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Sample records for neural stimulation system

  1. Radial Basis Function Neural Network-based PID model for functional electrical stimulation system control.

    Science.gov (United States)

    Cheng, Longlong; Zhang, Guangju; Wan, Baikun; Hao, Linlin; Qi, Hongzhi; Ming, Dong

    2009-01-01

    Functional electrical stimulation (FES) has been widely used in the area of neural engineering. It utilizes electrical current to activate nerves innervating extremities affected by paralysis. An effective combination of a traditional PID controller and a neural network, being capable of nonlinear expression and adaptive learning property, supply a more reliable approach to construct FES controller that help the paraplegia complete the action they want. A FES system tuned by Radial Basis Function (RBF) Neural Network-based Proportional-Integral-Derivative (PID) model was designed to control the knee joint according to the desired trajectory through stimulation of lower limbs muscles in this paper. Experiment result shows that the FES system with RBF Neural Network-based PID model get a better performance when tracking the preset trajectory of knee angle comparing with the system adjusted by Ziegler- Nichols tuning PID model.

  2. A low-cost multichannel wireless neural stimulation system for freely roaming animals

    Science.gov (United States)

    Alam, Monzurul; Chen, Xi; Fernandez, Eduardo

    2013-12-01

    Objectives. Electrical stimulation of nerve tissue and recording of neural activity are the basis of many therapies and neural prostheses. Conventional stimulation systems have a number of practical limitations, especially in experiments involving freely roaming subjects. Our main objective was to develop a modular, versatile and inexpensive multichannel wireless system able to overcome some of these constraints. Approach. We have designed and implemented a new multichannel wireless neural stimulator based on commercial components. The system is small (2 cm × 4 cm × 0.5 cm) and light in weight (9 g) which allows it to be easily carried in a small backpack. To test and validate the performance and reliability of the whole system we conducted several bench tests and in vivo experiments. Main results. The performance and accuracy of the stimulator were comparable to commercial threaded systems. Stimulation sequences can be constructed on-the-fly with 251 selectable current levels (from 0 to 250 µA) with 1 µA step resolution. The pulse widths and intervals can be as long as 65 ms in 2 µs time resolution. The system covers approximately 10 m of transmission range in a regular laboratory environment and 100 m in free space (line of sight). Furthermore it provides great flexibility for experiments since it allows full control of the stimulator and the stimulation parameters in real time. When there is no stimulation, the device automatically goes into low-power sleep mode to preserve battery power. Significance. We introduce the design of a powerful multichannel wireless stimulator assembled from commercial components. Key features of the system are their reliability, robustness and small size. The system has a flexible design that can be modified straightforwardly to tailor it to any specific experimental need. Furthermore it can be effortlessly adapted for use with any kind of multielectrode arrays.

  3. Non-invasive neural stimulation

    Science.gov (United States)

    Tyler, William J.; Sanguinetti, Joseph L.; Fini, Maria; Hool, Nicholas

    2017-05-01

    Neurotechnologies for non-invasively interfacing with neural circuits have been evolving from those capable of sensing neural activity to those capable of restoring and enhancing human brain function. Generally referred to as non-invasive neural stimulation (NINS) methods, these neuromodulation approaches rely on electrical, magnetic, photonic, and acoustic or ultrasonic energy to influence nervous system activity, brain function, and behavior. Evidence that has been surmounting for decades shows that advanced neural engineering of NINS technologies will indeed transform the way humans treat diseases, interact with information, communicate, and learn. The physics underlying the ability of various NINS methods to modulate nervous system activity can be quite different from one another depending on the energy modality used as we briefly discuss. For members of commercial and defense industry sectors that have not traditionally engaged in neuroscience research and development, the science, engineering and technology required to advance NINS methods beyond the state-of-the-art presents tremendous opportunities. Within the past few years alone there have been large increases in global investments made by federal agencies, foundations, private investors and multinational corporations to develop advanced applications of NINS technologies. Driven by these efforts NINS methods and devices have recently been introduced to mass markets via the consumer electronics industry. Further, NINS continues to be explored in a growing number of defense applications focused on enhancing human dimensions. The present paper provides a brief introduction to the field of non-invasive neural stimulation by highlighting some of the more common methods in use or under current development today.

  4. Closed-loop optical neural stimulation based on a 32-channel low-noise recording system with online spike sorting

    Science.gov (United States)

    Nguyen, T. K. T.; Navratilova, Z.; Cabral, H.; Wang, L.; Gielen, G.; Battaglia, F. P.; Bartic, C.

    2014-08-01

    Objective. Closed-loop operation of neuro-electronic systems is desirable for both scientific and clinical (neuroprosthesis) applications. Integrating optical stimulation with recording capability further enhances the selectivity of neural stimulation. We have developed a system enabling the local delivery of optical stimuli and the simultaneous electrical measuring of the neural activities in a closed-loop approach. Approach. The signal analysis is performed online through the implementation of a template matching algorithm. The system performance is demonstrated with the recorded data and in awake rats. Main results. Specifically, the neural activities are simultaneously recorded, detected, classified online (through spike sorting) from 32 channels, and used to trigger a light emitting diode light source using generated TTL signals. Significance. A total processing time of 8 ms is achieved, suitable for optogenetic studies of brain mechanisms online.

  5. A CMOS IC–based multisite measuring system for stimulation and recording in neural preparations in vitro

    Directory of Open Access Journals (Sweden)

    Takashi eTateno

    2014-10-01

    Full Text Available In this report, we describe the system integration of a complementary metal oxide semiconductor (CMOS integrated circuit (IC chip, capable of both stimulation and recording of neurons or neural tissues, to investigate electrical signal propagation within cellular networks in vitro. The overall system consisted of three major subunits: a 5.0 mm × 5.0 mm CMOS IC chip, a reconfigurable logic device (field-programmable gate array, FPGA, and a PC. To test the system, microelectrode arrays (MEAs were used to extracellularly measure the activity of cultured rat cortical neurons and mouse cortical slices. The MEA had 64 bidirectional (stimulation and recording electrodes. In addition, the CMOS IC chip was equipped with dedicated analog filters, amplification stages, and a stimulation buffer. Signals from the electrodes were sampled at 15.6 kHz with 16-bit resolution. The measured input-referred circuitry noise was 10.1 μV root mean square (10 Hz to 100 kHz, which allowed reliable detection of neural signals ranging from several millivolts down to approximately 33 μVpp. Experiments were performed involving the stimulation of neurons with several spatiotemporal patterns and the recording of the triggered activity. An advantage over current MEAs, as demonstrated by our experiments, includes the ability to stimulate (voltage stimulation, 5-bit resolution spatiotemporal patterns in arbitrary subsets of electrodes. Furthermore, the fast stimulation reset mechanism allowed us to record neuronal signals from a stimulating electrode around 3 ms after stimulation. We demonstrate that the system can be directly applied to, for example, auditory neural prostheses in conjunction with an acoustic sensor and a sound processing system.

  6. Nanomaterial-enabled neural stimulation

    Directory of Open Access Journals (Sweden)

    Yongchen eWang

    2016-03-01

    Full Text Available Neural stimulation is a critical technique in treating neurological diseases and investigating brain functions. Traditional electrical stimulation uses electrodes to directly create intervening electric fields in the immediate vicinity of neural tissues. Second-generation stimulation techniques directly use light, magnetic fields or ultrasound in a non-contact manner. An emerging generation of non- or minimally invasive neural stimulation techniques is enabled by nanotechnology to achieve a high spatial resolution and cell-type specificity. In these techniques, a nanomaterial converts a remotely transmitted primary stimulus such as a light, magnetic or ultrasonic signal to a localized secondary stimulus such as an electric field or heat to stimulate neurons. The ease of surface modification and bio-conjugation of nanomaterials facilitates cell-type-specific targeting, designated placement and highly localized membrane activation. This review focuses on nanomaterial-enabled neural stimulation techniques primarily involving opto-electric, opto-thermal, magneto-electric, magneto-thermal and acousto-electric transduction mechanisms. Stimulation techniques based on other possible transduction schemes and general consideration for these emerging neurotechnologies are also discussed.

  7. Design of efficient and safe neural stimulators a multidisciplinary approach

    CERN Document Server

    van Dongen, Marijn

    2016-01-01

    This book discusses the design of neural stimulator systems which are used for the treatment of a wide variety of brain disorders such as Parkinson’s, depression and tinnitus. Whereas many existing books treating neural stimulation focus on one particular design aspect, such as the electrical design of the stimulator, this book uses a multidisciplinary approach: by combining the fields of neuroscience, electrophysiology and electrical engineering a thorough understanding of the complete neural stimulation chain is created (from the stimulation IC down to the neural cell). This multidisciplinary approach enables readers to gain new insights into stimulator design, while context is provided by presenting innovative design examples. Provides a single-source, multidisciplinary reference to the field of neural stimulation, bridging an important knowledge gap among the fields of bioelectricity, neuroscience, neuroengineering and microelectronics;Uses a top-down approach to understanding the neural activation proc...

  8. Stimulation and recording electrodes for neural prostheses

    CERN Document Server

    Pour Aryan, Naser; Rothermel, Albrecht

    2015-01-01

    This book provides readers with basic principles of the electrochemistry of the electrodes used in modern, implantable neural prostheses. The authors discuss the boundaries and conditions in which the electrodes continue to function properly for long time spans, which are required when designing neural stimulator devices for long-term in vivo applications. Two kinds of electrode materials, titanium nitride and iridium are discussed extensively, both qualitatively and quantitatively. The influence of the counter electrode on the safety margins and electrode lifetime in a two electrode system is explained. Electrode modeling is handled in a final chapter.

  9. Evolvable synthetic neural system

    Science.gov (United States)

    Curtis, Steven A. (Inventor)

    2009-01-01

    An evolvable synthetic neural system includes an evolvable neural interface operably coupled to at least one neural basis function. Each neural basis function includes an evolvable neural interface operably coupled to a heuristic neural system to perform high-level functions and an autonomic neural system to perform low-level functions. In some embodiments, the evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy.

  10. Design of efficient and safe neural stimulators : A multidisciplinary approach

    NARCIS (Netherlands)

    Van Dongen, M.N.

    2015-01-01

    Neural stimulation is an established treatment methodology for an increasing number of diseases. Electrical Stimulation injects a stimulation signal through electrodes that are implanted in the target area of the central or peripheral nervous system in order to evoke a specific neuronal response

  11. Neural adaptations to electrical stimulation strength training

    NARCIS (Netherlands)

    Hortobagyi, Tibor; Maffiuletti, Nicola A.

    2011-01-01

    This review provides evidence for the hypothesis that electrostimulation strength training (EST) increases the force of a maximal voluntary contraction (MVC) through neural adaptations in healthy skeletal muscle. Although electrical stimulation and voluntary effort activate muscle differently, there

  12. A Chip for an Implantable Neural Stimulator

    DEFF Research Database (Denmark)

    Gudnason, Gunnar; Bruun, Erik; Haugland, Morten

    2000-01-01

    This paper describes a chip for a multichannel neural stimulator for functional electrical stimulation (FES). The purpose of FES is to restore muscular control in disabled patients. The chip performs all the signal processing required in an implanted neural stimulator. The power and digital data...... transmission to the stimulator passes through a 5 MHz inductive link. From the signals transmitted to the stimulator, the chip is able to generate charge-balanced current pulses with a controllable length up to 256 µs and an amplitude up to 2 mA, for stimulation of nerve fibers. The quiescent current...... consumption of the chip is approx. 650 µA at supply voltages of 6–12 V, and its size is 3.9×3.5 mm2. It has 4 output channels for use in a multipolar cuff electrode....

  13. Controlling selective stimulations below a spinal cord hemisection using brain recordings with a neural interface system approach

    Science.gov (United States)

    Panetsos, Fivos; Sanchez-Jimenez, Abel; Torets, Carlos; Largo, Carla; Micera, Silvestro

    2011-08-01

    In this work we address the use of realtime cortical recordings for the generation of coherent, reliable and robust motor activity in spinal-lesioned animals through selective intraspinal microstimulation (ISMS). The spinal cord of adult rats was hemisectioned and groups of multielectrodes were implanted in both the central nervous system (CNS) and the spinal cord below the lesion level to establish a neural system interface (NSI). To test the reliability of this new NSI connection, highly repeatable neural responses recorded from the CNS were used as a pattern generator of an open-loop control strategy for selective ISMS of the spinal motoneurons. Our experimental procedure avoided the spontaneous non-controlled and non-repeatable neural activity that could have generated spurious ISMS and the consequent undesired muscle contractions. Combinations of complex CNS patterns generated precisely coordinated, reliable and robust motor actions.

  14. An Implantable Mixed Analog/Digital Neural Stimulator Circuit

    DEFF Research Database (Denmark)

    Gudnason, Gunnar; Bruun, Erik; Haugland, Morten

    1999-01-01

    This paper describes a chip for a multichannel neural stimulator for functional electrical stimulation. The chip performs all the signal processing required in an implanted neural stimulator. The power and signal transmission to the stimulator is carried out via an inductive link. From the signals...

  15. Neural Systems Laboratory

    Data.gov (United States)

    Federal Laboratory Consortium — As part of the Electrical and Computer Engineering Department and The Institute for System Research, the Neural Systems Laboratory studies the functionality of the...

  16. Evolvable Neural Software System

    Science.gov (United States)

    Curtis, Steven A.

    2009-01-01

    The Evolvable Neural Software System (ENSS) is composed of sets of Neural Basis Functions (NBFs), which can be totally autonomously created and removed according to the changing needs and requirements of the software system. The resulting structure is both hierarchical and self-similar in that a given set of NBFs may have a ruler NBF, which in turn communicates with other sets of NBFs. These sets of NBFs may function as nodes to a ruler node, which are also NBF constructs. In this manner, the synthetic neural system can exhibit the complexity, three-dimensional connectivity, and adaptability of biological neural systems. An added advantage of ENSS over a natural neural system is its ability to modify its core genetic code in response to environmental changes as reflected in needs and requirements. The neural system is fully adaptive and evolvable and is trainable before release. It continues to rewire itself while on the job. The NBF is a unique, bilevel intelligence neural system composed of a higher-level heuristic neural system (HNS) and a lower-level, autonomic neural system (ANS). Taken together, the HNS and the ANS give each NBF the complete capabilities of a biological neural system to match sensory inputs to actions. Another feature of the NBF is the Evolvable Neural Interface (ENI), which links the HNS and ANS. The ENI solves the interface problem between these two systems by actively adapting and evolving from a primitive initial state (a Neural Thread) to a complicated, operational ENI and successfully adapting to a training sequence of sensory input. This simulates the adaptation of a biological neural system in a developmental phase. Within the greater multi-NBF and multi-node ENSS, self-similar ENI s provide the basis for inter-NBF and inter-node connectivity.

  17. Energy efficient neural stimulation: coupling circuit design and membrane biophysics.

    Science.gov (United States)

    Foutz, Thomas J; Ackermann, D Michael; Kilgore, Kevin L; McIntyre, Cameron C

    2012-01-01

    The delivery of therapeutic levels of electrical current to neural tissue is a well-established treatment for numerous indications such as Parkinson's disease and chronic pain. While the neuromodulation medical device industry has experienced steady clinical growth over the last two decades, much of the core technology underlying implanted pulse generators remain unchanged. In this study we propose some new methods for achieving increased energy-efficiency during neural stimulation. The first method exploits the biophysical features of excitable tissue through the use of a centered-triangular stimulation waveform. Neural activation with this waveform is achieved with a statistically significant reduction in energy compared to traditional rectangular waveforms. The second method demonstrates energy savings that could be achieved by advanced circuitry design. We show that the traditional practice of using a fixed compliance voltage for constant-current stimulation results in substantial energy loss. A portion of this energy can be recuperated by adjusting the compliance voltage to real-time requirements. Lastly, we demonstrate the potential impact of axon fiber diameter on defining the energy-optimal pulse-width for stimulation. When designing implantable pulse generators for energy efficiency, we propose that the future combination of a variable compliance system, a centered-triangular stimulus waveform, and an axon diameter specific stimulation pulse-width has great potential to reduce energy consumption and prolong battery life in neuromodulation devices.

  18. Neural dynamics during repetitive visual stimulation

    Science.gov (United States)

    Tsoneva, Tsvetomira; Garcia-Molina, Gary; Desain, Peter

    2015-12-01

    Objective. Steady-state visual evoked potentials (SSVEPs), the brain responses to repetitive visual stimulation (RVS), are widely utilized in neuroscience. Their high signal-to-noise ratio and ability to entrain oscillatory brain activity are beneficial for their applications in brain-computer interfaces, investigation of neural processes underlying brain rhythmic activity (steady-state topography) and probing the causal role of brain rhythms in cognition and emotion. This paper aims at analyzing the space and time EEG dynamics in response to RVS at the frequency of stimulation and ongoing rhythms in the delta, theta, alpha, beta, and gamma bands. Approach.We used electroencephalography (EEG) to study the oscillatory brain dynamics during RVS at 10 frequencies in the gamma band (40-60 Hz). We collected an extensive EEG data set from 32 participants and analyzed the RVS evoked and induced responses in the time-frequency domain. Main results. Stable SSVEP over parieto-occipital sites was observed at each of the fundamental frequencies and their harmonics and sub-harmonics. Both the strength and the spatial propagation of the SSVEP response seem sensitive to stimulus frequency. The SSVEP was more localized around the parieto-occipital sites for higher frequencies (>54 Hz) and spread to fronto-central locations for lower frequencies. We observed a strong negative correlation between stimulation frequency and relative power change at that frequency, the first harmonic and the sub-harmonic components over occipital sites. Interestingly, over parietal sites for sub-harmonics a positive correlation of relative power change and stimulation frequency was found. A number of distinct patterns in delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz) and beta (15-30 Hz) bands were also observed. The transient response, from 0 to about 300 ms after stimulation onset, was accompanied by increase in delta and theta power over fronto-central and occipital sites, which returned to baseline

  19. Consecutive Acupuncture Stimulations Lead to Significantly Decreased Neural Responses

    NARCIS (Netherlands)

    Yeo, S.; Choe, I.H.; Noort, M.W.M.L. van den; Bosch, M.P.C.; Lim, S.

    2010-01-01

    Objective: Functional magnetic resonance imaging (fMRI), in combination with block design paradigms with consecutive acupuncture stimulations, has often been used to investigate the neural responses to acupuncture. In this study, we investigated whether previous acupuncture stimulations can affect

  20. Analysis of fractal electrodes for efficient neural stimulation.

    Science.gov (United States)

    Golestanirad, Laleh; Elahi, Behzad; Molina, Alberto; Mosig, Juan R; Pollo, Claudio; Chen, Robert; Graham, Simon J

    2013-01-01

    Planar electrodes are increasingly used in therapeutic neural stimulation techniques such as functional electrical stimulation, epidural spinal cord stimulation (ESCS), and cortical stimulation. Recently, optimized electrode geometries have been shown to increase the efficiency of neural stimulation by increasing the variation of current density on the electrode surface. In the present work, a new family of modified fractal electrode geometries is developed to enhance the efficiency of neural stimulation. It is shown that a promising approach in increasing the neural activation function is to increase the "edginess" of the electrode surface, a concept that is explained and quantified by fractal mathematics. Rigorous finite element simulations were performed to compute electric potential produced by proposed modified fractal geometries. The activation of 256 model axons positioned around the electrodes was then quantified, showing that modified fractal geometries required a 22% less input power while maintaining the same level of neural activation. Preliminary in vivo experiments investigating muscle evoked potentials due to median nerve stimulation showed encouraging results, supporting the feasibility of increasing neural stimulation efficiency using modified fractal geometries.

