastewater quality parameter. Multivariate regression, artificial neural networks (ANN), and a hybrid technique that combines principal component analysis as a preprocessing stage are applied to data from industrial wastewater processes. The hybrid ANN technique shows an enhancement of prediction capability and reduces the overfitting problem of neural networks. The result shows that the hybrid ANN technique can be used to extract information from noisy data and to describe the nonlinearity of complex wastewater treatment processes.

Distributed adaptive neural network control for a class of heterogeneous nonlinear multi-agent systems subject to actuation failures

NASA Astrophysics Data System (ADS)

Cui, Bing; Zhao, Chunhui; Ma, Tiedong; Feng, Chi

2017-02-01

In this paper, the cooperative adaptive consensus tracking problem for heterogeneous nonlinear multi-agent systems on directed graph is addressed. Each follower is modelled as a general nonlinear system with the unknown and nonidentical nonlinear dynamics, disturbances and actuator failures. Cooperative fault tolerant neural network tracking controllers with online adaptive learning features are proposed to guarantee that all agents synchronise to the trajectory of one leader with bounded adjustable synchronisation errors. With the help of linear quadratic regulator-based optimal design, a graph-dependent Lyapunov proof provides error bounds that depend on the graph topology, one virtual matrix and some design parameters. Of particular interest is that if the control gain is selected appropriately, the proposed control scheme can be implemented in a unified framework no matter whether there are faults or not. Furthermore, the fault detection and isolation are not needed to implement. Finally, a simulation is given to verify the effectiveness of the proposed method.

Computational Models and Emergent Properties of Respiratory Neural Networks

PubMed Central

Lindsey, Bruce G.; Rybak, Ilya A.; Smith, Jeffrey C.

2012-01-01

Computational models of the neural control system for breathing in mammals provide a theoretical and computational framework bringing together experimental data obtained from different animal preparations under various experimental conditions. Many of these models were developed in parallel and iteratively with experimental studies and provided predictions guiding new experiments. This data-driven modeling approach has advanced our understanding of respiratory network architecture and neural mechanisms underlying generation of the respiratory rhythm and pattern, including their functional reorganization under different physiological conditions. Models reviewed here vary in neurobiological details and computational complexity and span multiple spatiotemporal scales of respiratory control mechanisms. Recent models describe interacting populations of respiratory neurons spatially distributed within the Bötzinger and pre-Bötzinger complexes and rostral ventrolateral medulla that contain core circuits of the respiratory central pattern generator (CPG). Network interactions within these circuits along with intrinsic rhythmogenic properties of neurons form a hierarchy of multiple rhythm generation mechanisms. The functional expression of these mechanisms is controlled by input drives from other brainstem components, including the retrotrapezoid nucleus and pons, which regulate the dynamic behavior of the core circuitry. The emerging view is that the brainstem respiratory network has rhythmogenic capabilities at multiple levels of circuit organization. This allows flexible, state-dependent expression of different neural pattern-generation mechanisms under various physiological conditions, enabling a wide repertoire of respiratory behaviors. Some models consider control of the respiratory CPG by pulmonary feedback and network reconfiguration during defensive behaviors such as cough. Future directions in modeling of the respiratory CPG are considered. PMID:23687564

Positive mood enhances reward-related neural activity

PubMed Central

Nusslock, Robin

2016-01-01

Although behavioral research has shown that positive mood leads to desired outcomes in nearly every major life domain, no studies have directly examined the effects of positive mood on the neural processes underlying reward-related affect and goal-directed behavior. To address this gap, participants in the present fMRI study experienced either a positive (n = 20) or neutral (n = 20) mood induction and subsequently completed a monetary incentive delay task that assessed reward and loss processing. Consistent with prediction, positive mood elevated activity specifically during reward anticipation in corticostriatal neural regions that have been implicated in reward processing and goal-directed behavior, including the nucleus accumbens, caudate, lateral orbitofrontal cortex and putamen, as well as related paralimbic regions, including the anterior insula and ventromedial prefrontal cortex. These effects were not observed during reward outcome, loss anticipation or loss outcome. Critically, this is the first study to report that positive mood enhances reward-related neural activity. Our findings have implications for uncovering the neural mechanisms by which positive mood enhances goal-directed behavior, understanding the malleability of reward-related neural activity, and developing targeted treatments for psychiatric disorders characterized by deficits in reward processing. PMID:26833919

Control of Complex Dynamic Systems by Neural Networks

NASA Technical Reports Server (NTRS)

Spall, James C.; Cristion, John A.

1993-01-01

This paper considers the use of neural networks (NN's) in controlling a nonlinear, stochastic system with unknown process equations. The NN is used to model the resulting unknown control law. The approach here is based on using the output error of the system to train the NN controller without the need to construct a separate model (NN or other type) for the unknown process dynamics. To implement such a direct adaptive control approach, it is required that connection weights in the NN be estimated while the system is being controlled. As a result of the feedback of the unknown process dynamics, however, it is not possible to determine the gradient of the loss function for use in standard (back-propagation-type) weight estimation algorithms. Therefore, this paper considers the use of a new stochastic approximation algorithm for this weight estimation, which is based on a 'simultaneous perturbation' gradient approximation that only requires the system output error. It is shown that this algorithm can greatly enhance the efficiency over more standard stochastic approximation algorithms based on finite-difference gradient approximations.

Titania nanotube arrays as potential interfaces for neurological prostheses

NASA Astrophysics Data System (ADS)

Sorkin, Jonathan Andrew

Neural prostheses can make a dramatic improvement for those suffering from visual and auditory, cognitive, and motor control disabilities, allowing them regained functionality by the use of stimulating or recording electrical signaling. However, the longevity of these devices is limited due to the neural tissue response to the implanted device. In response to the implant penetrating the blood brain barrier and causing trauma to the tissue, the body forms a to scar to isolate the implant in order to protect the nearby tissue. The scar tissue is a result of reactive gliosis and produces an insulated sheath, encapsulating the implant. The glial sheath limits the stimulating or recording capabilities of the implant, reducing its effectiveness over the long term. A favorable interaction with this tissue would be the direct adhesion of neurons onto the contacts of the implant, and the prevention of glial encapsulation. With direct neuronal adhesion the effectiveness and longevity of the device would be significantly improved. Titania nanotube arrays, fabricated using electrochemical anodization, provide a conductive architecture capable of altering cellular response. This work focuses on the fabrication of different titania nanotube array architectures to determine how their structures and properties influence the response of neural tissue, modeled using the C17.2 murine neural stem cell subclone, and if glial encapsulation can be reduced while neuronal adhesion is promoted.

Estimate the effective connectivity in multi-coupled neural mass model using particle swarm optimization

NASA Astrophysics Data System (ADS)

Shan, Bonan; Wang, Jiang; Deng, Bin; Zhang, Zhen; Wei, Xile

2017-03-01

Assessment of the effective connectivity among different brain regions during seizure is a crucial problem in neuroscience today. As a consequence, a new model inversion framework of brain function imaging is introduced in this manuscript. This framework is based on approximating brain networks using a multi-coupled neural mass model (NMM). NMM describes the excitatory and inhibitory neural interactions, capturing the mechanisms involved in seizure initiation, evolution and termination. Particle swarm optimization method is used to estimate the effective connectivity variation (the parameters of NMM) and the epileptiform dynamics (the states of NMM) that cannot be directly measured using electrophysiological measurement alone. The estimated effective connectivity includes both the local connectivity parameters within a single region NMM and the remote connectivity parameters between multi-coupled NMMs. When the epileptiform activities are estimated, a proportional-integral controller outputs control signal so that the epileptiform spikes can be inhibited immediately. Numerical simulations are carried out to illustrate the effectiveness of the proposed framework. The framework and the results have a profound impact on the way we detect and treat epilepsy.

Hypothalamic neural systems controlling the female reproductive life cycle: Gonadotropin-releasing hormone, glutamate, and GABA

PubMed Central

Maffucci, Jacqueline A.; Gore, Andrea C.

2009-01-01

The hypothalamic-pituitary-gonadal (HPG) axis undergoes a number of changes throughout the reproductive life cycle that are responsible for the development, puberty, adulthood, and senescence of reproductive systems. This natural progression is dictated by the neural network controlling the hypothalamus including the cells that synthesize and release gonadotropin-releasing hormone (GnRH) and their regulatory neurotransmitters. Glutamate and GABA are the primary excitatory and inhibitory neurotransmitters in the central nervous system, and as such contribute a great deal to modulating this axis throughout the lifetime via their actions on receptors in the hypothalamus, both directly on GnRH neurons as well as indirectly though other hypothalamic neural networks. Interactions among GnRH neurons, glutamate, and GABA, including the regulation of GnRH gene and protein expression, hormone release, and modulation by estrogen, are critical to age-appropriate changes in reproductive function. Here, we present evidence for the modulation of GnRH neurosecretory cells by the balance of glutamate and GABA in the hypothalamus, and the functional consequences of these interactions on reproductive physiology across the life cycle. PMID:19349036

Vortioxetine reduces BOLD signal during performance of the N-back working memory task: a randomised neuroimaging trial in remitted depressed patients and healthy controls

PubMed Central

Smith, J; Browning, M; Conen, S; Smallman, R; Buchbjerg, J; Larsen, K G; Olsen, C K; Christensen, S R; Dawson, G R; Deakin, J F; Hawkins, P; Morris, R; Goodwin, G; Harmer, C J

2018-01-01

Cognitive dysfunction is common in depression during both acute episodes and remission. Vortioxetine is a novel multimodal antidepressant that has improved cognitive function including executive function in depressed patients in randomised placebo-controlled clinical trials. However, it is unclear whether vortioxetine is able to target directly the neural circuitry implicated in the cognitive deficits in depression. Remitted depressed (n=48) and healthy volunteers (n=48) were randomised to receive 14 days treatment with 20 mg vortioxetine or placebo in a double-blind design. The effects of treatment on functional magnetic resonance imaging responses during an N-back working memory task were assessed at baseline and at the end of treatment. Neuropsychological measures of executive function, speed and information processing, attention and learning and memory were examined with the Trail Making Test (TMT), Rey Auditory Learning Test and Digit Symbol Substitution Test before and after treatment; subjective cognitive function was assessed using the Perceived Deficits Questionnaire (PDQ). Compared with placebo, vortioxetine reduced activation in the right dorsolateral prefrontal cortex and left hippocampus during the N-back task compared with placebo. Vortioxetine also increased TMT-A performance and self-reported cognitive function on the PDQ. These effects were seen across both subject groups. Vortioxetine modulates neural responses across a circuit subserving working memory in a direction opposite to the changes described in depression, when performance is maintained. This study provides evidence that vortioxetine has direct effects on the neural circuitry supporting cognitive function that can be dissociated from its effects on the mood symptoms of depression. PMID:28533517

Adaptive output feedback NN control of a class of discrete-time MIMO nonlinear systems with unknown control directions.

PubMed

Li, Yanan; Yang, Chenguang; Ge, Shuzhi Sam; Lee, Tong Heng

2011-04-01

In this paper, adaptive neural network (NN) control is investigated for a class of block triangular multiinput-multioutput nonlinear discrete-time systems with each subsystem in pure-feedback form with unknown control directions. These systems are of couplings in every equation of each subsystem, and different subsystems may have different orders. To avoid the noncausal problem in the control design, the system is transformed into a predictor form by rigorous derivation. By exploring the properties of the block triangular form, implicit controls are developed for each subsystem such that the couplings of inputs and states among subsystems have been completely decoupled. The radial basis function NN is employed to approximate the unknown control. Each subsystem achieves a semiglobal uniformly ultimately bounded stability with the proposed control, and simulation results are presented to demonstrate its efficiency.

Innervation of Extrahepatic Biliary Tract, With Special Reference to the Direct Bidirectional Neural Connections of the Gall Bladder, Sphincter of Oddi and Duodenum in Suncus murinus, in Whole-Mount Immunohistochemical Study.

PubMed

Yi, S-Q; Ren, K; Kinoshita, M; Takano, N; Itoh, M; Ozaki, N

2016-06-01

Sphincter of Oddi dysfunction is one of the most important symptoms in post-cholecystectomy syndrome. Using either electrical or mechanical stimulation and retrogradely transported neuronal dyes, it has been demonstrated that there are direct neural pathways connecting gall bladder and the sphincter of Oddi in the Australian opossum and the golden hamster. In the present study, we employed whole-mount immunohistochemistry staining to observe and verify that there are two different plexuses of the extrahepatic biliary tract in Suncus murinus. One, named Pathway One, showed a fine, irregular but dense network plexus that ran adhesively and resided on/in the extrahepatic biliary tract wall, and the plexus extended into the intrahepatic area. On the other hand, named Pathway Two, exhibiting simple, thicker and straight neural bundles, ran parallel to the surface of the extrahepatic biliary tract and passed between the gall bladder and duodenum, but did not give off any branches to the liver. Pathway Two was considered to involve direct bidirectional neural connections between the duodenum and the biliary tract system. For the first time, morphologically, we demonstrated direct neural connections between gall bladder and duodenum in S. murinus. Malfunction of the sphincter of Oddi may be caused by injury of the direct neural pathways between gall bladder and duodenum by cholecystectomy. From the viewpoint of preserving the function of the major duodenal papilla and common bile duct, we emphasize the importance of avoiding kocherization of the common bile duct so as to preserve the direct neural connections between gall bladder and sphincter of Oddi. © 2015 Blackwell Verlag GmbH.

Elimination of spiral waves in a locally connected chaotic neural network by a dynamic phase space constraint.

PubMed

Li, Yang; Oku, Makito; He, Guoguang; Aihara, Kazuyuki

2017-04-01

In this study, a method is proposed that eliminates spiral waves in a locally connected chaotic neural network (CNN) under some simplified conditions, using a dynamic phase space constraint (DPSC) as a control method. In this method, a control signal is constructed from the feedback internal states of the neurons to detect phase singularities based on their amplitude reduction, before modulating a threshold value to truncate the refractory internal states of the neurons and terminate the spirals. Simulations showed that with appropriate parameter settings, the network was directed from a spiral wave state into either a plane wave (PW) state or a synchronized oscillation (SO) state, where the control vanished automatically and left the original CNN model unaltered. Each type of state had a characteristic oscillation frequency, where spiral wave states had the highest, and the intra-control dynamics was dominated by low-frequency components, thereby indicating slow adjustments to the state variables. In addition, the PW-inducing and SO-inducing control processes were distinct, where the former generally had longer durations but smaller average proportions of affected neurons in the network. Furthermore, variations in the control parameter allowed partial selectivity of the control results, which were accompanied by modulation of the control processes. The results of this study broaden the applicability of DPSC to chaos control and they may also facilitate the utilization of locally connected CNNs in memory retrieval and the exploration of traveling wave dynamics in biological neural networks. Copyright © 2017 Elsevier Ltd. All rights reserved.

Real time hardware implementation of power converters for grid integration of distributed generation and STATCOM systems

NASA Astrophysics Data System (ADS)

Jaithwa, Ishan

Deployment of smart grid technologies is accelerating. Smart grid enables bidirectional flows of energy and energy-related communications. The future electricity grid will look very different from today's power system. Large variable renewable energy sources will provide a greater portion of electricity, small DERs and energy storage systems will become more common, and utilities will operate many different kinds of energy efficiency. All of these changes will add complexity to the grid and require operators to be able to respond to fast dynamic changes to maintain system stability and security. This thesis investigates advanced control technology for grid integration of renewable energy sources and STATCOM systems by verifying them on real time hardware experiments using two different systems: d SPACE and OPAL RT. Three controls: conventional, direct vector control and the intelligent Neural network control were first simulated using Matlab to check the stability and safety of the system and were then implemented on real time hardware using the d SPACE and OPAL RT systems. The thesis then shows how dynamic-programming (DP) methods employed to train the neural networks are better than any other controllers where, an optimal control strategy is developed to ensure effective power delivery and to improve system stability. Through real time hardware implementation it is proved that the neural vector control approach produces the fastest response time, low overshoot, and, the best performance compared to the conventional standard vector control method and DCC vector control technique. Finally the entrepreneurial approach taken to drive the technologies from the lab to market via ORANGE ELECTRIC is discussed in brief.

Fuzzy and neural control

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1992-01-01

Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning.

Application of Fuzzy-Logic Controller and Neural Networks Controller in Gas Turbine Speed Control and Overheating Control and Surge Control on Transient Performance

NASA Astrophysics Data System (ADS)

Torghabeh, A. A.; Tousi, A. M.

2007-08-01

This paper presents Fuzzy Logic and Neural Networks approach to Gas Turbine Fuel schedules. Modeling of non-linear system using feed forward artificial Neural Networks using data generated by a simulated gas turbine program is introduced. Two artificial Neural Networks are used , depicting the non-linear relationship between gas generator speed and fuel flow, and turbine inlet temperature and fuel flow respectively . Off-line fast simulations are used for engine controller design for turbojet engine based on repeated simulation. The Mamdani and Sugeno models are used to expression the Fuzzy system . The linguistic Fuzzy rules and membership functions are presents and a Fuzzy controller will be proposed to provide an Open-Loop control for the gas turbine engine during acceleration and deceleration . MATLAB Simulink was used to apply the Fuzzy Logic and Neural Networks analysis. Both systems were able to approximate functions characterizing the acceleration and deceleration schedules . Surge and Flame-out avoidance during acceleration and deceleration phases are then checked . Turbine Inlet Temperature also checked and controls by Neural Networks controller. This Fuzzy Logic and Neural Network Controllers output results are validated and evaluated by GSP software . The validation results are used to evaluate the generalization ability of these artificial Neural Networks and Fuzzy Logic controllers.

Neural network-based sliding mode control for atmospheric-actuated spacecraft formation using switching strategy

NASA Astrophysics Data System (ADS)

Sun, Ran; Wang, Jihe; Zhang, Dexin; Shao, Xiaowei

2018-02-01

This paper presents an adaptive neural networks-based control method for spacecraft formation with coupled translational and rotational dynamics using only aerodynamic forces. It is assumed that each spacecraft is equipped with several large flat plates. A coupled orbit-attitude dynamic model is considered based on the specific configuration of atmospheric-based actuators. For this model, a neural network-based adaptive sliding mode controller is implemented, accounting for system uncertainties and external perturbations. To avoid invalidation of the neural networks destroying stability of the system, a switching control strategy is proposed which combines an adaptive neural networks controller dominating in its active region and an adaptive sliding mode controller outside the neural active region. An optimal process is developed to determine the control commands for the plates system. The stability of the closed-loop system is proved by a Lyapunov-based method. Comparative results through numerical simulations illustrate the effectiveness of executing attitude control while maintaining the relative motion, and higher control accuracy can be achieved by using the proposed neural-based switching control scheme than using only adaptive sliding mode controller.

Optical Neural Interfaces

PubMed Central

Warden, Melissa R.; Cardin, Jessica A.; Deisseroth, Karl

2014-01-01

Genetically encoded optical actuators and indicators have changed the landscape of neuroscience, enabling targetable control and readout of specific components of intact neural circuits in behaving animals. Here, we review the development of optical neural interfaces, focusing on hardware designed for optical control of neural activity, integrated optical control and electrical readout, and optical readout of population and single-cell neural activity in freely moving mammals. PMID:25014785

Ataxin-2: A versatile posttranscriptional regulator and its implication in neural function.

PubMed

Lee, Jongbo; Kim, Minjong; Itoh, Taichi Q; Lim, Chunghun

2018-06-05

Ataxin-2 (ATXN2) is a eukaryotic RNA-binding protein that is conserved from yeast to human. Genetic expansion of a poly-glutamine tract in human ATXN2 has been implicated in several neurodegenerative diseases, likely acting through gain-of-function effects. Emerging evidence, however, suggests that ATXN2 plays more direct roles in neural function via specific molecular and cellular pathways. ATXN2 and its associated protein complex control distinct steps in posttranscriptional gene expression, including poly-A tailing, RNA stabilization, microRNA-dependent gene silencing, and translational activation. Specific RNA substrates have been identified for the functions of ATXN2 in aspects of neural physiology, such as circadian rhythms and olfactory habituation. Genetic models of ATXN2 loss-of-function have further revealed its significance in stress-induced cytoplasmic granules, mechanistic target of rapamycin signaling, and cellular metabolism, all of which are crucial for neural homeostasis. Accordingly, we propose that molecular evolution has been selecting the ATXN2 protein complex as an important trans-acting module for the posttranscriptional control of diverse neural functions. This explains how ATXN2 intimately interacts with various neurodegenerative disease genes, and suggests that loss-of-function effects of ATXN2 could be therapeutic targets for ATXN2-related neurological disorders. This article is categorized under: RNA in Disease and Development > RNA in Disease RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications. © 2018 Wiley Periodicals, Inc.

Combining neural and behavioral indicators in the assessment of internalizing psychopathology in children and adolescents.

PubMed

Moser, Jason S; Durbin, C Emily; Patrick, Christopher J; Schmidt, Norman B

2015-01-01

Anxiety and mood disorders are among the most prevalent mental health problems affecting our youth. We propose that assessment and treatment efforts in this area can benefit from a focus on developmentally sensitive neurobehavioral trait constructs, that is, individual difference constructs with direct referents in both neurobiology and behavior across the lifespan. This approach dovetails with the National Institute of Mental Health's Research Domain Criteria initiative, which aims to improve classification and treatment of psychopathology by delineating dimensions of functioning that transcend measurement domains and traditional diagnostic categories. We highlight two neurobehavioral dimensions with clear relevance for understanding internalizing problems at differing ages: (a) defensive reactivity and (b) cognitive control. Individual differences in defensive reactivity are posited to reflect variations in sensitivity of the brain's negative valence systems, whereas differences in cognitive control are theorized to reflect variations in neural systems dedicated to regulating behavior and affect. Focusing on these target constructs, we illustrate a psychoneurometric approach to assessment of internalizing psychopathology entailing use of neural, self-report, and behavioral indicators. We address the feasibility of the psychoneurometric approach for clinical application and present results from a pilot study demonstrating expected associations for neural, parent-report, and behavioral measures of defensive reactivity and cognitive control with internalizing symptoms in preschoolers. Together, our conceptual and empirical analyses highlight the promise of multimethod, dimensional assessment of internalizing psychopathology in the lab and in the clinic.

An Integrated Circuit for Simultaneous Extracellular Electrophysiology Recording and Optogenetic Neural Manipulation

PubMed Central

Chen, Chang Hao; McCullagh, Elizabeth A.; Pun, Sio Hang; Mak, Peng Un; Vai, Mang I; Mak, Pui In; Klug, Achim; Lei, Tim C.

2017-01-01

The ability to record and to control action potential firing in neuronal circuits of the brain is critical to understand how the brain functions on the cellular and network levels. Recent development of optogenetic proteins allows direct stimulation or inhibition of action potential firing of neurons upon optical illumination. In this paper, we combined a low-noise and high input impedance (or low input capacitance) neural recording amplifier, and a high current laser/LED driver in a monolithic integrated circuit (IC) for simultaneous neural recording and optogenetic neural control. The low input capacitance of the amplifier (9.7 pF) was achieved through adding a dedicated unity gain input stage optimized for high impedance metal electrodes. The input referred noise of the amplifier was measured to be 4.57 µVrms, which is lower than the estimated thermal noise of the metal electrode. Thus, action potentials originating from a single neuron can be recorded with a signal-to-noise ratio of ~6.6. The LED/laser current driver delivers a maximum current of 330 mA to generate adequate light for optogenetic control. We experimentally tested the functionality of the IC with an anesthetized Mongolian gerbil and recorded auditory stimulated action potentials from the inferior colliculus. Furthermore, we showed that spontaneous firing of 5th (trigeminal) nerve fibers was inhibited using the optogenetic protein Halorhodopsin. A noise model was also derived including the equivalent electronic components of the metal electrode and the high current driver to guide the design. PMID:28221990

Differential Neural Responses to Food Images in Women with Bulimia versus Anorexia Nervosa

PubMed Central

Brooks, Samantha J.; O′Daly, Owen G.; Uher, Rudolf; Friederich, Hans-Christoph; Giampietro, Vincent; Brammer, Michael; Williams, Steven C. R.; Schiöth, Helgi B.; Treasure, Janet; Campbell, Iain C.

2011-01-01

Background Previous fMRI studies show that women with eating disorders (ED) have differential neural activation to viewing food images. However, despite clinical differences in their responses to food, differential neural activation to thinking about eating food, between women with anorexia nervosa (AN) and bulimia nervosa (BN) is not known. Methods We compare 50 women (8 with BN, 18 with AN and 24 age-matched healthy controls [HC]) while they view food images during functional Magnetic Resonance Imaging (fMRI). Results In response to food (vs non-food) images, women with BN showed greater neural activation in the visual cortex, right dorsolateral prefrontal cortex, right insular cortex and precentral gyrus, women with AN showed greater activation in the right dorsolateral prefrontal cortex, cerebellum and right precuneus. HC women activated the cerebellum, right insular cortex, right medial temporal lobe and left caudate. Direct comparisons revealed that compared to HC, the BN group showed relative deactivation in the bilateral superior temporal gyrus/insula, and visual cortex, and compared to AN had relative deactivation in the parietal lobe and dorsal posterior cingulate cortex, but greater activation in the caudate, superior temporal gyrus, right insula and supplementary motor area. Conclusions Women with AN and BN activate top-down cognitive control in response to food images, yet women with BN have increased activation in reward and somatosensory regions, which might impinge on cognitive control over food consumption and binge eating. PMID:21799807

Bilingual Language Control in Perception versus Action: MEG Reveals Comprehension Control Mechanisms in Anterior Cingulate Cortex and Domain-General Control of Production in Dorsolateral Prefrontal Cortex.

PubMed

Blanco-Elorrieta, Esti; Pylkkänen, Liina

2016-01-13

For multilingual individuals, adaptive goal-directed behavior as enabled by cognitive control includes the management of two or more languages. This work used magnetoencephalography (MEG) to investigate the degree of neural overlap between language control and domain-general cognitive control both in action and perception. Highly proficient Arabic-English bilingual individuals participated in maximally parallel language-switching tasks in production and comprehension as well as in analogous tasks in which, instead of the used language, the semantic category of the comprehended/produced word changed. Our results indicated a clear dissociation of language control mechanisms in production versus comprehension. Language-switching in production recruited dorsolateral prefrontal regions bilaterally and, importantly, these regions were similarly recruited by category-switching. Conversely, effects of language-switching in comprehension were observed in the anterior cingulate cortex and were not shared by category-switching. These results suggest that bilingual individuals rely on adaptive language control strategies and that the neural involvement during language-switching could be extensively influenced by whether the switch is active (e.g., in production) or passive (e.g., in comprehension). In addition, these results support that humans require high-level cognitive control to switch languages in production, but the comprehension of language switches recruits a distinct neural circuitry. The use of MEG enabled us to obtain the first characterization of the spatiotemporal profile of these effects, establishing that switching processes begin ∼ 400 ms after stimulus presentation. This research addresses the neural mechanisms underlying multilingual individuals' ability to successfully manage two or more languages, critically targeting whether language control is uniform across linguistic domains (production and comprehension) and whether it is a subdomain of general cognitive control. The results showed that language production and comprehension rely on different networks: whereas language control in production recruited domain-general networks, the brain bases of switching during comprehension seemed language specific. Therefore, the crucial assumption of the bilingual advantage hypothesis, that there is a close relationship between language control and general cognitive control, seems to only hold during production. Copyright © 2016 the authors 0270-6474/16/360290-12$15.00/0.

Feasibility Study of Extended-Gate-Type Silicon Nanowire Field-Effect Transistors for Neural Recording

PubMed Central

Kang, Hongki; Kim, Jee-Yeon; Choi, Yang-Kyu; Nam, Yoonkey

2017-01-01

In this research, a high performance silicon nanowire field-effect transistor (transconductance as high as 34 µS and sensitivity as 84 nS/mV) is extensively studied and directly compared with planar passive microelectrode arrays for neural recording application. Electrical and electrochemical characteristics are carefully characterized in a very well-controlled manner. We especially focused on the signal amplification capability and intrinsic noise of the transistors. A neural recording system using both silicon nanowire field-effect transistor-based active-type microelectrode array and platinum black microelectrode-based passive-type microelectrode array are implemented and compared. An artificial neural spike signal is supplied as input to both arrays through a buffer solution and recorded simultaneously. Recorded signal intensity by the silicon nanowire transistor was precisely determined by an electrical characteristic of the transistor, transconductance. Signal-to-noise ratio was found to be strongly dependent upon the intrinsic 1/f noise of the silicon nanowire transistor. We found how signal strength is determined and how intrinsic noise of the transistor determines signal-to-noise ratio of the recorded neural signals. This study provides in-depth understanding of the overall neural recording mechanism using silicon nanowire transistors and solid design guideline for further improvement and development. PMID:28350370

Feasibility Study of Extended-Gate-Type Silicon Nanowire Field-Effect Transistors for Neural Recording.

PubMed

Kang, Hongki; Kim, Jee-Yeon; Choi, Yang-Kyu; Nam, Yoonkey

2017-03-28

In this research, a high performance silicon nanowire field-effect transistor (transconductance as high as 34 µS and sensitivity as 84 nS/mV) is extensively studied and directly compared with planar passive microelectrode arrays for neural recording application. Electrical and electrochemical characteristics are carefully characterized in a very well-controlled manner. We especially focused on the signal amplification capability and intrinsic noise of the transistors. A neural recording system using both silicon nanowire field-effect transistor-based active-type microelectrode array and platinum black microelectrode-based passive-type microelectrode array are implemented and compared. An artificial neural spike signal is supplied as input to both arrays through a buffer solution and recorded simultaneously. Recorded signal intensity by the silicon nanowire transistor was precisely determined by an electrical characteristic of the transistor, transconductance. Signal-to-noise ratio was found to be strongly dependent upon the intrinsic 1/f noise of the silicon nanowire transistor. We found how signal strength is determined and how intrinsic noise of the transistor determines signal-to-noise ratio of the recorded neural signals. This study provides in-depth understanding of the overall neural recording mechanism using silicon nanowire transistors and solid design guideline for further improvement and development.

Neural integration underlying a time-compensated sun compass in the migratory monarch butterfly

PubMed Central

Shlizerman, Eli; Phillips-Portillo, James; Reppert, Steven M.

2016-01-01

Migrating Eastern North American monarch butterflies use a time-compensated sun compass to adjust their flight to the southwest direction. While the antennal genetic circadian clock and the azimuth of the sun are instrumental for proper function of the compass, it is unclear how these signals are represented on a neuronal level and how they are integrated to produce flight control. To address these questions, we constructed a receptive field model of the compound eye that encodes the solar azimuth. We then derived a neural circuit model, which integrates azimuthal and circadian signals to correct flight direction. The model demonstrates an integration mechanism, which produces robust trajectories reaching the southwest regardless of the time of day and includes a configuration for remigration. Comparison of model simulations with flight trajectories of butterflies in a flight simulator shows analogous behaviors and affirms the prediction that midday is the optimal time for migratory flight. PMID:27149852

Conscious Action/Zombie Action

PubMed Central

Shepherd, Joshua

2015-01-01

Abstract I argue that the neural realizers of experiences of trying (that is, experiences of directing effort towards the satisfaction of an intention) are not distinct from the neural realizers of actual trying (that is, actual effort directed towards the satisfaction of an intention). I then ask how experiences of trying might relate to the perceptual experiences one has while acting. First, I assess recent zombie action arguments regarding conscious visual experience, and I argue that contrary to what some have claimed, conscious visual experience plays a causal role for action control in some circumstances. Second, I propose a multimodal account of the experience of acting. According to this account, the experience of acting is (at the very least) a temporally extended, co‐conscious collection of agentive and perceptual experiences, functionally integrated and structured both by multimodal perceptual processing as well as by what an agent is, at the time, trying to do. PMID:27667859

Active Control of Wind-Tunnel Model Aeroelastic Response Using Neural Networks

NASA Technical Reports Server (NTRS)

Scott, Robert C.

2000-01-01

NASA Langley Research Center, Hampton, VA 23681 Under a joint research and development effort conducted by the National Aeronautics and Space Administration and The Boeing Company (formerly McDonnell Douglas) three neural-network based control systems were developed and tested. The control systems were experimentally evaluated using a transonic wind-tunnel model in the Langley Transonic Dynamics Tunnel. One system used a neural network to schedule flutter suppression control laws, another employed a neural network in a predictive control scheme, and the third employed a neural network in an inverse model control scheme. All three of these control schemes successfully suppressed flutter to or near the limits of the testing apparatus, and represent the first experimental applications of neural networks to flutter suppression. This paper will summarize the findings of this project.

Neural network-based model reference adaptive control system.

PubMed

Patino, H D; Liu, D

2000-01-01

In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.

Motorized CPM/CAM physiotherapy device with sliding-mode Fuzzy Neural Network control loop.

PubMed

Ho, Hung-Jung; Chen, Tien-Chi

2009-11-01

Continuous passive motion (CPM) and controllable active motion (CAM) physiotherapy devices promote rehabilitation of damaged joints. This paper presents a computerized CPM/CAM system that obviates the need for mechanical resistance devices such as springs. The system is controlled by a computer which performs sliding-mode Fuzzy Neural Network (FNN) calculations online. CAM-type resistance force is generated by the active performance of an electric motor which is controlled so as to oppose the motion of the patient's leg. A force sensor under the patient's foot on the device pedal provides data for feedback in a sliding-mode FNN control loop built around the motor. Via an active impedance control feedback system, the controller drives the motor to behave similarly to a damped spring by generating and controlling the amplitude and direction of the pedal force in relation to the patient's leg. Experiments demonstrate the high sensitivity and speed of the device. The PC-based feedback nature of the control loop means that sophisticated auto-adaptable CPM/CAM custom-designed physiotherapy becomes possible. The computer base also allows extensive data recording, data analysis and network-connected remote patient monitoring.

Neural joint control for Space Shuttle Remote Manipulator System

NASA Technical Reports Server (NTRS)

Atkins, Mark A.; Cox, Chadwick J.; Lothers, Michael D.; Pap, Robert M.; Thomas, Charles R.

1992-01-01

Neural networks are being used to control a robot arm in a telerobotic operation. The concept uses neural networks for both joint and inverse kinematics in a robotic control application. An upper level neural network is trained to learn inverse kinematic mappings. The output, a trajectory, is then fed to the Decentralized Adaptive Joint Controllers. This neural network implementation has shown that the controlled arm recovers from unexpected payload changes while following the reference trajectory. The neural network-based decentralized joint controller is faster, more robust and efficient than conventional approaches. Implementations of this architecture are discussed that would relax assumptions about dynamics, obstacles, and heavy loads. This system is being developed to use with the Space Shuttle Remote Manipulator System.

Culture, gaze and the neural processing of fear expressions

PubMed Central

Franklin, Robert G.; Rule, Nicholas O.; Freeman, Jonathan B.; Kveraga, Kestutis; Hadjikhani, Nouchine; Yoshikawa, Sakiko; Ambady, Nalini

2010-01-01

The direction of others’ eye gaze has important influences on how we perceive their emotional expressions. Here, we examined differences in neural activation to direct- versus averted-gaze fear faces as a function of culture of the participant (Japanese versus US Caucasian), culture of the stimulus face (Japanese versus US Caucasian), and the relation between the two. We employed a previously validated paradigm to examine differences in neural activation in response to rapidly presented direct- versus averted-fear expressions, finding clear evidence for a culturally determined role of gaze in the processing of fear. Greater neural responsivity was apparent to averted- versus direct-gaze fear in several regions related to face and emotion processing, including bilateral amygdalae, when posed on same-culture faces, whereas greater response to direct- versus averted-gaze fear was apparent in these same regions when posed on other-culture faces. We also found preliminary evidence for intercultural variation including differential responses across participants to Japanese versus US Caucasian stimuli, and to a lesser degree differences in how Japanese and US Caucasian participants responded to these stimuli. These findings reveal a meaningful role of culture in the processing of eye gaze and emotion, and highlight their interactive influences in neural processing. PMID:20019073

Oscillatory phase dynamics in neural entrainment underpin illusory percepts of time.

PubMed

Herrmann, Björn; Henry, Molly J; Grigutsch, Maren; Obleser, Jonas

2013-10-02

Neural oscillatory dynamics are a candidate mechanism to steer perception of time and temporal rate change. While oscillator models of time perception are strongly supported by behavioral evidence, a direct link to neural oscillations and oscillatory entrainment has not yet been provided. In addition, it has thus far remained unaddressed how context-induced illusory percepts of time are coded for in oscillator models of time perception. To investigate these questions, we used magnetoencephalography and examined the neural oscillatory dynamics that underpin pitch-induced illusory percepts of temporal rate change. Human participants listened to frequency-modulated sounds that varied over time in both modulation rate and pitch, and judged the direction of rate change (decrease vs increase). Our results demonstrate distinct neural mechanisms of rate perception: Modulation rate changes directly affected listeners' rate percept as well as the exact frequency of the neural oscillation. However, pitch-induced illusory rate changes were unrelated to the exact frequency of the neural responses. The rate change illusion was instead linked to changes in neural phase patterns, which allowed for single-trial decoding of percepts. That is, illusory underestimations or overestimations of perceived rate change were tightly coupled to increased intertrial phase coherence and changes in cerebro-acoustic phase lag. The results provide insight on how illusory percepts of time are coded for by neural oscillatory dynamics.

Direct reprogramming of somatic cells into neural stem cells or neurons for neurological disorders.

PubMed

Hou, Shaoping; Lu, Paul

2016-01-01

Direct reprogramming of somatic cells into neurons or neural stem cells is one of the most important frontier fields in current neuroscience research. Without undergoing the pluripotency stage, induced neurons or induced neural stem cells are a safer and timelier manner resource in comparison to those derived from induced pluripotent stem cells. In this prospective, we review the recent advances in generation of induced neurons and induced neural stem cells in vitro and in vivo and their potential treatments of neurological disorders.

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.

Quantized Synchronization of Chaotic Neural Networks With Scheduled Output Feedback Control.

PubMed

Wan, Ying; Cao, Jinde; Wen, Guanghui

In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control gain matrix, allowable length of sampling intervals, and upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural networks. Lastly, Chua's circuit system and 4-D Hopfield neural network are simulated to validate the effectiveness of the main results.In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control gain matrix, allowable length of sampling intervals, and upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural networks. Lastly, Chua's circuit system and 4-D Hopfield neural network are simulated to validate the effectiveness of the main results.

Separate representations of dynamics in rhythmic and discrete movements: evidence from motor learning

PubMed Central

Ingram, James N.; Wolpert, Daniel M.

2011-01-01

Rhythmic and discrete arm movements occur ubiquitously in everyday life, and there is a debate as to whether these two classes of movements arise from the same or different underlying neural mechanisms. Here we examine interference in a motor-learning paradigm to test whether rhythmic and discrete movements employ at least partially separate neural representations. Subjects were required to make circular movements of their right hand while they were exposed to a velocity-dependent force field that perturbed the circularity of the movement path. The direction of the force-field perturbation reversed at the end of each block of 20 revolutions. When subjects made only rhythmic or only discrete circular movements, interference was observed when switching between the two opposing force fields. However, when subjects alternated between blocks of rhythmic and discrete movements, such that each was uniquely associated with one of the perturbation directions, interference was significantly reduced. Only in this case did subjects learn to corepresent the two opposing perturbations, suggesting that different neural resources were employed for the two movement types. Our results provide further evidence that rhythmic and discrete movements employ at least partially separate control mechanisms in the motor system. PMID:21273324

Exploring the neural correlates of visual creativity

PubMed Central

Liew, Sook-Lei; Dandekar, Francesco

2013-01-01

Although creativity has been called the most important of all human resources, its neural basis is still unclear. In the current study, we used fMRI to measure neural activity in participants solving a visuospatial creativity problem that involves divergent thinking and has been considered a canonical right hemisphere task. As hypothesized, both the visual creativity task and the control task as compared to rest activated a variety of areas including the posterior parietal cortex bilaterally and motor regions, which are known to be involved in visuospatial rotation of objects. However, directly comparing the two tasks indicated that the creative task more strongly activated left hemisphere regions including the posterior parietal cortex, the premotor cortex, dorsolateral prefrontal cortex (DLPFC) and the medial PFC. These results demonstrate that even in a task that is specialized to the right hemisphere, robust parallel activity in the left hemisphere supports creative processing. Furthermore, the results support the notion that higher motor planning may be a general component of creative improvisation and that such goal-directed planning of novel solutions may be organized top-down by the left DLPFC and by working memory processing in the medial prefrontal cortex. PMID:22349801

An Implantable Neural Sensing Microsystem with Fiber-Optic Data Transmission and Power Delivery

PubMed Central

Park, Sunmee; Borton, David A.; Kang, Mingyu; Nurmikko, Arto V.; Song, Yoon-Kyu

2013-01-01

We have developed a prototype cortical neural sensing microsystem for brain implantable neuroengineering applications. Its key feature is that both the transmission of broadband, multichannel neural data and power required for the embedded microelectronics are provided by optical fiber access. The fiber-optic system is aimed at enabling neural recording from rodents and primates by converting cortical signals to a digital stream of infrared light pulses. In the full microsystem whose performance is summarized in this paper, an analog-to-digital converter and a low power digital controller IC have been integrated with a low threshold, semiconductor laser to extract the digitized neural signals optically from the implantable unit. The microsystem also acquires electrical power and synchronization clocks via optical fibers from an external laser by using a highly efficient photovoltaic cell on board. The implantable unit employs a flexible polymer substrate to integrate analog and digital microelectronics and on-chip optoelectronic components, while adapting to the anatomical and physiological constraints of the environment. A low power analog CMOS chip, which includes preamplifier and multiplexing circuitry, is directly flip-chip bonded to the microelectrode array to form the cortical neurosensor device. PMID:23666130

Power prediction in mobile communication systems using an optimal neural-network structure.

PubMed

Gao, X M; Gao, X Z; Tanskanen, J A; Ovaska, S J

1997-01-01

Presents a novel neural-network-based predictor for received power level prediction in direct sequence code division multiple access (DS/CDMA) systems. The predictor consists of an adaptive linear element (Adaline) followed by a multilayer perceptron (MLP). An important but difficult problem in designing such a cascade predictor is to determine the complexity of the networks. We solve this problem by using the predictive minimum description length (PMDL) principle to select the optimal numbers of input and hidden nodes. This approach results in a predictor with both good noise attenuation and excellent generalization capability. The optimized neural networks are used for predictive filtering of very noisy Rayleigh fading signals with 1.8 GHz carrier frequency. Our results show that the optimal neural predictor can provide smoothed in-phase and quadrature signals with signal-to-noise ratio (SNR) gains of about 12 and 7 dB at the urban mobile speeds of 5 and 50 km/h, respectively. The corresponding power signal SNR gains are about 11 and 5 dB. Therefore, the neural predictor is well suitable for power control applications where ldquodelaylessrdquo noise attenuation and efficient reduction of fast fading are required.

Neural control of magnetic suspension systems

NASA Technical Reports Server (NTRS)

Gray, W. Steven

1993-01-01

The purpose of this research program is to design, build and test (in cooperation with NASA personnel from the NASA Langley Research Center) neural controllers for two different small air-gap magnetic suspension systems. The general objective of the program is to study neural network architectures for the purpose of control in an experimental setting and to demonstrate the feasibility of the concept. The specific objectives of the research program are: (1) to demonstrate through simulation and experimentation the feasibility of using neural controllers to stabilize a nonlinear magnetic suspension system; (2) to investigate through simulation and experimentation the performance of neural controllers designs under various types of parametric and nonparametric uncertainty; (3) to investigate through simulation and experimentation various types of neural architectures for real-time control with respect to performance and complexity; and (4) to benchmark in an experimental setting the performance of neural controllers against other types of existing linear and nonlinear compensator designs. To date, the first one-dimensional, small air-gap magnetic suspension system has been built, tested and delivered to the NASA Langley Research Center. The device is currently being stabilized with a digital linear phase-lead controller. The neural controller hardware is under construction. Two different neural network paradigms are under consideration, one based on hidden layer feedforward networks trained via back propagation and one based on using Gaussian radial basis functions trained by analytical methods related to stability conditions. Some advanced nonlinear control algorithms using feedback linearization and sliding mode control are in simulation studies.

Goal-Directed Decision Making with Spiking Neurons.

PubMed

Friedrich, Johannes; Lengyel, Máté

2016-02-03

Behavioral and neuroscientific data on reward-based decision making point to a fundamental distinction between habitual and goal-directed action selection. The formation of habits, which requires simple updating of cached values, has been studied in great detail, and the reward prediction error theory of dopamine function has enjoyed prominent success in accounting for its neural bases. In contrast, the neural circuit mechanisms of goal-directed decision making, requiring extended iterative computations to estimate values online, are still unknown. Here we present a spiking neural network that provably solves the difficult online value estimation problem underlying goal-directed decision making in a near-optimal way and reproduces behavioral as well as neurophysiological experimental data on tasks ranging from simple binary choice to sequential decision making. Our model uses local plasticity rules to learn the synaptic weights of a simple neural network to achieve optimal performance and solves one-step decision-making tasks, commonly considered in neuroeconomics, as well as more challenging sequential decision-making tasks within 1 s. These decision times, and their parametric dependence on task parameters, as well as the final choice probabilities match behavioral data, whereas the evolution of neural activities in the network closely mimics neural responses recorded in frontal cortices during the execution of such tasks. Our theory provides a principled framework to understand the neural underpinning of goal-directed decision making and makes novel predictions for sequential decision-making tasks with multiple rewards. Goal-directed actions requiring prospective planning pervade decision making, but their circuit-level mechanisms remain elusive. We show how a model circuit of biologically realistic spiking neurons can solve this computationally challenging problem in a novel way. The synaptic weights of our network can be learned using local plasticity rules such that its dynamics devise a near-optimal plan of action. By systematically comparing our model results to experimental data, we show that it reproduces behavioral decision times and choice probabilities as well as neural responses in a rich set of tasks. Our results thus offer the first biologically realistic account for complex goal-directed decision making at a computational, algorithmic, and implementational level. Copyright © 2016 the authors 0270-6474/16/361529-18$15.00/0.

Goal-Directed Decision Making with Spiking Neurons

PubMed Central

Lengyel, Máté

2016-01-01

Behavioral and neuroscientific data on reward-based decision making point to a fundamental distinction between habitual and goal-directed action selection. The formation of habits, which requires simple updating of cached values, has been studied in great detail, and the reward prediction error theory of dopamine function has enjoyed prominent success in accounting for its neural bases. In contrast, the neural circuit mechanisms of goal-directed decision making, requiring extended iterative computations to estimate values online, are still unknown. Here we present a spiking neural network that provably solves the difficult online value estimation problem underlying goal-directed decision making in a near-optimal way and reproduces behavioral as well as neurophysiological experimental data on tasks ranging from simple binary choice to sequential decision making. Our model uses local plasticity rules to learn the synaptic weights of a simple neural network to achieve optimal performance and solves one-step decision-making tasks, commonly considered in neuroeconomics, as well as more challenging sequential decision-making tasks within 1 s. These decision times, and their parametric dependence on task parameters, as well as the final choice probabilities match behavioral data, whereas the evolution of neural activities in the network closely mimics neural responses recorded in frontal cortices during the execution of such tasks. Our theory provides a principled framework to understand the neural underpinning of goal-directed decision making and makes novel predictions for sequential decision-making tasks with multiple rewards. SIGNIFICANCE STATEMENT Goal-directed actions requiring prospective planning pervade decision making, but their circuit-level mechanisms remain elusive. We show how a model circuit of biologically realistic spiking neurons can solve this computationally challenging problem in a novel way. The synaptic weights of our network can be learned using local plasticity rules such that its dynamics devise a near-optimal plan of action. By systematically comparing our model results to experimental data, we show that it reproduces behavioral decision times and choice probabilities as well as neural responses in a rich set of tasks. Our results thus offer the first biologically realistic account for complex goal-directed decision making at a computational, algorithmic, and implementational level. PMID:26843636

The application of neural network PID controller to control the light gasoline etherification

NASA Astrophysics Data System (ADS)

Cheng, Huanxin; Zhang, Yimin; Kong, Lingling; Meng, Xiangyong

2017-06-01

Light gasoline etherification technology can effectively improve the quality of gasoline, which is environmental- friendly and economical. By combining BP neural network and PID control and using BP neural network self-learning ability for online parameter tuning, this method optimizes the parameters of PID controller and applies this to the Fcc gas flow control to achieve the control of the final product- heavy oil concentration. Finally, through MATLAB simulation, it is found that the PID control based on BP neural network has better controlling effect than traditional PID control.

Cognitive Control Predicts Use of Model-Based Reinforcement-Learning

PubMed Central

Otto, A. Ross; Skatova, Anya; Madlon-Kay, Seth; Daw, Nathaniel D.

2015-01-01

Accounts of decision-making and its neural substrates have long posited the operation of separate, competing valuation systems in the control of choice behavior. Recent theoretical and experimental work suggest that this classic distinction between behaviorally and neurally dissociable systems for habitual and goal-directed (or more generally, automatic and controlled) choice may arise from two computational strategies for reinforcement learning (RL), called model-free and model-based RL, but the cognitive or computational processes by which one system may dominate over the other in the control of behavior is a matter of ongoing investigation. To elucidate this question, we leverage the theoretical framework of cognitive control, demonstrating that individual differences in utilization of goal-related contextual information—in the service of overcoming habitual, stimulus-driven responses—in established cognitive control paradigms predict model-based behavior in a separate, sequential choice task. The behavioral correspondence between cognitive control and model-based RL compellingly suggests that a common set of processes may underpin the two behaviors. In particular, computational mechanisms originally proposed to underlie controlled behavior may be applicable to understanding the interactions between model-based and model-free choice behavior. PMID:25170791

Ground-based telescope pointing and tracking optimization using a neural controller.

PubMed

Mancini, D; Brescia, M; Schipani, P

2003-01-01

Neural network models (NN) have emerged as important components for applications of adaptive control theories. Their basic generalization capability, based on acquired knowledge, together with execution rapidity and correlation ability between input stimula, are basic attributes to consider NN as an extremely powerful tool for on-line control of complex systems. By a control system point of view, not only accuracy and speed, but also, in some cases, a high level of adaptation capability is required in order to match all working phases of the whole system during its lifetime. This is particularly remarkable for a new generation ground-based telescope control system. Infact, strong changes in terms of system speed and instantaneous position error tolerance are necessary, especially in case of trajectory disturb induced by wind shake. The classical control scheme adopted in such a system is based on the proportional integral (PI) filter, already applied and implemented on a large amount of new generation telescopes, considered as a standard in this technological environment. In this paper we introduce the concept of a new approach, the neural variable structure proportional integral, (NVSPI), related to the implementation of a standard multi layer perceptron network in new generation ground-based Alt-Az telescope control systems. Its main purpose is to improve adaptive capability of the Variable structure proportional integral model, an already innovative control scheme recently introduced by authors [Proc SPIE (1997)], based on a modified version of classical PI control model, in terms of flexibility and accuracy of the dynamic response range also in presence of wind noise effects. The realization of a powerful well tested and validated telescope model simulation system allowed the possibility to directly compare performances of the two control schemes on simulated tracking trajectories, revealing extremely encouraging results in terms of NVSPI control robustness and reliability.

Social reinforcement can regulate localized brain activity.

PubMed

Mathiak, Krystyna A; Koush, Yury; Dyck, Miriam; Gaber, Tilman J; Alawi, Eliza; Zepf, Florian D; Zvyagintsev, Mikhail; Mathiak, Klaus

2010-11-01

Social learning is essential for adaptive behavior in humans. Neurofeedback based on functional magnetic resonance imaging (fMRI) trains control over localized brain activity. It can disentangle learning processes at the neural level and thus investigate the mechanisms of operant conditioning with explicit social reinforcers. In a pilot study, a computer-generated face provided a positive feedback (smiling) when activity in the anterior cingulate cortex (ACC) increased and gradually returned to a neutral expression when the activity dropped. One female volunteer without previous experience in fMRI underwent training based on a social reinforcer. Directly before and after the neurofeedback runs, neural responses to a cognitive interference task (Simon task) were recorded. We observed a significant increase in activity within ACC during the neurofeedback blocks, correspondent with the a-priori defined anatomical region of interest. In the course of the neurofeedback training, the subject learned to regulate ACC activity and could maintain the control even without direct feedback. Moreover, ACC was activated significantly stronger during Simon task after the neurofeedback training when compared to before. Localized brain activity can be controlled by social reward. The increased ACC activity transferred to a cognitive task with the potential to reduce cognitive interference. Systematic studies are required to explore long-term effects on social behavior and clinical applications.

Early changes in emotional processing as a marker of clinical response to SSRI treatment in depression.

PubMed

Godlewska, B R; Browning, M; Norbury, R; Cowen, P J; Harmer, C J

2016-11-22

Antidepressant treatment reduces behavioural and neural markers of negative emotional bias early in treatment and has been proposed as a mechanism of antidepressant drug action. Here, we provide a critical test of this hypothesis by assessing whether neural markers of early emotional processing changes predict later clinical response in depression. Thirty-five unmedicated patients with major depression took the selective serotonin re-uptake inhibitor (SSRI), escitalopram (10 mg), over 6 weeks, and were classified as responders (22 patients) versus non-responders (13 patients), based on at least a 50% reduction in symptoms by the end of treatment. The neural response to fearful and happy emotional facial expressions was assessed before and after 7 days of treatment using functional magnetic resonance imaging. Changes in the neural response to these facial cues after 7 days of escitalopram were compared in patients as a function of later clinical response. A sample of healthy controls was also assessed. At baseline, depressed patients showed greater activation to fear versus happy faces than controls in the insula and dorsal anterior cingulate. Depressed patients who went on to respond to the SSRI had a greater reduction in neural activity to fearful versus happy facial expressions after just 7 days of escitalopram across a network of regions including the anterior cingulate, insula, amygdala and thalamus. Mediation analysis confirmed that the direct effect of neural change on symptom response was not mediated by initial changes in depressive symptoms. These results support the hypothesis that early changes in emotional processing with antidepressant treatment are the basis of later clinical improvement. As such, early correction of negative bias may be a key mechanism of antidepressant drug action and a potentially useful predictor of therapeutic response.

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.

Health and Pleasure in Consumers' Dietary Food Choices: Individual Differences in the Brain's Value System

PubMed Central

Petit, Olivia; Merunka, Dwight; Anton, Jean-Luc; Nazarian, Bruno; Spence, Charles; Cheok, Adrian David; Raccah, Denis; Oullier, Olivier

2016-01-01

Taking into account how people value the healthiness and tastiness of food at both the behavioral and brain levels may help to better understand and address overweight and obesity-related issues. Here, we investigate whether brain activity in those areas involved in self-control may increase significantly when individuals with a high body-mass index (BMI) focus their attention on the taste rather than on the health benefits related to healthy food choices. Under such conditions, BMI is positively correlated with both the neural responses to healthy food choices in those brain areas associated with gustation (insula), reward value (orbitofrontal cortex), and self-control (inferior frontal gyrus), and with the percent of healthy food choices. By contrast, when attention is directed towards health benefits, BMI is negatively correlated with neural activity in gustatory and reward-related brain areas (insula, inferior frontal operculum). Taken together, these findings suggest that those individuals with a high BMI do not necessarily have reduced capacities for self-control but that they may be facilitated by external cues that direct their attention toward the tastiness of healthy food. Thus, promoting the taste of healthy food in communication campaigns and/or food packaging may lead to more successful self-control and healthy food behaviors for consumers with a higher BMI, an issue which needs to be further researched. PMID:27428267

PDGF controls contact inhibition of locomotion by regulating N-cadherin during neural crest migration.

PubMed

Bahm, Isabel; Barriga, Elias H; Frolov, Antonina; Theveneau, Eric; Frankel, Paul; Mayor, Roberto

2017-07-01

A fundamental property of neural crest (NC) migration is contact inhibition of locomotion (CIL), a process by which cells change their direction of migration upon cell contact. CIL has been proven to be essential for NC migration in amphibians and zebrafish by controlling cell polarity in a cell contact-dependent manner. Cell contact during CIL requires the participation of the cell adhesion molecule N-cadherin, which starts to be expressed by NC cells as a consequence of the switch between E- and N-cadherins during epithelial-to-mesenchymal transition (EMT). However, the mechanism that controls the upregulation of N-cadherin remains unknown. Here, we show that platelet-derived growth factor receptor alpha (PDGFRα) and its ligand platelet-derived growth factor A (PDGF-A) are co-expressed in migrating cranial NC. Inhibition of PDGF-A/PDGFRα blocks NC migration by inhibiting N-cadherin and, consequently, impairing CIL. Moreover, we identify phosphatidylinositol-3-kinase (PI3K)/AKT as a downstream effector of the PDGFRα cellular response during CIL. Our results lead us to propose PDGF-A/PDGFRα signalling as a tissue-autonomous regulator of CIL by controlling N-cadherin upregulation during EMT. Finally, we show that once NC cells have undergone EMT, the same PDGF-A/PDGFRα works as an NC chemoattractant, guiding their directional migration. © 2017. Published by The Company of Biologists Ltd.

Why all the confusion? Experimental task explains discrepant semantic priming effects in schizophrenia under "automatic" conditions: evidence from Event-Related Potentials.

PubMed

Kreher, Donna A; Goff, Donald; Kuperberg, Gina R

2009-06-01

The schizophrenia research literature contains many differing accounts of semantic memory function in schizophrenia as assessed through the semantic priming paradigm. Most recently, Event-Related Potentials (ERPs) have been used to demonstrate both increased and decreased semantic priming at a neural level in schizophrenia patients, relative to healthy controls. The present study used ERPs to investigate the role of behavioral task in determining neural semantic priming effects in schizophrenia. The same schizophrenia patients and healthy controls completed two experiments in which word stimuli were identical, and the time between the onset of prime and target remained constant at 350 ms: in the first, participants monitored for words within a particular semantic category that appeared only in filler items (implicit task); in the second, participants explicitly rated the relatedness of word-pairs (explicit task). In the explicit task, schizophrenia patients showed reduced direct and indirect semantic priming in comparison with healthy controls. In contrast, in the implicit task, schizophrenia patients showed normal or, in positively thought-disordered patients, increased direct and indirect N400 priming effects compared with healthy controls. These data confirm that, although schizophrenia patients with positive thought disorder may show an abnormally increased automatic spreading activation, the introduction of semantic decision-making can result in abnormally reduced semantic priming in schizophrenia, even when other experimental conditions bias toward automatic processing.

Health and Pleasure in Consumers' Dietary Food Choices: Individual Differences in the Brain's Value System.

PubMed

Petit, Olivia; Merunka, Dwight; Anton, Jean-Luc; Nazarian, Bruno; Spence, Charles; Cheok, Adrian David; Raccah, Denis; Oullier, Olivier

2016-01-01

Taking into account how people value the healthiness and tastiness of food at both the behavioral and brain levels may help to better understand and address overweight and obesity-related issues. Here, we investigate whether brain activity in those areas involved in self-control may increase significantly when individuals with a high body-mass index (BMI) focus their attention on the taste rather than on the health benefits related to healthy food choices. Under such conditions, BMI is positively correlated with both the neural responses to healthy food choices in those brain areas associated with gustation (insula), reward value (orbitofrontal cortex), and self-control (inferior frontal gyrus), and with the percent of healthy food choices. By contrast, when attention is directed towards health benefits, BMI is negatively correlated with neural activity in gustatory and reward-related brain areas (insula, inferior frontal operculum). Taken together, these findings suggest that those individuals with a high BMI do not necessarily have reduced capacities for self-control but that they may be facilitated by external cues that direct their attention toward the tastiness of healthy food. Thus, promoting the taste of healthy food in communication campaigns and/or food packaging may lead to more successful self-control and healthy food behaviors for consumers with a higher BMI, an issue which needs to be further researched.

A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder.

PubMed

Boi, Fabio; Moraitis, Timoleon; De Feo, Vito; Diotalevi, Francesco; Bartolozzi, Chiara; Indiveri, Giacomo; Vato, Alessandro

2016-01-01

Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link between the brain and the external world. A decoder translates recorded neural activity into motor commands and an encoder delivers sensory information collected from the environment directly to the brain creating a closed-loop system. These two modules are typically integrated in bulky external devices. However, the clinical support of patients with severe motor and sensory deficits requires compact, low-power, and fully implantable systems that can decode neural signals to control external devices. As a first step toward this goal, we developed a modular bidirectional BMI setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits. On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn to decode neural signals recorded from the brain into motor outputs controlling the movements of an external device. The modularity of the BMI allowed us to tune the individual components of the setup without modifying the whole system. In this paper, we present the features of this modular BMI and describe how we configured the network of spiking neuron circuits to implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm that connects bidirectionally the brain of an anesthetized rat with an external object. We show that the chip learned the decoding task correctly, allowing the interfaced brain to control the object's trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is mature enough for the development of BMI modules that are sufficiently low-power and compact, while being highly computationally powerful and adaptive.

A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder

PubMed Central

Boi, Fabio; Moraitis, Timoleon; De Feo, Vito; Diotalevi, Francesco; Bartolozzi, Chiara; Indiveri, Giacomo; Vato, Alessandro

2016-01-01

Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link between the brain and the external world. A decoder translates recorded neural activity into motor commands and an encoder delivers sensory information collected from the environment directly to the brain creating a closed-loop system. These two modules are typically integrated in bulky external devices. However, the clinical support of patients with severe motor and sensory deficits requires compact, low-power, and fully implantable systems that can decode neural signals to control external devices. As a first step toward this goal, we developed a modular bidirectional BMI setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits. On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn to decode neural signals recorded from the brain into motor outputs controlling the movements of an external device. The modularity of the BMI allowed us to tune the individual components of the setup without modifying the whole system. In this paper, we present the features of this modular BMI and describe how we configured the network of spiking neuron circuits to implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm that connects bidirectionally the brain of an anesthetized rat with an external object. We show that the chip learned the decoding task correctly, allowing the interfaced brain to control the object's trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is mature enough for the development of BMI modules that are sufficiently low-power and compact, while being highly computationally powerful and adaptive. PMID:28018162

Hippocampal Ghrelin-positive neurons directly project to arcuate hypothalamic and medial amygdaloid nuclei. Could they modulate food-intake?

PubMed

Russo, Cristina; Russo, Antonella; Pellitteri, Rosalia; Stanzani, Stefania

2017-07-13

Feeding is a process controlled by a complex of associations between external and internal stimuli. The processes that involve learning and memory seem to exert a strong control over appetite and food intake, which is modulated by a gastrointestinal hormone, Ghrelin (Ghre). Recent studies claim that Ghre is involved in cognitive and neurobiological mechanisms that underlie the conditioning of eating behaviors. The expression of Ghre increases in anticipation of food intake based on learned behaviors. The hippocampal Ghre-containing neurons neurologically influence the orexigenic hypothalamus and consequently the learned feeding behavior. The CA1 field of Ammon's horn of the hippocampus (H-CA1) constitutes the most important neural substrate to control both appetitive and ingestive behavior. It also innervates amygdala regions that in turn innervate the hypothalamus. A recent study also implies that Ghre effects on cue-potentiated feeding behavior occur, at the least, via indirect action on the amygdala. In the present study, we investigate the neural substrates through which endogenous Ghre communicates conditioned appetite and feeding behavior within the CNS. We show the existence of a neural Ghre dependent pathway whereby peripherally-derived Ghre activates H-CA1 neurons, which in turn activate Ghre-expressing hypothalamic and amygdaloid neurons to stimulate appetite and feeding behavior. To highlight this pathway, we use two fluorescent retrograde tracers (Fluoro Gold and Dil) and immunohistochemical detection of Ghre expression in the hippocampus. Triple fluorescent-labeling has determined the presence of H-CA1 Ghre-containing collateralized neurons that project to the hypothalamus and amygdala monosynaptically. We hypothesize that H-Ghre-containing neurons in H-CA1 modulate food-intake behavior through direct pathways to the arcuate hypothalamic nucleus and medial amygdaloid nucleus. Copyright © 2017 Elsevier B.V. All rights reserved.