  1. Harmine stimulates proliferation of human neural progenitors

    Directory of Open Access Journals (Sweden)

    Vanja Dakic

    2016-12-01

    Full Text Available Harmine is the β-carboline alkaloid with the highest concentration in the psychotropic plant decoction Ayahuasca. In rodents, classical antidepressants reverse the symptoms of depression by stimulating neuronal proliferation. It has been shown that Ayahuasca presents antidepressant effects in patients with depressive disorder. In the present study, we investigated the effects of harmine in cell cultures containing human neural progenitor cells (hNPCs, 97% nestin-positive derived from pluripotent stem cells. After 4 days of treatment, the pool of proliferating hNPCs increased by 71.5%. Harmine has been reported as a potent inhibitor of the dual specificity tyrosine-phosphorylation-regulated kinase (DYRK1A, which regulates cell proliferation and brain development. We tested the effect of analogs of harmine, an inhibitor of DYRK1A (INDY, and an irreversible selective inhibitor of monoamine oxidase (MAO but not DYRK1A (pargyline. INDY but not pargyline induced proliferation of hNPCs similarly to harmine, suggesting that inhibition of DYRK1A is a possible mechanism to explain harmine effects upon the proliferation of hNPCs. Our findings show that harmine enhances proliferation of hNPCs and suggest that inhibition of DYRK1A may explain its effects upon proliferation in vitro and antidepressant effects in vivo.

  2. Exploring infrared neural stimulation with multimodal nonlinear imaging (Conference Presentation)

    Science.gov (United States)

    Adams, Wilson R.; Mahadevan-Jansen, Anita

    2017-02-01

    Infrared neural stimulation (INS) provides optical control of neural excitability using near to mid-infrared (mid-IR) light, which allows for spatially selective, artifact-free excitation without the introduction of exogenous agents or genetic modification. Although neural excitability is mediated by a transient temperature increase due to water absorption of IR energy, the molecular nature of IR excitability in neural tissue remains unknown. Current research suggests that transient changes in local tissue temperature give rise to a myriad of cellular responses that have been individually attributed to IR mediated excitability. To further elucidate the underlying biophysical mechanisms, we have begun work towards employing a novel multimodal nonlinear imaging platform to probe the molecular underpinnings of INS. Our imaging system performs coherent anti-Stokes Raman scattering (CARS), stimulated Raman scattering (SRS), two-photon excitation fluorescence (TPEF), second-harmonic generation (SHG) and thermal imaging into a single platform that allows for unprecedented co-registration of thermal and biochemical information in real-time. Here, we present our work leveraging CARS and SRS in acute thalamocortical brain slice preparations. We observe the evolution of lipid and protein-specific Raman bands during INS and electrically evoked activity in real-time. Combined with two-photon fluorescence and second harmonic generation, we offer insight to cellular metabolism and membrane dynamics during INS. Thermal imaging allows for the coregistration of acquired biochemical information with temperature information. Our work previews the versatility and capabilities of coherent Raman imaging combined with multiphoton imaging to observe biophysical phenomena for neuroscience applications.

  3. Carbon Nanofiber Nanoelectrodes for Neural Stimulation and Chemical Detection: The Era of "Smart" Deep Brain Stimulation

    Science.gov (United States)

    Koehne, Jessica E.

    2016-01-01

    A sensor platform based on vertically aligned carbon nanofibers (CNFs) has been developed. Their inherent nanometer scale, high conductivity, wide potential window, good biocompatibility and well-defined surface chemistry make them ideal candidates as biosensor electrodes. Here, we report two studies using vertically aligned CNF nanoelectrodes for biomedical applications. CNF arrays are investigated as neural stimulation and neurotransmitter recording electrodes for application in deep brain stimulation (DBS). Polypyrrole coated CNF nanoelectrodes have shown great promise as stimulating electrodes due to their large surface area, low impedance, biocompatibility and capacity for highly localized stimulation. CNFs embedded in SiO2 have been used as sensing electrodes for neurotransmitter detection. Our approach combines a multiplexed CNF electrode chip, developed at NASA Ames Research Center, with the Wireless Instantaneous Neurotransmitter Concentration Sensor (WINCS) system, developed at the Mayo Clinic. Preliminary results indicate that the CNF nanoelectrode arrays are easily integrated with WINCS for neurotransmitter detection in a multiplexed array format. In the future, combining CNF based stimulating and recording electrodes with WINCS may lay the foundation for an implantable smart therapeutic system that utilizes neurochemical feedback control while likely resulting in increased DBS application in various neuropsychiatric disorders. In total, our goal is to take advantage of the nanostructure of CNF arrays for biosensing studies requiring ultrahigh sensitivity, high-degree of miniaturization, and selective biofunctionalization.

  4. Carbon Nanofiber Nanoelectrodes for Neural Stimulation and Chemical Detection: The Era of Smart Deep Brain Stimulation

    Science.gov (United States)

    Koehne, Jessica E.

    2016-01-01

    A sensor platform based on vertically aligned carbon nanofibers (CNFs) has been developed. Their inherent nanometer scale, high conductivity, wide potential window, good biocompatibility and well-defined surface chemistry make them ideal candidates as biosensor electrodes. Here, we report two studies using vertically aligned CNF nanoelectrodes for biomedical applications. CNF arrays are investigated as neural stimulation and neurotransmitter recording electrodes for application in deep brain stimulation (DBS). Polypyrrole coated CNF nanoelectrodes have shown great promise as stimulating electrodes due to their large surface area, low impedance, biocompatibility and capacity for highly localized stimulation. CNFs embedded in SiO2 have been used as sensing electrodes for neurotransmitter detection. Our approach combines a multiplexed CNF electrode chip, developed at NASA Ames Research Center, with the Wireless Instantaneous Neurotransmitter Concentration Sensor (WINCS) system, developed at the Mayo Clinic. Preliminary results indicate that the CNF nanoelectrode arrays are easily integrated with WINCS for neurotransmitter detection in a multiplexed array format. In the future, combining CNF based stimulating and recording electrodes with WINCS may lay the foundation for an implantable "smart" therapeutic system that utilizes neurochemical feedback control while likely resulting in increased DBS application in various neuropsychiatric disorders. In total, our goal is to take advantage of the nanostructure of CNF arrays for biosensing studies requiring ultrahigh sensitivity, high-degree of miniaturization, and selective biofunctionalization.

  5. Nanoparticles: a challenging vehicle for neural stimulation

    Directory of Open Access Journals (Sweden)

    Elisabetta eColombo

    2016-03-01

    Full Text Available Neurostimulation represents a powerful and well-established tool for the treatment of several diseases affecting the central nervous system. Although effective in reducing the symptoms or the progression of brain disorders, the poor accessibility of the deepest areas of the brain currently hampers the possibility of a more specific and controlled therapeutic stimulation, depending on invasive surgical approaches and long-term stability and biocompatibility issues. The massive research of the last decades on nanomaterials and nanoscale devices favored the development of new tools to address the limitations of the available neurostimulation approaches. This mini-review focuses on the employment of nanoparticles for the modulation of the electrophysiological activity of neuronal networks and the related transduction mechanisms underlying the nanostructure-neuron interfaces.

  6. Combining BMI stimulation and mathematical modeling for acute stroke recovery and neural repair

    Directory of Open Access Journals (Sweden)

    Sara L Gonzalez Andino

    2011-07-01

    Full Text Available Rehabilitation is a neural plasticity-exploiting approach that forces undamaged neural circuits to undertake the functionality of other circuits damaged by stroke. It aims to partial restoration of the neural functions by circuit remodeling rather than by the regeneration of damaged circuits. The core hypothesis of the present paper is that - in stroke - Brain Machine Interfaces can be designed to target neural repair instead of rehabilitation. To support this hypothesis we first review existing evidence on the role of endogenous or externally applied electric fields on all processes involved in CNS repair. We then describe our own results to illustrate the neuroprotective and neuroregenerative effects of BMI- electrical stimulation on sensory deprivation-related degenerative processes of the CNS. Finally, we discuss three of the crucial issues involved in the design of neural repair-oriented BMIs: when to stimulate, where to stimulate and - the particularly important but unsolved issue of - how to stimulate. We argue that optimal parameters for the electrical stimulation can be determined from studying and modeling the dynamics of the electric fields that naturally emerge at the central and peripheral nervous system during spontaneous healing in both, experimental animals and human patients. We conclude that a closed-loop BMI that defines the optimal stimulation parameters from a priori developed experimental models of the dynamics of spontaneous repair and the on-line monitoring of neural activity might place BMIs as an alternative or complement to stem-cell transplantation or pharmacological approaches, intensively pursued nowadays.

  7. Rewiring neural interactions by micro-stimulation

    Directory of Open Access Journals (Sweden)

    James M Rebesco

    2010-08-01

    Full Text Available Plasticity is a crucial component of normal brain function and a critical mechanism for recovery from injury. In vitro, associative pairing of presynaptic spiking and stimulus-induced postsynaptic depolarization causes changes in the synaptic efficacy of the presynaptic neuron, when activated by extrinsic stimulation. In vivo, such paradigms can alter the responses of whole groups of neurons to stimulation. Here, we used in vivo spike-triggered stimulation to drive plastic changes in rat forelimb sensorimotor cortex, which we monitored using a statistical measure of functional connectivity inferred from the spiking statistics of the neurons during normal, spontaneous behavior. These induced plastic changes in inferred functional connectivity depended on the latency between trigger spike and stimulation, and appear to reflect a robust reorganization of the network. Such targeted connectivity changes might provide a tool for rerouting the flow of information through a network, with implications for both rehabilitation and brain-machine interface applications.

  8. Prediction and control of neural responses to pulsatile electrical stimulation

    Science.gov (United States)

    Campbell, Luke J.; Sly, David James; O'Leary, Stephen John

    2012-04-01

    This paper aims to predict and control the probability of firing of a neuron in response to pulsatile electrical stimulation of the type delivered by neural prostheses such as the cochlear implant, bionic eye or in deep brain stimulation. Using the cochlear implant as a model, we developed an efficient computational model that predicts the responses of auditory nerve fibers to electrical stimulation and evaluated the model's accuracy by comparing the model output with pooled responses from a group of guinea pig auditory nerve fibers. It was found that the model accurately predicted the changes in neural firing probability over time to constant and variable amplitude electrical pulse trains, including speech-derived signals, delivered at rates up to 889 pulses s-1. A simplified version of the model that did not incorporate adaptation was used to adaptively predict, within its limitations, the pulsatile electrical stimulus required to cause a desired response from neurons up to 250 pulses s-1. Future stimulation strategies for cochlear implants and other neural prostheses may be enhanced using similar models that account for the way that neural responses are altered by previous stimulation.

  9. Stimulated Deep Neural Network for Speech Recognition

    Science.gov (United States)

    2016-09-08

    approaches yield state-of-the-art performance in a range of tasks, including speech recognition . However, the parameters of the network are hard to analyze...advantage of the smoothness con- straints that stimulated training offers. The approaches are eval- uated on two large vocabulary speech recognition

  10. Infrared neural stimulation of human spinal nerve roots in vivo.

    Science.gov (United States)

    Cayce, Jonathan M; Wells, Jonathon D; Malphrus, Jonathan D; Kao, Chris; Thomsen, Sharon; Tulipan, Noel B; Konrad, Peter E; Jansen, E Duco; Mahadevan-Jansen, Anita

    2015-01-01

    Infrared neural stimulation (INS) is a neurostimulation modality that uses pulsed infrared light to evoke artifact-free, spatially precise neural activity with a noncontact interface; however, the technique has not been demonstrated in humans. The objective of this study is to demonstrate the safety and efficacy of INS in humans in vivo. The feasibility of INS in humans was assessed in patients ([Formula: see text]) undergoing selective dorsal root rhizotomy, where hyperactive dorsal roots, identified for transection, were stimulated in vivo with INS on two to three sites per nerve with electromyogram recordings acquired throughout the stimulation. The stimulated dorsal root was removed and histology was performed to determine thermal damage thresholds of INS. Threshold activation of human dorsal rootlets occurred in 63% of nerves for radiant exposures between 0.53 and [Formula: see text]. In all cases, only one or two monitored muscle groups were activated from INS stimulation of a hyperactive spinal root identified by electrical stimulation. Thermal damage was first noted at [Formula: see text] and a [Formula: see text] safety ratio was identified. These findings demonstrate the success of INS as a fresh approach for activating human nerves in vivo and providing the necessary safety data needed to pursue clinically driven therapeutic and diagnostic applications of INS in humans.

  11. A Sub-millimeter, Inductively Powered Neural Stimulator

    Directory of Open Access Journals (Sweden)

    Daniel K. Freeman

    2017-11-01

    Full Text Available Wireless neural stimulators are being developed to address problems associated with traditional lead-based implants. However, designing wireless stimulators on the sub-millimeter scale (<1 mm3 is challenging. As device size shrinks, it becomes difficult to deliver sufficient wireless power to operate the device. Here, we present a sub-millimeter, inductively powered neural stimulator consisting only of a coil to receive power, a capacitor to tune the resonant frequency of the receiver, and a diode to rectify the radio-frequency signal to produce neural excitation. By replacing any complex receiver circuitry with a simple rectifier, we have reduced the required voltage levels that are needed to operate the device from 0.5 to 1 V (e.g., for CMOS to ~0.25–0.5 V. This reduced voltage allows the use of smaller receive antennas for power, resulting in a device volume of 0.3–0.5 mm3. The device was encapsulated in epoxy, and successfully passed accelerated lifetime tests in 80°C saline for 2 weeks. We demonstrate a basic proof-of-concept using stimulation with tens of microamps of current delivered to the sciatic nerve in rat to produce a motor response.

  12. A hybrid hardware and software approach for cancelling stimulus artifacts during same-electrode neural stimulation and recording.

    Science.gov (United States)

    Culaclii, Stanislav; Kim, Brian; Yi-Kai Lo; Wentai Liu

    2016-08-01

    Recovering neural responses from electrode recordings is fundamental for understanding the dynamics of neural networks. This effort is often obscured by stimulus artifacts in the recordings, which result from stimuli injected into the electrode-tissue interface. Stimulus artifacts, which can be orders of magnitude larger than the neural responses of interest, can mask short-latency evoked responses. Furthermore, simultaneous neural stimulation and recording on the same electrode generates artifacts with larger amplitudes compared to a separate electrode setup, which inevitably overwhelm the amplifier operation and cause unrecoverable neural signal loss. This paper proposes an end-to-end system combining hardware and software techniques for actively cancelling stimulus artifacts, avoiding amplifier saturation, and recovering neural responses during current-controlled in-vivo neural stimulation and recording. The proposed system is tested in-vitro under various stimulation settings by stimulating and recording on the same electrode with a superimposed pre-recorded neural signal. Experimental results show that neural responses can be recovered with minimal distortion even during stimulus artifacts that are several orders greater in magnitude.

  13. Simulation of morphologically structured photo-thermal neural stimulation

    Science.gov (United States)

    Weissler, Y.; Farah, N.; Shoham, S.

    2017-10-01

    Objective. Rational design of next-generation techniques for photo-thermal excitation requires the development of tools capable of modeling the effects of spatially- and temporally-dependent temperature distribution on cellular neuronal structures. Approach. We present a new computer simulation tool for predicting the effects of arbitrary spatiotemporally-structured photo-thermal stimulation on 3D, morphologically realistic neurons. The new simulation tool is based on interfacing two generic platforms, NEURON and MATLAB and is therefore suited for capturing different kinds of stimuli and neural models. Main results. Simulation results are validated using photo-absorber induced neuro-thermal stimulation (PAINTS) empirical results, and advanced features are explored. Significance. The new simulation tool could have an important role in understanding and investigating complex optical stimulation at the single-cell and network levels.

  14. Electric stimulation with sinusoids and white noise for neural prostheses

    Directory of Open Access Journals (Sweden)

    Daniel K Freeman

    2010-02-01

    Full Text Available We are investigating the use of novel stimulus waveforms in neural prostheses to determine whether they can provide more precise control over the temporal and spatial pattern of elicited activity as compared to conventional pulsatile stimulation. To study this, we measured the response of retinal ganglion cells to both sinusoidal and white noise waveforms. The use of cell-attached and whole cell patch clamp recordings allowed the responses to be observed without significant obstruction from the stimulus artifact. Electric stimulation with sinusoids elicited robust responses. White noise analysis was used to derive the linear kernel for the ganglion cell’s spiking response as well as for the underlying excitatory currents. These results suggest that in response to electric stimulation, presynaptic retinal neurons exhibit bandpass filtering characteristics with peak response that occur 25ms after onset. The experimental approach demonstrated here may be useful for studying the temporal response properties of other neurons in the CNS.

  15. EDITORIAL: Special issue on optical neural engineering: advances in optical stimulation technology Special issue on optical neural engineering: advances in optical stimulation technology

    Science.gov (United States)

    Shoham, Shy; Deisseroth, Karl

    2010-08-01

    a single spine, with two-photon uncaging) and in rapid, flexible spatial-temporal patterns [10-14]. Nevertheless, current technology generally requires damaging doses of UV or violet illumination and the continuous re-introduction of the caged compound, which, despite interest, makes for a difficult transition beyond in vitro preparations. Thus, the tremendous progress in the in vivo application of photo-stimulation tools over the past five years has been largely facilitated by two 'exciting' new photo-stimulation technologies: photo-biological stimulation of a rapidly increasing arsenal of light-sensitive ion channels and pumps ('optogenetic' probes[15-18]) and direct photo-thermal stimulation of neural tissue with an IR laser [19-21]. The Journal of Neural Engineering has dedicated a special section in this issue to highlight advances in optical stimulation technology, which includes original peer-reviewed contributions dealing with the design of modern optical systems for spatial-temporal control of optical excitation patterns and with the biophysics of neural-thermal interaction mediated by electromagnetic waves. The paper by Nikolenko, Peterka and Yuste [22] presents a compact design of a microscope-photo-stimulator based on a transmissive phase-modulating spatial-light modulator (SLM). Computer-generated holographic photo-stimulation using SLMs [12-14, 23] allows the efficient parallel projection of intense sparse patterns of light, and the welcome development of compact, user-friendly systems will likely reduce the barrier to its widespread adoption. The paper by Losavio et al [24] presents the design and functional characteristics of their acousto-optical deflector (AOD) systems for studying spatial-temporal dendritic integration in single neurons in vitro. Both single-photon (UV) and two-photon (femtosecond pulsed IR) AOD uncaging systems are described in detail. The paper presents an excellent overview of the current state of the art and limitations of

  16. Parametric characterization of neural activity in the locus coeruleus in response to vagus nerve stimulation.

    Science.gov (United States)

    Hulsey, Daniel R; Riley, Jonathan R; Loerwald, Kristofer W; Rennaker, Robert L; Kilgard, Michael P; Hays, Seth A

    2017-03-01

    Vagus nerve stimulation (VNS) has emerged as a therapy to treat a wide range of neurological disorders, including epilepsy, depression, stroke, and tinnitus. Activation of neurons in the locus coeruleus (LC) is believed to mediate many of the effects of VNS in the central nervous system. Despite the importance of the LC, there is a dearth of direct evidence characterizing neural activity in response to VNS. A detailed understanding of the brain activity evoked by VNS across a range of stimulation parameters may guide selection of stimulation regimens for therapeutic use. In this study, we recorded neural activity in the LC and the mesencephalic trigeminal nucleus (Me5) in response to VNS over a broad range of current amplitudes, pulse frequencies, train durations, inter-train intervals, and pulse widths. Brief 0.5s trains of VNS drive rapid, phasic firing of LC neurons at 0.1mA. Higher current intensities and longer pulse widths drive greater increases in LC firing rate. Varying the pulse frequency substantially affects the timing, but not the total amount, of phasic LC activity. VNS drives pulse-locked neural activity in the Me5 at current levels above 1.2mA. These results provide insight into VNS-evoked phasic neural activity in multiple neural structures and may be useful in guiding the selection of VNS parameters to enhance clinical efficacy. Copyright © 2016 Elsevier Inc. All rights reserved.