A Neurocomputational Model of Goal-Directed Navigation in Insect-Inspired Artificial Agents.

PubMed

Goldschmidt, Dennis; Manoonpong, Poramate; Dasgupta, Sakyasingha

2017-01-01

Despite their small size, insect brains are able to produce robust and efficient navigation in complex environments. Specifically in social insects, such as ants and bees, these navigational capabilities are guided by orientation directing vectors generated by a process called path integration. During this process, they integrate compass and odometric cues to estimate their current location as a vector, called the home vector for guiding them back home on a straight path. They further acquire and retrieve path integration-based vector memories globally to the nest or based on visual landmarks. Although existing computational models reproduced similar behaviors, a neurocomputational model of vector navigation including the acquisition of vector representations has not been described before. Here we present a model of neural mechanisms in a modular closed-loop control-enabling vector navigation in artificial agents. The model consists of a path integration mechanism, reward-modulated global learning, random search, and action selection. The path integration mechanism integrates compass and odometric cues to compute a vectorial representation of the agent's current location as neural activity patterns in circular arrays. A reward-modulated learning rule enables the acquisition of vector memories by associating the local food reward with the path integration state. A motor output is computed based on the combination of vector memories and random exploration. In simulation, we show that the neural mechanisms enable robust homing and localization, even in the presence of external sensory noise. The proposed learning rules lead to goal-directed navigation and route formation performed under realistic conditions. Consequently, we provide a novel approach for vector learning and navigation in a simulated, situated agent linking behavioral observations to their possible underlying neural substrates.

Natural neural projection dynamics underlying social behavior

PubMed Central

Gunaydin, Lisa A.; Grosenick, Logan; Finkelstein, Joel C.; Kauvar, Isaac V.; Fenno, Lief E.; Adhikari, Avishek; Lammel, Stephan; Mirzabekov, Julie J.; Airan, Raag D.; Zalocusky, Kelly A.; Tye, Kay M.; Anikeeva, Polina; Malenka, Robert C.; Deisseroth, Karl

2014-01-01

Social interaction is a complex behavior essential for many species, and is impaired in major neuropsychiatric disorders. Pharmacological studies have implicated certain neurotransmitter systems in social behavior, but circuit-level understanding of endogenous neural activity during social interaction is lacking. We therefore developed and applied a new methodology, termed fiber photometry, to optically record natural neural activity in genetically- and connectivity-defined projections to elucidate the real-time role of specified pathways in mammalian behavior. Fiber photometry revealed that activity dynamics of a ventral tegmental area (VTA)-to-nucleus accumbens (NAc) projection could encode and predict key features of social but not novel-object interaction. Consistent with this observation, optogenetic control of cells specifically contributing to this projection was sufficient to modulate social behavior, which was mediated by type-1 dopamine receptor signaling downstream in the NAc. Direct observation of projection-specific activity in this way captures a fundamental and previously inaccessible dimension of circuit dynamics. PMID:24949967

Us versus Them: Social Identity Shapes Neural Responses to Intergroup Competition and Harm

PubMed Central

Cikara, Mina; Botvinick, Matthew M.; Fiske, Susan T.

2013-01-01

Intergroup competition makes social identity salient, which affects how people respond to competitors’ hardships. The failures of a fellow group member are painful, while those of a rival group member may give pleasure—a feeling that may motivate harming rivals. The present study examines whether valuation-related neural responses to rival groups’ failures correlate with likelihood of harming individuals associated with those rivals. Avid fans of the Red Sox and Yankees teams viewed baseball plays while undergoing fMRI. Subjectively negative outcomes (favored-failure, rival-success) activated anterior cingulate cortex and insula, while positive outcomes (favored-success, rival-failure—even against a third team) activated ventral striatum. The ventral striatum effect, associated with subjective pleasure, also correlated with self-reported likelihood of aggressing against a fan of the rival team (controlling for general aggression). Outcomes of social group competition can directly affect primary reward-processing neural systems, with implications for intergroup harm. PMID:21270447

Neural networks for self-learning control systems

NASA Technical Reports Server (NTRS)

Nguyen, Derrick H.; Widrow, Bernard

1990-01-01

It is shown how a neural network can learn of its own accord to control a nonlinear dynamic system. An emulator, a multilayered neural network, learns to identify the system's dynamic characteristics. The controller, another multilayered neural network, next learns to control the emulator. The self-trained controller is then used to control the actual dynamic system. The learning process continues as the emulator and controller improve and track the physical process. An example is given to illustrate these ideas. The 'truck backer-upper,' a neural network controller that steers a trailer truck while the truck is backing up to a loading dock, is demonstrated. The controller is able to guide the truck to the dock from almost any initial position. The technique explored should be applicable to a wide variety of nonlinear control problems.

An online brain-machine interface using decoding of movement direction from the human electrocorticogram

NASA Astrophysics Data System (ADS)

Milekovic, Tomislav; Fischer, Jörg; Pistohl, Tobias; Ruescher, Johanna; Schulze-Bonhage, Andreas; Aertsen, Ad; Rickert, Jörn; Ball, Tonio; Mehring, Carsten

2012-08-01

A brain-machine interface (BMI) can be used to control movements of an artificial effector, e.g. movements of an arm prosthesis, by motor cortical signals that control the equivalent movements of the corresponding body part, e.g. arm movements. This approach has been successfully applied in monkeys and humans by accurately extracting parameters of movements from the spiking activity of multiple single neurons. We show that the same approach can be realized using brain activity measured directly from the surface of the human cortex using electrocorticography (ECoG). Five subjects, implanted with ECoG implants for the purpose of epilepsy assessment, took part in our study. Subjects used directionally dependent ECoG signals, recorded during active movements of a single arm, to control a computer cursor in one out of two directions. Significant BMI control was achieved in four out of five subjects with correct directional decoding in 69%-86% of the trials (75% on average). Our results demonstrate the feasibility of an online BMI using decoding of movement direction from human ECoG signals. Thus, to achieve such BMIs, ECoG signals might be used in conjunction with or as an alternative to intracortical neural signals.

Neural mechanisms of rhythm perception: current findings and future perspectives.

PubMed

Grahn, Jessica A

2012-10-01

Perception of temporal patterns is fundamental to normal hearing, speech, motor control, and music. Certain types of pattern understanding are unique to humans, such as musical rhythm. Although human responses to musical rhythm are universal, there is much we do not understand about how rhythm is processed in the brain. Here, I consider findings from research into basic timing mechanisms and models through to the neuroscience of rhythm and meter. A network of neural areas, including motor regions, is regularly implicated in basic timing as well as processing of musical rhythm. However, fractionating the specific roles of individual areas in this network has remained a challenge. Distinctions in activity patterns appear between "automatic" and "cognitively controlled" timing processes, but the perception of musical rhythm requires features of both automatic and controlled processes. In addition, many experimental manipulations rely on participants directing their attention toward or away from certain stimulus features, and measuring corresponding differences in neural activity. Many temporal features, however, are implicitly processed whether attended to or not, making it difficult to create controlled baseline conditions for experimental comparisons. The variety of stimuli, paradigms, and definitions can further complicate comparisons across domains or methodologies. Despite these challenges, the high level of interest and multitude of methodological approaches from different cognitive domains (including music, language, and motor learning) have yielded new insights and hold promise for future progress. Copyright © 2012 Cognitive Science Society, Inc.

Glove-TalkII--a neural-network interface which maps gestures to parallel formant speech synthesizer controls.

PubMed

Fels, S S; Hinton, G E

1998-01-01

Glove-TalkII is a system which translates hand gestures to speech through an adaptive interface. Hand gestures are mapped continuously to ten control parameters of a parallel formant speech synthesizer. The mapping allows the hand to act as an artificial vocal tract that produces speech in real time. This gives an unlimited vocabulary in addition to direct control of fundamental frequency and volume. Currently, the best version of Glove-TalkII uses several input devices (including a Cyberglove, a ContactGlove, a three-space tracker, and a foot pedal), a parallel formant speech synthesizer, and three neural networks. The gesture-to-speech task is divided into vowel and consonant production by using a gating network to weight the outputs of a vowel and a consonant neural network. The gating network and the consonant network are trained with examples from the user. The vowel network implements a fixed user-defined relationship between hand position and vowel sound and does not require any training examples from the user. Volume, fundamental frequency, and stop consonants are produced with a fixed mapping from the input devices. One subject has trained to speak intelligibly with Glove-TalkII. He speaks slowly but with far more natural sounding pitch variations than a text-to-speech synthesizer.

Alternating current electric fields of varying frequencies: effects on proliferation and differentiation of porcine neural progenitor cells.

PubMed

Lim, Ji-Hey; McCullen, Seth D; Piedrahita, Jorge A; Loboa, Elizabeth G; Olby, Natasha J

2013-10-01

Application of sinusoidal electric fields (EFs) has been observed to affect cellular processes, including alignment, proliferation, and differentiation. In the present study, we applied low-frequency alternating current (AC) EFs to porcine neural progenitor cells (pNPCs) and investigated the effects on cell patterning, proliferation, and differentiation. pNPCs were grown directly on interdigitated electrodes (IDEs) localizing the EFs to a region accessible visually for fluorescence-based assays. Cultures of pNPCs were exposed to EFs (1 V/cm) of 1 Hz, 10 Hz, and 50 Hz for 3, 7, and 14 days and compared to control cultures. Immunocytochemistry was performed to evaluate the expression of neural markers. pNPCs grew uniformly with no evidence of alignment to the EFs and no change in cell numbers when compared with controls. Nestin expression was shown in all groups at 3 and 7 days, but not at 14 days. NG2 expression was low in all groups. Co-expression of glial fibrillary acidic protein (GFAP) and TUJ1 was significantly higher in the cultures exposed to 10- and 50-Hz EFs than the controls. In summary, sinusoidal AC EFs via IDEs did not alter the alignment and proliferation of pNPCs, but higher frequency stimulation appeared to delay differentiation into mature astrocytes.

Multipotent Caudal Neural Progenitors Derived from Human Pluripotent Stem Cells That Give Rise to Lineages of the Central and Peripheral Nervous System

PubMed Central

Hasegawa, Kouichi; Menheniott, Trevelyan; Rollo, Ben; Zhang, Dongcheng; Hough, Shelley; Alshawaf, Abdullah; Febbraro, Fabia; Ighaniyan, Samiramis; Leung, Jessie; Elliott, David A.; Newgreen, Donald F.; Pera, Martin F.

2015-01-01

Abstract The caudal neural plate is a distinct region of the embryo that gives rise to major progenitor lineages of the developing central and peripheral nervous system, including neural crest and floor plate cells. We show that dual inhibition of the glycogen synthase kinase 3β and activin/nodal pathways by small molecules differentiate human pluripotent stem cells (hPSCs) directly into a preneuroepithelial progenitor population we named “caudal neural progenitors” (CNPs). CNPs coexpress caudal neural plate and mesoderm markers, and, share high similarities to embryonic caudal neural plate cells in their lineage differentiation potential. Exposure of CNPs to BMP2/4, sonic hedgehog, or FGF2 signaling efficiently directs their fate to neural crest/roof plate cells, floor plate cells, and caudally specified neuroepithelial cells, respectively. Neural crest derived from CNPs differentiated to neural crest derivatives and demonstrated extensive migratory properties in vivo. Importantly, we also determined the key extrinsic factors specifying CNPs from human embryonic stem cell include FGF8, canonical WNT, and IGF1. Our studies are the first to identify a multipotent neural progenitor derived from hPSCs, that is the precursor for major neural lineages of the embryonic caudal neural tube. Stem Cells 2015;33:1759–1770 PMID:25753817

Laser Direct Metal Deposition of 2024 Al Alloy: Trace Geometry Prediction via Machine Learning.

PubMed

Caiazzo, Fabrizia; Caggiano, Alessandra

2018-03-19

Laser direct metal deposition is an advanced additive manufacturing technology suitably applicable in maintenance, repair, and overhaul of high-cost products, allowing for minimal distortion of the workpiece, reduced heat affected zones, and superior surface quality. Special interest is growing for the repair and coating of 2024 aluminum alloy parts, extensively utilized for a wide range of applications in the automotive, military, and aerospace sectors due to its excellent plasticity, corrosion resistance, electric conductivity, and strength-to-weight ratio. A critical issue in the laser direct metal deposition process is related to the geometrical parameters of the cross-section of the deposited metal trace that should be controlled to meet the part specifications. In this research, a machine learning approach based on artificial neural networks is developed to find the correlation between the laser metal deposition process parameters and the output geometrical parameters of the deposited metal trace produced by laser direct metal deposition on 5-mm-thick 2024 aluminum alloy plates. The results show that the neural network-based machine learning paradigm is able to accurately estimate the appropriate process parameters required to obtain a specified geometry for the deposited metal trace.

Laser Direct Metal Deposition of 2024 Al Alloy: Trace Geometry Prediction via Machine Learning

PubMed Central

2018-01-01

Laser direct metal deposition is an advanced additive manufacturing technology suitably applicable in maintenance, repair, and overhaul of high-cost products, allowing for minimal distortion of the workpiece, reduced heat affected zones, and superior surface quality. Special interest is growing for the repair and coating of 2024 aluminum alloy parts, extensively utilized for a wide range of applications in the automotive, military, and aerospace sectors due to its excellent plasticity, corrosion resistance, electric conductivity, and strength-to-weight ratio. A critical issue in the laser direct metal deposition process is related to the geometrical parameters of the cross-section of the deposited metal trace that should be controlled to meet the part specifications. In this research, a machine learning approach based on artificial neural networks is developed to find the correlation between the laser metal deposition process parameters and the output geometrical parameters of the deposited metal trace produced by laser direct metal deposition on 5-mm-thick 2024 aluminum alloy plates. The results show that the neural network-based machine learning paradigm is able to accurately estimate the appropriate process parameters required to obtain a specified geometry for the deposited metal trace. PMID:29562682

Bio-inspired spiking neural network for nonlinear systems control.

PubMed

Pérez, Javier; Cabrera, Juan A; Castillo, Juan J; Velasco, Juan M

2018-08-01

Spiking neural networks (SNN) are the third generation of artificial neural networks. SNN are the closest approximation to biological neural networks. SNNs make use of temporal spike trains to command inputs and outputs, allowing a faster and more complex computation. As demonstrated by biological organisms, they are a potentially good approach to designing controllers for highly nonlinear dynamic systems in which the performance of controllers developed by conventional techniques is not satisfactory or difficult to implement. SNN-based controllers exploit their ability for online learning and self-adaptation to evolve when transferred from simulations to the real world. SNN's inherent binary and temporary way of information codification facilitates their hardware implementation compared to analog neurons. Biological neural networks often require a lower number of neurons compared to other controllers based on artificial neural networks. In this work, these neuronal systems are imitated to perform the control of non-linear dynamic systems. For this purpose, a control structure based on spiking neural networks has been designed. Particular attention has been paid to optimizing the structure and size of the neural network. The proposed structure is able to control dynamic systems with a reduced number of neurons and connections. A supervised learning process using evolutionary algorithms has been carried out to perform controller training. The efficiency of the proposed network has been verified in two examples of dynamic systems control. Simulations show that the proposed control based on SNN exhibits superior performance compared to other approaches based on Neural Networks and SNNs. Copyright © 2018 Elsevier Ltd. All rights reserved.

Synchronization of Switched Neural Networks With Communication Delays via the Event-Triggered Control.

PubMed

Wen, Shiping; Zeng, Zhigang; Chen, Michael Z Q; Huang, Tingwen

2017-10-01

This paper addresses the issue of synchronization of switched delayed neural networks with communication delays via event-triggered control. For synchronizing coupled switched neural networks, we propose a novel event-triggered control law which could greatly reduce the number of control updates for synchronization tasks of coupled switched neural networks involving embedded microprocessors with limited on-board resources. The control signals are driven by properly defined events, which depend on the measurement errors and current-sampled states. By using a delay system method, a novel model of synchronization error system with delays is proposed with the communication delays and event-triggered control in the unified framework for coupled switched neural networks. The criteria are derived for the event-triggered synchronization analysis and control synthesis of switched neural networks via the Lyapunov-Krasovskii functional method and free weighting matrix approach. A numerical example is elaborated on to illustrate the effectiveness of the derived results.

Neuromechanism Study of Insect–Machine Interface: Flight Control by Neural Electrical Stimulation

PubMed Central

Zhao, Huixia; Zheng, Nenggan; Ribi, Willi A.; Zheng, Huoqing; Xue, Lei; Gong, Fan; Zheng, Xiaoxiang; Hu, Fuliang

2014-01-01

The insect–machine interface (IMI) is a novel approach developed for man-made air vehicles, which directly controls insect flight by either neuromuscular or neural stimulation. In our previous study of IMI, we induced flight initiation and cessation reproducibly in restrained honeybees (Apis mellifera L.) via electrical stimulation of the bilateral optic lobes. To explore the neuromechanism underlying IMI, we applied electrical stimulation to seven subregions of the honeybee brain with the aid of a new method for localizing brain regions. Results showed that the success rate for initiating honeybee flight decreased in the order: α-lobe (or β-lobe), ellipsoid body, lobula, medulla and antennal lobe. Based on a comparison with other neurobiological studies in honeybees, we propose that there is a cluster of descending neurons in the honeybee brain that transmits neural excitation from stimulated brain areas to the thoracic ganglia, leading to flight behavior. This neural circuit may involve the higher-order integration center, the primary visual processing center and the suboesophageal ganglion, which is also associated with a possible learning and memory pathway. By pharmacologically manipulating the electrically stimulated honeybee brain, we have shown that octopamine, rather than dopamine, serotonin and acetylcholine, plays a part in the circuit underlying electrically elicited honeybee flight. Our study presents a new brain stimulation protocol for the honeybee–machine interface and has solved one of the questions with regard to understanding which functional divisions of the insect brain participate in flight control. It will support further studies to uncover the involved neurons inside specific brain areas and to test the hypothesized involvement of a visual learning and memory pathway in IMI flight control. PMID:25409523

Neuromechanism study of insect-machine interface: flight control by neural electrical stimulation.

PubMed

Zhao, Huixia; Zheng, Nenggan; Ribi, Willi A; Zheng, Huoqing; Xue, Lei; Gong, Fan; Zheng, Xiaoxiang; Hu, Fuliang

2014-01-01

The insect-machine interface (IMI) is a novel approach developed for man-made air vehicles, which directly controls insect flight by either neuromuscular or neural stimulation. In our previous study of IMI, we induced flight initiation and cessation reproducibly in restrained honeybees (Apis mellifera L.) via electrical stimulation of the bilateral optic lobes. To explore the neuromechanism underlying IMI, we applied electrical stimulation to seven subregions of the honeybee brain with the aid of a new method for localizing brain regions. Results showed that the success rate for initiating honeybee flight decreased in the order: α-lobe (or β-lobe), ellipsoid body, lobula, medulla and antennal lobe. Based on a comparison with other neurobiological studies in honeybees, we propose that there is a cluster of descending neurons in the honeybee brain that transmits neural excitation from stimulated brain areas to the thoracic ganglia, leading to flight behavior. This neural circuit may involve the higher-order integration center, the primary visual processing center and the suboesophageal ganglion, which is also associated with a possible learning and memory pathway. By pharmacologically manipulating the electrically stimulated honeybee brain, we have shown that octopamine, rather than dopamine, serotonin and acetylcholine, plays a part in the circuit underlying electrically elicited honeybee flight. Our study presents a new brain stimulation protocol for the honeybee-machine interface and has solved one of the questions with regard to understanding which functional divisions of the insect brain participate in flight control. It will support further studies to uncover the involved neurons inside specific brain areas and to test the hypothesized involvement of a visual learning and memory pathway in IMI flight control.

Neural net controller for inlet pressure control of rocket engine testing

NASA Technical Reports Server (NTRS)

Trevino, Luis C.

1994-01-01

Many dynamic systems operate in select operating regions, each exhibiting characteristic modes of behavior. It is traditional to employ standard adjustable gain proportional-integral-derivative (PID) loops in such systems where no apriori model information is available. However, for controlling inlet pressure for rocket engine testing, problems in fine tuning, disturbance accommodation, and control gains for new profile operating regions (for research and development) are typically encountered. Because of the capability of capturing I/O peculiarities, using NETS, a back propagation trained neural network is specified. For select operating regions, the neural network controller is simulated to be as robust as the PID controller. For a comparative analysis, the higher order moment neural array (HOMNA) method is used to specify a second neural controller by extracting critical exemplars from the I/O data set. Furthermore, using the critical exemplars from the HOMNA method, a third neural controller is developed using NETS back propagation algorithm. All controllers are benchmarked against each other.

Application of neural models as controllers in mobile robot velocity control loop

NASA Astrophysics Data System (ADS)

Cerkala, Jakub; Jadlovska, Anna

2017-01-01

This paper presents the application of an inverse neural models used as controllers in comparison to classical PI controllers for velocity tracking control task used in two-wheel, differentially driven mobile robot. The PI controller synthesis is based on linear approximation of actuators with equivalent load. In order to obtain relevant datasets for training of feed-forward multi-layer perceptron based neural network used as neural model, the mathematical model of mobile robot, that combines its kinematic and dynamic properties such as chassis dimensions, center of gravity offset, friction and actuator parameters is used. Neural models are trained off-line to act as an inverse dynamics of DC motors with particular load using data collected in simulation experiment for motor input voltage step changes within bounded operating area. The performances of PI controllers versus inverse neural models in mobile robot internal velocity control loops are demonstrated and compared in simulation experiment of navigation control task for line segment motion in plane.

Biomimetic hydrogels direct spinal progenitor cell differentiation and promote functional recovery after spinal cord injury.

PubMed

Geissler, Sydney A; Sabin, Alexandra L; Besser, Rachel R; Gooden, Olivia M; Shirk, Bryce D; Nguyen, Quan M; Khaing, Zin Z; Schmidt, Christine E

2018-04-01

Demyelination that results from disease or traumatic injury, such as spinal cord injury (SCI), can have a devastating effect on neural function and recovery. Many researchers are examining treatments to minimize demyelination by improving oligodendrocyte availability in vivo. Transplantation of stem and oligodendrocyte progenitor cells is a promising option, however, trials are plagued by undirected differentiation. Here we introduce a biomaterial that has been optimized to direct the differentiation of neural progenitor cells (NPCs) toward oligodendrocytes as a cell delivery vehicle after SCI. A collagen-based hydrogel was modified to mimic the mechanical properties of the neonatal spinal cord, and components present in the developing extracellular matrix were included to provide appropriate chemical cues to the NPCs to direct their differentiation toward oligodendrocytes. The hydrogel with cells was then transplanted into a unilateral cervical contusion model of SCI to examine the functional recovery with this treatment. Six behavioral tests and histological assessment were performed to examine the in vivo response to this treatment. Our results demonstrate that we can achieve a significant increase in oligodendrocyte differentiation of NPCs compared to standard culture conditions using a three-component biomaterial composed of collagen, hyaluronic acid, and laminin that has mechanical properties matched to those of neonatal neural tissue. Additionally, SCI rats with hydrogel transplants, with and without NPCs, showed functional recovery. Animals transplanted with hydrogels with NPCs showed significantly increased functional recovery over six weeks compared to the media control group. The three-component hydrogel presented here has the potential to provide cues to direct differentiation in vivo to encourage regeneration of the central nervous system.

Nonlinear neural control with power systems applications

NASA Astrophysics Data System (ADS)

Chen, Dingguo

1998-12-01

Extensive studies have been undertaken on the transient stability of large interconnected power systems with flexible ac transmission systems (FACTS) devices installed. Varieties of control methodologies have been proposed to stabilize the postfault system which would otherwise eventually lose stability without a proper control. Generally speaking, regular transient stability is well understood, but the mechanism of load-driven voltage instability or voltage collapse has not been well understood. The interaction of generator dynamics and load dynamics makes synthesis of stabilizing controllers even more challenging. There is currently increasing interest in the research of neural networks as identifiers and controllers for dealing with dynamic time-varying nonlinear systems. This study focuses on the development of novel artificial neural network architectures for identification and control with application to dynamic electric power systems so that the stability of the interconnected power systems, following large disturbances, and/or with the inclusion of uncertain loads, can be largely enhanced, and stable operations are guaranteed. The latitudinal neural network architecture is proposed for the purpose of system identification. It may be used for identification of nonlinear static/dynamic loads, which can be further used for static/dynamic voltage stability analysis. The properties associated with this architecture are investigated. A neural network methodology is proposed for dealing with load modeling and voltage stability analysis. Based on the neural network models of loads, voltage stability analysis evolves, and modal analysis is performed. Simulation results are also provided. The transient stability problem is studied with consideration of load effects. The hierarchical neural control scheme is developed. Trajectory-following policy is used so that the hierarchical neural controller performs as almost well for non-nominal cases as they do for the nominal cases. The adaptive hierarchical neural control scheme is also proposed to deal with the time-varying nature of loads. Further, adaptive neural control, which is based on the on-line updating of the weights and biases of the neural networks, is studied. Simulations provided on the faulted power systems with unknown loads suggest that the proposed adaptive hierarchical neural control schemes should be useful for practical power applications.

An integrated interface for peripheral neural system recording and stimulation: system design, electrical tests and in-vivo results.

PubMed

Carboni, Caterina; Bisoni, Lorenzo; Carta, Nicola; Puddu, Roberto; Raspopovic, Stanisa; Navarro, Xavier; Raffo, Luigi; Barbaro, Massimo

2016-04-01

The prototype of an electronic bi-directional interface between the Peripheral Nervous System (PNS) and a neuro-controlled hand prosthesis is presented. The system is composed of 2 integrated circuits: a standard CMOS device for neural recording and a HVCMOS device for neural stimulation. The integrated circuits have been realized in 2 different 0.35μ m CMOS processes available from ams. The complete system incorporates 8 channels each including the analog front-end, the A/D conversion, based on a sigma delta architecture and a programmable stimulation module implemented as a 5-bit current DAC; two voltage boosters supply the output stimulation stage with a programmable voltage scalable up to 17V. Successful in-vivo experiments with rats having a TIME electrode implanted in the sciatic nerve were carried out, showing the capability of recording neural signals in the tens of microvolts, with a global noise of 7μ V r m s , and to selectively elicit the tibial and plantar muscles using different active sites of the electrode.

Thermoelastic steam turbine rotor control based on neural network

NASA Astrophysics Data System (ADS)

Rzadkowski, Romuald; Dominiczak, Krzysztof; Radulski, Wojciech; Szczepanik, R.

2015-12-01

Considered here are Nonlinear Auto-Regressive neural networks with eXogenous inputs (NARX) as a mathematical model of a steam turbine rotor for controlling steam turbine stress on-line. In order to obtain neural networks that locate critical stress and temperature points in the steam turbine during transient states, an FE rotor model was built. This model was used to train the neural networks on the basis of steam turbine transient operating data. The training included nonlinearity related to steam turbine expansion, heat exchange and rotor material properties during transients. Simultaneous neural networks are algorithms which can be implemented on PLC controllers. This allows for the application neural networks to control steam turbine stress in industrial power plants.

Video game training enhances cognitive control in older adults.

PubMed

Anguera, J A; Boccanfuso, J; Rintoul, J L; Al-Hashimi, O; Faraji, F; Janowich, J; Kong, E; Larraburo, Y; Rolle, C; Johnston, E; Gazzaley, A

2013-09-05

Cognitive control is defined by a set of neural processes that allow us to interact with our complex environment in a goal-directed manner. Humans regularly challenge these control processes when attempting to simultaneously accomplish multiple goals (multitasking), generating interference as the result of fundamental information processing limitations. It is clear that multitasking behaviour has become ubiquitous in today's technologically dense world, and substantial evidence has accrued regarding multitasking difficulties and cognitive control deficits in our ageing population. Here we show that multitasking performance, as assessed with a custom-designed three-dimensional video game (NeuroRacer), exhibits a linear age-related decline from 20 to 79 years of age. By playing an adaptive version of NeuroRacer in multitasking training mode, older adults (60 to 85 years old) reduced multitasking costs compared to both an active control group and a no-contact control group, attaining levels beyond those achieved by untrained 20-year-old participants, with gains persisting for 6 months. Furthermore, age-related deficits in neural signatures of cognitive control, as measured with electroencephalography, were remediated by multitasking training (enhanced midline frontal theta power and frontal-posterior theta coherence). Critically, this training resulted in performance benefits that extended to untrained cognitive control abilities (enhanced sustained attention and working memory), with an increase in midline frontal theta power predicting the training-induced boost in sustained attention and preservation of multitasking improvement 6 months later. These findings highlight the robust plasticity of the prefrontal cognitive control system in the ageing brain, and provide the first evidence, to our knowledge, of how a custom-designed video game can be used to assess cognitive abilities across the lifespan, evaluate underlying neural mechanisms, and serve as a powerful tool for cognitive enhancement.

An Artificial Neural Network Controller for Intelligent Transportation Systems Applications

DOT National Transportation Integrated Search

1996-01-01

An Autonomous Intelligent Cruise Control (AICC) has been designed using a feedforward artificial neural network, as an example for utilizing artificial neural networks for nonlinear control problems arising in intelligent transportation systems appli...

Out of control: attentional selection for orientation is thwarted by properties of the underlying neural mechanisms.

PubMed

Du, Feng; Abrams, Richard A

2012-09-01

To avoid sensory overload, people are able to selectively attend to a particular color or direction of motion while ignoring irrelevant stimuli that differ from the desired one. We show here for the first time that it is also possible to selectively attend to a specific line orientation-but with an important caveat: orientations that are perpendicular to the target orientation cannot be suppressed. This effect reflects properties of the neural mechanisms selective for orientation and reveals the extent to which contingent capture is constrained not only by one's top-down goals but also by feature preferences of visual neurons. Copyright © 2012 Elsevier B.V. All rights reserved.

Reprint of “Non-causal spike filtering improves decoding of movement intention for intracortical BCIs”☆

PubMed Central

Masse, Nicolas Y.; Jarosiewicz, Beata; Simeral, John D.; Bacher, Daniel; Stavisky, Sergey D.; Cash, Sydney S.; Oakley, Erin M.; Berhanu, Etsub; Eskandar, Emad; Friehs, Gerhard; Hochberg, Leigh R.; Donoghue, John P.

2015-01-01

Background Multiple types of neural signals are available for controlling assistive devices through brain–computer interfaces (BCIs). Intracortically recorded spiking neural signals are attractive for BCIs because they can in principle provide greater fidelity of encoded information compared to electrocorticographic (ECoG) signals and electroencephalograms (EEGs). Recent reports show that the information content of these spiking neural signals can be reliably extracted simply by causally band-pass filtering the recorded extracellular voltage signals and then applying a spike detection threshold, without relying on “sorting” action potentials. New method We show that replacing the causal filter with an equivalent non-causal filter increases the information content extracted from the extracellular spiking signal and improves decoding of intended movement direction. This method can be used for real-time BCI applications by using a 4 ms lag between recording and filtering neural signals. Results Across 18 sessions from two people with tetraplegia enrolled in the BrainGate2 pilot clinical trial, we found that threshold crossing events extracted using this non-causal filtering method were significantly more informative of each participant’s intended cursor kinematics compared to threshold crossing events derived from causally filtered signals. This new method decreased the mean angular error between the intended and decoded cursor direction by 9.7° for participant S3, who was implanted 5.4 years prior to this study, and by 3.5° for participant T2, who was implanted 3 months prior to this study. PMID:25681017

Goal-Directed Decision Making as Probabilistic Inference: A Computational Framework and Potential Neural Correlates

ERIC Educational Resources Information Center

Solway, Alec; Botvinick, Matthew M.