  17. Neural stimulation and recording with bidirectional, soft carbon nanotube fiber microelectrodes.

    Science.gov (United States)

    Vitale, Flavia; Summerson, Samantha R; Aazhang, Behnaam; Kemere, Caleb; Pasquali, Matteo

    2015-01-01

    The development of microelectrodes capable of safely stimulating and recording neural activity is a critical step in the design of many prosthetic devices, brain-machine interfaces, and therapies for neurologic or nervous-system-mediated disorders. Metal electrodes are inadequate prospects for the miniaturization needed to attain neuronal-scale stimulation and recording because of their poor electrochemical properties, high stiffness, and propensity to fail due to bending fatigue. Here we demonstrate neural recording and stimulation using carbon nanotube (CNT) fiber electrodes. In vitro characterization shows that the tissue contact impedance of CNT fibers is remarkably lower than that of state-of-the-art metal electrodes, making them suitable for recording single-neuron activity without additional surface treatments. In vivo chronic studies in parkinsonian rodents show that CNT fiber microelectrodes stimulate neurons as effectively as metal electrodes with 10 times larger surface area, while eliciting a significantly reduced inflammatory response. The same CNT fiber microelectrodes can record neural activity for weeks, paving the way for the development of novel multifunctional and dynamic neural interfaces with long-term stability.

  18. Control strategies for underactuated neural ensembles driven by optogenetic stimulation

    Directory of Open Access Journals (Sweden)

    ShiNung eChing

    2013-04-01

    Full Text Available Motivated by experiments employing optogenetic stimulation of cortical regions, we consider spike control strategies for ensembles of uncoupled integrate and fire neurons with a common conductance input. We construct strategies for control of spike patterns, that is, multineuron trains of action potentials, up to some maximal spike rate determined by the neural biophysics. We emphasize a constructive role for parameter heterogeneity, and find a simple rule for controllability in pairs of neurons. In particular, we determine parameters for which common drive is not limited to inducing synchronous spiking. For large ensembles, we determine how the number of controllable neurons varies with the number of observed (recorded neurons, and what collateral spiking occurs in the full ensemble during control of the subensemble. While complete control of spiking in every neuron is not possible with a single input, we find that a degree of subensemble control is made possible by exploiting dynamical heterogeneity. As most available technologies for neural stimulation are underactuated, in the sense that the number of target neurons far exceeds the number of independent channels of stimulation, these results suggest partial control strategies that may be important in the development of sensory neuroprosthetics and other neurocontrol applications.

  19. Control strategies for underactuated neural ensembles driven by optogenetic stimulation.

    Science.gov (United States)

    Ching, ShiNung; Ritt, Jason T

    2013-01-01

    Motivated by experiments employing optogenetic stimulation of cortical regions, we consider spike control strategies for ensembles of uncoupled integrate and fire neurons with a common conductance input. We construct strategies for control of spike patterns, that is, multineuron trains of action potentials, up to some maximal spike rate determined by the neural biophysics. We emphasize a constructive role for parameter heterogeneity, and find a simple rule for controllability in pairs of neurons. In particular, we determine parameters for which common drive is not limited to inducing synchronous spiking. For large ensembles, we determine how the number of controllable neurons varies with the number of observed (recorded) neurons, and what collateral spiking occurs in the full ensemble during control of the subensemble. While complete control of spiking in every neuron is not possible with a single input, we find that a degree of subensemble control is made possible by exploiting dynamical heterogeneity. As most available technologies for neural stimulation are underactuated, in the sense that the number of target neurons far exceeds the number of independent channels of stimulation, these results suggest partial control strategies that may be important in the development of sensory neuroprosthetics and other neurocontrol applications.

  20. Neural systems for control

    National Research Council Canada - National Science Library

    Omidvar, Omid; Elliott, David L

    1997-01-01

    ... is reprinted with permission from A. Barto, "Reinforcement Learning," Handbook of Brain Theory and Neural Networks, M.A. Arbib, ed.. The MIT Press, Cambridge, MA, pp. 804-809, 1995. Chapter 4, Figures 4-5 and 7-9 and Tables 2-5, are reprinted with permission, from S. Cho, "Map Formation in Proprioceptive Cortex," International Jour...

  1. Polymer neural interface with dual-sided electrodes for neural stimulation and recording.

    Science.gov (United States)

    Tooker, Angela; Tolosa, Vanessa; Shah, Kedar G; Sheth, Heeral; Felix, Sarah; Delima, Terri; Pannu, Satinderpall

    2012-01-01

    We present here a demonstration of a dual-sided, 4-layer metal, polyimide-based electrode array suitable for neural stimulation and recording. The fabrication process outlined here utilizes simple polymer and metal deposition and etching steps, with no potentially harmful backside etches or long exposures to extremely toxic chemicals. These polyimide-based electrode arrays have been tested to ensure they are fully biocompatible and suitable for long-term implantation; their flexibility minimizes the injury and glial scarring that can occur at the implantation site. The creation of dual-side electrode arrays with more than two layers of trace metal enables the fabrication of neural probes with more electrodes without a significant increase in probe size. This allows for more stimulation/recording sites without inducing additional injury and glial scarring.

  2. Photovoltaic Pixels for Neural Stimulation: Circuit Models and Performance.

    Science.gov (United States)

    Boinagrov, David; Lei, Xin; Goetz, Georges; Kamins, Theodore I; Mathieson, Keith; Galambos, Ludwig; Harris, James S; Palanker, Daniel

    2016-02-01

    Photovoltaic conversion of pulsed light into pulsed electric current enables optically-activated neural stimulation with miniature wireless implants. In photovoltaic retinal prostheses, patterns of near-infrared light projected from video goggles onto subretinal arrays of photovoltaic pixels are converted into patterns of current to stimulate the inner retinal neurons. We describe a model of these devices and evaluate the performance of photovoltaic circuits, including the electrode-electrolyte interface. Characteristics of the electrodes measured in saline with various voltages, pulse durations, and polarities were modeled as voltage-dependent capacitances and Faradaic resistances. The resulting mathematical model of the circuit yielded dynamics of the electric current generated by the photovoltaic pixels illuminated by pulsed light. Voltages measured in saline with a pipette electrode above the pixel closely matched results of the model. Using the circuit model, our pixel design was optimized for maximum charge injection under various lighting conditions and for different stimulation thresholds. To speed discharge of the electrodes between the pulses of light, a shunt resistor was introduced and optimized for high frequency stimulation.

  3. Is neural hyperpolarization by cathodal stimulation always detrimental at the behavioral level?

    Directory of Open Access Journals (Sweden)

    Cornelia ePirulli

    2014-06-01

    Full Text Available Cathodal transcranial direct current stimulation (c-tDCS is usually considered an inhibitory stimulation. From a physiological perspective, c-tDCS stimulation induces hyperpolarization at the neural level. However, from a behavioral perspective, c-tDCS application does not always result in performance deterioration. In this work, we investigated the role of several important stimulation parameters (i.e., timing, presence of pauses, duration and intensity in shaping the behavioral effects of c-tDCS over the primary visual cortex.In Experiment 1, we applied c-tDCS at two different times (before or during an orientation discrimination task. We also studied the effects of pauses during the stimulation. In Experiments 2 and 3, we compared different durations (9 minutes vs. 22 minutes and intensities (0.75 mA vs. 1.5 mA of stimulation.c-tDCS applied before task execution induced an improvement of performance, highlighting the importance of the activation state of the cortex. However, this result depended on the duration and intensity of stimulation.We suggest that the application of c-tDCS induces depression of cortical activity over a specific stimulated area; but to keep reactivity within given limits, the brain react in order to restore the equilibrium and this might result in increased sensitivity in visual performance. This is a further example of how the nervous system dynamically maintains a condition that permits adequate performance in different environments.

  4. Degenerate coding in neural systems.

    Science.gov (United States)

    Leonardo, Anthony

    2005-11-01

    When the dimensionality of a neural circuit is substantially larger than the dimensionality of the variable it encodes, many different degenerate network states can produce the same output. In this review I will discuss three different neural systems that are linked by this theme. The pyloric network of the lobster, the song control system of the zebra finch, and the odor encoding system of the locust, while different in design, all contain degeneracies between their internal parameters and the outputs they encode. Indeed, although the dynamics of song generation and odor identification are quite different, computationally, odor recognition can be thought of as running the song generation circuitry backwards. In both of these systems, degeneracy plays a vital role in mapping a sparse neural representation devoid of correlations onto external stimuli (odors or song structure) that are strongly correlated. I argue that degeneracy between input and output states is an inherent feature of many neural systems, which can be exploited as a fault-tolerant method of reliably learning, generating, and discriminating closely related patterns.

  5. Investigation of sputtered iridium oxide as a stimulating/sensing material for neural prostheses

    Science.gov (United States)

    Negi, Sandeep

    Neural interface devices are developed for neuroscience and neuroprosthetics applications to record and stimulate nerve signals. Microelectrodes represent the direct interface between the biological tissue and the electronic system in neural prostheses that serve to record electrical signals from the nerves to obtain information from the natural sensors of the body or the motor fibers of the muscles. Also, the microelectrodes can inject charge into the targeted tissue to functionally excite nerves and muscles by electrical stimulation. Overall, the neural microelectrodes have to measure electrical potentials and have to exchange charge between the solid state of the electrode and the fluid state of the electrolyte in the body. Therefore, the interface between the microelectrode and biological fluid is a critical factor for the performance of the neural device. The interface properties depend mainly on the physical, electrical and chemical property of the electrode material. Even though a large selection of electrode materials has been tested for this purpose, to date no electrode material or coating process presented in scientific literature has been identified or qualified for long-term stimulation and recording neural signals. In this work, sputtered iridium oxide film (SIROF) was investigated as a potential electrode material. SIROF was deposited on the microelectrodes by reactive pulsed DC sputtering. The deposition parameters and corresponding film properties were examined and correlated with the stimulation and recording characteristics. Furthermore, for chronic applications, the stability of SIROF was investigated and stimulation protocol was determined for damage threshold of the film. The sputtering pressure was varied to obtain SIROF with desired properties. The SIROF properties were optimized based on its ability to inject charge in the tissue and its mechanical strength. The electrochemical characterization of SIROF was studied by electrochemical

  6. Memory Storage and Neural Systems.

    Science.gov (United States)

    Alkon, Daniel L.

    1989-01-01

    Investigates memory storage and molecular nature of associative-memory formation by analyzing Pavlovian conditioning in marine snails and rabbits. Presented is the design of a computer-based memory system (neural networks) using the rules acquired in the investigation. Reports that the artificial network recognized patterns well. (YP)

  7. Evoked EMG-based torque prediction under muscle fatigue in implanted neural stimulation

    Science.gov (United States)

    Hayashibe, Mitsuhiro; Zhang, Qin; Guiraud, David; Fattal, Charles

    2011-10-01

    In patients with complete spinal cord injury, fatigue occurs rapidly and there is no proprioceptive feedback regarding the current muscle condition. Therefore, it is essential to monitor the muscle state and assess the expected muscle response to improve the current FES system toward adaptive force/torque control in the presence of muscle fatigue. Our team implanted neural and epimysial electrodes in a complete paraplegic patient in 1999. We carried out a case study, in the specific case of implanted stimulation, in order to verify the corresponding torque prediction based on stimulus evoked EMG (eEMG) when muscle fatigue is occurring during electrical stimulation. Indeed, in implanted stimulation, the relationship between stimulation parameters and output torques is more stable than external stimulation in which the electrode location strongly affects the quality of the recruitment. Thus, the assumption that changes in the stimulation-torque relationship would be mainly due to muscle fatigue can be made reasonably. The eEMG was proved to be correlated to the generated torque during the continuous stimulation while the frequency of eEMG also decreased during fatigue. The median frequency showed a similar variation trend to the mean absolute value of eEMG. Torque prediction during fatigue-inducing tests was performed based on eEMG in model cross-validation where the model was identified using recruitment test data. The torque prediction, apart from the potentiation period, showed acceptable tracking performances that would enable us to perform adaptive closed-loop control through implanted neural stimulation in the future.

  8. Microchip-Embedded Capacitors for Implantable Neural Stimulators

    Science.gov (United States)

    Auciello, Orlando

    Miniaturization of microchips for implantation in the human body (e.g., microchip for the artificial retina to restore sight to people blinded by retina photoreceptors degeneration) requires the integration of high-capacitance (≥ 10 μF) energy-storage capacitors into the microchip. These capacitors would be based on high-dielectric constant layers, preferably made of materials that are bioinert (not affected by human body fluids) and are biocompatible (do not elicit adverse reactions in the human body). This chapter focuses on reviewing the work being done at Argonne National Laboratory (Materials Science Division and Center for Nanoscale Materials) to develop high-capacitance microchip-embedded capacitors based on novel high-K dielectric layers (TiAlOx or TiO2/Al2O3 superlattices). The microchip-embedded capacitor provides energy storage and electromagnetic signal coupling needed for neural stimulations. Advances in neural prostheses such as artificial retinas and cochlear implants require miniaturization of device size to minimize tissue damage and improve device/tissue interfaces in the human body. Therefore, development of microchip-embedded capacitors is critical to achieve full-implantable biomedical device miniaturization.

  9. Neural adaptations to strength training: moving beyond transcranial magnetic stimulation and reflex studies.

    Science.gov (United States)

    Carroll, T J; Selvanayagam, V S; Riek, S; Semmler, J G

    2011-06-01

    It has long been believed that training for increased strength not only affects muscle tissue, but also results in adaptive changes in the central nervous system. However, only in the last 10 years has the use of methods to study the neurophysiological details of putative neural adaptations to training become widespread. There are now many published reports that have used single motor unit recordings, electrical stimulation of peripheral nerves, and non-invasive stimulation of the human brain [i.e. transcranial magnetic stimulation (TMS)] to study neural responses to strength training. In this review, we aim to summarize what has been learned from single motor unit, reflex and TMS studies, and identify the most promising avenues to advance our conceptual understanding with these methods. We also consider the few strength training studies that have employed alternative neurophysiological techniques such as functional magnetic resonance imaging and electroencephalography. The nature of the information that these techniques can provide, as well as their major technical and conceptual pitfalls, are briefly described. The overall conclusion of the review is that the current evidence regarding neural adaptations to strength training is inconsistent and incomplete. In order to move forward in our understanding, it will be necessary to design studies that are based on a rigorous consideration of the limitations of the available techniques, and that are specifically targeted to address important conceptual questions. © 2011 The Authors. Acta Physiologica © 2011 Scandinavian Physiological Society.

  10. Improved Selectivity From a Wavelength Addressable Device for Wireless Stimulation of Neural Tissue

    Directory of Open Access Journals (Sweden)

    Elif Ç. Seymour

    2014-02-01

    Full Text Available Electrical neural stimulation with micro electrodes is a promising technique for restoring lost functions in the central nervous system as a result of injury or disease. One of the problems related to current neural stimulators is the tissue response due to the connecting wires and the presence of a rigid electrode inside soft neural tissue. We have developed a novel, optically activated, microscale photovoltaic neurostimulator based on a custom layered compound semiconductor heterostructure that is both wireless and has a comparatively small volume. Optical activation provides a wireless means of energy transfer to the neurostimulator, eliminating wires and the associated complications. This neurostimulator was shown to evoke action potentials and a functional motor response in the rat spinal cord. In this work, we extend our design to include wavelength selectivity and thus allowing independent activation of devices. As a proof of concept, we fabricated two different microscale devices with different spectral responsivities in the near-infrared region. We assessed the improved addressability of individual devices via wavelength selectivity as compared to spatial selectivity alone through on-bench optical measurements of the devices in combination with an in vivo light intensity profile in the rat cortex obtained in a previous study. We show that wavelength selectivity improves the individual addressability of the floating stimulators, thus increasing the number of devices that can be implanted in close proximity to each other.

  11. Amorphous silicon carbide ultramicroelectrode arrays for neural stimulation and recording.

    Science.gov (United States)

    Deku, Felix; Cohen, Yarden; Joshi-Imre, Alexandra; Kanneganti, Aswini; Gardner, Timothy; Cogan, Stuart

    2017-09-27

    Foreign body response to indwelling cortical microelectrodes limits the reliability of neural stimulation and recording, particularly for extended chronic applications in behaving animals. The extent to which this response compromises the chronic stability of neural devices depends on many factors including the materials used in the electrode construction, the size, and geometry of the indwelling structure. Here, we report on the development of microelectrode arrays (MEAs) based on amorphous silicon carbide (a-SiC). This technology utilizes a-SiC for its chronic stability and employs semiconductor manufacturing processes to create MEAs with small shank dimensions. The a-SiC films were deposited by plasma enhanced chemical vapor deposition and patterned by thin-film photolithographic techniques. To improve stimulation and recording capabilities with small contact areas, we investigated low impedance coatings on the electrode sites. The assembled devices were characterized in phosphate buffered saline for their electrochemical properties. MEAs utilizing a-SiC as both the primary structural element and encapsulation were fabricated successfully. These a-SiC MEAs had 16 penetrating shanks. Each shank has a cross-sectional area less than 60 µm2 and electrode sites with a geometric surface area varying from 20-200 μm2. Electrode coatings of TiN and SIROF reduced 1 kHz electrode impedance to less than 100 kΩ from ~2.8 MΩ for 100 µm2 Au electrode sites and increased the charge injection capacities to values greater than 3 mC/cm2. Finally, we demonstrated functionality by recording neural activity from basal ganglia nucleus of Zebra Finches and motor cortex of rat. The a-SiC MEAs provide a significant advancement in the development of microelectrodes that over the years has relied on silicon platforms for device manufacture. These flexible a-SiC MEAs have the potential for decreased tissue damage and reduced foreign body

  12. Original electronic design to perform epimysial and neural stimulation in paraplegia

    Science.gov (United States)

    Guiraud, David; Stieglitz, Thomas; Taroni, Gérard; Divoux, Jean-Louis

    2006-12-01

    This paper presents an original electronic architecture to manage epimysial and neural stimulation using the same implantable device. All the muscles needed to achieve lower limb movements such as standing and walking can thus be activated. Mainly for surgical reasons, some muscles need to be stimulated through different inputs: epimysium or motor nerve. We developed an electronic solution, including the design of an application-specific integrated circuit, to meet the requirements of both types of stimulation. Five years after the successful implantation of the system, we were able to evaluate the system's performance. The patient is still using the system at home and no failure occurred during this 5-year period. We conclude that the electronic design not only provides a unique investigative tool for research, but that it can also be used to restore the motor function of the lower limb. This technology has an advantage over external stimulation because the patient can safely use the system at home. However, improvements such as lower power consumption, and thus greater autonomy, are needed. We further conclude that the modelling of the electrical behaviour of the electrodes is reliable and the estimated parameter values are homogeneous and consistent for the same type of electrode. Thus, the three parameters of the first-order model can be identified from an acute animal experiment and provide a means to optimize the design of the output stage of implanted stimulators.

  13. A Power-Efficient Multichannel Neural Stimulator Using High-Frequency Pulsed Excitation From an Unfiltered Dynamic Supply.

    Science.gov (United States)

    van Dongen, Marijn N; Serdijn, Wouter A

    2016-02-01

    This paper presents a neural stimulator system that employs a fundamentally different way of stimulating neural tissue compared to classical constant current stimulation. A stimulation pulse is composed of a sequence of current pulses injected at a frequency of 1 MHz for which the duty cycle is used to control the stimulation intensity. The system features 8 independent channels that connect to any of the 16 electrodes at the output. A sophisticated control system allows for individual control of each channel's stimulation and timing parameters. This flexibility makes the system suitable for complex electrode configurations and current steering applications. Simultaneous multichannel stimulation is implemented using a high frequency alternating technique, which reduces the amount of electrode switches by a factor 8. The system has the advantage of requiring a single inductor as its only external component. Furthermore it offers a high power efficiency, which is nearly independent on both the voltage over the load as well as on the number of simultaneously operated channels. Measurements confirm this: in multichannel mode the power efficiency can be increased for specific cases to 40% compared to 20% that is achieved by state-of-the-art classical constant current stimulators with adaptive power supply.

  14. Learning by stimulation avoidance: A principle to control spiking neural networks dynamics.

    Science.gov (United States)

    Sinapayen, Lana; Masumori, Atsushi; Ikegami, Takashi

    2017-01-01

    Learning based on networks of real neurons, and learning based on biologically inspired models of neural networks, have yet to find general learning rules leading to widespread applications. In this paper, we argue for the existence of a principle allowing to steer the dynamics of a biologically inspired neural network. Using carefully timed external stimulation, the network can be driven towards a desired dynamical state. We term this principle "Learning by Stimulation Avoidance" (LSA). We demonstrate through simulation that the minimal sufficient conditions leading to LSA in artificial networks are also sufficient to reproduce learning results similar to those obtained in biological neurons by Shahaf and Marom, and in addition explains synaptic pruning. We examined the underlying mechanism by simulating a small network of 3 neurons, then scaled it up to a hundred neurons. We show that LSA has a higher explanatory power than existing hypotheses about the response of biological neural networks to external simulation, and can be used as a learning rule for an embodied application: learning of wall avoidance by a simulated robot. In other works, reinforcement learning with spiking networks can be obtained through global reward signals akin simulating the dopamine system; we believe that this is the first project demonstrating sensory-motor learning with random spiking networks through Hebbian learning relying on environmental conditions without a separate reward system.

  15. Optogenetic stimulation of multiwell MEA plates for neural and cardiac applications

    Science.gov (United States)

    Clements, Isaac P.; Millard, Daniel C.; Nicolini, Anthony M.; Preyer, Amanda J.; Grier, Robert; Heckerling, Andrew; Blum, Richard A.; Tyler, Phillip; McSweeney, K. M.; Lu, Yi-Fan; Hall, Diana; Ross, James D.

    2016-03-01

    Microelectrode array (MEA) technology enables advanced drug screening and "disease-in-a-dish" modeling by measuring the electrical activity of cultured networks of neural or cardiac cells. Recent developments in human stem cell technologies, advancements in genetic models, and regulatory initiatives for drug screening have increased the demand for MEA-based assays. In response, Axion Biosystems previously developed a multiwell MEA platform, providing up to 96 MEA culture wells arrayed into a standard microplate format. Multiwell MEA-based assays would be further enhanced by optogenetic stimulation, which enables selective excitation and inhibition of targeted cell types. This capability for selective control over cell culture states would allow finer pacing and probing of cell networks for more reliable and complete characterization of complex network dynamics. Here we describe a system for independent optogenetic stimulation of each well of a 48-well MEA plate. The system enables finely graded control of light delivery during simultaneous recording of network activity in each well. Using human induced pluripotent stem cell (hiPSC) derived cardiomyocytes and rodent primary neuronal cultures, we demonstrate high channel-count light-based excitation and suppression in several proof-of-concept experimental models. Our findings demonstrate advantages of combining multiwell optical stimulation and MEA recording for applications including cardiac safety screening, neural toxicity assessment, and advanced characterization of complex neuronal diseases.

  16. Effective electric fields along realistic DTI-based neural trajectories for modelling the stimulation mechanisms of TMS

    NARCIS (Netherlands)

    De Geeter, N.; Crevecoeur, G.; Leemans, A.; Dupré, L.

    2015-01-01

    In transcranial magnetic stimulation (TMS), an applied alternating magnetic field induces an electric field in the brain that can interact with the neural system. It is generally assumed that this induced electric field is the crucial effect exciting a certain region of the brain. More specifically,

  17. A wireless and batteryless neural headstage with optical stimulation and electrophysiological recording.

    Science.gov (United States)

    Ameli, Reza; Mirbozorgi, Abdollah; Neron, Jean-Luc; Lechasseur, Yoan; Gosselin, Benoit

    2013-01-01

    This paper presents a miniature Optogenetics headstage for wirelessly stimulating the brain of rodents with an implanted LED while recording electrophysiological data from a two-channel custom readout. The headstage is powered wirelessly using an inductive link, and is built using inexpensive commercial off-the-shelf electronic components, including a RF microcontroller and a printed antenna. This device has the capability to drive one light-stimulating LED and, at the same time, capture and send back neural signals recorded from two microelectrode readout channels. Light stimulation uses flexible patterns that allow for easy tuning of light intensity and stimulation periods. For driving the LED, a low-pass filtered digitally-generated PWM signal is employed for providing a flexible pulse generation method that alleviates the need for D/A converters. The proposed device can be powered wirelessly into an animal chamber using inductive energy transfer, which enables compact, light-weight and cost-effective smart animal research systems. The device dimensions are 15×25×17 mm; it weighs 7.4 grams and has a data transmission range of more than 2 meters. Different types of LEDs with different power consumptions can be used for this system. The power consumption of the system without the LED is 94.52 mW.

  18. The LILARTI neural network system

    Energy Technology Data Exchange (ETDEWEB)

    Allen, J.D. Jr.; Schell, F.M.; Dodd, C.V.

    1992-10-01

    The material of this Technical Memorandum is intended to provide the reader with conceptual and technical background information on the LILARTI neural network system of detail sufficient to confer an understanding of the LILARTI method as it is presently allied and to facilitate application of the method to problems beyond the scope of this document. Of particular importance in this regard are the descriptive sections and the Appendices which include operating instructions, partial listings of program output and data files, and network construction information.

  19. Alteration of neural action potential patterns by axonal stimulation: the importance of stimulus location.

    Science.gov (United States)

    Crago, Patrick E; Makowski, Nathaniel S

    2014-10-01

    Stimulation of peripheral nerves is often superimposed on ongoing motor and sensory activity in the same axons, without a quantitative model of the net action potential train at the axon endpoint. We develop a model of action potential patterns elicited by superimposing constant frequency axonal stimulation on the action potentials arriving from a physiologically activated neural source. The model includes interactions due to collision block, resetting of the neural impulse generator, and the refractory period of the axon at the point of stimulation. Both the mean endpoint firing rate and the probability distribution of the action potential firing periods depend strongly on the relative firing rates of the two sources and the intersite conduction time between them. When the stimulus rate exceeds the neural rate, neural action potentials do not reach the endpoint and the rate of endpoint action potentials is the same as the stimulus rate, regardless of the intersite conduction time. However, when the stimulus rate is less than the neural rate, and the intersite conduction time is short, the two rates partially sum. Increases in stimulus rate produce non-monotonic increases in endpoint rate and continuously increasing block of neurally generated action potentials. Rate summation is reduced and more neural action potentials are blocked as the intersite conduction time increases. At long intersite conduction times, the endpoint rate simplifies to being the maximum of either the neural or the stimulus rate. This study highlights the potential of increasing the endpoint action potential rate and preserving neural information transmission by low rate stimulation with short intersite conduction times. Intersite conduction times can be decreased with proximal stimulation sites for muscles and distal stimulation sites for sensory endings. The model provides a basis for optimizing experiments and designing neuroprosthetic interventions involving motor or sensory stimulation.

  20. Evidence-Based Systematic Review: Effects of Neuromuscular Electrical Stimulation on Swallowing and Neural Activation

    Science.gov (United States)

    Clark, Heather; Lazarus, Cathy; Arvedson, Joan; Schooling, Tracy; Frymark, Tobi

    2009-01-01

    Purpose: To systematically review the literature examining the effects of neuromuscular electrical stimulation (NMES) on swallowing and neural activation. The review was conducted as part of a series examining the effects of oral motor exercises (OMEs) on speech, swallowing, and neural activation. Method: A systematic search was conducted to…

  1. Complement peptide C3a stimulates neural plasticity after experimental brain ischaemia.

    Science.gov (United States)

    Stokowska, Anna; Atkins, Alison L; Morán, Javier; Pekny, Tulen; Bulmer, Linda; Pascoe, Michaela C; Barnum, Scott R; Wetsel, Rick A; Nilsson, Jonas A; Dragunow, Mike; Pekna, Marcela

    2017-02-01

    Ischaemic stroke induces endogenous repair processes that include proliferation and differentiation of neural stem cells and extensive rewiring of the remaining neural connections, yet about 50% of stroke survivors live with severe long-term disability. There is an unmet need for drug therapies to improve recovery by promoting brain plasticity in the subacute to chronic phase after ischaemic stroke. We previously showed that complement-derived peptide C3a regulates neural progenitor cell migration and differentiation in vitro and that C3a receptor signalling stimulates neurogenesis in unchallenged adult mice. To determine the role of C3a-C3a receptor signalling in ischaemia-induced neural plasticity, we subjected C3a receptor-deficient mice, GFAP-C3a transgenic mice expressing biologically active C3a in the central nervous system, and their respective wild-type controls to photothrombotic stroke. We found that C3a overexpression increased, whereas C3a receptor deficiency decreased post-stroke expression of GAP43 (P plasticity, in the peri-infarct cortex. To verify the translational potential of these findings, we used a pharmacological approach. Daily intranasal treatment of wild-type mice with C3a beginning 7 days after stroke induction robustly increased synaptic density (P neural plasticity and intranasal treatment with C3a receptor agonists is an attractive approach to improve functional recovery after ischaemic brain injury. © The Author (2016). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  2. Using Neural Response Telemetry to Monitor Physiological Responses to Acoustic Stimulation in Hybrid Cochlear Implant Users.

    Science.gov (United States)

    Abbas, Paul J; Tejani, Viral D; Scheperle, Rachel A; Brown, Carolyn J

    This report describes the results of a series of experiments where we use the neural response telemetry (NRT) system of the Nucleus cochlear implant (CI) to measure the response of the peripheral auditory system to acoustic stimulation in Nucleus Hybrid CI users. The objectives of this study were to determine whether they could separate responses from hair cells and neurons and to evaluate the stability of these measures over time. Forty-four CI users participated. They all had residual acoustic hearing and used a Nucleus Hybrid S8, S12, or L24 CI or the standard lateral wall CI422 implant. The NRT system of the CI was used to trigger an acoustic stimulus (500-Hz tone burst or click), which was presented at a low stimulation rate (10, 15, or 50 per second) to the implanted ear via an insert earphone and to record the cochlear microphonic, the auditory nerve neurophonic and the compound action potential (CAP) from an apical intracochlear electrode. To record acoustically evoked responses, a longer time window than is available with the commercial NRT software is required. This limitation was circumvented by making multiple recordings for each stimulus using different time delays between the onset of stimulation and the onset of averaging. These recordings were then concatenated off-line. Matched recordings elicited using positive and negative polarity stimuli were added off-line to emphasize neural potentials (SUM) and subtracted off-line to emphasize potentials primarily generated by cochlear hair cells (DIF). These assumptions regarding the origin of the SUM and DIF components were tested by comparing the magnitude of these derived responses recorded using various stimulation rates. Magnitudes of the SUM and DIF components were compared with each other and with behavioral thresholds. SUM and DIF components were identified for most subjects, consistent with both hair cell and neural responses to acoustic stimulation. For a subset of the study participants, the DIF

  3. Visible Light Neural Stimulation on graphitic-Carbon Nitride/Graphene Photocatalytic Fibers

    DEFF Research Database (Denmark)

    Zhang, Zhongyang; Xu, Ruodan; Wang, Zegao

    2017-01-01

    Light stimulation allows remote and spatiotemporally accurate operation that has been applied as effective, non-invasive means of therapeutic interventions. Here, visible light neural stimulation of graphitic carbon nitride (g-C3N4), an emerging photocatalyst with visible-light optoelectronic...

  4. Angiogenic factors stimulate growth of adult neural stem cells.

    Directory of Open Access Journals (Sweden)

    Andreas Androutsellis-Theotokis

    2010-02-01

    Full Text Available The ability to grow a uniform cell type from the adult central nervous system (CNS is valuable for developing cell therapies and new strategies for drug discovery. The adult mammalian brain is a source of neural stem cells (NSC found in both neurogenic and non-neurogenic zones but difficulties in culturing these hinders their use as research tools.Here we show that NSCs can be efficiently grown in adherent cell cultures when angiogenic signals are included in the medium. These signals include both anti-angiogenic factors (the soluble form of the Notch receptor ligand, Dll4 and pro-angiogenic factors (the Tie-2 receptor ligand, Angiopoietin 2. These treatments support the self renewal state of cultured NSCs and expression of the transcription factor Hes3, which also identifies the cancer stem cell population in human tumors. In an organotypic slice model, angiogenic factors maintain vascular structure and increase the density of dopamine neuron processes.We demonstrate new properties of adult NSCs and a method to generate efficient adult NSC cultures from various central nervous system areas. These findings will help establish cellular models relevant to cancer and regeneration.

  5. Neural network based system for equipment surveillance

    Science.gov (United States)

    Vilim, R.B.; Gross, K.C.; Wegerich, S.W.

    1998-04-28

    A method and system are disclosed for performing surveillance of transient signals of an industrial device to ascertain the operating state. The method and system involves the steps of reading into a memory training data, determining neural network weighting values until achieving target outputs close to the neural network output. If the target outputs are inadequate, wavelet parameters are determined to yield neural network outputs close to the desired set of target outputs and then providing signals characteristic of an industrial process and comparing the neural network output to the industrial process signals to evaluate the operating state of the industrial process. 33 figs.

  6. A Thalamocortical Neural Mass Model of the EEG during NREM Sleep and Its Response to Auditory Stimulation.

    Science.gov (United States)

    Schellenberger Costa, Michael; Weigenand, Arne; Ngo, Hong-Viet V; Marshall, Lisa; Born, Jan; Martinetz, Thomas; Claussen, Jens Christian

    2016-09-01

    Few models exist that accurately reproduce the complex rhythms of the thalamocortical system that are apparent in measured scalp EEG and at the same time, are suitable for large-scale simulations of brain activity. Here, we present a neural mass model of the thalamocortical system during natural non-REM sleep, which is able to generate fast sleep spindles (12-15 Hz), slow oscillations (EEG-data from a recent sleep study in humans, where closed-loop auditory stimulation was applied. The model output relates directly to the EEG, which makes it a useful basis to develop new stimulation protocols.

  7. Model study of combined electrical and near-infrared neural stimulation on the bullfrog sciatic nerve.

    Science.gov (United States)

    You, Mengxian; Mou, Zongxia

    2017-07-01

    This paper implemented a model study of combined electrical and near-infrared (808 nm) neural stimulation (NINS) on the bullfrog sciatic nerve. The model includes a COMSOL model to calculate the electric-field distribution of the surrounding area of the nerve, a Monte Carlo model to simulate light transport and absorption in the bullfrog sciatic nerve during NINS, and a NEURON model to simulate the neural electrophysiology changes under electrical stimulus and laser irradiation. The optical thermal effect is considered the main mechanism during NINS. Therefore, thermal change during laser irradiation was calculated by the Monte Carlo method, and the temperature distribution was then transferred to the NEURON model to stimulate the sciatic nerve. The effects on thermal response by adjusting the laser spot size, energy of the beam, and the absorption coefficient of the nerve are analyzed. The effect of the ambient temperature on the electrical stimulation or laser stimulation and the interaction between laser irradiation and electrical stimulation are also studied. The results indicate that the needed stimulus threshold for neural activation or inhibition is reduced by laser irradiation. Additionally, the needed laser energy for blocking the action potential is reduced by electrical stimulus. Both electrical and laser stimulation are affected by the ambient temperature. These results provide references for subsequent animal experiments and could be of great help to future basic and applied studies of infrared neural stimulation (INS).

  8. Targeted therapies using electrical and magnetic neural stimulation for the treatment of chronic pain in spinal cord injury.