2012-01-01

Recent work has given rise to the view that reward-based decision making is governed by two key controllers: a habit system, which stores stimulus-response associations shaped by past reward, and a goal-oriented system that selects actions based on their anticipated outcomes. The current literature provides a rich body of computational theory…

Neural Regulation Of Chromatophore Function In Cephalopods

DTIC Science & Technology

2015-05-19

which include octopus , squid and cuttlefish, are the only animals able to generate active body patterns directly controlled by the nervous system...Pattering Behavior, the ability of cephalopod mollusks to generate numerous and highly complex body patterns. Cephalopods, which include octopus , squid...cephalopod species, Octopus vulgaris with the Fiorito lab at the Stazione Zoologica in Napoli, Italy and showed that regeneration follows a

Optimization of matrix tablets controlled drug release using Elman dynamic neural networks and decision trees.

PubMed

Petrović, Jelena; Ibrić, Svetlana; Betz, Gabriele; Đurić, Zorica

2012-05-30

The main objective of the study was to develop artificial intelligence methods for optimization of drug release from matrix tablets regardless of the matrix type. Static and dynamic artificial neural networks of the same topology were developed to model dissolution profiles of different matrix tablets types (hydrophilic/lipid) using formulation composition, compression force used for tableting and tablets porosity and tensile strength as input data. Potential application of decision trees in discovering knowledge from experimental data was also investigated. Polyethylene oxide polymer and glyceryl palmitostearate were used as matrix forming materials for hydrophilic and lipid matrix tablets, respectively whereas selected model drugs were diclofenac sodium and caffeine. Matrix tablets were prepared by direct compression method and tested for in vitro dissolution profiles. Optimization of static and dynamic neural networks used for modeling of drug release was performed using Monte Carlo simulations or genetic algorithms optimizer. Decision trees were constructed following discretization of data. Calculated difference (f(1)) and similarity (f(2)) factors for predicted and experimentally obtained dissolution profiles of test matrix tablets formulations indicate that Elman dynamic neural networks as well as decision trees are capable of accurate predictions of both hydrophilic and lipid matrix tablets dissolution profiles. Elman neural networks were compared to most frequently used static network, Multi-layered perceptron, and superiority of Elman networks have been demonstrated. Developed methods allow simple, yet very precise way of drug release predictions for both hydrophilic and lipid matrix tablets having controlled drug release. Copyright © 2012 Elsevier B.V. All rights reserved.

[Motivation and Emotional States: Structural Systemic, Neurochemical, Molecular and Cellular Mechanisms].

PubMed

Bazyan, A S

2016-01-01

The structural, systemic, neurochemical, molecular and cellular mechanisms of organization and coding motivation and emotional states are describe. The GABA and glutamatergic synaptic systems of basal ganglia form a neural network and participate in the implementation of voluntary behavior. Neuropeptides, neurohormones and paracrine neuromodulators involved in the organization of motivation and emotional states, integrated with synaptic systems, controlled by neural networks and organizing goal-directed behavior. Structural centers for united and integrated of information in voluntary and goal-directed behavior are globus pallidus. Substantia nigra pars reticulata switches the information from corticobasal networks to thalamocortical networks, induces global dopaminergic (DA) signal and organize interaction of mesolimbic and nigostriatnoy DA systems controlled by prefrontal and motor cortex. Together with the motor cortex, substantia nigra displays information in the brainstem and spinal cord to implementation of behavior. Motivation states are formed in the interaction of neurohormonal and neuropeptide systems by monoaminergic systems of brain. Emotional states are formed by monoaminergic systems of the mid-brain, where the leading role belongs to the mesolimbic DA system. The emotional and motivation state of the encoded specific epigenetic molecular and chemical pattern of neuron.

Direct Current Contamination of Kilohertz Frequency Alternating Current Waveforms

PubMed Central

Franke, Manfred; Bhadra, Niloy; Bhadra, Narendra; Kilgore, Kevin

2014-01-01

Kilohertz Frequency Alternating Current (KHFAC) waveforms are being evaluated in a variety of physiological settings because of their potential to modulate neural activity uniquely when compared to frequencies in the sub-kilohertz range. However, the use of waveforms in this frequency range presents some unique challenges regarding the generator output. In this study we explored the possibility of undesirable contamination of the KHFAC waveforms by direct current (DC). We evaluated current- and voltage-controlled KHFAC waveform generators in configurations that included a capacitive coupling between generator and electrode, a resistive coupling and combinations of capacitive with inductive coupling. Our results demonstrate that both voltage- and current-controlled signal generators can unintentionally add DC-contamination to a KHFAC signal, and that capacitive coupling is not always sufficient to eliminate this contamination. We furthermore demonstrated that high value inductors, placed in parallel with the electrode, can be effective in eliminating DC-contamination irrespective of the type of stimulator, reducing the DC contamination to less than 1 μA. This study highlights the importance of carefully designing the electronic setup used in KHFAC studies and suggests specific testing that should be performed and reported in all studies that assess the neural response to KHFAC waveforms. PMID:24820914

Neurocontrol and fuzzy logic: Connections and designs

NASA Technical Reports Server (NTRS)

Werbos, Paul J.

1991-01-01

Artificial neural networks (ANNs) and fuzzy logic are complementary technologies. ANNs extract information from systems to be learned or controlled, while fuzzy techniques mainly use verbal information from experts. Ideally, both sources of information should be combined. For example, one can learn rules in a hybrid fashion, and then calibrate them for better whole-system performance. ANNs offer universal approximation theorems, pedagogical advantages, very high-throughput hardware, and links to neurophysiology. Neurocontrol - the use of ANNs to directly control motors or actuators, etc. - uses five generalized designs, related to control theory, which can work on fuzzy logic systems as well as ANNs. These designs can copy what experts do instead of what they say, learn to track trajectories, generalize adaptive control, and maximize performance or minimize cost over time, even in noisy environments. Design tradeoffs and future directions are discussed throughout.

Propagating Neural Source Revealed by Doppler Shift of Population Spiking Frequency

PubMed Central

Zhang, Mingming; Shivacharan, Rajat S.; Chiang, Chia-Chu; Gonzalez-Reyes, Luis E.

2016-01-01

Electrical activity in the brain during normal and abnormal function is associated with propagating waves of various speeds and directions. It is unclear how both fast and slow traveling waves with sometime opposite directions can coexist in the same neural tissue. By recording population spikes simultaneously throughout the unfolded rodent hippocampus with a penetrating microelectrode array, we have shown that fast and slow waves are causally related, so a slowly moving neural source generates fast-propagating waves at ∼0.12 m/s. The source of the fast population spikes is limited in space and moving at ∼0.016 m/s based on both direct and Doppler measurements among 36 different spiking trains among eight different hippocampi. The fact that the source is itself moving can account for the surprising direction reversal of the wave. Therefore, these results indicate that a small neural focus can move and that this phenomenon could explain the apparent wave reflection at tissue edges or multiple foci observed at different locations in neural tissue. SIGNIFICANCE STATEMENT The use of novel techniques with an unfolded hippocampus and penetrating microelectrode array to record and analyze neural activity has revealed the existence of a source of neural signals that propagates throughout the hippocampus. The source itself is electrically silent, but its location can be inferred by building isochrone maps of population spikes that the source generates. The movement of the source can also be tracked by observing the Doppler frequency shift of these spikes. These results have general implications for how neural signals are generated and propagated in the hippocampus; moreover, they have important implications for the understanding of seizure generation and foci localization. PMID:27013678

Neural stem cell implantation extends life in Niemann-Pick C1 mice.

PubMed

Ahmad, Iram; Hunter, Robert E; Flax, Jonathan D; Snyder, Evan Y; Erickson, Robert P

2007-01-01

In order to evaluate the phenotypic effects of implanted neural stem cells (NSCs) in the mouse model of Niemann-Pick C (NPC) disease, we injected a well-characterized clone of murine NSCs into the cerebella of neonatal Npc1(-/-) and control mice. The implanted cells survived and were abundant in some regions of the cerebellum. Life span was lengthened in NPC mice with the implanted NSCs. However, the rate of weight gain and subsequent weight loss, resulting from neurodegeneration, was not significantly different from un-injected controls. Ataxia was measured by Rota-Rod performance. The overall rate of decline in time on the Rota-Rod was not significantly slowed down. Thus, in this small group of NPC mice, a single administration in the neonatal period of the NSCs (which were not engineered to over-express the missing gene and not directed into the parenchyma) was only partially therapeutic.

Switching Adaptability in Human-Inspired Sidesteps: A Minimal Model.

PubMed

Fujii, Keisuke; Yoshihara, Yuki; Tanabe, Hiroko; Yamamoto, Yuji

2017-01-01

Humans can adapt to abruptly changing situations by coordinating redundant components, even in bipedality. Conventional adaptability has been reproduced by various computational approaches, such as optimal control, neural oscillator, and reinforcement learning; however, the adaptability in bipedal locomotion necessary for biological and social activities, such as unpredicted direction change in chase-and-escape, is unknown due to the dynamically unstable multi-link closed-loop system. Here we propose a switching adaptation model for performing bipedal locomotion by improving autonomous distributed control, where autonomous actuators interact without central control and switch the roles for propulsion, balancing, and leg swing. Our switching mobility model achieved direction change at any time using only three actuators, although it showed higher motor costs than comparable models without direction change. Our method of evaluating such adaptation at any time should be utilized as a prerequisite for understanding universal motor control. The proposed algorithm may simply explain and predict the adaptation mechanism in human bipedality to coordinate the actuator functions within and between limbs.

Neural networks for function approximation in nonlinear control

NASA Technical Reports Server (NTRS)

Linse, Dennis J.; Stengel, Robert F.

1990-01-01

Two neural network architectures are compared with a classical spline interpolation technique for the approximation of functions useful in a nonlinear control system. A standard back-propagation feedforward neural network and a cerebellar model articulation controller (CMAC) neural network are presented, and their results are compared with a B-spline interpolation procedure that is updated using recursive least-squares parameter identification. Each method is able to accurately represent a one-dimensional test function. Tradeoffs between size requirements, speed of operation, and speed of learning indicate that neural networks may be practical for identification and adaptation in a nonlinear control environment.

Neural Networks for Modeling and Control of Particle Accelerators

DOE Office of Scientific and Technical Information (OSTI.GOV)

Edelen, A. L.; Biedron, S. G.; Chase, B. E.

Myriad nonlinear and complex physical phenomena are host to particle accelerators. They often involve a multitude of interacting systems, are subject to tight performance demands, and should be able to run for extended periods of time with minimal interruptions. Often times, traditional control techniques cannot fully meet these requirements. One promising avenue is to introduce machine learning and sophisticated control techniques inspired by artificial intelligence, particularly in light of recent theoretical and practical advances in these fields. Within machine learning and artificial intelligence, neural networks are particularly well-suited to modeling, control, and diagnostic analysis of complex, nonlinear, and time-varying systems,more » as well as systems with large parameter spaces. Consequently, the use of neural network-based modeling and control techniques could be of significant benefit to particle accelerators. For the same reasons, particle accelerators are also ideal test-beds for these techniques. Moreover, many early attempts to apply neural networks to particle accelerators yielded mixed results due to the relative immaturity of the technology for such tasks. For the purpose of this paper is to re-introduce neural networks to the particle accelerator community and report on some work in neural network control that is being conducted as part of a dedicated collaboration between Fermilab and Colorado State University (CSU). We also describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.« less

Neural Networks for Modeling and Control of Particle Accelerators

NASA Astrophysics Data System (ADS)

Edelen, A. L.; Biedron, S. G.; Chase, B. E.; Edstrom, D.; Milton, S. V.; Stabile, P.

2016-04-01

Particle accelerators are host to myriad nonlinear and complex physical phenomena. They often involve a multitude of interacting systems, are subject to tight performance demands, and should be able to run for extended periods of time with minimal interruptions. Often times, traditional control techniques cannot fully meet these requirements. One promising avenue is to introduce machine learning and sophisticated control techniques inspired by artificial intelligence, particularly in light of recent theoretical and practical advances in these fields. Within machine learning and artificial intelligence, neural networks are particularly well-suited to modeling, control, and diagnostic analysis of complex, nonlinear, and time-varying systems, as well as systems with large parameter spaces. Consequently, the use of neural network-based modeling and control techniques could be of significant benefit to particle accelerators. For the same reasons, particle accelerators are also ideal test-beds for these techniques. Many early attempts to apply neural networks to particle accelerators yielded mixed results due to the relative immaturity of the technology for such tasks. The purpose of this paper is to re-introduce neural networks to the particle accelerator community and report on some work in neural network control that is being conducted as part of a dedicated collaboration between Fermilab and Colorado State University (CSU). We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.

Neural Networks for Modeling and Control of Particle Accelerators

DOE PAGES

Edelen, A. L.; Biedron, S. G.; Chase, B. E.; ...

2016-04-01

Myriad nonlinear and complex physical phenomena are host to particle accelerators. They often involve a multitude of interacting systems, are subject to tight performance demands, and should be able to run for extended periods of time with minimal interruptions. Often times, traditional control techniques cannot fully meet these requirements. One promising avenue is to introduce machine learning and sophisticated control techniques inspired by artificial intelligence, particularly in light of recent theoretical and practical advances in these fields. Within machine learning and artificial intelligence, neural networks are particularly well-suited to modeling, control, and diagnostic analysis of complex, nonlinear, and time-varying systems,more » as well as systems with large parameter spaces. Consequently, the use of neural network-based modeling and control techniques could be of significant benefit to particle accelerators. For the same reasons, particle accelerators are also ideal test-beds for these techniques. Moreover, many early attempts to apply neural networks to particle accelerators yielded mixed results due to the relative immaturity of the technology for such tasks. For the purpose of this paper is to re-introduce neural networks to the particle accelerator community and report on some work in neural network control that is being conducted as part of a dedicated collaboration between Fermilab and Colorado State University (CSU). We also describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.« less

Microscale architecture in biomaterial scaffolds for spatial control of neural cell behavior

NASA Astrophysics Data System (ADS)

Meco, Edi; Lampe, Kyle J.

2018-02-01

Biomaterial scaffolds mimic aspects of the native central nervous system (CNS) extracellular matrix (ECM) and have been extensively utilized to influence neural cell (NC) behavior in in vitro and in vivo settings. These biomimetic scaffolds support NC cultures, can direct the differentiation of NCs, and have recapitulated some native NC behavior in an in vitro setting. However, NC transplant therapies and treatments used in animal models of CNS disease and injury have not fully restored functionality. The observed lack of functional recovery occurs despite improvements in transplanted NC viability when incorporating biomaterial scaffolds and the potential of NC to replace damaged native cells. The behavior of NCs within biomaterial scaffolds must be directed in order to improve the efficacy of transplant therapies and treatments. Biomaterial scaffold topography and imbedded bioactive cues, designed at the microscale level, can alter NC phenotype, direct migration, and differentiation. Microscale patterning in biomaterial scaffolds for spatial control of NC behavior has enhanced the capabilities of in vitro models to capture properties of the native CNS tissue ECM. Patterning techniques such as lithography, electrospinning and 3D bioprinting can be employed to design the microscale architecture of biomaterial scaffolds. Here, the progress and challenges of the prevalent biomaterial patterning techniques of lithography, electrospinning, and 3D bioprinting are reported. This review analyzes NC behavioral response to specific microscale topographical patterns and spatially organized bioactive cues.

Experimental and theoretical characterization of the voltage distribution generated by deep brain stimulation.

PubMed

Miocinovic, Svjetlana; Lempka, Scott F; Russo, Gary S; Maks, Christopher B; Butson, Christopher R; Sakaie, Ken E; Vitek, Jerrold L; McIntyre, Cameron C

2009-03-01

Deep brain stimulation (DBS) is an established therapy for the treatment of Parkinson's disease and shows great promise for numerous other disorders. While the fundamental purpose of DBS is to modulate neural activity with electric fields, little is known about the actual voltage distribution generated in the brain by DBS electrodes and as a result it is difficult to accurately predict which brain areas are directly affected by the stimulation. The goal of this study was to characterize the spatial and temporal characteristics of the voltage distribution generated by DBS electrodes. We experimentally recorded voltages around active DBS electrodes in either a saline bath or implanted in the brain of a non-human primate. Recordings were made during voltage-controlled and current-controlled stimulation. The experimental findings were compared to volume conductor electric field models of DBS parameterized to match the different experiments. Three factors directly affected the experimental and theoretical voltage measurements: 1) DBS electrode impedance, primarily dictated by a voltage drop at the electrode-electrolyte interface and the conductivity of the tissue medium, 2) capacitive modulation of the stimulus waveform, and 3) inhomogeneity and anisotropy of the tissue medium. While the voltage distribution does not directly predict the neural response to DBS, the results of this study do provide foundational building blocks for understanding the electrical parameters of DBS and characterizing its effects on the nervous system.

Direct gaze elicits atypical activation of the theory-of-mind network in autism spectrum conditions.

PubMed

von dem Hagen, Elisabeth A H; Stoyanova, Raliza S; Rowe, James B; Baron-Cohen, Simon; Calder, Andrew J

2014-06-01

Eye contact plays a key role in social interaction and is frequently reported to be atypical in individuals with autism spectrum conditions (ASCs). Despite the importance of direct gaze, previous functional magnetic resonance imaging in ASC has generally focused on paradigms using averted gaze. The current study sought to determine the neural processing of faces displaying direct and averted gaze in 18 males with ASC and 23 matched controls. Controls showed an increased response to direct gaze in brain areas implicated in theory-of-mind and gaze perception, including medial prefrontal cortex, temporoparietal junction, posterior superior temporal sulcus region, and amygdala. In contrast, the same regions showed an increased response to averted gaze in individuals with an ASC. This difference was confirmed by a significant gaze direction × group interaction. Relative to controls, participants with ASC also showed reduced functional connectivity between these regions. We suggest that, in the typical brain, perceiving another person gazing directly at you triggers spontaneous attributions of mental states (e.g. he is "interested" in me), and that such mental state attributions to direct gaze may be reduced or absent in the autistic brain.

A Rotational Motion Perception Neural Network Based on Asymmetric Spatiotemporal Visual Information Processing.

PubMed

Hu, Bin; Yue, Shigang; Zhang, Zhuhong

All complex motion patterns can be decomposed into several elements, including translation, expansion/contraction, and rotational motion. In biological vision systems, scientists have found that specific types of visual neurons have specific preferences to each of the three motion elements. There are computational models on translation and expansion/contraction perceptions; however, little has been done in the past to create computational models for rotational motion perception. To fill this gap, we proposed a neural network that utilizes a specific spatiotemporal arrangement of asymmetric lateral inhibited direction selective neural networks (DSNNs) for rotational motion perception. The proposed neural network consists of two parts-presynaptic and postsynaptic parts. In the presynaptic part, there are a number of lateral inhibited DSNNs to extract directional visual cues. In the postsynaptic part, similar to the arrangement of the directional columns in the cerebral cortex, these direction selective neurons are arranged in a cyclic order to perceive rotational motion cues. In the postsynaptic network, the delayed excitation from each direction selective neuron is multiplied by the gathered excitation from this neuron and its unilateral counterparts depending on which rotation, clockwise (cw) or counter-cw (ccw), to perceive. Systematic experiments under various conditions and settings have been carried out and validated the robustness and reliability of the proposed neural network in detecting cw or ccw rotational motion. This research is a critical step further toward dynamic visual information processing.All complex motion patterns can be decomposed into several elements, including translation, expansion/contraction, and rotational motion. In biological vision systems, scientists have found that specific types of visual neurons have specific preferences to each of the three motion elements. There are computational models on translation and expansion/contraction perceptions; however, little has been done in the past to create computational models for rotational motion perception. To fill this gap, we proposed a neural network that utilizes a specific spatiotemporal arrangement of asymmetric lateral inhibited direction selective neural networks (DSNNs) for rotational motion perception. The proposed neural network consists of two parts-presynaptic and postsynaptic parts. In the presynaptic part, there are a number of lateral inhibited DSNNs to extract directional visual cues. In the postsynaptic part, similar to the arrangement of the directional columns in the cerebral cortex, these direction selective neurons are arranged in a cyclic order to perceive rotational motion cues. In the postsynaptic network, the delayed excitation from each direction selective neuron is multiplied by the gathered excitation from this neuron and its unilateral counterparts depending on which rotation, clockwise (cw) or counter-cw (ccw), to perceive. Systematic experiments under various conditions and settings have been carried out and validated the robustness and reliability of the proposed neural network in detecting cw or ccw rotational motion. This research is a critical step further toward dynamic visual information processing.

Induction of the neural crest state: Control of stem cell attributes by gene regulatory, post-transcriptional and epigenetic interactions

PubMed Central

Prasad, Maneeshi S.; Sauka-Spengler, Tatjana; LaBonne, Carole

2012-01-01

Neural crest cells are a population of multipotent stem cell-like progenitors that arise at the neural plate border in vertebrates, migrate extensively, and give rise to diverse derivatives such as melanocytes, craniofacial cartilage and bone, smooth muscle, peripheral and enteric neurons and glia. The neural crest gene regulatory network (NC-GRN) includes a number of key factors that are used reiteratively to control multiple steps in the development of neural crest cells, including the acquisition of stem cell attributes. It is therefore essential to understand the mechanisms that control the distinct functions of such reiteratively used factors in different cellular contexts. The context-dependent control of neural crest specification is achieved through combinatorial interaction with other factors, post-transcriptional and post-translational modifications, and the epigenetic status and chromatin state of target genes. Here we review the current understanding of the NC-GRN, including the role of the neural crest specifiers, their links to the control of “stemness,” and their dynamic context-dependent regulation during the formation of neural crest progenitors. PMID:22583479

Flight Test Results from the NF-15B Intelligent Flight Control System (IFCS) Project with Adaptation to a Simulated Stabilator Failure

NASA Technical Reports Server (NTRS)

Bosworth, John T.; Williams-Hayes, Peggy S.

2007-01-01

Adaptive flight control systems have the potential to be more resilient to extreme changes in airplane behavior. Extreme changes could be a result of a system failure or of damage to the airplane. A direct adaptive neural-network-based flight control system was developed for the National Aeronautics and Space Administration NF-15B Intelligent Flight Control System airplane and subjected to an inflight simulation of a failed (frozen) (unmovable) stabilator. Formation flight handling qualities evaluations were performed with and without neural network adaptation. The results of these flight tests are presented. Comparison with simulation predictions and analysis of the performance of the adaptation system are discussed. The performance of the adaptation system is assessed in terms of its ability to decouple the roll and pitch response and reestablish good onboard model tracking. Flight evaluation with the simulated stabilator failure and adaptation engaged showed that there was generally improvement in the pitch response; however, a tendency for roll pilot-induced oscillation was experienced. A detailed discussion of the cause of the mixed results is presented.

Flight Test Results from the NF-15B Intelligent Flight Control System (IFCS) Project with Adaptation to a Simulated Stabilator Failure

NASA Technical Reports Server (NTRS)

Bosworth, John T.; Williams-Hayes, Peggy S.

2010-01-01

Adaptive flight control systems have the potential to be more resilient to extreme changes in airplane behavior. Extreme changes could be a result of a system failure or of damage to the airplane. A direct adaptive neural-network-based flight control system was developed for the National Aeronautics and Space Administration NF-15B Intelligent Flight Control System airplane and subjected to an inflight simulation of a failed (frozen) (unmovable) stabilator. Formation flight handling qualities evaluations were performed with and without neural network adaptation. The results of these flight tests are presented. Comparison with simulation predictions and analysis of the performance of the adaptation system are discussed. The performance of the adaptation system is assessed in terms of its ability to decouple the roll and pitch response and reestablish good onboard model tracking. Flight evaluation with the simulated stabilator failure and adaptation engaged showed that there was generally improvement in the pitch response; however, a tendency for roll pilot-induced oscillation was experienced. A detailed discussion of the cause of the mixed results is presented.

Short-term escitalopram treatment normalizes aberrant self-referential processing in major depressive disorder.

PubMed

Komulainen, Emma; Heikkilä, Roope; Nummenmaa, Lauri; Raij, Tuukka T; Harmer, Catherine J; Isometsä, Erkki; Ekelund, Jesper

2018-04-22

Increased self-focus and negative self-concept play an important role in depression. Antidepressants influence self-referential processing in healthy volunteers, but their function in self-processing of depressed patients remains unknown. Thirty-two depressed patients were randomly allocated to receive either escitalopram 10 mg or placebo for one week. After one week, neural responses to positive and negative self-referential adjectives and neutral control stimuli were assessed with functional magnetic resonance imaging. A group of matched healthy volunteers served as a control group. Escitalopram decreased responses of medial fronto-parietal regions to self-referential words relative to non-emotional control stimuli, driven by increased responses to the control condition. Escitalopram also increased responses in the pre-defined region of the medial prefrontal cortex (MPFC) and the anterior cingulate cortex (ACC) to positive relative to negative words. Importantly, the changes in neural responses occurred before any effect on depressive symptoms, implying a direct effect of escitalopram. Furthermore, the placebo group had decreased responses of the MPFC and the ACC to positive self-referential processing relative to the matched healthy controls. However, neural responses of the escitalopram group and the healthy unmedicated controls were similar. Differences between the groups in self-reported depression symptoms and personality traits may have influenced the results. One-week treatment with escitalopram normalized aberrant self-referential processing in depressed patients, shifting the focus from the self to the external environment and potentiating positive self-referential processing. This may be an important factor in mechanism of action of antidepressants. Copyright © 2018 Elsevier B.V. All rights reserved.

Selection and parameterization of cortical neurons for neuroprosthetic control.

PubMed

Wahnoun, Remy; He, Jiping; Helms Tillery, Stephen I

2006-06-01

When designing neuroprosthetic interfaces for motor function, it is crucial to have a system that can extract reliable information from available neural signals and produce an output suitable for real life applications. Systems designed to date have relied on establishing a relationship between neural discharge patterns in motor cortical areas and limb movement, an approach not suitable for patients who require such implants but who are unable to provide proper motor behavior to initially tune the system. We describe here a method that allows rapid tuning of a population vector-based system for neural control without arm movements. We trained highly motivated primates to observe a 3D center-out task as the computer played it very slowly. Based on only 10-12 s of neuronal activity observed in M1 and PMd, we generated an initial mapping between neural activity and device motion that the animal could successfully use for neuroprosthetic control. Subsequent tunings of the parameters led to improvements in control, but the initial selection of neurons and estimated preferred direction for those cells remained stable throughout the remainder of the day. Using this system, we have observed that the contribution of individual neurons to the overall control of the system is very heterogeneous. We thus derived a novel measure of unit quality and an indexing scheme that allowed us to rate each neuron's contribution to the overall control. In offline tests, we found that fewer than half of the units made positive contributions to the performance. We tested this experimentally by having the animals control the neuroprosthetic system using only the 20 best neurons. We found that performance in this case was better than when the entire set of available neurons was used. Based on these results, we believe that, with careful task design, it is feasible to parameterize control systems without any overt behaviors and that subsequent control system design will be enhanced with cautious unit selection. These improvements can lead to systems demanding lower bandwidth and computational power, and will pave the way for more feasible clinical systems.

Neural Excitability and Joint Laxity in Chronic Ankle Instability, Coper, and Control Groups

PubMed Central

Bowker, Samantha; Terada, Masafumi; Thomas, Abbey C.; Pietrosimone, Brian G.; Hiller, Claire E.; Gribble, Phillip A.

2016-01-01

Context:  Neuromuscular and mechanical deficiencies are commonly studied in participants with chronic ankle instability (CAI). Few investigators have attempted to comprehensively consider sensorimotor and mechanical differences among people with CAI, copers who did not present with prolonged dysfunctions after an initial ankle sprain, and a healthy control group. Objective:  To determine if differences exist in spinal reflex excitability and ankle laxity among participants with CAI, copers, and healthy controls. Design:  Case-control study. Setting:  Research laboratory. Patients or Other Participants:  Thirty-seven participants with CAI, 30 participants categorized as copers, and 26 healthy control participants. Main Outcome Measure(s):  We assessed spinal reflex excitability of the soleus using the Hoffmann reflex protocol. Participants' ankle laxity was measured with an instrumented ankle arthrometer. The maximum Hoffmann reflex : maximal muscle response ratio was calculated. Ankle laxity was measured as the total displacement in the anterior-posterior directions (mm) and total rotation in the inversion and eversion directions (°). Results:  Spinal reflex excitability was diminished in participants with CAI compared with copers and control participants (P = .01). No differences were observed among any of the groups for ankle laxity. Conclusion:  Changes in the spinal reflex excitability of the soleus that likely affect ankle stability were seen only in the CAI group, yet no mechanical differences were noted across the groups. These findings support the importance of finding effective ways to increase spinal reflex excitability for the purpose of treating neural excitability dysfunction in patients with CAI. PMID:27065189

Neural basis of forward flight control and landing in honeybees.

PubMed

Ibbotson, M R; Hung, Y-S; Meffin, H; Boeddeker, N; Srinivasan, M V

2017-11-06

The impressive repertoire of honeybee visually guided behaviors, and their ability to learn has made them an important tool for elucidating the visual basis of behavior. Like other insects, bees perform optomotor course correction to optic flow, a response that is dependent on the spatial structure of the visual environment. However, bees can also distinguish the speed of image motion during forward flight and landing, as well as estimate flight distances (odometry), irrespective of the visual scene. The neural pathways underlying these abilities are unknown. Here we report on a cluster of descending neurons (DNIIIs) that are shown to have the directional tuning properties necessary for detecting image motion during forward flight and landing on vertical surfaces. They have stable firing rates during prolonged periods of stimulation and respond to a wide range of image speeds, making them suitable to detect image flow during flight behaviors. While their responses are not strictly speed tuned, the shape and amplitudes of their speed tuning functions are resistant to large changes in spatial frequency. These cells are prime candidates not only for the control of flight speed and landing, but also the basis of a neural 'front end' of the honeybee's visual odometer.

Dance and music share gray matter structural correlates.

PubMed

Karpati, Falisha J; Giacosa, Chiara; Foster, Nicholas E V; Penhune, Virginia B; Hyde, Krista L

2017-02-15

Intensive practise of sensorimotor skills, such as music and dance, is associated with brain structural plasticity. While the neural correlates of music have been well-investigated, less is known about the neural correlates of dance. Additionally, the gray matter structural correlates of dance versus music training have not yet been directly compared. The objectives of the present study were to compare gray matter structure as measured by surface- and voxel-based morphometry between expert dancers, expert musicians and untrained controls, as well as to correlate gray matter structure with performance on dance- and music-related tasks. Dancers and musicians were found to have increased cortical thickness compared to controls in superior temporal regions. Gray matter structure in the superior temporal gyrus was also correlated with performance on dance imitation, rhythm synchronization and melody discrimination tasks. These results suggest that superior temporal regions are important in both dance- and music-related skills and may be affected similarly by both types of long-term intensive training. This work advances knowledge of the neural correlates of dance and music, as well as training-associated brain plasticity in general. Copyright © 2016 Elsevier B.V. All rights reserved.