    Science.gov (United States)

    Moreno-Duarte, Ingrid; Morse, Leslie R; Alam, Mahtab; Bikson, Marom; Zafonte, Ross; Fregni, Felipe

    2014-01-15

    Chronic neuropathic pain is one of the most common and disabling symptoms in individuals with spinal cord injury (SCI). Over two-thirds of subjects with SCI suffer from chronic pain influencing quality of life, rehabilitation, and recovery. Given the refractoriness of chronic pain to most pharmacological treatments, the majority of individuals with SCI report worsening of this condition over time. Moreover, only 4-6% of patients in this cohort report improvement. Novel treatments targeting mechanisms associated with pain-maladaptive plasticity, such as electromagnetic neural stimulation, may be desirable to improve outcomes. To date, few, small clinical trials have assessed the effects of invasive and noninvasive nervous system stimulation on pain after SCI. We aimed to review initial efficacy, safety and potential predictors of response by assessing the effects of neural stimulation techniques to treat SCI pain. A literature search was performed using the PubMed database including studies using the following targeted stimulation strategies: transcranial Direct Current Stimulation (tDCS), High Definition tDCS (HD-tDCS), repetitive Transcranial Magnetical Stimulation (rTMS), Cranial Electrotherapy Stimulation (CES), Transcutaneous Electrical Nerve Stimulation (TENS), Spinal Cord Stimulation (SCS) and Motor Cortex Stimulation (MCS), published prior to June of 2012. We included studies from 1998 to 2012. Eight clinical trials and one naturalistic observational study (nine studies in total) met the inclusion criteria. Among the clinical trials, three studies assessed the effects of tDCS, two of CES, two of rTMS and one of TENS. The naturalistic study investigated the analgesic effects of SCS. No clinical trials for epidural motor cortex stimulation (MCS) or HD-tDCS were found. Parameters of stimulation and also clinical characteristics varied significantly across studies. Three out of eight studies showed larger effects sizes (0.73, 0.88 and 1.86 respectively) for pain

  9. Bayesian neural adjustment of inhibitory control predicts emergence of problem stimulant use.

    Science.gov (United States)

    Harlé, Katia M; Stewart, Jennifer L; Zhang, Shunan; Tapert, Susan F; Yu, Angela J; Paulus, Martin P

    2015-11-01

    Bayesian ideal observer models quantify individuals' context- and experience-dependent beliefs and expectations about their environment, which provides a powerful approach (i) to link basic behavioural mechanisms to neural processing; and (ii) to generate clinical predictors for patient populations. Here, we focus on (ii) and determine whether individual differences in the neural representation of the need to stop in an inhibitory task can predict the development of problem use (i.e. abuse or dependence) in individuals experimenting with stimulants. One hundred and fifty-seven non-dependent occasional stimulant users, aged 18-24, completed a stop-signal task while undergoing functional magnetic resonance imaging. These individuals were prospectively followed for 3 years and evaluated for stimulant use and abuse/dependence symptoms. At follow-up, 38 occasional stimulant users met criteria for a stimulant use disorder (problem stimulant users), while 50 had discontinued use (desisted stimulant users). We found that those individuals who showed greater neural responses associated with Bayesian prediction errors, i.e. the difference between actual and expected need to stop on a given trial, in right medial prefrontal cortex/anterior cingulate cortex, caudate, anterior insula, and thalamus were more likely to exhibit problem use 3 years later. Importantly, these computationally based neural predictors outperformed clinical measures and non-model based neural variables in predicting clinical status. In conclusion, young adults who show exaggerated brain processing underlying whether to 'stop' or to 'go' are more likely to develop stimulant abuse. Thus, Bayesian cognitive models provide both a computational explanation and potential predictive biomarkers of belief processing deficits in individuals at risk for stimulant addiction. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please

  10. Facilitating access to emotions: neural signature of EMDR stimulation.

    Directory of Open Access Journals (Sweden)

    Deborah Herkt

    Full Text Available Eye Movement Desensitisation and Reprocessing (EMDR is a method in psychotherapy effective in treating symptoms of posttraumatic stress disorder. The client attends to alternating bilateral visual, auditory or sensory stimulation while confronted with emotionally disturbing material. It is thought that the bilateral stimulation as a specific element of EMDR facilitates accessing and processing of negative material while presumably creating new associative links. We hypothesized that the putatively facilitated access should be reflected in increased activation of the amygdala upon bilateral EMDR stimulation even in healthy subjects.We investigated 22 healthy female university students (mean 23.5 years with fMRI. Subjects were scanned while confronted with blocks of disgusting and neutral picture stimuli. One third of the blocks was presented without any additional stimulation, one third with bilateral simultaneous auditory stimulation, and one third with bilateral alternating auditory stimulation as used in EMDR.Contrasting disgusting vs. neutral picture stimuli confirmed the expected robust effect of amygdala activation for all auditory stimulation conditions. The interaction analysis with the type of auditory stimulation revealed a specific increase in activation of the right amygdala for the bilateral alternating auditory stimulation. Activation of the left dorsolateral prefrontal cortex showed the opposite effect with decreased activation.We demonstrate first time evidence for a putative neurobiological basis of the bilateral alternating stimulation as used in the EMDR method. The increase in limbic processing along with decreased frontal activation is in line with theoretical models of how bilateral alternating stimulation could help with therapeutic reintegration of information, and present findings may pave the way for future research on EMDR in the context of posttraumatic stress disorder.

  11. Facilitating access to emotions: neural signature of EMDR stimulation.

    Science.gov (United States)

    Herkt, Deborah; Tumani, Visal; Grön, Georg; Kammer, Thomas; Hofmann, Arne; Abler, Birgit

    2014-01-01

    Eye Movement Desensitisation and Reprocessing (EMDR) is a method in psychotherapy effective in treating symptoms of posttraumatic stress disorder. The client attends to alternating bilateral visual, auditory or sensory stimulation while confronted with emotionally disturbing material. It is thought that the bilateral stimulation as a specific element of EMDR facilitates accessing and processing of negative material while presumably creating new associative links. We hypothesized that the putatively facilitated access should be reflected in increased activation of the amygdala upon bilateral EMDR stimulation even in healthy subjects. We investigated 22 healthy female university students (mean 23.5 years) with fMRI. Subjects were scanned while confronted with blocks of disgusting and neutral picture stimuli. One third of the blocks was presented without any additional stimulation, one third with bilateral simultaneous auditory stimulation, and one third with bilateral alternating auditory stimulation as used in EMDR. Contrasting disgusting vs. neutral picture stimuli confirmed the expected robust effect of amygdala activation for all auditory stimulation conditions. The interaction analysis with the type of auditory stimulation revealed a specific increase in activation of the right amygdala for the bilateral alternating auditory stimulation. Activation of the left dorsolateral prefrontal cortex showed the opposite effect with decreased activation. We demonstrate first time evidence for a putative neurobiological basis of the bilateral alternating stimulation as used in the EMDR method. The increase in limbic processing along with decreased frontal activation is in line with theoretical models of how bilateral alternating stimulation could help with therapeutic reintegration of information, and present findings may pave the way for future research on EMDR in the context of posttraumatic stress disorder.

  12. Neural correlates of unihemispheric and bihemispheric motor cortex stimulation in healthy young adults.

    Science.gov (United States)

    Lindenberg, R; Sieg, M M; Meinzer, M; Nachtigall, L; Flöel, A

    2016-10-15

    Bihemispheric non-invasive motor cortex stimulation has shown promise for facilitating motor learning and recovery after stroke. However, previous studies yielded mixed results that can primarily be attributed to inter-individual variability in response. We therefore aimed at investigating neural correlates of bihemispheric transcranial direct current stimulation (tDCS) effects using multimodal magnetic resonance imaging (MRI). Twenty-four young healthy adults underwent diffusion tensor imaging (DTI), resting state and task-related functional MRI in a randomized sham-controlled, double-blind study using a triple cross-over design. We compared two active stimulation conditions-bihemispheric (or "dual") and unihemispheric anodal tDCS-with sham tDCS. The anode was placed over the left primary motor cortex in all conditions, and subgroups of responders were defined according to task-related activity in this area while subjects pressed a response button with their right index fingers during a choice reaction time task. Compared to sham, "dual responders" and "anodal responders" were characterized by mean beta value increases of 86±55% and 126±55%, respectively. In line with electrophysiological studies, tDCS effects on motor cortex activation appeared to be highly variable across the group. At rest, dual tDCS caused widespread bihemispheric alterations of functional connectivity, possibly mediating its most striking effect, which consisted of bilateral motor cortex disinhibition during the task-related functional MRI. In contrast, unihemispheric anodal tDCS was characterized by more local modulations of functional motor networks. As in aging and after stroke, the impact of dual tDCS on the motor system in young adults seems to depend on the microstructural status of transcallosal motor tracts as well. In sum, these results shed light on the neural correlates of dual and anodal tDCS effects in young adults and help in explaining the great inter-individual variability

  13. Transcutaneous parasacral electrical neural stimulation in children with primary monosymptomatic enuresis: a prospective randomized clinical trial.

    Science.gov (United States)

    de Oliveira, Liliana Fajardo; de Oliveira, Dayana Maria; da Silva de Paula, Lidyanne Ilídia; de Figueiredo, André Avarese; de Bessa, José; de Sá, Cacilda Andrade; Bastos Netto, José Murillo

    2013-10-01

    Parasacral transcutaneous electrical neural stimulation is widely used to treat hyperactive bladder in children and adults. Its use in nonmonosymptomatic enuresis has demonstrated improvement in number of dry nights. We assessed the effectiveness of parasacral transcutaneous electrical neural stimulation in the treatment of monosymptomatic primary enuresis. This prospective randomized clinical trial included 29 girls and 16 boys older than 6 years with primary monosymptomatic enuresis. Children were randomly divided into 2 groups consisting of controls, who were treated with behavioral therapy, and an experimental group, who were treated with behavioral therapy plus 10 sessions of parasacral transcutaneous electrical neural stimulation. Neural stimulation was performed with the electrodes placed in the sacral region (S2/S3). Sessions always followed the same pattern, with duration of 20 minutes, frequency of 10 Hz, a generated pulse of 700 μs and intensity determined by the sensitivity threshold of the child. Sessions were done 3 times weekly on alternate days. Patients in both groups were followed at 2-week intervals for the first month and then monthly for 6 consecutive months. Rate of wet nights was 77% in controls and 78.3% in the experimental group at onset of treatment (p = 0.82), and 49.5% and 31.2%, respectively, at the end of treatment (p = 0.02). Analyzing the average rate of improvement, there was a significantly greater increase in dry nights in the group undergoing neural stimulation (61.8%) compared to controls (37.3%, p = 0.0038). At the end of treatment percent improvement in children undergoing electrical stimulation had no relation to gender (p = 0.391) or age (p = 0.911). Treatment of primary monosymptomatic enuresis with 10 sessions of parasacral transcutaneous electrical neural stimulation plus behavioral therapy proved to be effective. However, no patient had complete resolution of symptoms. Copyright © 2013 American Urological Association

  14. Neural mechanisms underlying transcranial direct current stimulation in aphasia: A feasibility study.

    Directory of Open Access Journals (Sweden)

    Lena eUlm

    2015-10-01

    Full Text Available Little is known about the neural mechanisms by which transcranial direct current stimulation (tDCS impacts on language processing in post-stroke aphasia. This was addressed in a proof-of-principle study that explored the effects of tDCS application in aphasia during simultaneous functional magnetic resonance imaging (fMRI. We employed a single subject, cross-over, sham-tDCS controlled design and the stimulation was administered to an individualized perilesional stimulation site that was identified by a baseline fMRI scan and a picture naming task. Peak activity during the baseline scan was located in the spared left inferior frontal gyrus (IFG and this area was stimulated during a subsequent cross-over phase. tDCS was successfully administered to the target region and anodal- vs. sham-tDCS resulted in selectively increased activity at the stimulation site. Our results thus demonstrate that it is feasible to precisely target an individualized stimulation site in aphasia patients during simultaneous fMRI which allows assessing the neural mechanisms underlying tDCS application. The functional imaging results of this case report highlight one possible mechanism that may have contributed to beneficial behavioural stimulation effects in previous clinical tDCS trials in aphasia. In the future, this approach will allow identifying distinct patterns of stimulation effects on neural processing in larger cohorts of patients. This may ultimately yield information about the variability of tDCS-effects on brain functions in aphasia.

  15. Fast simulation and optimization tool to explore selective neural stimulation

    Directory of Open Access Journals (Sweden)

    Mélissa Dali

    2016-06-01

    Full Text Available In functional electrical stimulation, selective stimulation of axons is desirable to activate a specific target, in particular muscular function. This implies to simulate a fascicule without activating neighboring ones i.e. to be spatially selective. Spatial selectivity is achieved by the use of multicontact cuff electrodes over which the stimulation current is distributed. Because of the large number of parameters involved, numerical simulations provide a way to find and optimize electrode configuration. The present work offers a computation effective scheme and associated tool chain capable of simulating electrode-nerve interface and find the best spread of current to achieve spatial selectivity.

  16. Neural systems for tactual memories.

    Science.gov (United States)

    Bonda, E; Petrides, M; Evans, A

    1996-04-01

    1. The aim of this study was to investigate the neural systems involved in the memory processing of experiences through touch. 2. Regional cerebral blood flow was measured with positron emission tomography by means of the water bolus H2(15)O methodology in human subjects as they performed tasks involving different levels of tactual memory. In one of the experimental tasks, the subjects had to palpate nonsense shapes to match each one to a previously learned set, thus requiring constant reference to long-term memory. The other experimental task involved judgements of the recent recurrence of shapes during the scanning period. A set of three control tasks was used to control for the type of exploratory movements and sensory processing inherent in the two experimental tasks. 3. Comparisons of the distribution of activity between the experimental and the control tasks were carried out by means of the subtraction method. In relation to the control conditions, the two experimental tasks requiring memory resulted in significant changes within the posteroventral insula and the central opercular region. In addition, the task requiring recall from long-term memory yielded changes in the perirhinal cortex. 4. The above findings demonstrated that a ventrally directed parietoinsular pathway, leading to the posteroventral insula and the perirhinal cortex, constitutes a system by which long-lasting representations of tactual experiences are formed. It is proposed that the posteroventral insula is involved in tactual feature analysis, by analogy with the similar role of the inferotemporal cortex in vision, whereas the perirhinal cortex is further involved in the integration of these features into long-lasting representations of somatosensory experiences.

  17. KCNQ potassium channels in sensory system and neural circuits.

    Science.gov (United States)

    Wang, Jing-jing; Li, Yang

    2016-01-01

    M channels, an important regulator of neural excitability, are composed of four subunits of the Kv7 (KCNQ) K(+) channel family. M channels were named as such because their activity was suppressed by stimulation of muscarinic acetylcholine receptors. These channels are of particular interest because they are activated at the subthreshold membrane potentials. Furthermore, neural KCNQ channels are drug targets for the treatments of epilepsy and a variety of neurological disorders, including chronic and neuropathic pain, deafness, and mental illness. This review will update readers on the roles of KCNQ channels in the sensory system and neural circuits as well as discuss their respective mechanisms and the implications for physiology and medicine. We will also consider future perspectives and the development of additional pharmacological models, such as seizure, stroke, pain and mental illness, which work in combination with drug-design targeting of KCNQ channels. These models will hopefully deepen our understanding of KCNQ channels and provide general therapeutic prospects of related channelopathies.

  18. Neural Probes with Integrated Temperature Sensors for Monitoring Retina and Brain Implantation and Stimulation.

    Science.gov (United States)

    Wang, Jiaqi; Xie, Hui; Chung, Tsing; Chan, Leanne Lai Hang; Pang, Stella W

    2017-09-01

    Gold (Au) resistive temperature sensors were integrated on flexible polyimide-based neural probes to monitor temperature changes during neural probe implantation and stimulation. Temperature changes were measured as neural probes were implanted to infer the positions of the neural probes, and as the retina or the deep brain region was stimulated electrically. The temperature sensor consisted of a serpentine Au resistor and surrounded by four Au electrodes with 200 and [Formula: see text] diameter (dia.). The Au temperature sensors had temperature coefficient of 0.32%, and they were biocompatible and small in size. In vivo measurements of temperature changes during implantation and stimulation were carried out in the retina and deep brain region in rats. The desired implantation position was reached when temperature measured by the sensor increased to the calibrated level and became stable. There was no temperature increase when low level stimulation current of 8 and [Formula: see text] each for the two 200- and 400- [Formula: see text]-dia. electrodes, respectively, were applied. When higher level stimulation current of 100 and [Formula: see text] each were applied to the two 200- and 400- [Formula: see text]-dia. electrodes, respectively, maximum temperature increases of 1.2 °C in retina and 1 °C in deep brain region were found.

  19. Defining the neural fulcrum for chronic vagus nerve stimulation: implications for integrated cardiac control.

    Science.gov (United States)

    Ardell, Jeffrey L; Nier, Heath; Hammer, Matthew; Southerland, E Marie; Ardell, Christopher L; Beaumont, Eric; KenKnight, Bruce H; Armour, J Andrew

    2017-11-15

    VNS active phase owing to afferent modulation of parasympathetic central drive. When functional effects of afferent and efferent fibre activation were balanced, a null HRR was evoked (defined as 'neural fulcrum') during which HRR ≈ 0. As intensity increased further, HR was reduced during the active phase of VNS. While qualitatively similar, VNS delivered in the epilepsy configuration resulted in more pronounced HR acceleration and reduced HR deceleration during VNS. At termination, under anaesthesia, transection of the vagi rostral to the stimulation site eliminated the augmenting response to VNS and enhanced the parasympathetic efferent-mediated suppressing effect on electrical and mechanical function of the heart. In conclusion, VNS activates central then peripheral aspects of the cardiac nervous system. VNS control over cardiac function is maintained during chronic therapy. © 2017 The Authors. The Journal of Physiology © 2017 The Physiological Society.

  20. Fiber-array based optogenetic prosthetic system for stimulation therapy

    Science.gov (United States)

    Gu, Ling; Cote, Chris; Tejeda, Hector; Mohanty, Samarendra

    2012-02-01

    Recent advent of optogenetics has enabled activation of genetically-targeted neuronal cells using low intensity blue light with high temporal precision. Since blue light is attenuated rapidly due to scattering and absorption in neural tissue, optogenetic treatment of neurological disorders may require stimulation of specific cell types in multiple regions of the brain. Further, restoration of certain neural functions (vision, and auditory etc) requires accurate spatio-temporal stimulation patterns rather than just precise temporal stimulation. In order to activate multiple regions of the central nervous system in 3D, here, we report development of an optogenetic prosthetic comprising of array of fibers coupled to independently-controllable LEDs. This design avoids direct contact of LEDs with the brain tissue and thus does not require electrical and heat isolation, which can non-specifically stimulate and damage the local brain regions. The intensity, frequency, and duty cycle of light pulses from each fiber in the array was controlled independently using an inhouse developed LabView based program interfaced with a microcontroller driving the individual LEDs. While the temporal profile of the light pulses was controlled by varying the current driving the LED, the beam profile emanating from each fiber tip could be sculpted by microfabrication of the fiber tip. The fiber array was used to stimulate neurons, expressing channelrhodopsin-2, in different locations within the brain or retina. Control of neural activity in the mice cortex, using the fiber-array based prosthetic, is evaluated from recordings made with multi-electrode array (MEA). We also report construction of a μLED array based prosthetic for spatio-temporal stimulation of cortex.

  1. Contributions to muscle force and EMG by combined neural excitation and electrical stimulation

    Science.gov (United States)

    Crago, Patrick E.; Makowski, Nathaniel S.; Cole, Natalie M.

    2014-10-01

    Objective. Stimulation of muscle for research or clinical interventions is often superimposed on ongoing physiological activity without a quantitative understanding of the impact of the stimulation on the net muscle activity and the physiological response. Experimental studies show that total force during stimulation is less than the sum of the isolated voluntary and stimulated forces, but the occlusion mechanism is not understood. Approach. We develop a model of efferent motor activity elicited by superimposing stimulation during a physiologically activated contraction. The model combines action potential interactions due to collision block, source resetting, and refractory periods with previously published models of physiological motor unit recruitment, rate modulation, force production, and EMG generation in human first dorsal interosseous muscle to investigate the mechanisms and effectiveness of stimulation on the net muscle force and EMG. Main results. Stimulation during a physiological contraction demonstrates partial occlusion of force and the neural component of the EMG, due to action potential interactions in motor units activated by both sources. Depending on neural and stimulation firing rates as well as on force-frequency properties, individual motor unit forces can be greater, smaller, or unchanged by the stimulation. In contrast, voluntary motor unit EMG potentials in simultaneously stimulated motor units show progressive occlusion with increasing stimulus rate. The simulations predict that occlusion would be decreased by a reverse stimulation recruitment order. Significance. The results are consistent with and provide a mechanistic interpretation of previously published experimental evidence of force occlusion. The models also predict two effects that have not been reported previously—voluntary EMG occlusion and the advantages of a proximal stimulation site. This study provides a basis for the rational design of both future experiments and clinical

  2. Closed-loop neural stimulation for pentylenetetrazole-induced seizures in zebrafish

    Directory of Open Access Journals (Sweden)

    Ricardo Pineda

    2013-01-01

    Neural stimulation can reduce the frequency of seizures in persons with epilepsy, but rates of seizure-free outcome are low. Vagus nerve stimulation prevents seizures by continuously activating noradrenergic projections from the brainstem to the cortex. Cortical norepinephrine then increases GABAergic transmission and increases seizure threshold. Another approach, responsive nervous stimulation, prevents seizures by reactively shocking the seizure onset zone in precise synchrony with seizure onset. The electrical shocks abort seizures before they can spread and manifest clinically. The goal of this study was to determine whether a hybrid platform in which brainstem activation triggered in response to impending seizure activity could prevent seizures. We chose the zebrafish as a model organism for this study because of its ability to recapitulate human disease, in conjunction with its innate capacity for tightly controlled high-throughput experimentation. We first set out to determine whether electrical stimulation of the zebrafish hindbrain could have an anticonvulsant effect. We found that pulse train electrical stimulation of the hindbrain significantly increased the latency to onset of pentylenetetrazole-induced seizures, and that this apparent anticonvulsant effect was blocked by noradrenergic antagonists, as is also the case with rodents and humans. We also found that the anticonvulsant effect of hindbrain stimulation could be potentiated by reactive triggering of single pulse electrical stimulations in response to impending seizure activity. Finally, we found that the rate of stimulation triggering was directly proportional to pentylenetetrazole concentration and that the stimulation rate was reduced by the anticonvulsant valproic acid and by larger stimulation currents. Taken as a whole, these results show that that the anticonvulsant effect of brainstem activation can be efficiently utilized by reactive triggering, which suggests that alternative

  3. An improved genetic algorithm for designing optimal temporal patterns of neural stimulation

    Science.gov (United States)

    Cassar, Isaac R.; Titus, Nathan D.; Grill, Warren M.

    2017-12-01

    Objective. Electrical neuromodulation therapies typically apply constant frequency stimulation, but non-regular temporal patterns of stimulation may be more effective and more efficient. However, the design space for temporal patterns is exceedingly large, and model-based optimization is required for pattern design. We designed and implemented a modified genetic algorithm (GA) intended for design optimal temporal patterns of electrical neuromodulation. Approach. We tested and modified standard GA methods for application to designing temporal patterns of neural stimulation. We evaluated each modification individually and all modifications collectively by comparing performance to the standard GA across three test functions and two biophysically-based models of neural stimulation. Main results. The proposed modifications of the GA significantly improved performance across the test functions and performed best when all were used collectively. The standard GA found patterns that outperformed fixed-frequency, clinically-standard patterns in biophysically-based models of neural stimulation, but the modified GA, in many fewer iterations, consistently converged to higher-scoring, non-regular patterns of stimulation. Significance. The proposed improvements to standard GA methodology reduced the number of iterations required for convergence and identified superior solutions.

  4. An improved genetic algorithm for designing optimal temporal patterns of neural stimulation.

    Science.gov (United States)

    Cassar, Isaac R; Titus, Nathan D; Grill, Warren M

    2017-12-01

    Electrical neuromodulation therapies typically apply constant frequency stimulation, but non-regular temporal patterns of stimulation may be more effective and more efficient. However, the design space for temporal patterns is exceedingly large, and model-based optimization is required for pattern design. We designed and implemented a modified genetic algorithm (GA) intended for design optimal temporal patterns of electrical neuromodulation. We tested and modified standard GA methods for application to designing temporal patterns of neural stimulation. We evaluated each modification individually and all modifications collectively by comparing performance to the standard GA across three test functions and two biophysically-based models of neural stimulation. The proposed modifications of the GA significantly improved performance across the test functions and performed best when all were used collectively. The standard GA found patterns that outperformed fixed-frequency, clinically-standard patterns in biophysically-based models of neural stimulation, but the modified GA, in many fewer iterations, consistently converged to higher-scoring, non-regular patterns of stimulation. The proposed improvements to standard GA methodology reduced the number of iterations required for convergence and identified superior solutions.

  5. Effective electric fields along realistic DTI-based neural trajectories for modelling the stimulation mechanisms of TMS.

    Science.gov (United States)

    De Geeter, N; Crevecoeur, G; Leemans, A; Dupré, L

    2015-01-21

    In transcranial magnetic stimulation (TMS), an applied alternating magnetic field induces an electric field in the brain that can interact with the neural system. It is generally assumed that this induced electric field is the crucial effect exciting a certain region of the brain. More specifically, it is the component of this field parallel to the neuron's local orientation, the so-called effective electric field, that can initiate neuronal stimulation. Deeper insights on the stimulation mechanisms can be acquired through extensive TMS modelling. Most models study simple representations of neurons with assumed geometries, whereas we embed realistic neural trajectories computed using tractography based on diffusion tensor images. This way of modelling ensures a more accurate spatial distribution of the effective electric field that is in addition patient and case specific. The case study of this paper focuses on the single pulse stimulation of the left primary motor cortex with a standard figure-of-eight coil. Including realistic neural geometry in the model demonstrates the strong and localized variations of the effective electric field between the tracts themselves and along them due to the interplay of factors such as the tract's position and orientation in relation to the TMS coil, the neural trajectory and its course along the white and grey matter interface. Furthermore, the influence of changes in the coil orientation is studied. Investigating the impact of tissue anisotropy confirms that its contribution is not negligible. Moreover, assuming isotropic tissues lead to errors of the same size as rotating or tilting the coil with 10 degrees. In contrast, the model proves to be less sensitive towards the not well-known tissue conductivity values.

  6. Cutaneous retinal activation and neural entrainment in transcranial alternating current stimulation: A systematic review

    NARCIS (Netherlands)

    Schutter, D.J.L.G.

    2016-01-01

    Transcranial alternating current stimulation (tACS) applies exogenous oscillatory electric field potentials to entrain neural rhythms and is used to investigate brain-function relationships and its potential to enhance perceptual and cognitive performance. However, due to current spread tACS can

  7. High Frequency Deep Brain Stimulation and Neural Rhythms in Parkinson's Disease.

    Science.gov (United States)

    Blumenfeld, Zack; Brontë-Stewart, Helen

    2015-12-01

    High frequency (HF) deep brain stimulation (DBS) is an established therapy for the treatment of Parkinson's disease (PD). It effectively treats the cardinal motor signs of PD, including tremor, bradykinesia, and rigidity. The most common neural target is the subthalamic nucleus, located within the basal ganglia, the region most acutely affected by PD pathology. Using chronically-implanted DBS electrodes, researchers have been able to record underlying neural rhythms from several nodes in the PD network as well as perturb it using DBS to measure the ensuing neural and behavioral effects, both acutely and over time. In this review, we provide an overview of the PD neural network, focusing on the pathophysiological signals that have been recorded from PD patients as well as the mechanisms underlying the therapeutic benefits of HF DBS. We then discuss evidence for the relationship between specific neural oscillations and symptoms of PD, including the aberrant relationships potentially underlying functional connectivity in PD as well as the use of different frequencies of stimulation to more specifically target certain symptoms. Finally, we briefly describe several current areas of investigation and how the ability to record neural data in ecologically-valid settings may allow researchers to explore the relationship between brain and behavior in an unprecedented manner, culminating in the future automation of neurostimulation therapy for the treatment of a variety of neuropsychiatric diseases.

  8. Fatigue in multiple sclerosis: neural correlates and the role of non-invasive brain stimulation

    Directory of Open Access Journals (Sweden)

    Moussa A. Chalah

    2015-11-01

    Full Text Available Multiple sclerosis (MS is a chronic progressive inflammatory disease of the central nervous system and the major cause of non-traumatic disability in young adults. Fatigue is a frequent symptom reported by the majority of MS patients during their disease course and drastically af-fects their quality of life. Despite its significant prevalence and impact, the underlying patho-physiological mechanisms are not well elucidated. MS fatigue is still considered the result of multifactorial and complex constellations, and is commonly classified into primary fatigue related to the pathological changes of the disease itself, and secondary fatigue attributed to mimicking symptoms, comorbid sleep and mood disorders, and medications side effects. Data from neuroimaging, neurophysiology, neuroendocrine and neuroimmune studies have raised hypotheses regarding the origin of this symptom, some of which have succeeded in identifying an association between MS fatigue and structural or functional abnormalities within various brain networks. Hence, the aim of this work is to reappraise the neural correlates of MS fatigue and to discuss the rationale for the emergent use of noninvasive brain stimulation (NIBS techniques as potential treatments. This will include a presentation of the various NIBS modalities and a proposition of their potential mechanisms of action in this context. Specific issues related to the value of transcranial direct current stimulation will be addressed.

  9. Auditory Responses to Electric and Infrared Neural Stimulation of the Rat Cochlear Nucleus

    Science.gov (United States)

    Verma, Rohit; Guex, Amelie A.; Hancock, Kenneth E.; Durakovic, Nedim; McKay, Colette M.; Slama, Michaël C. C.; Brown, M. Christian; Lee, Daniel J.

    2014-01-01

    In an effort to improve the auditory brainstem implant, a prosthesis in which user outcomes are modest, we applied electric and infrared neural stimulation (INS) to the cochlear nucleus in a rat animal model. Electric stimulation evoked regions of neural activation in the inferior colliculus and short-latency, multipeaked auditory brainstem responses (ABRs). Pulsed INS, delivered to the surface of the cochlear nucleus via an optical fiber, evoked broad neural activation in the inferior colliculus. Strongest responses were recorded when the fiber was placed at lateral positions on the cochlear nucleus, close to the temporal bone. INS-evoked ABRs were multipeaked but longer in latency than those for electric stimulation; they resembled the responses to acoustic stimulation. After deafening, responses to electric stimulation persisted, whereas those to INS disappeared, consistent with a reported “optophonic” effect, a laser-induced acoustic artifact. Thus, for deaf individuals who use the auditory brainstem implant, INS alone did not appear promising as a new approach. PMID:24508368

  10. Auditory responses to electric and infrared neural stimulation of the rat cochlear nucleus.

    Science.gov (United States)

    Verma, Rohit U; Guex, Amélie A; Hancock, Kenneth E; Durakovic, Nedim; McKay, Colette M; Slama, Michaël C C; Brown, M Christian; Lee, Daniel J

    2014-04-01

    In an effort to improve the auditory brainstem implant, a prosthesis in which user outcomes are modest, we applied electric and infrared neural stimulation (INS) to the cochlear nucleus in a rat animal model. Electric stimulation evoked regions of neural activation in the inferior colliculus and short-latency, multipeaked auditory brainstem responses (ABRs). Pulsed INS, delivered to the surface of the cochlear nucleus via an optical fiber, evoked broad neural activation in the inferior colliculus. Strongest responses were recorded when the fiber was placed at lateral positions on the cochlear nucleus, close to the temporal bone. INS-evoked ABRs were multipeaked but longer in latency than those for electric stimulation; they resembled the responses to acoustic stimulation. After deafening, responses to electric stimulation persisted, whereas those to INS disappeared, consistent with a reported "optophonic" effect, a laser-induced acoustic artifact. Thus, for deaf individuals who use the auditory brainstem implant, INS alone did not appear promising as a new approach. Copyright © 2014 Elsevier B.V. All rights reserved.

  11. Brain-controlled neuromuscular stimulation to drive neural plasticity and functional recovery.

    Science.gov (United States)

    Ethier, C; Gallego, J A; Miller, L E

    2015-08-01

    There is mounting evidence that appropriately timed neuromuscular stimulation can induce neural plasticity and generate functional recovery from motor disorders. This review addresses the idea that coordinating stimulation with a patient's voluntary effort might further enhance neurorehabilitation. Studies in cell cultures and behaving animals have delineated the rules underlying neural plasticity when single neurons are used as triggers. However, the rules governing more complex stimuli and larger networks are less well understood. We argue that functional recovery might be optimized if stimulation were modulated by a brain machine interface, to match the details of the patient's voluntary intent. The potential of this novel approach highlights the need for a better understanding of the complex rules underlying this form of plasticity. Copyright © 2015 Elsevier Ltd. All rights reserved.

  12. Characterizing Deep Brain Stimulation effects in computationally efficient neural network models.

    Science.gov (United States)

    Latteri, Alberta; Arena, Paolo; Mazzone, Paolo

    2011-04-15

    Recent studies on the medical treatment of Parkinson's disease (PD) led to the introduction of the so called Deep Brain Stimulation (DBS) technique. This particular therapy allows to contrast actively the pathological activity of various Deep Brain structures, responsible for the well known PD symptoms. This technique, frequently joined to dopaminergic drugs administration, replaces the surgical interventions implemented to contrast the activity of specific brain nuclei, called Basal Ganglia (BG). This clinical protocol gave the possibility to analyse and inspect signals measured from the electrodes implanted into the deep brain regions. The analysis of these signals led to the possibility to study the PD as a specific case of dynamical synchronization in biological neural networks, with the advantage to apply the theoretical analysis developed in such scientific field to find efficient treatments to face with this important disease. Experimental results in fact show that the PD neurological diseases are characterized by a pathological signal synchronization in BG. Parkinsonian tremor, for example, is ascribed to be caused by neuron populations of the Thalamic and Striatal structures that undergo an abnormal synchronization. On the contrary, in normal conditions, the activity of the same neuron populations do not appear to be correlated and synchronized. To study in details the effect of the stimulation signal on a pathological neural medium, efficient models of these neural structures were built, which are able to show, without any external input, the intrinsic properties of a pathological neural tissue, mimicking the BG synchronized dynamics.We start considering a model already introduced in the literature to investigate the effects of electrical stimulation on pathologically synchronized clusters of neurons. This model used Morris Lecar type neurons. This neuron model, although having a high level of biological plausibility, requires a large computational effort

  13. Acupuncture stimulation on GB34 activates neural responses associated with Parkinson's disease.

    Science.gov (United States)

    Yeo, Sujung; Lim, Sabina; Choe, Il-Hwan; Choi, Yeong-Gon; Chung, Kyung-Cheon; Jahng, Geon-Ho; Kim, Sung-Hoon

    2012-09-01

    Parkinson's disease (PD) is a degenerative brain disorder that is caused by neural defects in the substantia nigra. Numerous studies have reported that acupuncture treatment on GB34 (Yanglingquan) leads to significant improvements in patients with PD and in PD animal models. Studies using functional magnetic resonance imaging (fMRI) have shown that patients with PD, compared to healthy participants, have lower neural responses in extensive brain regions including the putamen, thalamus, and the supplementary motor area. This study investigated the reported association between acupuncture point GB34 and PD. Using fMRI, neural responses of 12 patients with PD and 12 healthy participants were examined before and after acupuncture stimulation. Acupuncture stimulation increased neural responses in regions including the substantia nigra, caudate, thalamus, and putamen, which are impaired caused by PD. Areas associated with PD were activated by the acupuncture stimulation on GB34. This shows that acupuncture treatment on GB34 may be effective in improving the symptoms of PD. Although more randomized controlled trials on the topic will be needed, this study shows that acupuncture may be helpful in the treatment of symptoms involving PD. © 2012 Blackwell Publishing Ltd.

  14. Nanowire electrodes for high-density stimulation and measurement of neural circuits

    Directory of Open Access Journals (Sweden)

    Jacob T. Robinson

    2013-03-01

    Full Text Available Brain-machine interfaces (BMIs that can precisely monitor and control neural activity will likely require new hardware with improved resolution and specificity. New nanofabricated electrodes with feature sizes and densities comparable to neural circuits may lead to such improvements. In this perspective, we review the recent development of vertical nanowire (NW electrodes that could provide highly parallel single-cell recording and stimulation for future BMIs. We compare the advantages of these devices and discuss some of the technical challenges that must be overcome for this technology to become a platform for next-generation closed-loop BMIs.

  15. Prolonged fasting impairs neural reactivity to visual stimulation.

    Science.gov (United States)

    Kohn, N; Wassenberg, A; Toygar, T; Kellermann, T; Weidenfeld, C; Berthold-Losleben, M; Chechko, N; Orfanos, S; Vocke, S; Laoutidis, Z G; Schneider, F; Karges, W; Habel, U

    2016-01-01

    Previous literature has shown that hypoglycemia influences the intensity of the BOLD signal. A similar but smaller effect may also be elicited by low normal blood glucose levels in healthy individuals. This may not only confound the BOLD signal measured in fMRI, but also more generally interact with cognitive processing, and thus indirectly influence fMRI results. Here we show in a placebo-controlled, crossover, double-blind study on 40 healthy subjects, that overnight fasting and low normal levels of glucose contrasted to an activated, elevated glucose condition have an impact on brain activation during basal visual stimulation. Additionally, functional connectivity of the visual cortex shows a strengthened association with higher-order attention-related brain areas in an elevated blood glucose condition compared to the fasting condition. In a fasting state visual brain areas show stronger coupling to the inferior temporal gyrus. Results demonstrate that prolonged overnight fasting leads to a diminished BOLD signal in higher-order occipital processing areas when compared to an elevated blood glucose condition. Additionally, functional connectivity patterns underscore the modulatory influence of fasting on visual brain networks. Patterns of brain activation and functional connectivity associated with a broad range of attentional processes are affected by maturation and aging and associated with psychiatric disease and intoxication. Thus, we conclude that prolonged fasting may decrease fMRI design sensitivity in any task involving attentional processes when fasting status or blood glucose is not controlled.

  16. Spiking neural P systems with multiple channels.

    Science.gov (United States)

    Peng, Hong; Yang, Jinyu; Wang, Jun; Wang, Tao; Sun, Zhang; Song, Xiaoxiao; Luo, Xiaohui; Huang, Xiangnian

    2017-11-01

    Spiking neural P systems (SNP systems, in short) are a class of distributed parallel computing systems inspired from the neurophysiological behavior of biological spiking neurons. In this paper, we investigate a new variant of SNP systems in which each neuron has one or more synaptic channels, called spiking neural P systems with multiple channels (SNP-MC systems, in short). The spiking rules with channel label are introduced to handle the firing mechanism of neurons, where the channel labels indicate synaptic channels of transmitting the generated spikes. The computation power of SNP-MC systems is investigated. Specifically, we prove that SNP-MC systems are Turing universal as both number generating and number accepting devices. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. Visible Light Neural Stimulation on graphitic-Carbon Nitride/Graphene Photocatalytic Fibers

    DEFF Research Database (Denmark)

    Zhang, Zhongyang; Xu, Ruodan; Wang, Zegao

    2017-01-01

    Light stimulation allows remote and spatiotemporally accurate operation that has been applied as effective, non-invasive means of therapeutic interventions. Here, visible light neural stimulation of graphitic carbon nitride (g-C3N4), an emerging photocatalyst with visible-light optoelectronic...... conversion, was for the first time investigated. Specifically, g-C3N4 was combined with graphene oxide (GO) in a 3D manner on the surfaces of electrospun polycaprolactone/gelatin (PG) fibers and functioned as a biocompatible interface for visible-light stimulating neuronal differentiation. The enhanced...... was confirmed by the Lactate Dehydrogenase (LDH) assay, live dead staining and colorimetric cell viability assay CCK-8. Under a bidaily, monochromatic light stimulation at a wavelength of 450 nm at 10mW/cm2, a 18.5-fold increase of neurite outgrowth of PC12 was found on g-C3N4 coated fibers; while AA reduced GO...