Limited Transfer of Newly Acquired Movement Patterns across Walking and Running in Humans

PubMed Central

Ogawa, Tetsuya; Kawashima, Noritaka; Ogata, Toru; Nakazawa, Kimitaka

2012-01-01

The two major modes of locomotion in humans, walking and running, may be regarded as a function of different speed (walking as slower and running as faster). Recent results using motor learning tasks in humans, as well as more direct evidence from animal models, advocate for independence in the neural control mechanisms underlying different locomotion tasks. In the current study, we investigated the possible independence of the neural mechanisms underlying human walking and running. Subjects were tested on a split-belt treadmill and adapted to walking or running on an asymmetrically driven treadmill surface. Despite the acquisition of asymmetrical movement patterns in the respective modes, the emergence of asymmetrical movement patterns in the subsequent trials was evident only within the same modes (walking after learning to walk and running after learning to run) and only partial in the opposite modes (walking after learning to run and running after learning to walk) (thus transferred only limitedly across the modes). Further, the storage of the acquired movement pattern in each mode was maintained independently of the opposite mode. Combined, these results provide indirect evidence for independence in the neural control mechanisms underlying the two locomotive modes. PMID:23029490

Maximization of Learning Speed in the Motor Cortex Due to Neuronal Redundancy

PubMed Central

Takiyama, Ken; Okada, Masato

2012-01-01

Many redundancies play functional roles in motor control and motor learning. For example, kinematic and muscle redundancies contribute to stabilizing posture and impedance control, respectively. Another redundancy is the number of neurons themselves; there are overwhelmingly more neurons than muscles, and many combinations of neural activation can generate identical muscle activity. The functional roles of this neuronal redundancy remains unknown. Analysis of a redundant neural network model makes it possible to investigate these functional roles while varying the number of model neurons and holding constant the number of output units. Our analysis reveals that learning speed reaches its maximum value if and only if the model includes sufficient neuronal redundancy. This analytical result does not depend on whether the distribution of the preferred direction is uniform or a skewed bimodal, both of which have been reported in neurophysiological studies. Neuronal redundancy maximizes learning speed, even if the neural network model includes recurrent connections, a nonlinear activation function, or nonlinear muscle units. Furthermore, our results do not rely on the shape of the generalization function. The results of this study suggest that one of the functional roles of neuronal redundancy is to maximize learning speed. PMID:22253586

Newly developed double neural network concept for reliable fast plasma position control

NASA Astrophysics Data System (ADS)

Jeon, Young-Mu; Na, Yong-Su; Kim, Myung-Rak; Hwang, Y. S.

2001-01-01

Neural network is considered as a parameter estimation tool in plasma controls for next generation tokamak such as ITER. The neural network has been reported to be so accurate and fast for plasma equilibrium identification that it may be applied to the control of complex tokamak plasmas. For this application, the reliability of the conventional neural network needs to be improved. In this study, a new idea of double neural network is developed to achieve this. The new idea has been applied to simple plasma position identification of KSTAR tokamak for feasibility test. Characteristics of the concept show higher reliability and fault tolerance even in severe faulty conditions, which may make neural network applicable to plasma control reliably and widely in future tokamaks.

Synchronization criteria for generalized reaction-diffusion neural networks via periodically intermittent control.

PubMed

Gan, Qintao; Lv, Tianshi; Fu, Zhenhua

2016-04-01

In this paper, the synchronization problem for a class of generalized neural networks with time-varying delays and reaction-diffusion terms is investigated concerning Neumann boundary conditions in terms of p-norm. The proposed generalized neural networks model includes reaction-diffusion local field neural networks and reaction-diffusion static neural networks as its special cases. By establishing a new inequality, some simple and useful conditions are obtained analytically to guarantee the global exponential synchronization of the addressed neural networks under the periodically intermittent control. According to the theoretical results, the influences of diffusion coefficients, diffusion space, and control rate on synchronization are analyzed. Finally, the feasibility and effectiveness of the proposed methods are shown by simulation examples, and by choosing different diffusion coefficients, diffusion spaces, and control rates, different controlled synchronization states can be obtained.

Manifold decoding for neural representations of face viewpoint and gaze direction using magnetoencephalographic data.

PubMed

Kuo, Po-Chih; Chen, Yong-Sheng; Chen, Li-Fen

2018-05-01

The main challenge in decoding neural representations lies in linking neural activity to representational content or abstract concepts. The transformation from a neural-based to a low-dimensional representation may hold the key to encoding perceptual processes in the human brain. In this study, we developed a novel model by which to represent two changeable features of faces: face viewpoint and gaze direction. These features are embedded in spatiotemporal brain activity derived from magnetoencephalographic data. Our decoding results demonstrate that face viewpoint and gaze direction can be represented by manifold structures constructed from brain responses in the bilateral occipital face area and right superior temporal sulcus, respectively. Our results also show that the superposition of brain activity in the manifold space reveals the viewpoints of faces as well as directions of gazes as perceived by the subject. The proposed manifold representation model provides a novel opportunity to gain further insight into the processing of information in the human brain. © 2018 Wiley Periodicals, Inc.

The Relationship of Aluminium and Silver to Neural Tube Defects; a Case Control

PubMed Central

Ramírez-Altamirano, María de Jesús; Fenton-Navarro, Patricia; Sivet-Chiñas, Elvira; Harp-Iturribarria, Flor de María; Martínez-Cruz, Ruth; Cruz, Pedro Hernández; Cruz, Margarito Martínez; Pérez-Campos, Eduardo

2012-01-01

Objective The purpose of this study was to identify the relationship of neurotoxic inorganic elements in the hair of patients with the diagnosis of Neural Tube Defects. Our initial hypothesis was that neurotoxic inorganic elements were associated with Neural Tube Defects. Methods Twenty-three samples of hair from newborns were obtained from the General Hospital, “Aurelio Valdivieso” in the city of Oaxaca, Mexico. The study group included 8 newborn infants with neural tube pathology. The control group was composed of 15 newborns without this pathology. The presence of inorganic elements in the hair samples was determined by inductively-coupled plasma spectroscopy (spectroscopic emission of the plasma). Findings The population of newborns with Neural Tube Defects showed significantly higher values of the following elements than the control group: Aluminium, Neural Tube Defects 152.77±51.06 µg/g, control group 76.24±27.89 µg/g; Silver, Neural Tube Defects 1.45±0.76, control group 0.25±0.53 µg/g; Potassium, Neural Tube Defects 553.87±77.91 µg/g, control group 341.13±205.90 µg/g. Association was found at 75 percentile between aluminium plus silver, aluminium plus potassium, silver plus potassium, and potassium plus sodium. Conclusion In the hair of newborns with Neural Tube Defects, the following metals were increased: aluminium, silver. Given the neurotoxicity of the same, and association of Neural Tube Defects with aluminum and silver, one may infer that they may be participating as factors in the development of Neural Tube Defects. PMID:23400307

Method for neural network control of motion using real-time environmental feedback

NASA Technical Reports Server (NTRS)

Buckley, Theresa M. (Inventor)

1997-01-01

A method of motion control for robotics and other automatically controlled machinery using a neural network controller with real-time environmental feedback. The method is illustrated with a two-finger robotic hand having proximity sensors and force sensors that provide environmental feedback signals. The neural network controller is taught to control the robotic hand through training sets using back- propagation methods. The training sets are created by recording the control signals and the feedback signal as the robotic hand or a simulation of the robotic hand is moved through a representative grasping motion. The data recorded is divided into discrete increments of time and the feedback data is shifted out of phase with the control signal data so that the feedback signal data lag one time increment behind the control signal data. The modified data is presented to the neural network controller as a training set. The time lag introduced into the data allows the neural network controller to account for the temporal component of the robotic motion. Thus trained, the neural network controlled robotic hand is able to grasp a wide variety of different objects by generalizing from the training sets.

Reconfigurable Control with Neural Network Augmentation for a Modified F-15 Aircraft

NASA Technical Reports Server (NTRS)

Burken, John J.; Williams-Hayes, Peggy; Kaneshige, John 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.

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.

An Artificial Neural Network Control System for Spacecraft Attitude Stabilization

DTIC Science & Technology

1990-06-01

NAVAL POSTGRADUATE SCHOOL Monterey, California ’-DTIC 0 ELECT f NMARO 5 191 N S, U, THESIS B . AN ARTIFICIAL NEURAL NETWORK CONTROL SYSTEM FOR...NO. NO. NO ACCESSION NO 11. TITLE (Include Security Classification) AN ARTIFICIAL NEURAL NETWORK CONTROL SYSTEM FOR SPACECRAFT ATTITUDE STABILIZATION...obsolete a U.S. G v pi.. iim n P.. oiice! toog-eo.5s43 i Approved for public release; distribution is unlimited. AN ARTIFICIAL NEURAL NETWORK CONTROL

Closing the Loop for Memory Prostheses: Detecting the Role of Hippocampal Neural Ensembles Using Nonlinear Models

PubMed Central

Hampson, Robert E.; Song, Dong; Chan, Rosa H.M.; Sweatt, Andrew J.; Riley, Mitchell R.; Goonawardena, Anushka V.; Marmarelis, Vasilis Z.; Gerhardt, Greg A.; Berger, Theodore W.; Deadwyler, Sam A.

2012-01-01

A major factor involved in providing closed loop feedback for control of neural function is to understand how neural ensembles encode online information critical to the final behavioral endpoint. This issue was directly assessed in rats performing a short-term delay memory task in which successful encoding of task information is dependent upon specific spatiotemporal firing patterns recorded from ensembles of CA3 and CA1 hippocampal neurons. Such patterns, extracted by a specially designed nonlinear multi-input multi-output (MIMO) nonlinear mathematical model, were used to predict successful performance online via a closed loop paradigm which regulated trial difficulty (time of retention) as a function of the “strength” of stimulus encoding. The significance of the MIMO model as a neural prosthesis has been demonstrated by substituting trains of electrical stimulation pulses to mimic these same ensemble firing patterns. This feature was used repeatedly to vary “normal” encoding as a means of understanding how neural ensembles can be “tuned” to mimic the inherent process of selecting codes of different strength and functional specificity. The capacity to enhance and tune hippocampal encoding via MIMO model detection and insertion of critical ensemble firing patterns shown here provides the basis for possible extension to other disrupted brain circuitry. PMID:22498704

Pulsed DC Electric Field–Induced Differentiation of Cortical Neural Precursor Cells

PubMed Central

Chang, Hui-Fang; Lee, Ying-Shan; Tang, Tang K.; Cheng, Ji-Yen

2016-01-01

We report the differentiation of neural stem and progenitor cells solely induced by direct current (DC) pulses stimulation. Neural stem and progenitor cells in the adult mammalian brain are promising candidates for the development of therapeutic neuroregeneration strategies. The differentiation of neural stem and progenitor cells depends on various in vivo environmental factors, such as nerve growth factor and endogenous EF. In this study, we demonstrated that the morphologic and phenotypic changes of mouse neural stem and progenitor cells (mNPCs) could be induced solely by exposure to square-wave DC pulses (magnitude 300 mV/mm at frequency of 100-Hz). The DC pulse stimulation was conducted for 48 h, and the morphologic changes of mNPCs were monitored continuously. The length of primary processes and the amount of branching significantly increased after stimulation by DC pulses for 48 h. After DC pulse treatment, the mNPCs differentiated into neurons, astrocytes, and oligodendrocytes simultaneously in stem cell maintenance medium. Our results suggest that simple DC pulse treatment could control the fate of NPCs. With further studies, DC pulses may be applied to manipulate NPC differentiation and may be used for the development of therapeutic strategies that employ NPCs to treat nervous system disorders. PMID:27352251

Effect of short-term escitalopram treatment on neural activation during emotional processing.

PubMed

Maron, Eduard; Wall, Matt; Norbury, Ray; Godlewska, Beata; Terbeck, Sylvia; Cowen, Philip; Matthews, Paul; Nutt, David J

2016-01-01

Recent functional magnetic resonance (fMRI) imaging studies have revealed that subchronic medication with escitalopram leads to significant reduction in both amygdala and medial frontal gyrus reactivity during processing of emotional faces, suggesting that escitalopram may have a distinguishable modulatory effect on neural activation as compared with other serotonin-selective antidepressants. In this fMRI study we aimed to explore whether short-term medication with escitalopram in healthy volunteers is associated with reduced neural response to emotional processing, and whether this effect is predicted by drug plasma concentration. The neural response to fearful and happy faces was measured before and on day 7 of treatment with escitalopram (10mg) in 15 healthy volunteers and compared with those in a control unmedicated group (n=14). Significantly reduced activation to fearful, but not to happy facial expressions was observed in the bilateral amygdala, cingulate and right medial frontal gyrus following escitalopram medication. This effect was not correlated with plasma drug concentration. In accordance with previous data, we showed that escitalopram exerts its rapid direct effect on emotional processing via attenuation of neural activation in pathways involving medial frontal gyrus and amygdala, an effect that seems to be distinguishable from that of other SSRIs. © The Author(s) 2015.

Neural Correlates of Consumer Buying Motivations: A 7T functional Magnetic Resonance Imaging (fMRI) Study

PubMed Central

Goodman, Adam M.; Wang, Yun; Kwon, Wi-Suk; Byun, Sang-Eun; Katz, Jeffrey S.; Deshpande, Gopikrishna

2017-01-01

Consumer buying motivations can be distinguished into three categories: functional, experiential, or symbolic motivations (Keller, 1993). Although prior neuroimaging studies have examined the neural substrates which enable these motivations, direct comparisons between these three types of consumer motivations have yet to be made. In the current study, we used 7 Tesla (7T) functional magnetic resonance imaging (fMRI) to assess the neural correlates of each motivation by instructing participants to view common consumer goods while emphasizing either functional, experiential, or symbolic values of these products. The results demonstrated mostly consistent activations between symbolic and experiential motivations. Although, these motivations differed in that symbolic motivation was associated with medial frontal gyrus (MFG) activation, whereas experiential motivation was associated with posterior cingulate cortex (PCC) activation. Functional motivation was associated with dorsolateral prefrontal cortex (DLPFC) activation, as compared to other motivations. These findings provide a neural basis for how symbolic and experiential motivations may be similar, yet different in subtle ways. Furthermore, the dissociation of functional motivation within the DLPFC supports the notion that this motivation relies on executive function processes relatively more than hedonic motivation. These findings provide a better understanding of the underlying neural functioning which may contribute to poor self-control choices. PMID:28959182

Neural network based automatic limit prediction and avoidance system and method

NASA Technical Reports Server (NTRS)

Calise, Anthony J. (Inventor); Prasad, Jonnalagadda V. R. (Inventor); Horn, Joseph F. (Inventor)

2001-01-01

A method for performance envelope boundary cueing for a vehicle control system comprises the steps of formulating a prediction system for a neural network and training the neural network to predict values of limited parameters as a function of current control positions and current vehicle operating conditions. The method further comprises the steps of applying the neural network to the control system of the vehicle, where the vehicle has capability for measuring current control positions and current vehicle operating conditions. The neural network generates a map of current control positions and vehicle operating conditions versus the limited parameters in a pre-determined vehicle operating condition. The method estimates critical control deflections from the current control positions required to drive the vehicle to a performance envelope boundary. Finally, the method comprises the steps of communicating the critical control deflection to the vehicle control system; and driving the vehicle control system to provide a tactile cue to an operator of the vehicle as the control positions approach the critical control deflections.

Neural substrates underlying stimulation-enhanced motor skill learning after stroke

PubMed Central

Lefebvre, Stéphanie; Dricot, Laurence; Laloux, Patrice; Gradkowski, Wojciech; Desfontaines, Philippe; Evrard, Frédéric; Peeters, André; Jamart, Jacques

2015-01-01

Motor skill learning is one of the key components of motor function recovery after stroke, especially recovery driven by neurorehabilitation. Transcranial direct current stimulation can enhance neurorehabilitation and motor skill learning in stroke patients. However, the neural mechanisms underlying the retention of stimulation-enhanced motor skill learning involving a paretic upper limb have not been resolved. These neural substrates were explored by means of functional magnetic resonance imaging. Nineteen chronic hemiparetic stroke patients participated in a double-blind, cross-over randomized, sham-controlled experiment with two series. Each series consisted of two sessions: (i) an intervention session during which dual transcranial direct current stimulation or sham was applied during motor skill learning with the paretic upper limb; and (ii) an imaging session 1 week later, during which the patients performed the learned motor skill. The motor skill learning task, called the ‘circuit game’, involves a speed/accuracy trade-off and consists of moving a pointer controlled by a computer mouse along a complex circuit as quickly and accurately as possible. Relative to the sham series, dual transcranial direct current stimulation applied bilaterally over the primary motor cortex during motor skill learning with the paretic upper limb resulted in (i) enhanced online motor skill learning; (ii) enhanced 1-week retention; and (iii) superior transfer of performance improvement to an untrained task. The 1-week retention’s enhancement driven by the intervention was associated with a trend towards normalization of the brain activation pattern during performance of the learned motor skill relative to the sham series. A similar trend towards normalization relative to sham was observed during performance of a simple, untrained task without a speed/accuracy constraint, despite a lack of behavioural difference between the dual transcranial direct current stimulation and sham series. Finally, dual transcranial direct current stimulation applied during the first session enhanced continued learning with the paretic limb 1 week later, relative to the sham series. This lasting behavioural enhancement was associated with more efficient recruitment of the motor skill learning network, that is, focused activation on the motor-premotor areas in the damaged hemisphere, especially on the dorsal premotor cortex. Dual transcranial direct current stimulation applied during motor skill learning with a paretic upper limb resulted in prolonged shaping of brain activation, which supported behavioural enhancements in stroke patients. PMID:25488186

Acute exposure to 2G phase shifts the rat circadian timing system

NASA Technical Reports Server (NTRS)

Hoban-Higgins, T. M.; Murakami, D. M.; Tandon, T.; Fuller, C. A.

1995-01-01

The circadian timing system (CTS) provides internal and external temporal coordination of an animal's physiology and behavior. In mammals, the generation and coordination of these circadian rhythms is controlled by a neural pacemaker, the suprachiasmatic nucleus (SCN), located within the hypothalamus. The pacemaker is synchronized to the 24 hour day by time cures (zeitgebers) such as the light/dark cycle. When an animal is exposed to an environment without time cues, the circadian rhythms maintain internal temporal coordination, but exhibit a 'free-running' condition in which the period length is determined by the internal pacemaker. Maintenance of internal and external temporal coordination are critical for normal physiological and psychological function in human and non-human primates. Exposure to altered gravitational environments has been shown to affect the amplitude, mean, and timing of circadian rhythms in species ranging from unicellular organisms to man. However, it has not been determined whether altered gravitational fields have a direct effect on the neural pacemaker, or affect peripheral parameters. In previous studies, the ability of a stimulus to phase shift circadian rhythms was used to determine whether a stimulus has a direct effect on the neural pacemaker. The present experiment was performed in order to determine whether acute exposure to a hyperdynamic field could phase shift circadian rhythms.

Control of autonomous robot using neural networks

NASA Astrophysics Data System (ADS)

Barton, Adam; Volna, Eva

2017-07-01

The aim of the article is to design a method of control of an autonomous robot using artificial neural networks. The introductory part describes control issues from the perspective of autonomous robot navigation and the current mobile robots controlled by neural networks. The core of the article is the design of the controlling neural network, and generation and filtration of the training set using ART1 (Adaptive Resonance Theory). The outcome of the practical part is an assembled Lego Mindstorms EV3 robot solving the problem of avoiding obstacles in space. To verify models of an autonomous robot behavior, a set of experiments was created as well as evaluation criteria. The speed of each motor was adjusted by the controlling neural network with respect to the situation in which the robot was found.

Robustness of a distributed neural network controller for locomotion in a hexapod robot

NASA Technical Reports Server (NTRS)

Chiel, Hillel J.; Beer, Randall D.; Quinn, Roger D.; Espenschied, Kenneth S.

1992-01-01

A distributed neural-network controller for locomotion, based on insect neurobiology, has been used to control a hexapod robot. How robust is this controller? Disabling any single sensor, effector, or central component did not prevent the robot from walking. Furthermore, statically stable gaits could be established using either sensor input or central connections. Thus, a complex interplay between central neural elements and sensor inputs is responsible for the robustness of the controller and its ability to generate a continuous range of gaits. These results suggest that biologically inspired neural-network controllers may be a robust method for robotic control.

Reconfigurable Control Design with Neural Network Augmentation for a Modified F-15 Aircraft

NASA Technical Reports Server (NTRS)

Burken, John J.

2007-01-01

The viewgraphs present background information about reconfiguration control design, design methods used for paper, control failure survivability results, and results and time histories of tests. Topics examined include control reconfiguration, general information about adaptive controllers, model reference adaptive control (MRAC), the utility of neural networks, radial basis functions (RBF) neural network outputs, neurons, and results of investigations of failures.

New genes in the evolution of the neural crest differentiation program

PubMed Central

2007-01-01

Background Development of the vertebrate head depends on the multipotency and migratory behavior of neural crest derivatives. This cell population is considered a vertebrate innovation and, accordingly, chordate ancestors lacked neural crest counterparts. The identification of neural crest specification genes expressed in the neural plate of basal chordates, in addition to the discovery of pigmented migratory cells in ascidians, has challenged this hypothesis. These new findings revive the debate on what is new and what is ancient in the genetic program that controls neural crest formation. Results To determine the origin of neural crest genes, we analyzed Phenotype Ontology annotations to select genes that control the development of this tissue. Using a sequential blast pipeline, we phylogenetically classified these genes, as well as those associated with other tissues, in order to define tissue-specific profiles of gene emergence. Of neural crest genes, 9% are vertebrate innovations. Our comparative analyses show that, among different tissues, the neural crest exhibits a particularly high rate of gene emergence during vertebrate evolution. A remarkable proportion of the new neural crest genes encode soluble ligands that control neural crest precursor specification into each cell lineage, including pigmented, neural, glial, and skeletal derivatives. Conclusion We propose that the evolution of the neural crest is linked not only to the recruitment of ancestral regulatory genes but also to the emergence of signaling peptides that control the increasingly complex lineage diversification of this plastic cell population. PMID:17352807

Intelligent complementary sliding-mode control for LUSMS-based X-Y-theta motion control stage.

PubMed

Lin, Faa-Jeng; Chen, Syuan-Yi; Shyu, Kuo-Kai; Liu, Yen-Hung

2010-07-01

An intelligent complementary sliding-mode control (ICSMC) system using a recurrent wavelet-based Elman neural network (RWENN) estimator is proposed in this study to control the mover position of a linear ultrasonic motors (LUSMs)-based X-Y-theta motion control stage for the tracking of various contours. By the addition of a complementary generalized error transformation, the complementary sliding-mode control (CSMC) can efficiently reduce the guaranteed ultimate bound of the tracking error by half compared with the slidingmode control (SMC) while using the saturation function. To estimate a lumped uncertainty on-line and replace the hitting control of the CSMC directly, the RWENN estimator is adopted in the proposed ICSMC system. In the RWENN, each hidden neuron employs a different wavelet function as an activation function to improve both the convergent precision and the convergent time compared with the conventional Elman neural network (ENN). The estimation laws of the RWENN are derived using the Lyapunov stability theorem to train the network parameters on-line. A robust compensator is also proposed to confront the uncertainties including approximation error, optimal parameter vectors, and higher-order terms in Taylor series. Finally, some experimental results of various contours tracking show that the tracking performance of the ICSMC system is significantly improved compared with the SMC and CSMC systems.

Integrating Artificial Immune, Neural and Endrocine Systems in Autonomous Sailing Robots

DTIC Science & Technology

2010-09-24

system - Development of an adaptive hormone system capable of changing operation and control of the neural network depending on changing enviromental ...and control of the neural network depending on changing enviromental conditions • First basic design of the MOOP and a simple neural-endocrine based

Instructional control of reinforcement learning: A behavioral and neurocomputational investigation

PubMed Central

Doll, Bradley B.; Jacobs, W. Jake; Sanfey, Alan G.; Frank, Michael J.

2011-01-01

Humans learn how to behave directly through environmental experience and indirectly through rules and instructions. Behavior analytic research has shown that instructions can control behavior, even when such behavior leads to sub-optimal outcomes (Hayes, S. (Ed.). 1989. Rule-governed behavior: cognition, contingencies, and instructional control. Plenum Press.). Here we examine the control of behavior through instructions in a reinforcement learning task known to depend on striatal dopaminergic function. Participants selected between probabilistically reinforced stimuli, and were (incorrectly) told that a specific stimulus had the highest (or lowest) reinforcement probability. Despite experience to the contrary, instructions drove choice behavior. We present neural network simulations that capture the interactions between instruction-driven and reinforcement-driven behavior via two potential neural circuits: one in which the striatum is inaccurately trained by instruction representations coming from prefrontal cortex/hippocampus (PFC/HC), and another in which the striatum learns the environmentally based reinforcement contingencies, but is “overridden” at decision output. Both models capture the core behavioral phenomena but, because they differ fundamentally on what is learned, make distinct predictions for subsequent behavioral and neuroimaging experiments. Finally, we attempt to distinguish between the proposed computational mechanisms governing instructed behavior by fitting a series of abstract “Q-learning” and Bayesian models to subject data. The best-fitting model supports one of the neural models, suggesting the existence of a “confirmation bias” in which the PFC/HC system trains the reinforcement system by amplifying outcomes that are consistent with instructions while diminishing inconsistent outcomes. PMID:19595993

Artificial neural networks for control of a grid-connected rectifier/inverter under disturbance, dynamic and power converter switching conditions.

PubMed

Li, Shuhui; Fairbank, Michael; Johnson, Cameron; Wunsch, Donald C; Alonso, Eduardo; Proaño, Julio L

2014-04-01

Three-phase grid-connected converters are widely used in renewable and electric power system applications. Traditionally, grid-connected converters are controlled with standard decoupled d-q vector control mechanisms. However, recent studies indicate that such mechanisms show limitations in their applicability to dynamic systems. This paper investigates how to mitigate such restrictions using a neural network to control a grid-connected rectifier/inverter. The neural network implements a dynamic programming algorithm and is trained by using back-propagation through time. To enhance performance and stability under disturbance, additional strategies are adopted, including the use of integrals of error signals to the network inputs and the introduction of grid disturbance voltage to the outputs of a well-trained network. The performance of the neural-network controller is studied under typical vector control conditions and compared against conventional vector control methods, which demonstrates that the neural vector control strategy proposed in this paper is effective. Even in dynamic and power converter switching environments, the neural vector controller shows strong ability to trace rapidly changing reference commands, tolerate system disturbances, and satisfy control requirements for a faulted power system.

Fast and slow transitions in frontal ensemble activity during flexible sensorimotor behavior.

PubMed

Siniscalchi, Michael J; Phoumthipphavong, Victoria; Ali, Farhan; Lozano, Marc; Kwan, Alex C

2016-09-01

The ability to shift between repetitive and goal-directed actions is a hallmark of cognitive control. Previous studies have reported that adaptive shifts in behavior are accompanied by changes of neural activity in frontal cortex. However, neural and behavioral adaptations can occur at multiple time scales, and their relationship remains poorly defined. Here we developed an adaptive sensorimotor decision-making task for head-fixed mice, requiring them to shift flexibly between multiple auditory-motor mappings. Two-photon calcium imaging of secondary motor cortex (M2) revealed different ensemble activity states for each mapping. When adapting to a conditional mapping, transitions in ensemble activity were abrupt and occurred before the recovery of behavioral performance. By contrast, gradual and delayed transitions accompanied shifts toward repetitive responding. These results demonstrate distinct ensemble signatures associated with the start versus end of sensory-guided behavior and suggest that M2 leads in engaging goal-directed response strategies that require sensorimotor associations.

Out of Control: Attentional Selection for Orientation Is Thwarted by Properties of the Underlying Neural Mechanisms

ERIC Educational Resources Information Center

Du, Feng; Abrams, Richard A.

2012-01-01

To avoid sensory overload, people are able to selectively attend to a particular color or direction of motion while ignoring irrelevant stimuli that differ from the desired one. We show here for the first time that it is also possible to selectively attend to a specific line orientation--but with an important caveat: orientations that are…

Experimental evaluation of a neural-oscillator-driven active mass damper system

NASA Astrophysics Data System (ADS)

Iba, Daisuke; Hongu, Junichi

2014-03-01

This paper proposes a new active dynamic absorber control system for high-rise buildings using a neural oscillator and a map, which estimates the amplitude level of the oscillator, and shows some experimental results by using an apparatus, which realizes the proposed control algorithm. The proposed system decides the travel distance and direction of the auxiliary mass of the dynamic absorber using the output of oscillator, which is the filtering result of structure acceleration responses by the property of the oscillator, and Amplitude-Phase map (AP-map) for estimation of the structural response in specific frequency between synchronization region, and then, transfer the auxiliary mass to the predetermined location by using a position controller. In addition, the developed active dynamic absorber system is mounted on the top of the experimental single degree of freedom structure, which represents high-rise buildings, and consists of the auxiliary mass, a DC motor, a ball screw, a microcomputer, a laser displacement sensor, and an acceleration sensor. The proposed AP-map and the algorithm to determine the travel direction of the mass using the oscillator output are embedded in the microcomputer. This paper starts by illuminating the relation among subsystems of the proposed system with reference to a block diagram, and then, shows experimental responses of the whole system excited by earthquakes to confirm the validity of the proposed system.

Intelligent neural network and fuzzy logic control of industrial and power systems

NASA Astrophysics Data System (ADS)

Kuljaca, Ognjen

The main role played by neural network and fuzzy logic intelligent control algorithms today is to identify and compensate unknown nonlinear system dynamics. There are a number of methods developed, but often the stability analysis of neural network and fuzzy control systems was not provided. This work will meet those problems for the several algorithms. Some more complicated control algorithms included backstepping and adaptive critics will be designed. Nonlinear fuzzy control with nonadaptive fuzzy controllers is also analyzed. An experimental method for determining describing function of SISO fuzzy controller is given. The adaptive neural network tracking controller for an autonomous underwater vehicle is analyzed. A novel stability proof is provided. The implementation of the backstepping neural network controller for the coupled motor drives is described. Analysis and synthesis of adaptive critic neural network control is also provided in the work. Novel tuning laws for the system with action generating neural network and adaptive fuzzy critic are given. Stability proofs are derived for all those control methods. It is shown how these control algorithms and approaches can be used in practical engineering control. Stability proofs are given. Adaptive fuzzy logic control is analyzed. Simulation study is conducted to analyze the behavior of the adaptive fuzzy system on the different environment changes. A novel stability proof for adaptive fuzzy logic systems is given. Also, adaptive elastic fuzzy logic control architecture is described and analyzed. A novel membership function is used for elastic fuzzy logic system. The stability proof is proffered. Adaptive elastic fuzzy logic control is compared with the adaptive nonelastic fuzzy logic control. The work described in this dissertation serves as foundation on which analysis of particular representative industrial systems will be conducted. Also, it gives a good starting point for analysis of learning abilities of adaptive and neural network control systems, as well as for the analysis of the different algorithms such as elastic fuzzy systems.

Flight control with adaptive critic neural network

NASA Astrophysics Data System (ADS)

Han, Dongchen

2001-10-01

In this dissertation, the adaptive critic neural network technique is applied to solve complex nonlinear system control problems. Based on dynamic programming, the adaptive critic neural network can embed the optimal solution into a neural network. Though trained off-line, the neural network forms a real-time feedback controller. Because of its general interpolation properties, the neurocontroller has inherit robustness. The problems solved here are an agile missile control for U.S. Air Force and a midcourse guidance law for U.S. Navy. In the first three papers, the neural network was used to control an air-to-air agile missile to implement a minimum-time heading-reverse in a vertical plane corresponding to following conditions: a system without constraint, a system with control inequality constraint, and a system with state inequality constraint. While the agile missile is a one-dimensional problem, the midcourse guidance law is the first test-bed for multiple-dimensional problem. In the fourth paper, the neurocontroller is synthesized to guide a surface-to-air missile to a fixed final condition, and to a flexible final condition from a variable initial condition. In order to evaluate the adaptive critic neural network approach, the numerical solutions for these cases are also obtained by solving two-point boundary value problem with a shooting method. All of the results showed that the adaptive critic neural network could solve complex nonlinear system control problems.

When Habits Are Dangerous: Alcohol Expectancies and Habitual Decision Making Predict Relapse in Alcohol Dependence.