  18. Modulating neural plasticity with non-invasive brain stimulation in schizophrenia.

    Science.gov (United States)

    Hasan, Alkomiet; Wobrock, Thomas; Rajji, Tarek; Malchow, Berend; Daskalakis, Zafiris J

    2013-12-01

    Schizophrenia is a severe mental disorder characterised by a complex phenotype including positive, negative, affective and cognitive symptoms. Various theories have been developed to integrate the clinical phenotype into a strong neurobiological framework. One theory describes schizophrenia as a disorder of impaired neural plasticity. Recently, non-invasive brain stimulation techniques have garnered much attention to their ability to modulate plasticity and treat schizophrenia. The aim of this review is to introduce the basic physiological principles of conventional non-invasive brain stimulation techniques and to review the available evidence for schizophrenia. Despite promising evidence for efficacy in a large number of clinical trials, we continue to have a rudimentary understanding of the underlying neurobiology. Additional investigation is required to improve the response rates to non-invasive brain stimulation, to reduce the interindividual variability and to improve the understanding of non-invasive brain stimulation in schizophrenia.

  19. Attenuated Neural Processing of Risk in Young Adults at Risk for Stimulant Dependence.

    Directory of Open Access Journals (Sweden)

    Martina Reske

    Full Text Available Approximately 10% of young adults report non-medical use of stimulants (cocaine, amphetamine, methylphenidate, which puts them at risk for the development of dependence. This fMRI study investigates whether subjects at early stages of stimulant use show altered decision making processing.158 occasional stimulants users (OSU and 50 comparison subjects (CS performed a "risky gains" decision making task during which they could select safe options (cash in 20 cents or gamble them for double or nothing in two consecutive gambles (win or lose 40 or 80 cents, "risky decisions". The primary analysis focused on risky versus safe decisions. Three secondary analyses were conducted: First, a robust regression examined the effect of lifetime exposure to stimulants and marijuana; second, subgroups of OSU with >1000 (n = 42, or <50 lifetime marijuana uses (n = 32, were compared to CS with <50 lifetime uses (n = 46 to examine potential marijuana effects; third, brain activation associated with behavioral adjustment following monetary losses was probed.There were no behavioral differences between groups. OSU showed attenuated activation across risky and safe decisions in prefrontal cortex, insula, and dorsal striatum, exhibited lower anterior cingulate cortex (ACC and dorsal striatum activation for risky decisions and greater inferior frontal gyrus activation for safe decisions. Those OSU with relatively more stimulant use showed greater dorsal ACC and posterior insula attenuation. In comparison, greater lifetime marijuana use was associated with less neural differentiation between risky and safe decisions. OSU who chose more safe responses after losses exhibited similarities with CS relative to those preferring risky options.Individuals at risk for the development of stimulant use disorders presented less differentiated neural processing of risky and safe options. Specifically, OSU show attenuated brain response in regions critical for performance monitoring

  20. Evaluating neural networks and artificial intelligence systems

    Science.gov (United States)

    Alberts, David S.

    1994-02-01

    Systems have no intrinsic value in and of themselves, but rather derive value from the contributions they make to the missions, decisions, and tasks they are intended to support. The estimation of the cost-effectiveness of systems is a prerequisite for rational planning, budgeting, and investment documents. Neural network and expert system applications, although similar in their incorporation of a significant amount of decision-making capability, differ from each other in ways that affect the manner in which they can be evaluated. Both these types of systems are, by definition, evolutionary systems, which also impacts their evaluation. This paper discusses key aspects of neural network and expert system applications and their impact on the evaluation process. A practical approach or methodology for evaluating a certain class of expert systems that are particularly difficult to measure using traditional evaluation approaches is presented.

  1. Renewal-process approximation of a stochastic threshold model for electrical neural stimulation.

    Science.gov (United States)

    Bruce, I C; Irlicht, L S; White, M W; O'Leary, S J; Clark, G M

    2000-01-01

    In a recent set of modeling studies we have developed a stochastic threshold model of auditory nerve response to single biphasic electrical pulses (Bruce et al., 1999c) and moderate rate (less than 800 pulses per second) pulse trains (Bruce et al., 1999a). In this article we derive an analytical approximation for the single-pulse model, which is then extended to describe the pulse-train model in the case of evenly timed, uniform pulses. This renewal-process description provides an accurate and computationally efficient model of electrical stimulation of single auditory nerve fibers by a cochlear implant that may be extended to other forms of electrical neural stimulation.

  2. A Programmable High-Voltage Compliance Neural Stimulator for Deep Brain Stimulation in Vivo

    Directory of Open Access Journals (Sweden)

    Cihun-Siyong Alex Gong

    2015-05-01

    Full Text Available Deep brain stimulation (DBS is one of the most effective therapies for movement and other disorders. The DBS neurosurgical procedure involves the implantation of a DBS device and a battery-operated neurotransmitter, which delivers electrical impulses to treatment targets through implanted electrodes. The DBS modulates the neuronal activities in the brain nucleus for improving physiological responses as long as an electric discharge above the stimulation threshold can be achieved. In an effort to improve the performance of an implanted DBS device, the device size, implementation cost, and power efficiency are among the most important DBS device design aspects. This study aims to present preliminary research results of an efficient stimulator, with emphasis on conversion efficiency. The prototype stimulator features high-voltage compliance, implemented with only a standard semiconductor process, without the use of extra masks in the foundry through our proposed circuit structure. The results of animal experiments, including evaluation of evoked responses induced by thalamic electrical stimuli with our fabricated chip, were shown to demonstrate the proof of concept of our design.

  3. Selectively stimulating neural populations in the subthalamic region using a novel deep brain stimulation lead design

    NARCIS (Netherlands)

    van Dijk, Kees Joab; Verhagen, R.; Bour, L.J.; Heida, Tjitske

    2013-01-01

    Deep brain stimulation (DBS) of the Subthalamic Nucleus (STN) is widely used in advanced stages of Parkinson's disease(PD) and has proven to be an effective treatment of the various motor symptoms. The therapy involves implanting a lead consisting of multiple electrodes in the STN through which

  4. Differentiation-Dependent Motility-Responses of Developing Neural Progenitors to Optogenetic Stimulation

    Directory of Open Access Journals (Sweden)

    Tímea Köhidi

    2017-12-01

    Full Text Available During neural tissue genesis, neural stem/progenitor cells are exposed to bioelectric stimuli well before synaptogenesis and neural circuit formation. Fluctuations in the electrochemical potential in the vicinity of developing cells influence the genesis, migration and maturation of neuronal precursors. The complexity of the in vivo environment and the coexistence of various progenitor populations hinder the understanding of the significance of ionic/bioelectric stimuli in the early phases of neuronal differentiation. Using optogenetic stimulation, we investigated the in vitro motility responses of radial glia-like neural stem/progenitor populations to ionic stimuli. Radial glia-like neural stem cells were isolated from CAGloxpStoploxpChR2(H134-eYFP transgenic mouse embryos. After transfection with Cre-recombinase, ChR2(channelrhodopsin-2-expressing and non-expressing cells were separated by eYFP fluorescence. Expression of light-gated ion channels were checked by patch clamp and fluorescence intensity assays. Neurogenesis by ChR2-expressing and non-expressing cells was induced by withdrawal of EGF from the medium. Cells in different (stem cell, migrating progenitor and maturing precursor stages of development were illuminated with laser light (λ = 488 nm; 1.3 mW/mm2; 300 ms in every 5 min for 12 h. The displacement of the cells was analyzed on images taken at the end of each light pulse. Results demonstrated that the migratory activity decreased with the advancement of neuronal differentiation regardless of stimulation. Light-sensitive cells, however, responded on a differentiation-dependent way. In non-differentiated ChR2-expressing stem cell populations, the motility did not change significantly in response to light-stimulation. The displacement activity of migrating progenitors was enhanced, while the motility of differentiating neuronal precursors was markedly reduced by illumination.

  5. VEGF-mediated angiogenesis stimulates neural stem cell proliferation and differentiation in the premature brain

    Energy Technology Data Exchange (ETDEWEB)

    Sun, Jinqiao, E-mail: jinqiao1977@163.com [Institute of Pediatrics, Children' s Hospital of Fudan University (China); Sha, Bin [Department of Neonatology, Children' s Hospital of Fudan University, 399 Wanyuan Road, Shanghai 201102 (China); Zhou, Wenhao, E-mail: zhou_wenhao@yahoo.com.cn [Department of Neonatology, Children' s Hospital of Fudan University, 399 Wanyuan Road, Shanghai 201102 (China); Yang, Yi [Institute of Pediatrics, Children' s Hospital of Fudan University (China)

    2010-03-26

    This study investigated the effects of angiogenesis on the proliferation and differentiation of neural stem cells in the premature brain. We observed the changes in neurogenesis that followed the stimulation and inhibition of angiogenesis by altering vascular endothelial growth factor (VEGF) expression in a 3-day-old rat model. VEGF expression was overexpressed by adenovirus transfection and down-regulated by siRNA interference. Using immunofluorescence assays, Western blot analysis, and real-time PCR methods, we observed angiogenesis and the proliferation and differentiation of neural stem cells. Immunofluorescence assays showed that the number of vWF-positive areas peaked at day 7, and they were highest in the VEGF up-regulation group and lowest in the VEGF down-regulation group at every time point. The number of neural stem cells, neurons, astrocytes, and oligodendrocytes in the subventricular zone gradually increased over time in the VEGF up-regulation group. Among the three groups, the number of these cells was highest in the VEGF up-regulation group and lowest in the VEGF down-regulation group at the same time point. Western blot analysis and real-time PCR confirmed these results. These data suggest that angiogenesis may stimulate the proliferation of neural stem cells and differentiation into neurons, astrocytes, and oligodendrocytes in the premature brain.

  6. Electrocutaneous stimulation system for Braille reading.

    Science.gov (United States)

    Echenique, Ana Maria; Graffigna, Juan Pablo; Mut, Vicente

    2010-01-01

    This work is an assistive technology for people with visual disabilities and aims to facilitate access to written information in order to achieve better social inclusion and integration into work and educational activities. Two methods of electrical stimulation (by current and voltage) of the mechanoreceptors was tested to obtain tactile sensations on the fingertip. Current and voltage stimulation were tested in a Braille cell and line prototype, respectively. These prototypes are evaluated in 33 blind and visually impaired subjects. The result of experimentation with both methods showed that electrical stimulation causes sensations of touch defined in the fingertip. Better results in the Braille characters reading were obtained with current stimulation (85% accuracy). However this form of stimulation causes uncomfortable sensations. The latter feeling was minimized with the method of voltage stimulation, but with low efficiency (50% accuracy) in terms of identification of the characters. We concluded that electrical stimulation is a promising method for the development of a simple and unexpensive Braille reading system for blind people. We observed that voltage stimulation is preferred by the users. However, more experimental tests must be carry out in order to find the optimum values of the stimulus parameters and increase the accuracy the Braille characters reading.

  7. Neural Plasticity in the Gustatory System

    OpenAIRE

    Hill, David L.

    2004-01-01

    Sensory systems adapt to changing environmental influences by coordinated alterations in structure and function. These alterations are referred to as plastic changes. The gustatory system displays numerous plastic changes even in receptor cells. This review focuses on the plasticity of gustatory structures through the first synaptic relay in the brain. Unlike other sensory systems, there is a remarkable amount of environmentally induced changes in these peripheral-most neural structures. The ...

  8. Injectable electronic identification, monitoring, and stimulation systems.

    Science.gov (United States)

    Troyk, P R

    1999-01-01

    Historically, electronic devices such as pacemakers and neuromuscular stimulators have been surgically implanted into animals and humans. A new class of implants made possible by advances in monolithic electronic design and implant packaging is small enough to be implanted by percutaneous injection through large-gauge hypodermic needles and does not require surgical implantation. Among these, commercially available implants, known as radio frequency identification (RFID) tags, are used for livestock, pet, laboratory animal, and endangered-species identification. The RFID tag is a subminiature glass capsule containing a solenoidal coil and an integrated circuit. Acting as the implanted half of a transcutaneous magnetic link, the RFID tag is powered by and communicates with an extracorporeal magnetic reader. The tag transmits a unique identification code that serves the function of identifying the animal. Millions of RFID tags have been sold since the early 1980s. Based on the success of the RFID tags, research laboratories have developed injectable medical implants, known as micromodules. One type of micromodule, the microstimulator, is designed for use in functional-neuromuscular stimulation. Each microstimulator is uniquely addressable and could comprise one channel of a multichannel functional-neuromuscular stimulation system. Using bidirectional telemetry and commands, from a single extracorporeal transmitter, as many as 256 microstimulators could form the hardware basis for a complex functional-neuromuscular stimulation feedback-control system. Uses include stimulation of paralyzed muscle, therapeutic functional-neuromuscular stimulation, and neuromodulatory functions such as laryngeal stimulation and sleep apnea.

  9. A novel lead design enables selective deep brain stimulation of neural populations in the subthalamic region

    Science.gov (United States)

    van Dijk, Kees J.; Verhagen, Rens; Chaturvedi, Ashutosh; McIntyre, Cameron C.; Bour, Lo J.; Heida, Ciska; Veltink, Peter H.

    2015-08-01

    Objective. The clinical effects of deep brain stimulation (DBS) of the subthalamic nucleus (STN-DBS) as a treatment for Parkinson’s disease are sensitive to the location of the DBS lead within the STN. New high density (HD) lead designs have been created which are hypothesized to provide additional degrees of freedom in shaping the stimulating electric field. The objective of this study is to compare the performances of a new HD lead with a conventional cylindrical contact (CC) lead. Approach. A computational model, consisting of a finite element electric field model combined with multi-compartment neuron and axon models representing different neural populations in the subthalamic region, was used to evaluate the two leads. We compared ring-mode and steering-mode stimulation with the HD lead to single contact stimulation with the CC lead. These stimulation modes were tested for the lead: (1) positioned in the centroid of the STN, (2) shifted 1 mm towards the internal capsule (IC), and (3) shifted 2 mm towards the IC. Under these conditions, we quantified the number of STN neurons that were activated without activating IC fibers, which are known to cause side-effects. Main results. The modeling results show that the HD lead is able to mimic the stimulation effect of the CC lead. Additionally, in steering-mode stimulation there was a significant increase of activated STN neurons compared to the CC mode. Significance. From the model simulations we conclude that the HD lead in steering-mode with optimized stimulation parameter selection can stimulate more STN cells. Next, the clinical impact of the increased number of activated STN cells should be tested and balanced across the increased complexity of identifying the optimized stimulation parameter settings for the HD lead.

  10. System and method for determining stability of a neural system

    Science.gov (United States)

    Curtis, Steven A. (Inventor)

    2011-01-01

    Disclosed are methods, systems, and computer-readable media for determining stability of a neural system. The method includes tracking a function world line of an N element neural system within at least one behavioral space, determining whether the tracking function world line is approaching a psychological stability surface, and implementing a quantitative solution that corrects instability if the tracked function world line is approaching the psychological stability surface.

  11. Unveiling neural coupling within the sensorimotor system : directionality and nonlinearity

    NARCIS (Netherlands)

    Yang, Y.; Dewald, J.P.A.; van der Helm, F.C.T.; Schouten, A.C.

    2017-01-01

    Neural coupling between the central nervous system and the periphery is essential for the neural control of movement. Corticomuscular coherence is a popular linear technique to assess synchronised oscillatory activity in the sensorimotor system. This oscillatory coupling originates from ascending

  12. Calcium dependent plasticity applied to repetitive transcranial magnetic stimulation with a neural field model.

    Science.gov (United States)

    Wilson, M T; Fung, P K; Robinson, P A; Shemmell, J; Reynolds, J N J

    2016-08-01

    The calcium dependent plasticity (CaDP) approach to the modeling of synaptic weight change is applied using a neural field approach to realistic repetitive transcranial magnetic stimulation (rTMS) protocols. A spatially-symmetric nonlinear neural field model consisting of populations of excitatory and inhibitory neurons is used. The plasticity between excitatory cell populations is then evaluated using a CaDP approach that incorporates metaplasticity. The direction and size of the plasticity (potentiation or depression) depends on both the amplitude of stimulation and duration of the protocol. The breaks in the inhibitory theta-burst stimulation protocol are crucial to ensuring that the stimulation bursts are potentiating in nature. Tuning the parameters of a spike-timing dependent plasticity (STDP) window with a Monte Carlo approach to maximize agreement between STDP predictions and the CaDP results reproduces a realistically-shaped window with two regions of depression in agreement with the existing literature. Developing understanding of how TMS interacts with cells at a network level may be important for future investigation.

  13. High-frequency limit of neural stimulation with ChR2.

    Science.gov (United States)

    Grossman, N; Nikolic, K; Grubb, M S; Burrone, J; Toumazou, C; Degenaar, P

    2011-01-01

    Optogenetic technology based on light activation of genetically targeted single component opsins such as Channelrhodopsin-2 (ChR2) has been changing the way neuroscience research is conducted. This technology is becoming increasingly important for neural engineering as well. The efficiency of neural stimulation with ChR2 drops at high frequencies, often before the natural limit of the neuron is reached. This study aims to investigate the underlying mechanisms that limit the efficiency of the stimulation at high frequencies. The study analyzes the dynamics of the spikes induced by ChR2 in comparison to control stimulations using patch clamp current injection. It shows that the stimulation dynamics is limited by two mechanisms: 1) a frequency independent reduction in the conductance-to-irradiance yield due to the ChR2 light adaptation process and 2) a frequency dependent reduction in the conductance-to-current yield due to a decrease in membrane re-polarization level between spikes that weakens the ionic driving force. The effect of the first mechanism can be minimized by using ChR2 mutants with lower irradiance threshold. In contrast the effect of the second mechanism is fundamentally limited by the rate the native ion channels re-polarize the membrane potential.

  14. A scheme of de-synchronization in globally coupled neural networks and its possible implications for vagus nerve stimulation

    Energy Technology Data Exchange (ETDEWEB)

    Li Yanlong [Institute of Theoretical Physics, Lanzhou University of Technology, Lanzhou 730050 (China) and Institute of Theoretical Physics, Lanzhou University, Lanzhou 730000 (China)], E-mail: liyl20031@126.com; Wu Min; Ma Jun [Institute of Theoretical Physics, Lanzhou University of Technology, Lanzhou 730050 (China); Chen Zhaoyang [Department of Chemistry, George Washington University, Washington, DC 20052 (United States); Wang Yinghai [Institute of Theoretical Physics, Lanzhou University, Lanzhou 730000 (China)

    2009-02-15

    A scheme of de-synchronization via pulse stimulation is numerically investigated in the Hindmarsh Rose globally coupled neural networks. The simulations show that synchronization evolves into de-synchronization in the globally coupled HR neural network when a part (about 10%) of neurons are stimulated with a pulse current signal. The network de-synchronization appears to be sensitive to the stimulation parameters. For the case of the same stimulation intensity, those weakly coupled networks reach de-synchronization more easily than strongly coupled networks. There exists a homologous asymptotic behavior in the region of higher frequency, and exist the optimal stimulation interval and period of continuous stimulation time when other stimulation parameters remain invariable.