PubMed

Sebold, Miriam; Nebe, Stephan; Garbusow, Maria; Guggenmos, Matthias; Schad, Daniel J; Beck, Anne; Kuitunen-Paul, Soeren; Sommer, Christian; Frank, Robin; Neu, Peter; Zimmermann, Ulrich S; Rapp, Michael A; Smolka, Michael N; Huys, Quentin J M; Schlagenhauf, Florian; Heinz, Andreas

2017-12-01

Addiction is supposedly characterized by a shift from goal-directed to habitual decision making, thus facilitating automatic drug intake. The two-step task allows distinguishing between these mechanisms by computationally modeling goal-directed and habitual behavior as model-based and model-free control. In addicted patients, decision making may also strongly depend upon drug-associated expectations. Therefore, we investigated model-based versus model-free decision making and its neural correlates as well as alcohol expectancies in alcohol-dependent patients and healthy controls and assessed treatment outcome in patients. Ninety detoxified, medication-free, alcohol-dependent patients and 96 age- and gender-matched control subjects underwent functional magnetic resonance imaging during the two-step task. Alcohol expectancies were measured with the Alcohol Expectancy Questionnaire. Over a follow-up period of 48 weeks, 37 patients remained abstinent and 53 patients relapsed as indicated by the Alcohol Timeline Followback method. Patients who relapsed displayed reduced medial prefrontal cortex activation during model-based decision making. Furthermore, high alcohol expectancies were associated with low model-based control in relapsers, while the opposite was observed in abstainers and healthy control subjects. However, reduced model-based control per se was not associated with subsequent relapse. These findings suggest that poor treatment outcome in alcohol dependence does not simply result from a shift from model-based to model-free control but is instead dependent on the interaction between high drug expectancies and low model-based decision making. Reduced model-based medial prefrontal cortex signatures in those who relapse point to a neural correlate of relapse risk. These observations suggest that therapeutic interventions should target subjective alcohol expectancies. Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Electrifying the motor engram: effects of tDCS on motor learning and control

PubMed Central

de Xivry, Jean-Jacques Orban; Shadmehr, Reza

2014-01-01

Learning to control our movements accompanies neuroplasticity of motor areas of the brain. The mechanisms of neuroplasticity are diverse and produce what is referred to as the motor engram, i.e. the neural trace of the motor memory. Transcranial direct current stimulation (tDCS) alters the neural and behavioral correlates of motor learning, but its precise influence on the motor engram is unknown. In this review, we summarize the effects of tDCS on neural activity and suggest a few key principles: 1) firing rates are increased by anodal polarization and decreased by cathodal polarization, 2) anodal polarization strengthens newly formed associations, and 3) polarization modulates the memory of new/preferred firing patterns. With these principles in mind, we review the effects of tDCS on motor control, motor learning, and clinical applications. The increased spontaneous and evoked firing rates may account for the modulation of dexterity in non-learning tasks by tDCS. The facilitation of new association may account for the effect of tDCS on learning in sequence tasks while the ability of tDCS to strengthen memories of new firing patterns may underlie the effect of tDCS on consolidation of skills. We then describe the mechanisms of neuroplasticity of motor cortical areas and how they might be influenced by tDCS. We end with current challenges for the fields of brain stimulation and motor learning. PMID:25200178

From neuro-pigments to neural efficiency: The relationship between retinal carotenoids and behavioral and neuroelectric indices of cognitive control in childhood.

PubMed

Walk, Anne M; Khan, Naiman A; Barnett, Sasha M; Raine, Lauren B; Kramer, Arthur F; Cohen, Neal J; Moulton, Christopher J; Renzi-Hammond, Lisa M; Hammond, Billy R; Hillman, Charles H

2017-08-01

Lutein and zeaxanthin are plant pigments known to preferentially accumulate in neural tissue. Macular Pigment Optical Density (MPOD), a non-invasive measure of retinal carotenoids and surrogate measure of brain carotenoid concentration, has been associated with disease prevention and cognitive health. Superior MPOD status in later adulthood has been shown to provide neuroprotective effects on cognition. Given that childhood signifies a critical period for carotenoid accumulation in brain, it is likely that the beneficial impact would be evident during development, though this relationship has not been directly investigated. The present study investigated the relationship between MPOD and the behavioral and neuroelectric indices elicited during a cognitive control task in preadolescent children. 49 participants completed a modified flanker task while event-related potentials (ERPs) were recorded to assess the P3 component of the ERP waveform. MPOD was associated with both behavioral performance and P3 amplitude such that children with higher MPOD had more accurate performance and lower P3 amplitudes. These relationships were more pronounced for trials requiring greater amounts of cognitive control. These results indicate that children with higher MPOD may respond to cognitive tasks more efficiently, maintaining high performance while displaying neural indices indicative of lower cognitive load. These findings provide novel support for the neuroprotective influence of retinal carotenoids during preadolescence. Copyright © 2017 Elsevier B.V. All rights reserved.

Electrifying the motor engram: effects of tDCS on motor learning and control.

PubMed

Orban de Xivry, Jean-Jacques; Shadmehr, Reza

2014-11-01

Learning to control our movements is accompanied by neuroplasticity of motor areas of the brain. The mechanisms of neuroplasticity are diverse and produce what is referred to as the motor engram, i.e., the neural trace of the motor memory. Transcranial direct current stimulation (tDCS) alters the neural and behavioral correlates of motor learning, but its precise influence on the motor engram is unknown. In this review, we summarize the effects of tDCS on neural activity and suggest a few key principles: (1) Firing rates are increased by anodal polarization and decreased by cathodal polarization, (2) anodal polarization strengthens newly formed associations, and (3) polarization modulates the memory of new/preferred firing patterns. With these principles in mind, we review the effects of tDCS on motor control, motor learning, and clinical applications. The increased spontaneous and evoked firing rates may account for the modulation of dexterity in non-learning tasks by tDCS. The facilitation of new association may account for the effect of tDCS on learning in sequence tasks while the ability of tDCS to strengthen memories of new firing patterns may underlie the effect of tDCS on consolidation of skills. We then describe the mechanisms of neuroplasticity of motor cortical areas and how they might be influenced by tDCS. We end with current challenges for the fields of brain stimulation and motor learning.

Hybrid adaptive ascent flight control for a flexible launch vehicle

NASA Astrophysics Data System (ADS)

Lefevre, Brian D.

For the purpose of maintaining dynamic stability and improving guidance command tracking performance under off-nominal flight conditions, a hybrid adaptive control scheme is selected and modified for use as a launch vehicle flight controller. This architecture merges a model reference adaptive approach, which utilizes both direct and indirect adaptive elements, with a classical dynamic inversion controller. This structure is chosen for a number of reasons: the properties of the reference model can be easily adjusted to tune the desired handling qualities of the spacecraft, the indirect adaptive element (which consists of an online parameter identification algorithm) continually refines the estimates of the evolving characteristic parameters utilized in the dynamic inversion, and the direct adaptive element (which consists of a neural network) augments the linear feedback signal to compensate for any nonlinearities in the vehicle dynamics. The combination of these elements enables the control system to retain the nonlinear capabilities of an adaptive network while relying heavily on the linear portion of the feedback signal to dictate the dynamic response under most operating conditions. To begin the analysis, the ascent dynamics of a launch vehicle with a single 1st stage rocket motor (typical of the Ares 1 spacecraft) are characterized. The dynamics are then linearized with assumptions that are appropriate for a launch vehicle, so that the resulting equations may be inverted by the flight controller in order to compute the control signals necessary to generate the desired response from the vehicle. Next, the development of the hybrid adaptive launch vehicle ascent flight control architecture is discussed in detail. Alterations of the generic hybrid adaptive control architecture include the incorporation of a command conversion operation which transforms guidance input from quaternion form (as provided by NASA) to the body-fixed angular rate commands needed by the hybrid adaptive flight controller, development of a Newton's method based online parameter update that is modified to include a step size which regulates the rate of change in the parameter estimates, comparison of the modified Newton's method and recursive least squares online parameter update algorithms, modification of the neural network's input structure to accommodate for the nature of the nonlinearities present in a launch vehicle's ascent flight, examination of both tracking error based and modeling error based neural network weight update laws, and integration of feedback filters for the purpose of preventing harmful interaction between the flight control system and flexible structural modes. To validate the hybrid adaptive controller, a high-fidelity Ares I ascent flight simulator and a classical gain-scheduled proportional-integral-derivative (PID) ascent flight controller were obtained from the NASA Marshall Space Flight Center. The classical PID flight controller is used as a benchmark when analyzing the performance of the hybrid adaptive flight controller. Simulations are conducted which model both nominal and off-nominal flight conditions with structural flexibility of the vehicle either enabled or disabled. First, rigid body ascent simulations are performed with the hybrid adaptive controller under nominal flight conditions for the purpose of selecting the update laws which drive the indirect and direct adaptive components. With the neural network disabled, the results revealed that the recursive least squares online parameter update caused high frequency oscillations to appear in the engine gimbal commands. This is highly undesirable for long and slender launch vehicles, such as the Ares I, because such oscillation of the rocket nozzle could excite unstable structural flex modes. In contrast, the modified Newton's method online parameter update produced smooth control signals and was thus selected for use in the hybrid adaptive launch vehicle flight controller. In the simulations where the online parameter identification algorithm was disabled, the tracking error based neural network weight update law forced the network's output to diverge despite repeated reductions of the adaptive learning rate. As a result, the modeling error based neural network weight update law (which generated bounded signals) is utilized by the hybrid adaptive controller in all subsequent simulations. Comparing the PID and hybrid adaptive flight controllers under nominal flight conditions in rigid body ascent simulations showed that their tracking error magnitudes are similar for a period of time during the middle of the ascent phase. Though the PID controller performs better for a short interval around the 20 second mark, the hybrid adaptive controller performs far better from roughly 70 to 120 seconds. Elevating the aerodynamic loads by increasing the force and moment coefficients produced results very similar to the nominal case. However, applying a 5% or 10% thrust reduction to the first stage rocket motor causes the tracking error magnitude observed by the PID controller to be significantly elevated and diverge rapidly as the simulation concludes. In contrast, the hybrid adaptive controller steadily maintains smaller errors (often less than 50% of the corresponding PID value). Under the same sets of flight conditions with flexibility enabled, the results exhibit similar trends with the hybrid adaptive controller performing even better in each case. Again, the reduction of the first stage rocket motor's thrust clearly illustrated the superior robustness of the hybrid adaptive flight controller.

Synchronization Control of Neural Networks With State-Dependent Coefficient Matrices.

PubMed

Zhang, Junfeng; Zhao, Xudong; Huang, Jun

2016-11-01

This brief is concerned with synchronization control of a class of neural networks with state-dependent coefficient matrices. Being different from the existing drive-response neural networks in the literature, a novel model of drive-response neural networks is established. The concepts of uniformly ultimately bounded (UUB) synchronization and convex hull Lyapunov function are introduced. Then, by using the convex hull Lyapunov function approach, the UUB synchronization design of the drive-response neural networks is proposed, and a delay-independent control law guaranteeing the bounded synchronization of the neural networks is constructed. All present conditions are formulated in terms of bilinear matrix inequalities. By comparison, it is shown that the neural networks obtained in this brief are less conservative than those ones in the literature, and the bounded synchronization is suitable for the novel drive-response neural networks. Finally, an illustrative example is given to verify the validity of the obtained results.

Biomimetic hydrogels direct spinal progenitor cell differentiation and promote functional recovery after spinal cord injury

NASA Astrophysics Data System (ADS)

Geissler, Sydney A.; Sabin, Alexandra L.; Besser, Rachel R.; Gooden, Olivia M.; Shirk, Bryce D.; Nguyen, Quan M.; Khaing, Zin Z.; Schmidt, Christine E.

2018-04-01

Objective. Demyelination that results from disease or traumatic injury, such as spinal cord injury (SCI), can have a devastating effect on neural function and recovery. Many researchers are examining treatments to minimize demyelination by improving oligodendrocyte availability in vivo. Transplantation of stem and oligodendrocyte progenitor cells is a promising option, however, trials are plagued by undirected differentiation. Here we introduce a biomaterial that has been optimized to direct the differentiation of neural progenitor cells (NPCs) toward oligodendrocytes as a cell delivery vehicle after SCI. Approach. A collagen-based hydrogel was modified to mimic the mechanical properties of the neonatal spinal cord, and components present in the developing extracellular matrix were included to provide appropriate chemical cues to the NPCs to direct their differentiation toward oligodendrocytes. The hydrogel with cells was then transplanted into a unilateral cervical contusion model of SCI to examine the functional recovery with this treatment. Six behavioral tests and histological assessment were performed to examine the in vivo response to this treatment. Main results. Our results demonstrate that we can achieve a significant increase in oligodendrocyte differentiation of NPCs compared to standard culture conditions using a three-component biomaterial composed of collagen, hyaluronic acid, and laminin that has mechanical properties matched to those of neonatal neural tissue. Additionally, SCI rats with hydrogel transplants, with and without NPCs, showed functional recovery. Animals transplanted with hydrogels with NPCs showed significantly increased functional recovery over six weeks compared to the media control group. Significance. The three-component hydrogel presented here has the potential to provide cues to direct differentiation in vivo to encourage regeneration of the central nervous system.

Light sensitive memristor with bi-directional and wavelength-dependent conductance control

DOE Office of Scientific and Technical Information (OSTI.GOV)

Maier, P.; Hartmann, F., E-mail: fabian.hartmann@physik.uni-wuerzburg.de; Emmerling, M.

2016-07-11

We report the optical control of localized charge on positioned quantum dots in an electro-photo-sensitive memristor. Interband absorption processes in the quantum dot barrier matrix lead to photo-generated electron-hole-pairs that, depending on the applied bias voltage, charge or discharge the quantum dots and hence decrease or increase the conductance. Wavelength-dependent conductance control is observed by illumination with red and infrared light, which leads to charging via interband and discharging via intraband absorption. The presented memristor enables optical conductance control and may thus be considered for sensory applications in artificial neural networks as light-sensitive synapses or optically tunable memories.

Positive Affect Versus Reward: Emotional and Motivational Influences on Cognitive Control

PubMed Central

Chiew, Kimberly S.; Braver, Todd S.

2011-01-01

It is becoming increasingly appreciated that affective influences can contribute strongly to goal-oriented cognition and behavior. However, much work is still needed to properly characterize these influences and the mechanisms by which they contribute to cognitive processing. An important question concerns the nature of emotional manipulations (i.e., direct induction of affectively valenced subjective experience) versus motivational manipulations (e.g., delivery of performance-contingent rewards and punishments) and their impact on cognitive control. Empirical evidence suggests that both kinds of manipulations can influence cognitive control in a systematic fashion, but investigations of both have largely been conducted independently of one another. Likewise, some theoretical accounts suggest that emotion and motivation may modulate cognitive control via common neural mechanisms, while others suggest the possibility of dissociable influences. Here, we provide an analysis and synthesis of these various accounts, suggesting potentially fruitful new research directions to test competing hypotheses. PMID:22022318

Impairment in Emotional Modulation of Attention and Memory in Schizophrenia

PubMed Central

Walsh-Messinger, Julie; Ramirez, Paul Michael; Wong, Philip; Antonius, Daniel; Aujero, Nicole; McMahon, Kevin; Opler, Lewis A.; Malaspina, Dolores

2014-01-01

Emotion plays a critical role in cognition and goal-directed behavior via complex interconnections between the emotional and motivational systems. It has been hypothesized that the impairment in goal-directed behavior widely noted in schizophrenia may result from defects in the interaction between the neural (ventral) emotional system and (rostral) cortical processes. The present study examined the impact of emotion on attention and memory in schizophrenia. Twenty-five individuals with schizophrenia related psychosis and 25 healthy control subjects were administered a computerized task in which they were asked to search for target images during a rapid serial visual presentation of pictures. Target stimuli were either positive, negative, or neutral images presented at either 200ms or 700ms lag. Additionally, a visual hedonics task was used to assess differences between the schizophrenia group and controls on ratings of valence and arousal from the picture stimuli. Compared to controls, individuals with schizophrenia detected fewer emotional images under both the 200ms and 700ms lag conditions. Multivariate analyses showed that the schizophrenia group also detected fewer positive images under the 700 lag condition and fewer negative images under the 200 lag condition. Individuals with schizophrenia reported higher pleasantness and unpleasantness ratings than controls in response to neutral stimuli, while controls reported higher arousal ratings for neutral and positive stimuli compared to the schizophrenia group. These results highlight dysfunction in the neural modulation of emotion, attention, and cortical processing in schizophrenia, adding to the growing but mixed body of literature on emotion processing in the disorder. PMID:24910446

Impairment in emotional modulation of attention and memory in schizophrenia.

PubMed

Walsh-Messinger, Julie; Ramirez, Paul Michael; Wong, Philip; Antonius, Daniel; Aujero, Nicole; McMahon, Kevin; Opler, Lewis A; Malaspina, Dolores

2014-08-01

Emotion plays a critical role in cognition and goal-directed behavior via complex interconnections between the emotional and motivational systems. It has been hypothesized that the impairment in goal-directed behavior widely noted in schizophrenia may result from defects in the interaction between the neural (ventral) emotional system and (rostral) cortical processes. The present study examined the impact of emotion on attention and memory in schizophrenia. Twenty-five individuals with schizophrenia related psychosis and 25 healthy control subjects were administered a computerized task in which they were asked to search for target images during a Rapid Serial Visual Presentation of pictures. Target stimuli were either positive or negative, or neutral images presented at either 200ms or 700ms lag. Additionally, a visual hedonic task was used to assess differences between the schizophrenia group and controls on ratings of valence and arousal from the picture stimuli. Compared to controls, individuals with schizophrenia detected fewer emotional images under both the 200ms and 700ms lag conditions. Multivariate analyses showed that the schizophrenia group also detected fewer positive images under the 700ms lag condition and fewer negative images under the 200ms lag condition. Individuals with schizophrenia reported higher pleasantness and unpleasantness ratings than controls in response to neutral stimuli, while controls reported higher arousal ratings for neutral and positive stimuli compared to the schizophrenia group. These results highlight dysfunction in the neural modulation of emotion, attention, and cortical processing in schizophrenia, adding to the growing but mixed body of literature on emotion processing in the disorder. Published by Elsevier B.V.

Nonlinear adaptive inverse control via the unified model neural network

NASA Astrophysics Data System (ADS)

Jeng, Jin-Tsong; Lee, Tsu-Tian

1999-03-01

In this paper, we propose a new nonlinear adaptive inverse control via a unified model neural network. In order to overcome nonsystematic design and long training time in nonlinear adaptive inverse control, we propose the approximate transformable technique to obtain a Chebyshev Polynomials Based Unified Model (CPBUM) neural network for the feedforward/recurrent neural networks. It turns out that the proposed method can use less training time to get an inverse model. Finally, we apply this proposed method to control magnetic bearing system. The experimental results show that the proposed nonlinear adaptive inverse control architecture provides a greater flexibility and better performance in controlling magnetic bearing systems.

Finite-Time Adaptive Control for a Class of Nonlinear Systems With Nonstrict Feedback Structure.

PubMed

Sun, Yumei; Chen, Bing; Lin, Chong; Wang, Honghong

2017-09-18

This paper focuses on finite-time adaptive neural tracking control for nonlinear systems in nonstrict feedback form. A semiglobal finite-time practical stability criterion is first proposed. Correspondingly, the finite-time adaptive neural control strategy is given by using this criterion. Unlike the existing results on adaptive neural/fuzzy control, the proposed adaptive neural controller guarantees that the tracking error converges to a sufficiently small domain around the origin in finite time, and other closed-loop signals are bounded. At last, two examples are used to test the validity of our results.

Global synchronization of memristive neural networks subject to random disturbances via distributed pinning control.

PubMed

Guo, Zhenyuan; Yang, Shaofu; Wang, Jun

2016-12-01

This paper presents theoretical results on global exponential synchronization of multiple memristive neural networks in the presence of external noise by means of two types of distributed pinning control. The multiple memristive neural networks are coupled in a general structure via a nonlinear function, which consists of a linear diffusive term and a discontinuous sign term. A pinning impulsive control law is introduced in the coupled system to synchronize all neural networks. Sufficient conditions are derived for ascertaining global exponential synchronization in mean square. In addition, a pinning adaptive control law is developed to achieve global exponential synchronization in mean square. Both pinning control laws utilize only partial state information received from the neighborhood of the controlled neural network. Simulation results are presented to substantiate the theoretical results. Copyright © 2016 Elsevier Ltd. All rights reserved.

How low can you go? Physical production mechanism of elephant infrasonic vocalizations.

PubMed

Herbst, Christian T; Stoeger, Angela S; Frey, Roland; Lohscheller, Jörg; Titze, Ingo R; Gumpenberger, Michaela; Fitch, W Tecumseh

2012-08-03

Elephants can communicate using sounds below the range of human hearing ("infrasounds" below 20 hertz). It is commonly speculated that these vocalizations are produced in the larynx, either by neurally controlled muscle twitching (as in cat purring) or by flow-induced self-sustained vibrations of the vocal folds (as in human speech and song). We used direct high-speed video observations of an excised elephant larynx to demonstrate flow-induced self-sustained vocal fold vibration in the absence of any neural signals, thus excluding the need for any "purring" mechanism. The observed physical principles of voice production apply to a wide variety of mammals, extending across a remarkably large range of fundamental frequencies and body sizes, spanning more than five orders of magnitude.

The effect of alcohol use on human adolescent brain structures and systems.

PubMed

Squeglia, Lindsay M; Jacobus, Joanna; Tapert, Susan F

2014-01-01

This article reviews the neurocognitive and neuroimaging literature regarding the effect of alcohol use on human adolescent brain structure and function. Adolescents who engage in heavy alcohol use, even at subdiagnostic levels, show differences in brain structure, function, and behavior when compared with non-drinking controls. Preliminary longitudinal studies have helped disentangle premorbid factors from consequences associated with drinking. Neural abnormalities and cognitive disadvantages both appear to predate drinking, particularly in youth who have a family history of alcoholism, and are directly related to the neurotoxic effect of alcohol use. Binge drinking and withdrawal and hangover symptoms have been associated with the greatest neural abnormalities during adolescence, particularly in frontal, parietal, and temporal regions. © 2014 Elsevier B.V. All rights reserved.

Neural systems implicated in obesity as an addictive disorder: from biological to behavioral mechanisms.

PubMed

Schulte, Erica M; Yokum, Sonja; Potenza, Marc N; Gearhardt, Ashley N

2016-01-01

Contributing factors to obesity have been identified, yet prevention and treatment efforts have had limited long-term success. It has recently been suggested that some individuals may experience an addictive-like response to certain foods, such as losing control over consumption and continued consumption despite negative consequences. In support, shared biological and behavioral features seem to exist between "food addiction" and traditional substance-use disorders. "Food addiction" may be another important contributor to obesity. The current chapter reviews existing literature regarding neural systems implicated similarly in obesity and addiction, discusses unique considerations for addictive-like eating, and proposes directions for future research regarding "food addiction" as an emerging construct for addiction medicine. © 2016 Elsevier B.V. All rights reserved.

Reconfigurable Flight Control Design using a Robust Servo LQR and Radial Basis Function Neural Networks

NASA Technical Reports Server (NTRS)

Burken, John J.

2005-01-01

This viewgraph presentation reviews the use of a Robust Servo Linear Quadratic Regulator (LQR) and a Radial Basis Function (RBF) Neural Network in reconfigurable flight control designs in adaptation to a aircraft part failure. The method uses a robust LQR servomechanism design with model Reference adaptive control, and RBF neural networks. During the failure the LQR servomechanism behaved well, and using the neural networks improved the tracking.

Multi-layer neural networks for robot control

NASA Technical Reports Server (NTRS)

Pourboghrat, Farzad

1989-01-01

Two neural learning controller designs for manipulators are considered. The first design is based on a neural inverse-dynamics system. The second is the combination of the first one with a neural adaptive state feedback system. Both types of controllers enable the manipulator to perform any given task very well after a period of training and to do other untrained tasks satisfactorily. The second design also enables the manipulator to compensate for unpredictable perturbations.

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.

Structural reliability calculation method based on the dual neural network and direct integration method.

PubMed

Li, Haibin; He, Yun; Nie, Xiaobo

2018-01-01

Structural reliability analysis under uncertainty is paid wide attention by engineers and scholars due to reflecting the structural characteristics and the bearing actual situation. The direct integration method, started from the definition of reliability theory, is easy to be understood, but there are still mathematics difficulties in the calculation of multiple integrals. Therefore, a dual neural network method is proposed for calculating multiple integrals in this paper. Dual neural network consists of two neural networks. The neural network A is used to learn the integrand function, and the neural network B is used to simulate the original function. According to the derivative relationships between the network output and the network input, the neural network B is derived from the neural network A. On this basis, the performance function of normalization is employed in the proposed method to overcome the difficulty of multiple integrations and to improve the accuracy for reliability calculations. The comparisons between the proposed method and Monte Carlo simulation method, Hasofer-Lind method, the mean value first-order second moment method have demonstrated that the proposed method is an efficient and accurate reliability method for structural reliability problems.

A Cognitive Framework for Understanding and Improving Interference Resolution in the Brain

PubMed Central

Mishra, Jyoti; Anguera, Joaquin A.; Ziegler, David A.; Gazzaley, Adam

2014-01-01

All of us are familiar with the negative impact of interference on achieving our task goals. We are referring to interference by information, which either impinges on our senses from an external environmental source or is internally generated by our thoughts. Informed by more than a decade of research on the cognitive and neural processing of interference, we have developed a framework for understanding how interference impacts our neural systems and especially how it is regulated and suppressed during efficient on-task performance. Importantly, externally and internally generated interferences have distinct neural signatures, and further, distinct neural processing emerges depending on whether individuals must ignore and suppress the interference, as for distractions, or engage with them in a secondary task, as during multitasking. Here, we elaborate on this cognitive framework and how it changes throughout the human lifespan, focusing mostly on research evidence from younger adults and comparing these findings to data from older adults, children, and cognitively impaired populations. With insights gleaned from our growing understanding, we then describe three novel translational efforts in our lab directed at improving distinct aspects of interference resolution using cognitive training. Critically, these training approaches were specifically developed to target improved interference resolution based on neuroplasticity principles and have shown much success in randomized controlled first version evaluations in healthy aging. Our results show not only on-task training improvements but also robust generalization of benefit to other cognitive control abilities. This research showcases how an in-depth understanding of neural mechanisms can then inform the development of effective deficit-targeted interventions, which can in turn benefit both healthy and cognitively impaired populations. PMID:24309262

Mammalian neurogenesis requires Treacle-Plk1 for precise control of spindle orientation, mitotic progression, and maintenance of neural progenitor cells.

PubMed

Sakai, Daisuke; Dixon, Jill; Dixon, Michael J; Trainor, Paul A

2012-01-01

The cerebral cortex is a specialized region of the brain that processes cognitive, motor, somatosensory, auditory, and visual functions. Its characteristic architecture and size is dependent upon the number of neurons generated during embryogenesis and has been postulated to be governed by symmetric versus asymmetric cell divisions, which mediate the balance between progenitor cell maintenance and neuron differentiation, respectively. The mechanistic importance of spindle orientation remains controversial, hence there is considerable interest in understanding how neural progenitor cell mitosis is controlled during neurogenesis. We discovered that Treacle, which is encoded by the Tcof1 gene, is a novel centrosome- and kinetochore-associated protein that is critical for spindle fidelity and mitotic progression. Tcof1/Treacle loss-of-function disrupts spindle orientation and cell cycle progression, which perturbs the maintenance, proliferation, and localization of neural progenitors during cortical neurogenesis. Consistent with this, Tcof1(+/-) mice exhibit reduced brain size as a consequence of defects in neural progenitor maintenance. We determined that Treacle elicits its effect via a direct interaction with Polo-like kinase1 (Plk1), and furthermore we discovered novel in vivo roles for Plk1 in governing mitotic progression and spindle orientation in the developing mammalian cortex. Increased asymmetric cell division, however, did not promote increased neuronal differentiation. Collectively our research has therefore identified Treacle and Plk1 as novel in vivo regulators of spindle fidelity, mitotic progression, and proliferation in the maintenance and localization of neural progenitor cells. Together, Treacle and Plk1 are critically required for proper cortical neurogenesis, which has important implications in the regulation of mammalian brain size and the pathogenesis of congenital neurodevelopmental disorders such as microcephaly.

Mammalian Neurogenesis Requires Treacle-Plk1 for Precise Control of Spindle Orientation, Mitotic Progression, and Maintenance of Neural Progenitor Cells

PubMed Central

Sakai, Daisuke; Dixon, Jill; Dixon, Michael J.; Trainor, Paul A.

2012-01-01

The cerebral cortex is a specialized region of the brain that processes cognitive, motor, somatosensory, auditory, and visual functions. Its characteristic architecture and size is dependent upon the number of neurons generated during embryogenesis and has been postulated to be governed by symmetric versus asymmetric cell divisions, which mediate the balance between progenitor cell maintenance and neuron differentiation, respectively. The mechanistic importance of spindle orientation remains controversial, hence there is considerable interest in understanding how neural progenitor cell mitosis is controlled during neurogenesis. We discovered that Treacle, which is encoded by the Tcof1 gene, is a novel centrosome- and kinetochore-associated protein that is critical for spindle fidelity and mitotic progression. Tcof1/Treacle loss-of-function disrupts spindle orientation and cell cycle progression, which perturbs the maintenance, proliferation, and localization of neural progenitors during cortical neurogenesis. Consistent with this, Tcof1 +/− mice exhibit reduced brain size as a consequence of defects in neural progenitor maintenance. We determined that Treacle elicits its effect via a direct interaction with Polo-like kinase1 (Plk1), and furthermore we discovered novel in vivo roles for Plk1 in governing mitotic progression and spindle orientation in the developing mammalian cortex. Increased asymmetric cell division, however, did not promote increased neuronal differentiation. Collectively our research has therefore identified Treacle and Plk1 as novel in vivo regulators of spindle fidelity, mitotic progression, and proliferation in the maintenance and localization of neural progenitor cells. Together, Treacle and Plk1 are critically required for proper cortical neurogenesis, which has important implications in the regulation of mammalian brain size and the pathogenesis of congenital neurodevelopmental disorders such as microcephaly. PMID:22479190

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. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

A modular architecture for transparent computation in recurrent neural networks.

PubMed

Carmantini, Giovanni S; Beim Graben, Peter; Desroches, Mathieu; Rodrigues, Serafim

2017-01-01

Computation is classically studied in terms of automata, formal languages and algorithms; yet, the relation between neural dynamics and symbolic representations and operations is still unclear in traditional eliminative connectionism. Therefore, we suggest a unique perspective on this central issue, to which we would like to refer as transparent connectionism, by proposing accounts of how symbolic computation can be implemented in neural substrates. In this study we first introduce a new model of dynamics on a symbolic space, the versatile shift, showing that it supports the real-time simulation of a range of automata. We then show that the Gödelization of versatile shifts defines nonlinear dynamical automata, dynamical systems evolving on a vectorial space. Finally, we present a mapping between nonlinear dynamical automata and recurrent artificial neural networks. The mapping defines an architecture characterized by its granular modularity, where data, symbolic operations and their control are not only distinguishable in activation space, but also spatially localizable in the network itself, while maintaining a distributed encoding of symbolic representations. The resulting networks simulate automata in real-time and are programmed directly, in the absence of network training. To discuss the unique characteristics of the architecture and their consequences, we present two examples: (i) the design of a Central Pattern Generator from a finite-state locomotive controller, and (ii) the creation of a network simulating a system of interactive automata that supports the parsing of garden-path sentences as investigated in psycholinguistics experiments. Copyright © 2016 Elsevier Ltd. All rights reserved.

Dissociable neural systems underwrite logical reasoning in the context of induced emotions with positive and negative valence.

PubMed

Smith, Kathleen W; Vartanian, Oshin; Goel, Vinod

2014-01-01

How emotions influence syllogistic reasoning is not well understood. fMRI was employed to investigate the effects of induced positive or negative emotion on syllogistic reasoning. Specifically, on a trial-by-trial basis participants were exposed to a positive, negative, or neutral picture, immediately prior to engagement in a reasoning task. After viewing and rating the valence and intensity of each picture, participants indicated by keypress whether or not the conclusion of the syllogism followed logically from the premises. The content of all syllogisms was neutral, and the influence of belief-bias was controlled for in the study design. Emotion did not affect reasoning performance, although there was a trend in the expected direction based on accuracy rates for the positive (63%) and negative (64%) versus neutral (70%) condition. Nevertheless, exposure to positive and negative pictures led to dissociable patterns of neural activation during reasoning. Therefore, the neural basis of deductive reasoning differs as a function of the valence of the context.

The Neural Basis of Typewriting: A Functional MRI Study.

PubMed

Higashiyama, Yuichi; Takeda, Katsuhiko; Someya, Yoshiaki; Kuroiwa, Yoshiyuki; Tanaka, Fumiaki

2015-01-01

To investigate the neural substrate of typewriting Japanese words and to detect the difference between the neural substrate of typewriting and handwriting, we conducted a functional magnetic resonance imaging (fMRI) study in 16 healthy volunteers. All subjects were skillful touch typists and performed five tasks: a typing task, a writing task, a reading task, and two control tasks. Three brain regions were activated during both the typing and the writing tasks: the left superior parietal lobule, the left supramarginal gyrus, and the left premotor cortex close to Exner's area. Although typing and writing involved common brain regions, direct comparison between the typing and the writing task revealed greater left posteromedial intraparietal cortex activation in the typing task. In addition, activity in the left premotor cortex was more rostral in the typing task than in the writing task. These findings suggest that, although the brain circuits involved in Japanese typewriting are almost the same as those involved in handwriting, there are brain regions that are specific for typewriting.

Dissociable Neural Systems Underwrite Logical Reasoning in the Context of Induced Emotions with Positive and Negative Valence

PubMed Central

Smith, Kathleen W.; Vartanian, Oshin; Goel, Vinod

2014-01-01

How emotions influence syllogistic reasoning is not well understood. fMRI was employed to investigate the effects of induced positive or negative emotion on syllogistic reasoning. Specifically, on a trial-by-trial basis participants were exposed to a positive, negative, or neutral picture, immediately prior to engagement in a reasoning task. After viewing and rating the valence and intensity of each picture, participants indicated by keypress whether or not the conclusion of the syllogism followed logically from the premises. The content of all syllogisms was neutral, and the influence of belief-bias was controlled for in the study design. Emotion did not affect reasoning performance, although there was a trend in the expected direction based on accuracy rates for the positive (63%) and negative (64%) versus neutral (70%) condition. Nevertheless, exposure to positive and negative pictures led to dissociable patterns of neural activation during reasoning. Therefore, the neural basis of deductive reasoning differs as a function of the valence of the context. PMID:25294997

The Neural Basis of Typewriting: A Functional MRI Study

PubMed Central

Higashiyama, Yuichi; Takeda, Katsuhiko; Someya, Yoshiaki; Kuroiwa, Yoshiyuki; Tanaka, Fumiaki

2015-01-01

To investigate the neural substrate of typewriting Japanese words and to detect the difference between the neural substrate of typewriting and handwriting, we conducted a functional magnetic resonance imaging (fMRI) study in 16 healthy volunteers. All subjects were skillful touch typists and performed five tasks: a typing task, a writing task, a reading task, and two control tasks. Three brain regions were activated during both the typing and the writing tasks: the left superior parietal lobule, the left supramarginal gyrus, and the left premotor cortex close to Exner’s area. Although typing and writing involved common brain regions, direct comparison between the typing and the writing task revealed greater left posteromedial intraparietal cortex activation in the typing task. In addition, activity in the left premotor cortex was more rostral in the typing task than in the writing task. These findings suggest that, although the brain circuits involved in Japanese typewriting are almost the same as those involved in handwriting, there are brain regions that are specific for typewriting. PMID:26218431

Axonal Control of the Adult Neural Stem Cell Niche

PubMed Central

Tong, Cheuk Ka; Chen, Jiadong; Cebrián-Silla, Arantxa; Mirzadeh, Zaman; Obernier, Kirsten; Guinto, Cristina D.; Tecott, Laurence H.; García-Verdugo, Jose Manuel; Kriegstein, Arnold; Alvarez-Buylla, Arturo

2014-01-01

SUMMARY The ventricular-subventricular zone (V-SVZ) is an extensive germinal niche containing neural stem cells (NSC) in the walls of the lateral ventricles of the adult brain. How the adult brain’s neural activity influences the behavior of adult NSCs remains largely unknown. We show that serotonergic (5HT) axons originating from a small group of neurons in the raphe form an extensive plexus on most of the ventricular walls. Electron microscopy revealed intimate contacts between 5HT axons and NSCs (B1) or ependymal cells (E1) and these cells were labeled by a transsynaptic viral tracer injected into the raphe. B1 cells express the 5HT receptors 2C and 5A. Electrophysiology showed that activation of these receptors in B1 cells induced small inward currents. Intraventricular infusion of 5HT2C agonist or antagonist increased or decreased V-SVZ proliferation, respectively. These results indicate that supraependymal 5HT axons directly interact with NSCs to regulate neurogenesis via 5HT2C. PMID:24561083

MEART: The Semi-Living Artist

PubMed Central

Bakkum, Douglas J.; Gamblen, Philip M.; Ben-Ary, Guy; Chao, Zenas C.; Potter, Steve M.

2007-01-01

Here, we and others describe an unusual neurorobotic project, a merging of art and science called MEART, the semi-living artist. We built a pneumatically actuated robotic arm to create drawings, as controlled by a living network of neurons from rat cortex grown on a multi-electrode array (MEA). Such embodied cultured networks formed a real-time closed-loop system which could now behave and receive electrical stimulation as feedback on its behavior. We used MEART and simulated embodiments, or animats, to study the network mechanisms that produce adaptive, goal-directed behavior. This approach to neural interfacing will help instruct the design of other hybrid neural-robotic systems we call hybrots. The interfacing technologies and algorithms developed have potential applications in responsive deep brain stimulation systems and for motor prosthetics using sensory components. In a broader context, MEART educates the public about neuroscience, neural interfaces, and robotics. It has paved the way for critical discussions on the future of bio-art and of biotechnology. PMID:18958276

Spatio-temporal neural stem cell behavior that leads to both perfect and imperfect structural brain regeneration in adult newts.

PubMed

Urata, Yuko; Yamashita, Wataru; Inoue, Takeshi; Agata, Kiyokazu

2018-06-14

Adult newts can regenerate large parts of their brain from adult neural stem cells (NSCs), but how adult NSCs reorganize brain structures during regeneration remains unclear. In development, elaborate brain structures are produced under broadly coordinated regulations of embryonic NSCs in the neural tube, whereas brain regeneration entails exquisite control of the reestablishment of certain brain parts, suggesting a yet-unknown mechanism directs NSCs upon partial brain excision. Here we report that upon one-quarter excision of the adult newt ( Pleurodeles waltl ) mesencephalon, active participation of local NSCs around specific brain subregions' boundaries leads to some imperfect and some perfect brain regeneration along an individual's rostrocaudal axis. Regeneration phenotypes depend on how the wound closing occurs using local NSCs, and perfect regeneration replicates development-like processes but takes more than one year. Our findings indicate that newt brain regeneration is supported by modularity of boundary-domain NSCs with self-organizing ability in neighboring fields. © 2018. Published by The Company of Biologists Ltd.

Artificial Neural Network Based Mission Planning Mechanism for Spacecraft

NASA Astrophysics Data System (ADS)

Li, Zhaoyu; Xu, Rui; Cui, Pingyuan; Zhu, Shengying

2018-04-01

The ability to plan and react fast in dynamic space environments is central to intelligent behavior of spacecraft. For space and robotic applications, many planners have been used. But it is difficult to encode the domain knowledge and directly use existing techniques such as heuristic to improve the performance of the application systems. Therefore, regarding planning as an advanced control problem, this paper first proposes an autonomous mission planning and action selection mechanism through a multiple layer perceptron neural network approach to select actions in planning process and improve efficiency. To prove the availability and effectiveness, we use autonomous mission planning problems of the spacecraft, which is a sophisticated system with complex subsystems and constraints as an example. Simulation results have shown that artificial neural networks (ANNs) are usable for planning problems. Compared with the existing planning method in EUROPA, the mechanism using ANNs is more efficient and can guarantee stable performance. Therefore, the mechanism proposed in this paper is more suitable for planning problems of spacecraft that require real time and stability.

The let-7 microRNA target gene, Mlin41/Trim71 is required for mouse embryonic survival and neural tube closure

PubMed Central

Schulman, Betsy R. Maller; Liang, Xianping; Stahlhut, Carlos; DelConte, Casey; Stefani, Giovanni; Slack, Frank J.

2010-01-01

In the nematode Caenorhabditis elegans, the let-7 microRNA (miRNA) controls the timing of key developmental events and terminal differentiation in part by directly regulating lin-41. C. elegans lin-41 mutants display precocious cell cycle exit and terminal differentiation of epidermal skin cells. lin-41 orthologues are found in more complex organisms including both mice and humans, but their roles are not known. We generated Mlin41 mouse mutants to ascertain a functional role for Mlin41. Strong loss of function Mlin41 gene-trap mutants demonstrated a striking neural tube closure defect during development, and embryonic lethality. Like C. elegans lin-41, Mlin41 also appears to be regulated by the let-7 and mir-125 miRNAs. Since Mlin41 is required for neural tube closure and survival it points to human lin-41 (HLIN41/TRIM71) as a potential human development and disease gene. PMID:19098426

An experimental vestibular neural prosthesis: design and preliminary results with rhesus monkeys stimulated with modulated pulses.

PubMed

Nie, Kaibao; Ling, Leo; Bierer, Steven M; Kaneko, Chris R S; Fuchs, Albert F; Oxford, Trey; Rubinstein, Jay T; Phillips, James O

2013-06-01

A vestibular neural prosthesis was designed on the basis of a cochlear implant for treatment of Meniere's disease and other vestibular disorders. Computer control software was developed to generate patterned pulse stimuli for exploring optimal parameters to activate the vestibular nerve. Two rhesus monkeys were implanted with the prototype vestibular prosthesis and they were behaviorally evaluated post implantation surgery. Horizontal and vertical eye movement responses to patterned electrical pulse stimulations were collected on both monkeys. Pulse amplitude modulated (PAM) and pulse rate modulated (PRM) trains were applied to the lateral canal of each implanted animal. Robust slow-phase nystagmus responses following the PAM or PRM modulation pattern were observed in both implanted monkeys in the direction consistent with the activation of the implanted canal. Both PAM and PRM pulse trains can elicit a significant amount of in-phase modulated eye velocity changes and they could potentially be used for efficiently coding head rotational signals in future vestibular neural prostheses.

Pilots Rate Augmented Generalized Predictive Control for Reconfiguration

NASA Technical Reports Server (NTRS)

Soloway, Don; Haley, Pam

2004-01-01

The objective of this paper is to report the results from the research being conducted in reconfigurable fight controls at NASA Ames. A study was conducted with three NASA Dryden test pilots to evaluate two approaches of reconfiguring an aircraft's control system when failures occur in the control surfaces and engine. NASA Ames is investigating both a Neural Generalized Predictive Control scheme and a Neural Network based Dynamic Inverse controller. This paper highlights the Predictive Control scheme where a simple augmentation to reduce zero steady-state error led to the neural network predictor model becoming redundant for the task. Instead of using a neural network predictor model, a nominal single point linear model was used and then augmented with an error corrector. This paper shows that the Generalized Predictive Controller and the Dynamic Inverse Neural Network controller perform equally well at reconfiguration, but with less rate requirements from the actuators. Also presented are the pilot ratings for each controller for various failure scenarios and two samples of the required control actuation during reconfiguration. Finally, the paper concludes by stepping through the Generalized Predictive Control's reconfiguration process for an elevator failure.

Engineering platform and experimental protocol for design and evaluation of a neurally-controlled powered transfemoral prosthesis.

PubMed

Zhang, Fan; Liu, Ming; Harper, Stephen; Lee, Michael; Huang, He

2014-07-22

To enable intuitive operation of powered artificial legs, an interface between user and prosthesis that can recognize the user's movement intent is desired. A novel neural-machine interface (NMI) based on neuromuscular-mechanical fusion developed in our previous study has demonstrated a great potential to accurately identify the intended movement of transfemoral amputees. However, this interface has not yet been integrated with a powered prosthetic leg for true neural control. This study aimed to report (1) a flexible platform to implement and optimize neural control of powered lower limb prosthesis and (2) an experimental setup and protocol to evaluate neural prosthesis control on patients with lower limb amputations. First a platform based on a PC and a visual programming environment were developed to implement the prosthesis control algorithms, including NMI training algorithm, NMI online testing algorithm, and intrinsic control algorithm. To demonstrate the function of this platform, in this study the NMI based on neuromuscular-mechanical fusion was hierarchically integrated with intrinsic control of a prototypical transfemoral prosthesis. One patient with a unilateral transfemoral amputation was recruited to evaluate our implemented neural controller when performing activities, such as standing, level-ground walking, ramp ascent, and ramp descent continuously in the laboratory. A novel experimental setup and protocol were developed in order to test the new prosthesis control safely and efficiently. The presented proof-of-concept platform and experimental setup and protocol could aid the future development and application of neurally-controlled powered artificial legs.

Neuronify: An Educational Simulator for Neural Circuits.

PubMed

Dragly, Svenn-Arne; Hobbi Mobarhan, Milad; Våvang Solbrå, Andreas; Tennøe, Simen; Hafreager, Anders; Malthe-Sørenssen, Anders; Fyhn, Marianne; Hafting, Torkel; Einevoll, Gaute T

2017-01-01

Educational software (apps) can improve science education by providing an interactive way of learning about complicated topics that are hard to explain with text and static illustrations. However, few educational apps are available for simulation of neural networks. Here, we describe an educational app, Neuronify, allowing the user to easily create and explore neural networks in a plug-and-play simulation environment. The user can pick network elements with adjustable parameters from a menu, i.e., synaptically connected neurons modelled as integrate-and-fire neurons and various stimulators (current sources, spike generators, visual, and touch) and recording devices (voltmeter, spike detector, and loudspeaker). We aim to provide a low entry point to simulation-based neuroscience by allowing students with no programming experience to create and simulate neural networks. To facilitate the use of Neuronify in teaching, a set of premade common network motifs is provided, performing functions such as input summation, gain control by inhibition, and detection of direction of stimulus movement. Neuronify is developed in C++ and QML using the cross-platform application framework Qt and runs on smart phones (Android, iOS) and tablet computers as well personal computers (Windows, Mac, Linux).

Medical Concept Normalization in Social Media Posts with Recurrent Neural Networks.

PubMed

Tutubalina, Elena; Miftahutdinov, Zulfat; Nikolenko, Sergey; Malykh, Valentin

2018-06-12

Text mining of scientific libraries and social media has already proven itself as a reliable tool for drug repurposing and hypothesis generation. The task of mapping a disease mention to a concept in a controlled vocabulary, typically to the standard thesaurus in the Unified Medical Language System (UMLS), is known as medical concept normalization. This task is challenging due to the differences in the use of medical terminology between health care professionals and social media texts coming from the lay public. To bridge this gap, we use sequence learning with recurrent neural networks and semantic representation of one- or multi-word expressions: we develop end-to-end architectures directly tailored to the task, including bidirectional Long Short-Term Memory, Gated Recurrent Units with an attention mechanism, and additional semantic similarity features based on UMLS. Our evaluation against a standard benchmark shows that recurrent neural networks improve results over an effective baseline for classification based on convolutional neural networks. A qualitative examination of mentions discovered in a dataset of user reviews collected from popular online health information platforms as well as a quantitative evaluation both show improvements in the semantic representation of health-related expressions in social media. Copyright © 2018. Published by Elsevier Inc.

Sex, lies and fMRI--gender differences in neural basis of deception.

PubMed

Marchewka, Artur; Jednorog, Katarzyna; Falkiewicz, Marcel; Szeszkowski, Wojciech; Grabowska, Anna; Szatkowska, Iwona

2012-01-01

Deception has always been a part of human communication as it helps to promote self-presentation. Although both men and women are equally prone to try to manage their appearance, their strategies, motivation and eagerness may be different. Here, we asked if lying could be influenced by gender on both the behavioral and neural levels. To test whether the hypothesized gender differences in brain activity related to deceptive responses were caused by differential socialization in men and women, we administered the Gender Identity Inventory probing the participants' subjective social sex role. In an fMRI session, participants were instructed either to lie or to tell the truth while answering a questionnaire focusing on general and personal information. Only for personal information, we found differences in neural responses during instructed deception in men and women. The women vs. men direct contrast revealed no significant differences in areas of activation, but men showed higher BOLD signal compared to women in the left middle frontal gyrus (MFG). Moreover, this effect remained unchanged when self-reported psychological gender was controlled for. Thus, our study showed that gender differences in the neural processes engaged during falsifying personal information might be independent from socialization.

Sex, Lies and fMRI—Gender Differences in Neural Basis of Deception

PubMed Central

Falkiewicz, Marcel; Szeszkowski, Wojciech; Grabowska, Anna; Szatkowska, Iwona

2012-01-01

Deception has always been a part of human communication as it helps to promote self-presentation. Although both men and women are equally prone to try to manage their appearance, their strategies, motivation and eagerness may be different. Here, we asked if lying could be influenced by gender on both the behavioral and neural levels. To test whether the hypothesized gender differences in brain activity related to deceptive responses were caused by differential socialization in men and women, we administered the Gender Identity Inventory probing the participants’ subjective social sex role. In an fMRI session, participants were instructed either to lie or to tell the truth while answering a questionnaire focusing on general and personal information. Only for personal information, we found differences in neural responses during instructed deception in men and women. The women vs. men direct contrast revealed no significant differences in areas of activation, but men showed higher BOLD signal compared to women in the left middle frontal gyrus (MFG). Moreover, this effect remained unchanged when self-reported psychological gender was controlled for. Thus, our study showed that gender differences in the neural processes engaged during falsifying personal information might be independent from socialization. PMID:22952631

Neural retina-specific Aldh1a1 controls dorsal choroidal vascular development via Sox9 expression in retinal pigment epithelial cells.

PubMed

Goto, So; Onishi, Akishi; Misaki, Kazuyo; Yonemura, Shigenobu; Sugita, Sunao; Ito, Hiromi; Ohigashi, Yoko; Ema, Masatsugu; Sakaguchi, Hirokazu; Nishida, Kohji; Takahashi, Masayo

2018-04-03

VEGF secreted from retinal pigment epithelial (RPE) cells is responsible for the choroidal vascular development; however, the molecular regulatory mechanism is unclear. We found that Aldh1a1 -/- mice showed choroidal hypoplasia with insufficient vascularization in the dorsal region, although Aldh1a1, an enzyme that synthesizes retinoic acids (RAs), is expressed in the dorsal neural retina, not in the RPE/choroid complex. The level of VEGF in the RPE/choroid was significantly decreased in Aldh1a1 -/- mice, and RA-dependent enhancement of VEGF was observed in primary RPE cells. An RA-deficient diet resulted in dorsal choroidal hypoplasia, and simple RA treatment of Aldh1a1 -/- pregnant females suppressed choroid hypoplasia in their offspring. We also found downregulation of Sox9 in the dorsal neural retina and RPE of Aldh1a1 -/- mice and RPE-specific disruption of Sox9 phenocopied Aldh1a1 -/- choroidal development. These results suggest that RAs produced by Aldh1a1 in the neural retina directs dorsal choroidal vascular development via Sox9 upregulation in the dorsal RPE cells to enhance RPE-derived VEGF secretion. © 2018, Goto et al.

Neuronify: An Educational Simulator for Neural Circuits

PubMed Central

Hafreager, Anders; Malthe-Sørenssen, Anders; Fyhn, Marianne

2017-01-01

Abstract Educational software (apps) can improve science education by providing an interactive way of learning about complicated topics that are hard to explain with text and static illustrations. However, few educational apps are available for simulation of neural networks. Here, we describe an educational app, Neuronify, allowing the user to easily create and explore neural networks in a plug-and-play simulation environment. The user can pick network elements with adjustable parameters from a menu, i.e., synaptically connected neurons modelled as integrate-and-fire neurons and various stimulators (current sources, spike generators, visual, and touch) and recording devices (voltmeter, spike detector, and loudspeaker). We aim to provide a low entry point to simulation-based neuroscience by allowing students with no programming experience to create and simulate neural networks. To facilitate the use of Neuronify in teaching, a set of premade common network motifs is provided, performing functions such as input summation, gain control by inhibition, and detection of direction of stimulus movement. Neuronify is developed in C++ and QML using the cross-platform application framework Qt and runs on smart phones (Android, iOS) and tablet computers as well personal computers (Windows, Mac, Linux). PMID:28321440

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.

"Roadblocks" Revisited: Neural Change, Stuttering Treatment, and Recovery from Stuttering

ERIC Educational Resources Information Center

Ingham, Roger J.; Finn, Patrick; Bothe, Anne K.

2005-01-01

In light of emerging findings concerning untreated recovery and neural plasticity, this paper re-examines the viability of an NIH conference recommendation [Cooper, J. A. (1990). Research directions in stuttering: Consensus and conflict. In Cooper, J. A. (Ed.), "Research needs in stuttering: Roadblocks and future directions" (pp. 98-100).…

Anaesthetic Tricaine Acts Preferentially on Neural Voltage-Gated Sodium Channels and Fails to Block Directly Evoked Muscle Contraction

PubMed Central

Attili, Seetharamaiah; Hughes, Simon M.

2014-01-01

Movements in animals arise through concerted action of neurons and skeletal muscle. General anaesthetics prevent movement and cause loss of consciousness by blocking neural function. Anaesthetics of the amino amide-class are thought to act by blockade of voltage-gated sodium channels. In fish, the commonly used anaesthetic tricaine methanesulphonate, also known as 3-aminobenzoic acid ethyl ester, metacaine or MS-222, causes loss of consciousness. However, its role in blocking action potentials in distinct excitable cells is unclear, raising the possibility that tricaine could act as a neuromuscular blocking agent directly causing paralysis. Here we use evoked electrical stimulation to show that tricaine efficiently blocks neural action potentials, but does not prevent directly evoked muscle contraction. Nifedipine-sensitive L-type Cav channels affecting movement are also primarily neural, suggesting that muscle Nav channels are relatively insensitive to tricaine. These findings show that tricaine used at standard concentrations in zebrafish larvae does not paralyse muscle, thereby diminishing concern that a direct action on muscle could mask a lack of general anaesthesia. PMID:25090007

Neural Control of Rising and Falling Tones in Mandarin Speakers Who Stutter

ERIC Educational Resources Information Center

Howell, Peter; Jiang, Jing; Peng, Danling; Lu, Chunming

2012-01-01

Neural control of rising and falling tones in Mandarin people who stutter (PWS) was examined by comparing with that which occurs in fluent speakers [Howell, Jiang, Peng, and Lu (2012). Neural control of fundamental frequency rise and fall in Mandarin tones. "Brain and Language, 121"(1), 35-46]. Nine PWS and nine controls were scanned. Functional…

An intermediate level of BMP signaling directly specifies cranial neural crest progenitor cells in zebrafish.

PubMed

Schumacher, Jennifer A; Hashiguchi, Megumi; Nguyen, Vu H; Mullins, Mary C

2011-01-01

The specification of the neural crest progenitor cell (NCPC) population in the early vertebrate embryo requires an elaborate network of signaling pathways, one of which is the Bone Morphogenetic Protein (BMP) pathway. Based on alterations in neural crest gene expression in zebrafish BMP pathway component mutants, we previously proposed a model in which the gastrula BMP morphogen gradient establishes an intermediate level of BMP activity establishing the future NCPC domain. Here, we tested this model and show that an intermediate level of BMP signaling acts directly to specify the NCPC. We quantified the effects of reducing BMP signaling on the number of neural crest cells and show that neural crest cells are significantly increased when BMP signaling is reduced and that this increase is not due to an increase in cell proliferation. In contrast, when BMP signaling is eliminated, NCPC fail to be specified. We modulated BMP signaling levels in BMP pathway mutants with expanded or no NCPCs to demonstrate that an intermediate level of BMP signaling specifies the NCPC. We further investigated the ability of Smad5 to act in a graded fashion by injecting smad5 antisense morpholinos and show that increasing doses first expand the NCPCs and then cause a loss of NCPCs, consistent with Smad5 acting directly in neural crest progenitor specification. Using Western blot analysis, we show that P-Smad5 levels are dose-dependently reduced in smad5 morphants, consistent with an intermediate level of BMP signaling acting through Smad5 to specify the neural crest progenitors. Finally, we performed chimeric analysis to demonstrate for the first time that BMP signal reception is required directly by NCPCs for their specification. Together these results add substantial evidence to a model in which graded BMP signaling acts as a morphogen to pattern the ectoderm, with an intermediate level acting in neural crest specification.

Increasing propensity to mind-wander with transcranial direct current stimulation

PubMed Central

Axelrod, Vadim; Rees, Geraint; Lavidor, Michal; Bar, Moshe

2015-01-01

Humans mind-wander quite intensely. Mind wandering is markedly different from other cognitive behaviors because it is spontaneous, self-generated, and inwardly directed (inner thoughts). However, can such an internal and intimate mental function also be modulated externally by means of brain stimulation? Addressing this question could also help identify the neural correlates of mind wandering in a causal manner, in contrast to the correlational methods used previously (primarily functional MRI). In our study, participants performed a monotonous task while we periodically sampled their thoughts to assess mind wandering. Concurrently, we applied transcranial direct current stimulation (tDCS). We found that stimulation of the frontal lobes [anode electrode at the left dorsolateral prefrontal cortex (DLPFC), cathode electrode at the right supraorbital area], but not of the occipital cortex or sham stimulation, increased the propensity to mind-wander. These results demonstrate for the first time, to our knowledge, that mind wandering can be enhanced externally using brain stimulation, and that the frontal lobes play a causal role in mind-wandering behavior. These results also suggest that the executive control network associated with the DLPFC might be an integral part of mind-wandering neural machinery. PMID:25691738

Direct hypothalamic and indirect trans-pallidal, trans-thalamic, or trans-septal control of accumbens signaling and their roles in food intake

PubMed Central

Urstadt, Kevin R.; Stanley, B. Glenn

2015-01-01

Due in part to the increasing incidence of obesity in developed nations, recent research aims to elucidate neural circuits that motivate humans to overeat. Earlier research has described how the nucleus accumbens shell (AcbSh) motivates organisms to feed by activating neuronal populations in the lateral hypothalamus (LH). However, more recent research suggests that the LH may in turn communicate with the AcbSh, both directly and indirectly, to re-tune the motivation to consume foods with homeostatic and food-related sensory signals. Here, we discuss the functional and anatomical evidence for an LH to AcbSh connection and its role in eating behaviors. The LH appears to modulate Acb activity directly, using neurotransmitters such as hypocretin/orexin or melanin concentrating hormone (MCH). The LH also indirectly regulates AcbSh activity through certain subcortical “relay” regions, such as the lateral septum (LS), ventral pallidum (VP), and paraventricular thalamus, using a variety of neurotransmitters. This review aims to summarize studies on these topics and outline a model by which LH circuits processing energy balance can modulate AcbSh neural activity to regulate feeding behavior. PMID:25741246

Transcription factor-based modulation of neural stem cell differentiation using direct protein transduction

PubMed Central

Stock, Kristin; Nolden, Lars; Edenhofer, Frank; Quandel, Tamara

2010-01-01

In contrast to conventional gene transfer strategies, the direct introduction of recombinant proteins into cells bypasses the risk of insertional mutagenesis and offers an alternative to genetic intervention. Here, we explore whether protein transduction of the gliogenic transcription factor Nkx2.2 can be used to promote oligodendroglial differentiation of mouse embryonic stem cell (ESC)-derived neural stem cells (NSC). To that end, a recombinant cell-permeant form of Nkx2.2 protein was generated. Exposure of ESC-derived NSC to the recombinant protein and initiation of differentiation resulted in a two-fold increase in the number of oligodendrocytes. Furthermore, Nkx2.2-transduced cells exhibited a more mature oligodendroglial phenotype. Comparative viral gene transfer studies showed that the biological effect of Nkx2.2 protein transduction is comparable to that obtained by lentiviral transduction. The results of this proof-of-concept study depict direct intracellular delivery of transcription factors as alternative modality to control lineage differentiation in NSC cultures without genetic modification. Electronic supplementary material The online version of this article (doi:10.1007/s00018-010-0347-1) contains supplementary material, which is available to authorized users. PMID:20352468

Increasing propensity to mind-wander with transcranial direct current stimulation.

PubMed

Axelrod, Vadim; Rees, Geraint; Lavidor, Michal; Bar, Moshe

2015-03-17

Humans mind-wander quite intensely. Mind wandering is markedly different from other cognitive behaviors because it is spontaneous, self-generated, and inwardly directed (inner thoughts). However, can such an internal and intimate mental function also be modulated externally by means of brain stimulation? Addressing this question could also help identify the neural correlates of mind wandering in a causal manner, in contrast to the correlational methods used previously (primarily functional MRI). In our study, participants performed a monotonous task while we periodically sampled their thoughts to assess mind wandering. Concurrently, we applied transcranial direct current stimulation (tDCS). We found that stimulation of the frontal lobes [anode electrode at the left dorsolateral prefrontal cortex (DLPFC), cathode electrode at the right supraorbital area], but not of the occipital cortex or sham stimulation, increased the propensity to mind-wander. These results demonstrate for the first time, to our knowledge, that mind wandering can be enhanced externally using brain stimulation, and that the frontal lobes play a causal role in mind-wandering behavior. These results also suggest that the executive control network associated with the DLPFC might be an integral part of mind-wandering neural machinery.

Experiments in Neural-Network Control of a Free-Flying Space Robot

NASA Technical Reports Server (NTRS)

Wilson, Edward

1995-01-01

Four important generic issues are identified and addressed in some depth in this thesis as part of the development of an adaptive neural network based control system for an experimental free flying space robot prototype. The first issue concerns the importance of true system level design of the control system. A new hybrid strategy is developed here, in depth, for the beneficial integration of neural networks into the total control system. A second important issue in neural network control concerns incorporating a priori knowledge into the neural network. In many applications, it is possible to get a reasonably accurate controller using conventional means. If this prior information is used purposefully to provide a starting point for the optimizing capabilities of the neural network, it can provide much faster initial learning. In a step towards addressing this issue, a new generic Fully Connected Architecture (FCA) is developed for use with backpropagation. A third issue is that neural networks are commonly trained using a gradient based optimization method such as backpropagation; but many real world systems have Discrete Valued Functions (DVFs) that do not permit gradient based optimization. One example is the on-off thrusters that are common on spacecraft. A new technique is developed here that now extends backpropagation learning for use with DVFs. The fourth issue is that the speed of adaptation is often a limiting factor in the implementation of a neural network control system. This issue has been strongly resolved in the research by drawing on the above new contributions.

A neural network-based estimator for the mixture ratio of the Space Shuttle Main Engine

NASA Astrophysics Data System (ADS)

Guo, T. H.; Musgrave, J.

1992-11-01

In order to properly utilize the available fuel and oxidizer of a liquid propellant rocket engine, the mixture ratio is closed loop controlled during main stage (65 percent - 109 percent power) operation. However, because of the lack of flight-capable instrumentation for measuring mixture ratio, the value of mixture ratio in the control loop is estimated using available sensor measurements such as the combustion chamber pressure and the volumetric flow, and the temperature and pressure at the exit duct on the low pressure fuel pump. This estimation scheme has two limitations. First, the estimation formula is based on an empirical curve fitting which is accurate only within a narrow operating range. Second, the mixture ratio estimate relies on a few sensor measurements and loss of any of these measurements will make the estimate invalid. In this paper, we propose a neural network-based estimator for the mixture ratio of the Space Shuttle Main Engine. The estimator is an extension of a previously developed neural network based sensor failure detection and recovery algorithm (sensor validation). This neural network uses an auto associative structure which utilizes the redundant information of dissimilar sensors to detect inconsistent measurements. Two approaches have been identified for synthesizing mixture ratio from measurement data using a neural network. The first approach uses an auto associative neural network for sensor validation which is modified to include the mixture ratio as an additional output. The second uses a new network for the mixture ratio estimation in addition to the sensor validation network. Although mixture ratio is not directly measured in flight, it is generally available in simulation and in test bed firing data from facility measurements of fuel and oxidizer volumetric flows. The pros and cons of these two approaches will be discussed in terms of robustness to sensor failures and accuracy of the estimate during typical transients using simulation data.

Revealing unobserved factors underlying cortical activity with a rectified latent variable model applied to neural population recordings.

PubMed

Whiteway, Matthew R; Butts, Daniel A

2017-03-01

The activity of sensory cortical neurons is not only driven by external stimuli but also shaped by other sources of input to the cortex. Unlike external stimuli, these other sources of input are challenging to experimentally control, or even observe, and as a result contribute to variability of neural responses to sensory stimuli. However, such sources of input are likely not "noise" and may play an integral role in sensory cortex function. Here we introduce the rectified latent variable model (RLVM) in order to identify these sources of input using simultaneously recorded cortical neuron populations. The RLVM is novel in that it employs nonnegative (rectified) latent variables and is much less restrictive in the mathematical constraints on solutions because of the use of an autoencoder neural network to initialize model parameters. We show that the RLVM outperforms principal component analysis, factor analysis, and independent component analysis, using simulated data across a range of conditions. We then apply this model to two-photon imaging of hundreds of simultaneously recorded neurons in mouse primary somatosensory cortex during a tactile discrimination task. Across many experiments, the RLVM identifies latent variables related to both the tactile stimulation as well as nonstimulus aspects of the behavioral task, with a majority of activity explained by the latter. These results suggest that properly identifying such latent variables is necessary for a full understanding of sensory cortical function and demonstrate novel methods for leveraging large population recordings to this end. NEW & NOTEWORTHY The rapid development of neural recording technologies presents new opportunities for understanding patterns of activity across neural populations. Here we show how a latent variable model with appropriate nonlinear form can be used to identify sources of input to a neural population and infer their time courses. Furthermore, we demonstrate how these sources are related to behavioral contexts outside of direct experimental control. Copyright © 2017 the American Physiological Society.

A neural network-based estimator for the mixture ratio of the Space Shuttle Main Engine

NASA Technical Reports Server (NTRS)

Guo, T. H.; Musgrave, J.

1992-01-01

In order to properly utilize the available fuel and oxidizer of a liquid propellant rocket engine, the mixture ratio is closed loop controlled during main stage (65 percent - 109 percent power) operation. However, because of the lack of flight-capable instrumentation for measuring mixture ratio, the value of mixture ratio in the control loop is estimated using available sensor measurements such as the combustion chamber pressure and the volumetric flow, and the temperature and pressure at the exit duct on the low pressure fuel pump. This estimation scheme has two limitations. First, the estimation formula is based on an empirical curve fitting which is accurate only within a narrow operating range. Second, the mixture ratio estimate relies on a few sensor measurements and loss of any of these measurements will make the estimate invalid. In this paper, we propose a neural network-based estimator for the mixture ratio of the Space Shuttle Main Engine. The estimator is an extension of a previously developed neural network based sensor failure detection and recovery algorithm (sensor validation). This neural network uses an auto associative structure which utilizes the redundant information of dissimilar sensors to detect inconsistent measurements. Two approaches have been identified for synthesizing mixture ratio from measurement data using a neural network. The first approach uses an auto associative neural network for sensor validation which is modified to include the mixture ratio as an additional output. The second uses a new network for the mixture ratio estimation in addition to the sensor validation network. Although mixture ratio is not directly measured in flight, it is generally available in simulation and in test bed firing data from facility measurements of fuel and oxidizer volumetric flows. The pros and cons of these two approaches will be discussed in terms of robustness to sensor failures and accuracy of the estimate during typical transients using simulation data.

Direct Inverse Control using an Artificial Neural Network for the Autonomous Hover of a Helicopter

DTIC Science & Technology

2014-10-05

that if r(t) is the command to hover; the output y(t) will simply track the input command and hold a hover for the helicopter. III. THE X- 4P ...being used to test the DIC techniques to simulate realistic flight conditions. The X- 4P was instrumented with a number of sensors and a data recording...research [1] for system identification. The X- 4P SISO transfer functions were developed by CIFER in order to compare results against the DIC control

Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

NASA Technical Reports Server (NTRS)

Williams-Hayes, Peggy S.

2004-01-01

The NASA F-15 Intelligent Flight Control System project team developed a series of flight control concepts designed to demonstrate neural network-based adaptive controller benefits, with the objective to develop and flight-test control systems using neural network technology to optimize aircraft performance under nominal conditions and stabilize the aircraft under failure conditions. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to baseline aerodynamic derivatives in flight. This open-loop flight test set was performed in preparation for a future phase in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed - pitch frequency sweep and automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. Flight data examination shows that addition of flight-identified aerodynamic derivative increments into the simulation improved aircraft pitch handling qualities.

Neural dynamic programming and its application to control systems

NASA Astrophysics Data System (ADS)

Seong, Chang-Yun

There are few general practical feedback control methods for nonlinear MIMO (multi-input-multi-output) systems, although such methods exist for their linear counterparts. Neural Dynamic Programming (NDP) is proposed as a practical design method of optimal feedback controllers for nonlinear MIMO systems. NDP is an offspring of both neural networks and optimal control theory. In optimal control theory, the optimal solution to any nonlinear MIMO control problem may be obtained from the Hamilton-Jacobi-Bellman equation (HJB) or the Euler-Lagrange equations (EL). The two sets of equations provide the same solution in different forms: EL leads to a sequence of optimal control vectors, called Feedforward Optimal Control (FOC); HJB yields a nonlinear optimal feedback controller, called Dynamic Programming (DP). DP produces an optimal solution that can reject disturbances and uncertainties as a result of feedback. Unfortunately, computation and storage requirements associated with DP solutions can be problematic, especially for high-order nonlinear systems. This dissertation presents an approximate technique for solving the DP problem based on neural network techniques that provides many of the performance benefits (e.g., optimality and feedback) of DP and benefits from the numerical properties of neural networks. We formulate neural networks to approximate optimal feedback solutions whose existence DP justifies. We show the conditions under which NDP closely approximates the optimal solution. Finally, we introduce the learning operator characterizing the learning process of the neural network in searching the optimal solution. The analysis of the learning operator provides not only a fundamental understanding of the learning process in neural networks but also useful guidelines for selecting the number of weights of the neural network. As a result, NDP finds---with a reasonable amount of computation and storage---the optimal feedback solutions to nonlinear MIMO control problems that would be very difficult to solve with DP. NDP was demonstrated on several applications such as the lateral autopilot logic for a Boeing 747, the minimum fuel control of a double-integrator plant with bounded control, the backward steering of a two-trailer truck, and the set-point control of a two-link robot arm.

Cognitive Control Mechanisms, Emotion & Memory: A neural perspective with implications for psychopathology

PubMed Central

Banich, Marie T.; Mackiewicz, Kristen L.; Depue, Brendan E.; Whitmer, Anson; Miller, Gregory A.; Heller, Wendy

2009-01-01

In this paper we provide a focused review of the literature examining neural mechanisms involved in cognitive control over memory processes that can influence, and in turn are influenced, by emotional processes. The review is divided into two parts, the first focusing on working memory and the second on long-term memory. With regard to working memory, we discuss the neural bases of 1) control mechanisms that can select against distracting emotional information, 2) mechanisms that can regulate emotional reactions or responses, 3) how mood state influences cognitive control, and 4) individual differences in control mechanisms. For long-term memory, we briefly review 1) the neural substrates of emotional memory, 2) the cognitive and neural mechanisms that are involved in controlling emotional memories and 3) how these systems are altered in post-traumatic stress disorder. Finally, we consider tentative generalizations that can be drawn from this relatively unexplored conjunction of research endeavors. PMID:18948135

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

PubMed

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

2017-06-01

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.

Modeling Aircraft Wing Loads from Flight Data Using Neural Networks

NASA Technical Reports Server (NTRS)

Allen, Michael J.; Dibley, Ryan P.

2003-01-01

Neural networks were used to model wing bending-moment loads, torsion loads, and control surface hinge-moments of the Active Aeroelastic Wing (AAW) aircraft. Accurate loads models are required for the development of control laws designed to increase roll performance through wing twist while not exceeding load limits. Inputs to the model include aircraft rates, accelerations, and control surface positions. Neural networks were chosen to model aircraft loads because they can account for uncharacterized nonlinear effects while retaining the capability to generalize. The accuracy of the neural network models was improved by first developing linear loads models to use as starting points for network training. Neural networks were then trained with flight data for rolls, loaded reversals, wind-up-turns, and individual control surface doublets for load excitation. Generalization was improved by using gain weighting and early stopping. Results are presented for neural network loads models of four wing loads and four control surface hinge moments at Mach 0.90 and an altitude of 15,000 ft. An average model prediction error reduction of 18.6 percent was calculated for the neural network models when compared to the linear models. This paper documents the input data conditioning, input parameter selection, structure, training, and validation of the neural network models.

ER fluid applications to vibration control devices and an adaptive neural-net controller

NASA Astrophysics Data System (ADS)

Morishita, Shin; Ura, Tamaki

1993-07-01

Four applications of electrorheological (ER) fluid to vibration control actuators and an adaptive neural-net control system suitable for the controller of ER actuators are described: a shock absorber system for automobiles, a squeeze film damper bearing for rotational machines, a dynamic damper for multidegree-of-freedom structures, and a vibration isolator. An adaptive neural-net control system composed of a forward model network for structural identification and a controller network is introduced for the control system of these ER actuators. As an example study of intelligent vibration control systems, an experiment was performed in which the ER dynamic damper was attached to a beam structure and controlled by the present neural-net controller so that the vibration in several modes of the beam was reduced with a single dynamic damper.

System Identification for Nonlinear Control Using Neural Networks

NASA Technical Reports Server (NTRS)

Stengel, Robert F.; Linse, Dennis J.

1990-01-01

An approach to incorporating artificial neural networks in nonlinear, adaptive control systems is described. The controller contains three principal elements: a nonlinear inverse dynamic control law whose coefficients depend on a comprehensive model of the plant, a neural network that models system dynamics, and a state estimator whose outputs drive the control law and train the neural network. Attention is focused on the system identification task, which combines an extended Kalman filter with generalized spline function approximation. Continual learning is possible during normal operation, without taking the system off line for specialized training. Nonlinear inverse dynamic control requires smooth derivatives as well as function estimates, imposing stringent goals on the approximating technique.

An fMRI study of musicians with focal dystonia during tapping tasks.

PubMed

Kadota, Hiroshi; Nakajima, Yasoichi; Miyazaki, Makoto; Sekiguchi, Hirofumi; Kohno, Yutaka; Amako, Masatoshi; Arino, Hiroshi; Nemoto, Koichi; Sakai, Naotaka

2010-07-01

Musician's dystonia is a type of task specific dystonia for which the pathophysiology is not clear. In this study, we performed functional magnetic resonance imaging to investigate the motor-related brain activity associated with musician's dystonia. We compared brain activities measured from subjects with focal hand dystonia and normal (control) musicians during right-hand, left-hand, and both-hands tapping tasks. We found activations in the thalamus and the basal ganglia during the tapping tasks in the control group but not in the dystonia group. For both groups, we detected significant activations in the contralateral sensorimotor areas, including the premotor area and cerebellum, during each tapping task. Moreover, direct comparison between the dystonia and control groups showed that the dystonia group had greater activity in the ipsilateral premotor area during the right-hand tapping task and less activity in the left cerebellum during the both-hands tapping task. Thus, the dystonic musicians showed irregular activation patterns in the motor-association system. We suggest that irregular neural activity patterns in dystonic subjects reflect dystonic neural malfunction and consequent compensatory activity to maintain appropriate voluntary movements.

High-order tracking differentiator based adaptive neural control of a flexible air-breathing hypersonic vehicle subject to actuators constraints.

PubMed

Bu, Xiangwei; Wu, Xiaoyan; Tian, Mingyan; Huang, Jiaqi; Zhang, Rui; Ma, Zhen

2015-09-01

In this paper, an adaptive neural controller is exploited for a constrained flexible air-breathing hypersonic vehicle (FAHV) based on high-order tracking differentiator (HTD). By utilizing functional decomposition methodology, the dynamic model is reasonably decomposed into the respective velocity subsystem and altitude subsystem. For the velocity subsystem, a dynamic inversion based neural controller is constructed. By introducing the HTD to adaptively estimate the newly defined states generated in the process of model transformation, a novel neural based altitude controller that is quite simpler than the ones derived from back-stepping is addressed based on the normal output-feedback form instead of the strict-feedback formulation. Based on minimal-learning parameter scheme, only two neural networks with two adaptive parameters are needed for neural approximation. Especially, a novel auxiliary system is explored to deal with the problem of control inputs constraints. Finally, simulation results are presented to test the effectiveness of the proposed control strategy in the presence of system uncertainties and actuators constraints. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Direct current contamination of kilohertz frequency alternating current waveforms.

PubMed

Franke, Manfred; Bhadra, Niloy; Bhadra, Narendra; Kilgore, Kevin

2014-07-30

Kilohertz frequency alternating current (KHFAC) waveforms are being evaluated in a variety of physiological settings because of their potential to modulate neural activity uniquely when compared to frequencies in the sub-kilohertz range. However, the use of waveforms in this frequency range presents some unique challenges regarding the generator output. In this study we explored the possibility of undesirable contamination of the KHFAC waveforms by direct current (DC). We evaluated current- and voltage-controlled KHFAC waveform generators in configurations that included a capacitive coupling between generator and electrode, a resistive coupling and combinations of capacitive with inductive coupling. Our results demonstrate that both voltage- and current-controlled signal generators can unintentionally add DC-contamination to a KHFAC signal, and that capacitive coupling is not always sufficient to eliminate this contamination. We furthermore demonstrated that high value inductors, placed in parallel with the electrode, can be effective in eliminating DC-contamination irrespective of the type of stimulator, reducing the DC contamination to less than 1 μA. This study highlights the importance of carefully designing the electronic setup used in KHFAC studies and suggests specific testing that should be performed and reported in all studies that assess the neural response to KHFAC waveforms. Published by Elsevier B.V.

Integrated Neural Flight and Propulsion Control System

NASA Technical Reports Server (NTRS)

Kaneshige, John; Gundy-Burlet, Karen; Norvig, Peter (Technical Monitor)

2001-01-01

This paper describes an integrated neural flight and propulsion control system. which uses a neural network based approach for applying alternate sources of control power in the presence of damage or failures. Under normal operating conditions, the system utilizes conventional flight control surfaces. Neural networks are used to provide consistent handling qualities across flight conditions and for different aircraft configurations. Under damage or failure conditions, the system may utilize unconventional flight control surface allocations, along with integrated propulsion control, when additional control power is necessary for achieving desired flight control performance. In this case, neural networks are used to adapt to changes in aircraft dynamics and control allocation schemes. Of significant importance here is the fact that this system can operate without emergency or backup flight control mode operations. An additional advantage is that this system can utilize, but does not require, fault detection and isolation information or explicit parameter identification. Piloted simulation studies were performed on a commercial transport aircraft simulator. Subjects included both NASA test pilots and commercial airline crews. Results demonstrate the potential for improving handing qualities and significantly increasing survivability rates under various simulated failure conditions.

Conservation and diversity in the cis-regulatory networks that integrate information controlling expression of Hoxa2 in hindbrain and cranial neural crest cells in vertebrates.

PubMed

Tümpel, Stefan; Maconochie, Mark; Wiedemann, Leanne M; Krumlauf, Robb

2002-06-01

The Hoxa2 and Hoxb2 genes are members of paralogy group II and display segmental patterns of expression in the developing vertebrate hindbrain and cranial neural crest cells. Functional analyses have demonstrated that these genes play critical roles in regulating morphogenetic pathways that direct the regional identity and anteroposterior character of hindbrain rhombomeres and neural crest-derived structures. Transgenic regulatory studies have also begun to characterize enhancers and cis-elements for those mouse and chicken genes that direct restricted patterns of expression in the hindbrain and neural crest. In light of the conserved role of Hoxa2 in neural crest patterning in vertebrates and the similarities between paralogs, it is important to understand the extent to which common regulatory networks and elements have been preserved between species and between paralogs. To investigate this problem, we have cloned and sequenced the intergenic region between Hoxa2 and Hoxa3 in the chick HoxA complex and used it for making comparative analyses with the respective human, mouse, and horn shark regions. We have also used transgenic assays in mouse and chick embryos to test the functional activity of Hoxa2 enhancers in heterologous species. Our analysis reveals that three of the critical individual components of the Hoxa2 enhancer region from mouse necessary for hindbrain expression (Krox20, BoxA, and TCT motifs) have been partially conserved. However, their number and organization are highly varied for the same gene in different species and between paralogs within a species. Other essential mouse elements appear to have diverged or are absent in chick and shark. We find the mouse r3/r5 enhancer fails to work in chick embryos and the chick enhancer works poorly in mice. This implies that new motifs have been recruited or utilized to mediate restricted activity of the enhancer in other species. With respect to neural crest regulation, cis-components are embedded among the hindbrain control elements and are highly diverged between species. Hence, there has been no widespread conservation of sequence identity over the entire enhancer domain from shark to humans, despite the common function of these genes in head patterning. This provides insight into how apparently equivalent regulatory regions from the same gene in different species have evolved different components to potentiate their activity in combination with a selection of core components. (c) 2002 Elsevier Science (USA).

Ventral striatal dopamine reflects behavioral and neural signatures of model-based control during sequential decision making.

PubMed

Deserno, Lorenz; Huys, Quentin J M; Boehme, Rebecca; Buchert, Ralph; Heinze, Hans-Jochen; Grace, Anthony A; Dolan, Raymond J; Heinz, Andreas; Schlagenhauf, Florian

2015-02-03

Dual system theories suggest that behavioral control is parsed between a deliberative "model-based" and a more reflexive "model-free" system. A balance of control exerted by these systems is thought to be related to dopamine neurotransmission. However, in the absence of direct measures of human dopamine, it remains unknown whether this reflects a quantitative relation with dopamine either in the striatum or other brain areas. Using a sequential decision task performed during functional magnetic resonance imaging, combined with striatal measures of dopamine using [(18)F]DOPA positron emission tomography, we show that higher presynaptic ventral striatal dopamine levels were associated with a behavioral bias toward more model-based control. Higher presynaptic dopamine in ventral striatum was associated with greater coding of model-based signatures in lateral prefrontal cortex and diminished coding of model-free prediction errors in ventral striatum. Thus, interindividual variability in ventral striatal presynaptic dopamine reflects a balance in the behavioral expression and the neural signatures of model-free and model-based control. Our data provide a novel perspective on how alterations in presynaptic dopamine levels might be accompanied by a disruption of behavioral control as observed in aging or neuropsychiatric diseases such as schizophrenia and addiction.

Neural control and precision of flight muscle activation in Drosophila.

PubMed

Lehmann, Fritz-Olaf; Bartussek, Jan

2017-01-01

Precision of motor commands is highly relevant in a large context of various locomotor behaviors, including stabilization of body posture, heading control and directed escape responses. While posture stability and heading control in walking and swimming animals benefit from high friction via ground reaction forces and elevated viscosity of water, respectively, flying animals have to cope with comparatively little aerodynamic friction on body and wings. Although low frictional damping in flight is the key to the extraordinary aerial performance and agility of flying birds, bats and insects, it challenges these animals with extraordinary demands on sensory integration and motor precision. Our review focuses on the dynamic precision with which Drosophila activates its flight muscular system during maneuvering flight, considering relevant studies on neural and muscular mechanisms of thoracic propulsion. In particular, we tackle the precision with which flies adjust power output of asynchronous power muscles and synchronous flight control muscles by monitoring muscle calcium and spike timing within the stroke cycle. A substantial proportion of the review is engaged in the significance of visual and proprioceptive feedback loops for wing motion control including sensory integration at the cellular level. We highlight that sensory feedback is the basis for precise heading control and body stability in flies.

Flight Test of an Intelligent Flight-Control System

NASA Technical Reports Server (NTRS)

Davidson, Ron; Bosworth, John T.; Jacobson, Steven R.; Thomson, Michael Pl; Jorgensen, Charles C.

2003-01-01

The F-15 Advanced Controls Technology for Integrated Vehicles (ACTIVE) airplane (see figure) was the test bed for a flight test of an intelligent flight control system (IFCS). This IFCS utilizes a neural network to determine critical stability and control derivatives for a control law, the real-time gains of which are computed by an algorithm that solves the Riccati equation. These derivatives are also used to identify the parameters of a dynamic model of the airplane. The model is used in a model-following portion of the control law, in order to provide specific vehicle handling characteristics. The flight test of the IFCS marks the initiation of the Intelligent Flight Control System Advanced Concept Program (IFCS ACP), which is a collaboration between NASA and Boeing Phantom Works. The goals of the IFCS ACP are to (1) develop the concept of a flight-control system that uses neural-network technology to identify aircraft characteristics to provide optimal aircraft performance, (2) develop a self-training neural network to update estimates of aircraft properties in flight, and (3) demonstrate the aforementioned concepts on the F-15 ACTIVE airplane in flight. The activities of the initial IFCS ACP were divided into three Phases, each devoted to the attainment of a different objective. The objective of Phase I was to develop a pre-trained neural network to store and recall the wind-tunnel-based stability and control derivatives of the vehicle. The objective of Phase II was to develop a neural network that can learn how to adjust the stability and control derivatives to account for failures or modeling deficiencies. The objective of Phase III was to develop a flight control system that uses the neural network outputs as a basis for controlling the aircraft. The flight test of the IFCS was performed in stages. In the first stage, the Phase I version of the pre-trained neural network was flown in a passive mode. The neural network software was running using flight data inputs with the outputs provided to instrumentation only. The IFCS was not used to control the airplane. In another stage of the flight test, the Phase I pre-trained neural network was integrated into a Phase III version of the flight control system. The Phase I pretrained neural network provided realtime stability and control derivatives to a Phase III controller that was based on a stochastic optimal feedforward and feedback technique (SOFFT). This combined Phase I/III system was operated together with the research flight-control system (RFCS) of the F-15 ACTIVE during the flight test. The RFCS enables the pilot to switch quickly from the experimental- research flight mode back to the safe conventional mode. These initial IFCS ACP flight tests were completed in April 1999. The Phase I/III flight test milestone was to demonstrate, across a range of subsonic and supersonic flight conditions, that the pre-trained neural network could be used to supply real-time aerodynamic stability and control derivatives to the closed-loop optimal SOFFT flight controller. Additional objectives attained in the flight test included (1) flight qualification of a neural-network-based control system; (2) the use of a combined neural-network/closed-loop optimal flight-control system to obtain level-one handling qualities; and (3) demonstration, through variation of control gains, that different handling qualities can be achieved by setting new target parameters. In addition, data for the Phase-II (on-line-learning) neural network were collected, during the use of stacked-frequency- sweep excitation, for post-flight analysis. Initial analysis of these data showed the potential for future flight tests that will incorporate the real-time identification and on-line learning aspects of the IFCS.

Synchronization of Hierarchical Time-Varying Neural Networks Based on Asynchronous and Intermittent Sampled-Data Control.

PubMed

Xiong, Wenjun; Patel, Ragini; Cao, Jinde; Zheng, Wei Xing

In this brief, our purpose is to apply asynchronous and intermittent sampled-data control methods to achieve the synchronization of hierarchical time-varying neural networks. The asynchronous and intermittent sampled-data controllers are proposed for two reasons: 1) the controllers may not transmit the control information simultaneously and 2) the controllers cannot always exist at any time . The synchronization is then discussed for a kind of hierarchical time-varying neural networks based on the asynchronous and intermittent sampled-data controllers. Finally, the simulation results are given to illustrate the usefulness of the developed criteria.In this brief, our purpose is to apply asynchronous and intermittent sampled-data control methods to achieve the synchronization of hierarchical time-varying neural networks. The asynchronous and intermittent sampled-data controllers are proposed for two reasons: 1) the controllers may not transmit the control information simultaneously and 2) the controllers cannot always exist at any time . The synchronization is then discussed for a kind of hierarchical time-varying neural networks based on the asynchronous and intermittent sampled-data controllers. Finally, the simulation results are given to illustrate the usefulness of the developed criteria.

Goal-Directed Behavior and Instrumental Devaluation: A Neural System-Level Computational Model

PubMed Central

Mannella, Francesco; Mirolli, Marco; Baldassarre, Gianluca

2016-01-01

Devaluation is the key experimental paradigm used to demonstrate the presence of instrumental behaviors guided by goals in mammals. We propose a neural system-level computational model to address the question of which brain mechanisms allow the current value of rewards to control instrumental actions. The model pivots on and shows the computational soundness of the hypothesis for which the internal representation of instrumental manipulanda (e.g., levers) activate the representation of rewards (or “action-outcomes”, e.g., foods) while attributing to them a value which depends on the current internal state of the animal (e.g., satiation for some but not all foods). The model also proposes an initial hypothesis of the integrated system of key brain components supporting this process and allowing the recalled outcomes to bias action selection: (a) the sub-system formed by the basolateral amygdala and insular cortex acquiring the manipulanda-outcomes associations and attributing the current value to the outcomes; (b) three basal ganglia-cortical loops selecting respectively goals, associative sensory representations, and actions; (c) the cortico-cortical and striato-nigro-striatal neural pathways supporting the selection, and selection learning, of actions based on habits and goals. The model reproduces and explains the results of several devaluation experiments carried out with control rats and rats with pre- and post-training lesions of the basolateral amygdala, the nucleus accumbens core, the prelimbic cortex, and the dorso-medial striatum. The results support the soundness of the hypotheses of the model and show its capacity to integrate, at the system-level, the operations of the key brain structures underlying devaluation. Based on its hypotheses and predictions, the model also represents an operational framework to support the design and analysis of new experiments on the motivational aspects of goal-directed behavior. PMID:27803652

Unity and diversity of executive functions: Individual differences as a window on cognitive structure.

PubMed

Friedman, Naomi P; Miyake, Akira

2017-01-01

Executive functions (EFs) are high-level cognitive processes, often associated with the frontal lobes, that control lower level processes in the service of goal-directed behavior. They include abilities such as response inhibition, interference control, working memory updating, and set shifting. EFs show a general pattern of shared but distinct functions, a pattern described as "unity and diversity". We review studies of EF unity and diversity at the behavioral and genetic levels, focusing on studies of normal individual differences and what they reveal about the functional organization of these cognitive abilities. In particular, we review evidence that across multiple ages and populations, commonly studied EFs (a) are robustly correlated but separable when measured with latent variables; (b) are not the same as general intelligence or g; (c) are highly heritable at the latent level and seemingly also highly polygenic; and (d) activate both common and specific neural areas and can be linked to individual differences in neural activation, volume, and connectivity. We highlight how considering individual differences at the behavioral and neural levels can add considerable insight to the investigation of the functional organization of the brain, and conclude with some key points about individual differences to consider when interpreting neuropsychological patterns of dissociation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Resource allocation in neural networks for motor control

NASA Astrophysics Data System (ADS)

Milton, J.; Cummins, J.; Gunnoe, J.; Tollefson, M.; Cabrera, J. L.; Ohira, T.

2006-03-01

Multiplicative noise plays an important part of a non-predictive control mechanism for stick balancing at the fingertip. However, intentionally-directed movements are also used in stick balancing, particularly by beginners. The interplay between intentional and non-predictive control mechanisms for stick balancing was assessed using two dual task paradigms: the subject was asked to either move one of their legs rhythmically or to imagine moving their leg while balancing a stick (55.4 cm, 35 g) at their fingertip. Performance was measured by determining the stick survival function, i.e. the fraction of trials (total >=25) for which the stick remained balanced at time t as a function of t. Performance was increased by concurrent rhythmic leg movements (50% survival time shifted from 8-9s to 15s in a typical subject). Imagined movements resulted in a similar improvement (50% survival time of 20s for the above subject) suggesting that this enhancement is not simply related to mechanical vibrations of the fingertip induced by leg movement. These observations emphasize the importance of the development of mathematical models for neural control of skilled motor movements that take into resource allocation of limited resources, such as intention.

The characterization of neural tissue ablation rate and corresponding heat affected zone of a 2 micron Tm3+ doped fiber laser(Conference Presentation)

NASA Astrophysics Data System (ADS)

Marques, Andrew J.; Jivraj, Jamil; Reyes, Robnier; Ramjist, Joel; Gu, Xijia J.; Yang, Victor X. D.

2017-02-01

Tissue removal using electrocautery is standard practice in neurosurgery since tissue can be cut and cauterized simultaneously. Thermally mediated tissue ablation using lasers can potentially possess the same benefits but with increased precision. However, given the critical nature of the spine, brain, and nerves, the effects of direct photo-thermal interaction on neural tissue needs to be known, yielding not only high precision of tissue removal but also increased control of peripheral heat damage. The proposed use of lasers as a neurosurgical tool requires that a common ground is found between ablation rates and resulting peripheral heat damage. Most surgical laser systems rely on the conversion of light energy into heat resulting in both desirable and undesirable thermal damage to the targeted tissue. Classifying the distribution of thermal energy in neural tissue, and thus characterizing the extent of undesirable thermal damage, can prove to be exceptionally challenging considering its highly inhomogenous composition when compared to other tissues such as muscle and bone. Here we present the characterization of neural tissue ablation rate and heat affected zone of a 1.94 micron thulium doped fiber laser for neural tissue ablation. In-Vivo ablation of porcine cerebral cortex is performed. Ablation volumes are studied in association with laser parameters. Histological samples are taken and examined to characterize the extent of peripheral heat damage.

Organization of the motor-unit pool for different directions of isometric contraction of the first dorsal interosseous muscle.

PubMed

Lei, Yuming; Suresh, Nina L; Rymer, William Z; Hu, Xiaogang

2018-01-01

Muscle force generation involves recruitment and firing rate modulation of motor units (MUs). The control of MUs in producing multidirectional forces remains unclear. We studied MU recruitment and firing properties, recorded from the first dorsal interosseous muscle, for 3 different directions of contraction: abduction; abduction/flexion combination; and flexion. MUs were recruited systematically at higher threshold force during flexion. Larger MUs were recruited and firing rates of MUs were lower during abduction. There was an orderly recruitment of MUs according to MU size regardless of contraction direction, obeying the "size principle." Firing rates of earlier-recruited MUs were consistently higher than later-recruited MUs, affirming the "onion-skin" property. Our findings suggest that the size principle and onion-skin organization together provide a general description of MU recruitment patterns and firing properties. The directional alternations of MU control properties likely reflect changes in neural drive to the muscle. Muscle Nerve 57: E85-E93, 2018. © 2017 Wiley Periodicals, Inc.

Neural control of renal tubular sodium reabsorption of the dog.

PubMed

DiBona, G F

1978-04-01

The evidence supporting a role for direct neurogenic control of renal tubular sodium reabsorption is reviewed. Electron microscopic and fluorescence histochemical studies demonstrate adrenergic nerve terminals in direct contact with basement membranes of mammalian renal tubular epithelial cells. Low level direct or baroreceptor reflex stimulation of renal sympathetic nerves produces an increase in renal tubular sodium reabsorption without alterations in glomerular filtration rate, renal blood flow, or intrarenal distribution of blood flow. The antinatriuresis is prevented by prior treatment of the kidney with guanethidine or phenoxybenzamine. Possible indirect mediation of the antinatriuresis by other humoral agents known to be released from the kidney upon renal nerve stimulation (angiotensin II, prostaglandin) was excluded by experiments with appropriate blocking agents. Reflex diminutions in renal nerve activity (left atrial distention, stellate ganglion stimulation) produce a decrease in renal tubular sodium reabsorption independent of glomerular filtration rate or renal blood flow. The anatomically described adrenergic innervation of the renal tubules participates in the direct regulation of renal tubular sodium reabsorption.

Human Neural Cell-Based Biosensor

DTIC Science & Technology

2011-03-11

following areas: (1) neural progenitor isolation from induced pluripotent stem cells , (2) directed differentiation of progenitors into dopaminergic...from induced pluripotent stem cells , (2) directed differentiation of progenitors into dopaminergic neurons, motoneurons and astrocytes using defined...progenitors from mixed populations, such as induced pluripotent stem cells (iPSCs). We also developed lentiviral based methods to generate iPSCs in