  15. Effect of temporal predictability on the neural processing of self-triggered auditory stimulation during vocalization

    Directory of Open Access Journals (Sweden)

    Chen Zhaocong

    2012-05-01

    Full Text Available Abstract Background Sensory consequences of our own actions are perceived differently from the sensory stimuli that are generated externally. The present event-related potential (ERP study examined the neural responses to self-triggered stimulation relative to externally-triggered stimulation as a function of delays between the motor act and the stimulus onset. While sustaining a vowel phonation, subjects clicked a mouse and heard pitch-shift stimuli (PSS in voice auditory feedback at delays of either 0 ms (predictable or 500–1000 ms (unpredictable. The motor effect resulting from the mouse click was corrected in the data analyses. For the externally-triggered condition, PSS were delivered by a computer with a delay of 500–1000 ms after the vocal onset. Results As compared to unpredictable externally-triggered PSS, P2 responses to predictable self-triggered PSS were significantly suppressed, whereas an enhancement effect for P2 responses was observed when the timing of self-triggered PSS was unpredictable. Conclusions These findings demonstrate the effect of the temporal predictability of stimulus delivery with respect to the motor act on the neural responses to self-triggered stimulation. Responses to self-triggered stimulation were suppressed or enhanced compared with the externally-triggered stimulation when the timing of stimulus delivery was predictable or unpredictable. Enhancement effect of unpredictable self-triggered stimulation in the present study supports the idea that sensory suppression of self-produced action may be primarily caused by an accurate prediction of stimulus timing, rather than a movement-related non-specific suppression.

  16. Wireless control of functional electrical stimulation systems.

    Science.gov (United States)

    Matjacić, Z; Munih, M; Bajd, T; Kralj, A; Benko, H; Obreza, P

    1997-03-01

    With the assistance of crutches and functional electrical stimulation (FES), we are able to restore standing and simple gait in some spinal cord injured (SCI) patients. In present rehabilitative systems, the patient divides the gait cycle into stance and swing phases via pushbuttons mounted on the handles of the crutches, which are hardwired to the functional electrical stimulator. The surface-mount technology based telemetry system, which makes use of the radiofrequency medium at 40 MHz, was developed to provide wireless control of the FES system. Signals from crutch pushbuttons were coded and transferred from the transmitter to the receiver. The receiver was firmly attached to the patient's waist and was connected to the stimulator.

  17. Simulating neural systems with Xyce.

    Energy Technology Data Exchange (ETDEWEB)

    Schiek, Richard Louis; Thornquist, Heidi K.; Mei, Ting; Warrender, Christina E.; Aimone, James Bradley; Teeter, Corinne; Duda, Alex M.

    2012-12-01

    Sandias parallel circuit simulator, Xyce, can address large scale neuron simulations in a new way extending the range within which one can perform high-fidelity, multi-compartment neuron simulations. This report documents the implementation of neuron devices in Xyce, their use in simulation and analysis of neuron systems.

  18. Transcranial magnetic stimulation and connectivity mapping: tools for studying the neural bases of brain disorders.

    Directory of Open Access Journals (Sweden)

    Michelle Hampson

    2010-08-01

    Full Text Available There has been an increasing emphasis on characterizing pathophysiology underlying psychiatric and neurological disorders in terms of altered neural connectivity and network dynamics. Transcranial magnetic stimulation (TMS provides a unique opportunity for investigating connectivity in the human brain. TMS allows researchers and clinicians to directly stimulate cortical regions accessible to electromagnetic coils positioned on the scalp. The induced activation can then propagate through long-range connections to other brain areas. Thus, by identifying distal regions activated during TMS, researchers can infer connectivity patterns in the healthy human brain and can examine how those patterns may be disrupted in patients with different brain disorders. Conversely, connectivity maps derived using neuroimaging methods can identify components of a dysfunctional network. Nodes in this dysfunctional network accessible as targets for TMS by virtue of their proximity to the scalp may then permit TMS-induced alterations of components of the network not directly accessible to TMS via propagated effects. Thus TMS can provide a portal for accessing and altering neural dynamics in networks that are widely distributed anatomically. Finally, when long-term modulation of network dynamics is induced by trains of repetitive TMS, changes in functional connectivity patterns can be studied in parallel with changes in patient symptoms. These correlational data can elucidate neural mechanisms underlying illness and recovery. In this review, we focus on the application of these approaches to the study of psychiatric and neurological illnesses.

  19. IMPLEMENTATION OF NEURAL - CRYPTOGRAPHIC SYSTEM USING FPGA

    Directory of Open Access Journals (Sweden)

    KARAM M. Z. OTHMAN

    2011-08-01

    Full Text Available Modern cryptography techniques are virtually unbreakable. As the Internet and other forms of electronic communication become more prevalent, electronic security is becoming increasingly important. Cryptography is used to protect e-mail messages, credit card information, and corporate data. The design of the cryptography system is a conventional cryptography that uses one key for encryption and decryption process. The chosen cryptography algorithm is stream cipher algorithm that encrypt one bit at a time. The central problem in the stream-cipher cryptography is the difficulty of generating a long unpredictable sequence of binary signals from short and random key. Pseudo random number generators (PRNG have been widely used to construct this key sequence. The pseudo random number generator was designed using the Artificial Neural Networks (ANN. The Artificial Neural Networks (ANN providing the required nonlinearity properties that increases the randomness statistical properties of the pseudo random generator. The learning algorithm of this neural network is backpropagation learning algorithm. The learning process was done by software program in Matlab (software implementation to get the efficient weights. Then, the learned neural network was implemented using field programmable gate array (FPGA.

  20. Neural correlates of erotic stimulation under different levels of female sexual hormones.

    Directory of Open Access Journals (Sweden)

    Birgit Abler

    Full Text Available Previous studies have demonstrated variable influences of sexual hormonal states on female brain activation and the necessity to control for these in neuroimaging studies. However, systematic investigations of these influences, particularly those of hormonal contraceptives as compared to the physiological menstrual cycle are scarce. In the present study, we investigated the hormonal modulation of neural correlates of erotic processing in a group of females under hormonal contraceptives (C group; N = 12, and a different group of females (nC group; N = 12 not taking contraceptives during their mid-follicular and mid-luteal phases of the cycle. We used functional magnetic resonance imaging to measure hemodynamic responses as an estimate of brain activation during three different experimental conditions of visual erotic stimulation: dynamic videos, static erotic pictures, and expectation of erotic pictures. Plasma estrogen and progesterone levels were assessed in all subjects. No strong hormonally modulating effect was detected upon more direct and explicit stimulation (viewing of videos or pictures with significant activations in cortical and subcortical brain regions previously linked to erotic stimulation consistent across hormonal levels and stimulation type. Upon less direct and less explicit stimulation (expectation, activation patterns varied between the different hormonal conditions with various, predominantly frontal brain regions showing significant within- or between-group differences. Activation in the precentral gyrus during the follicular phase in the nC group was found elevated compared to the C group and positively correlated with estrogen levels. From the results we conclude that effects of hormonal influences on brain activation during erotic stimulation are weak if stimulation is direct and explicit but that female sexual hormones may modulate more subtle aspects of sexual arousal and behaviour as involved in sexual

  1. Optical path of infrared neural stimulation in the guinea pig and cat cochlea

    Science.gov (United States)

    Rajguru, Suhrud M.; Hwang, Margaret; Moreno, Laura E.; Matic, Agnella I.; Stock, Stuart R.; Richter, Claus-Peter

    2011-03-01

    It has been demonstrated previously that infrared neural stimulation (INS) can be used to stimulate spiral ganglion cells in the cochlea. With INS, neural stimulation can be achieved without direct contact of the radiation source and the tissue and is spatially well resolved. The presence of fluids or bone between the target structure and the radiation source may lead to absorption or scattering of the radiation and limit the efficacy of INS. To develop INS based cochlear implants, it is critical to determine the beam path of the radiation in the cochlea. In the present study, we utilized noninvasive X-ray microtomography (microCT) to visualize the orientation and location of the optical fiber within the guinea pig and cat cochlea. Overall, the results indicated that the optical fiber was directed towards the spiral ganglion cells in the cochlea and not the nerve fibers in the center of the modiolus. The fiber was approximately 300 μm away from the target structures. In future studies, results from the microCT will be correlated with physiology obtained from recordings in the midbrain.

  2. Targeting Neural Endophenotypes of Eating Disorders with Non-invasive Brain Stimulation.

    Science.gov (United States)

    Dunlop, Katharine A; Woodside, Blake; Downar, Jonathan

    2016-01-01

    The term "eating disorders" (ED) encompasses a wide variety of disordered eating and compensatory behaviors, and so the term is associated with considerable clinical and phenotypic heterogeneity. This heterogeneity makes optimizing treatment techniques difficult. One class of treatments is non-invasive brain stimulation (NIBS). NIBS, including repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS), are accessible forms of neuromodulation that alter the cortical excitability of a target brain region. It is crucial for NIBS to be successful that the target is well selected for the patient population in question. Targets may best be selected by stepping back from conventional DSM-5 diagnostic criteria to identify neural substrates of more basic phenotypes, including behavior related to rewards and punishment, cognitive control, and social processes. These phenotypic dimensions have been recently laid out by the Research Domain Criteria (RDoC) initiative. Consequently, this review is intended to identify potential dimensions as outlined by the RDoC and the underlying behavioral and neurobiological targets associated with ED. This review will also identify candidate targets for NIBS based on these dimensions and review the available literature on rTMS and tDCS in ED. This review systematically reviews abnormal neural circuitry in ED within the RDoC framework, and also systematically reviews the available literature investigating NIBS as a treatment for ED.

  3. Targeting neural endophenotypes of eating disorders with non-invasive brain stimulation

    Directory of Open Access Journals (Sweden)

    Katharine A Dunlop

    2016-02-01

    Full Text Available The term eating disorders (ED encompasses a wide variety of disordered eating and compensatory behaviors, and so the term is associated with considerable clinical and phenotypic heterogeneity. This heterogeneity makes optimizing treatment techniques difficult. One class of treatments is non-invasive brain stimulation (NIBS. NIBS, including repetitive transcranial magnetic stimulation (rTMS and transcranial direct current stimulation (tDCS are accessible forms of neuromodulation that alter the cortical excitability of a target brain region. It is crucial for NIBS to be successful that the target is well selected for the patient population in question. Targets may best be selected by stepping back from conventional DSM-5 diagnostic criteria to identify neural substrates of more basic phenotypes, including behavior related rewards and punishment cognitive control, and social processes. These phenotypic dimensions have been recently laid out by the Research Domain Criteria (RDoC initiative. Consequently, this review is intended to identify potential dimensions as outlined by the RDoC and their underlying behavioral and neurobiological targets associated with ED as potential candidates for NIBS and review the available literature on rTMS and tDCS in ED. This review systematically reviews abnormal neural circuitry in ED within the RDoC framework, and also systematically reviews the available literature investigating NIBS as a treatment for ED.

  4. Neural Stimulation Has a Long-Term Effect on Foreign Vocabulary Acquisition

    Science.gov (United States)

    Pasqualotto, Achille; Kobanbay, Begüm; Proulx, Michael J.

    2015-01-01

    Acquisition of a foreign language is a challenging task that is becoming increasingly more important in the world nowadays. There is evidence suggesting that the frontal and temporal cortices are involved in language processing and comprehension, but it is still unknown whether foreign language acquisition recruits additional cortical areas in a causal manner. For the first time, we used transcranial random noise stimulation on the frontal and parietal brain areas, in order to compare its effect on the acquisition of unknown foreign words and a sham, or placebo, condition was also included. This type of noninvasive neural stimulation enhances cortical activity by boosting the spontaneous activity of neurons. Foreign vocabulary acquisition was tested both immediately and seven days after the stimulation. We found that stimulation on the posterior parietal, but not the dorsolateral prefrontal cortex or sham stimulation, significantly improved the memory performance in the long term. These results suggest that the posterior parietal cortex is directly involved in acquisition of foreign vocabulary, thus extending the “linguistic network” to this area. PMID:26075102

  5. Targeting Lumbar Spinal Neural Circuitry by Epidural Stimulation to Restore Motor Function After Spinal Cord Injury.

    Science.gov (United States)

    Minassian, Karen; McKay, W Barry; Binder, Heinrich; Hofstoetter, Ursula S

    2016-04-01

    Epidural spinal cord stimulation has a long history of application for improving motor control in spinal cord injury. This review focuses on its resurgence following the progress made in understanding the underlying neurophysiological mechanisms and on recent reports of its augmentative effects upon otherwise subfunctional volitional motor control. Early work revealed that the spinal circuitry involved in lower-limb motor control can be accessed by stimulating through electrodes placed epidurally over the posterior aspect of the lumbar spinal cord below a paralyzing injury. Current understanding is that such stimulation activates large-to-medium-diameter sensory fibers within the posterior roots. Those fibers then trans-synaptically activate various spinal reflex circuits and plurisegmentally organized interneuronal networks that control more complex contraction and relaxation patterns involving multiple muscles. The induced change in responsiveness of this spinal motor circuitry to any residual supraspinal input via clinically silent translesional neural connections that have survived the injury may be a likely explanation for rudimentary volitional control enabled by epidural stimulation in otherwise paralyzed muscles. Technological developments that allow dynamic control of stimulation parameters and the potential for activity-dependent beneficial plasticity may further unveil the remarkable capacity of spinal motor processing that remains even after severe spinal cord injuries.

  6. Neural Stimulation Has a Long-Term Effect on Foreign Vocabulary Acquisition.

    Science.gov (United States)

    Pasqualotto, Achille; Kobanbay, Begüm; Proulx, Michael J

    2015-01-01

    Acquisition of a foreign language is a challenging task that is becoming increasingly more important in the world nowadays. There is evidence suggesting that the frontal and temporal cortices are involved in language processing and comprehension, but it is still unknown whether foreign language acquisition recruits additional cortical areas in a causal manner. For the first time, we used transcranial random noise stimulation on the frontal and parietal brain areas, in order to compare its effect on the acquisition of unknown foreign words and a sham, or placebo, condition was also included. This type of noninvasive neural stimulation enhances cortical activity by boosting the spontaneous activity of neurons. Foreign vocabulary acquisition was tested both immediately and seven days after the stimulation. We found that stimulation on the posterior parietal, but not the dorsolateral prefrontal cortex or sham stimulation, significantly improved the memory performance in the long term. These results suggest that the posterior parietal cortex is directly involved in acquisition of foreign vocabulary, thus extending the "linguistic network" to this area.

  7. Miniature wireless recording and stimulation system for rodent behavioural testing

    Science.gov (United States)

    Pinnell, R. C.; Dempster, J.; Pratt, J.

    2015-12-01

    Objective. Elucidation of neural activity underpinning rodent behaviour has traditionally been hampered by the use of tethered systems and human involvement. Furthermore the combination of deep-brain stimulation (DBS) and various neural recording modalities can lead to complex and time-consuming laboratory setups. For studies of this type, novel tools are required to drive forward this research. Approach. A miniature wireless system weighing 8.5 g (including battery) was developed for rodent use that combined multichannel DBS and local-field potential (LFP) recordings. Its performance was verified in a working memory task that involved 4-channel fronto-hippocampal LFP recording and bilateral constant-current fimbria-fornix DBS. The system was synchronised with video-tracking for extraction of LFP at discrete task phases, and DBS was activated intermittently at discrete phases of the task. Main results. In addition to having a fast set-up time, the system could reliably transmit continuous LFP at over 8 hours across 3-5 m distances. During the working memory task, LFP pertaining to discrete task phases was extracted and compared with well-known neural correlates of active exploratory behaviour in rodents. DBS could be wirelessly activated/deactivated at any part of the experiment during EEG recording and transmission, allowing for a seamless integration of this modality. Significance. The wireless system combines a small size with a level of robustness and versatility that can greatly simplify rodent behavioural experiments involving EEG recording and DBS. Designed for versatility and simplicity, the small size and low-cost of the system and its receiver allow for enhanced portability, fast experimental setup times, and pave the way for integration with more complex behaviour.

  8. Investigating Tactile Stimulation in Symbiotic Systems

    DEFF Research Database (Denmark)

    Orso, Valeria; Mazza, Renato; Gamberini, Luciano

    2017-01-01

    The core characteristics of tactile stimuli, i.e., recognition reliability and tolerance to ambient interference, make them an ideal candidate to be integrated into a symbiotic system. The selection of the appropriate stimulation is indeed important in order not to hinder the interaction from the...

  9. A neural network-based design of an on-off adaptive control for Deep Brain Stimulation in movement disorders.

    Science.gov (United States)

    Shukla, Pitamber; Basu, Ishita; Graupe, Daniel; Tuninetti, Daniela; Slavin, Konstantin V

    2012-01-01

    The current Food and Drug Administration approved system for the treatment of tremor disorders through Deep Brain Stimulation (DBS) of the area of the brain that controls movement, operates open-loop. It does not automatically adapt to the instantaneous patient's needs or to the progression of the disease. This paper demonstrates an adaptive closed-loop controlled DBS that, after switching off stimulation, tracks few physiological signals to predict the reappearance of tremor before the patient experiences discomfort, at which point it instructs the DBS controller to switch on stimulation again. The core of the proposed approach is a Neural Network (NN) which effectively extracts tremor predictive information from non-invasively recorded surface-electromyogram(sEMG) and accelerometer signals measured at the symptomatic extremities. A simple feed-forward back-propagation NN architecture is shown to successfully predict tremor in 31 out of 33 trials in two Parkinson's Disease patients with an overall accuracy of 75.8% and sensitivity of 92.3%. This work therefore shows that closed-loop DBS control is feasible in the near future and that it can be achieved without modifications of the electrodes implanted in the brain, i.e., is backward compatible with approved DBS systems.

  10. Phase-dependent stimulation effects on bursting activity in a neural network cortical simulation.

    Science.gov (United States)

    Anderson, William S; Kudela, Pawel; Weinberg, Seth; Bergey, Gregory K; Franaszczuk, Piotr J

    2009-03-01

    A neural network simulation with realistic cortical architecture has been used to study synchronized bursting as a seizure representation. This model has the property that bursting epochs arise and cease spontaneously, and bursting epochs can be induced by external stimulation. We have used this simulation to study the time-frequency properties of the evolving bursting activity, as well as effects due to network stimulation. The model represents a cortical region of 1.6 mm x 1.6mm, and includes seven neuron classes organized by cortical layer, inhibitory or excitatory properties, and electrophysiological characteristics. There are a total of 65,536 modeled single compartment neurons that operate according to a version of Hodgkin-Huxley dynamics. The intercellular wiring is based on histological studies and our previous modeling efforts. The bursting phase is characterized by a flat frequency spectrum. Stimulation pulses are applied to this modeled network, with an electric field provided by a 1mm radius circular electrode represented mathematically in the simulation. A phase dependence to the post-stimulation quiescence is demonstrated, with local relative maxima in efficacy occurring before or during the network depolarization phase in the underlying activity. Brief periods of network insensitivity to stimulation are also demonstrated. The phase dependence was irregular and did not reach statistical significance when averaged over the full 2.5s of simulated bursting investigated. This result provides comparison with previous in vivo studies which have also demonstrated increased efficacy of stimulation when pulses are applied at the peak of the local field potential during cortical after discharges. The network bursting is synchronous when comparing the different neuron classes represented up to an uncertainty of 10 ms. Studies performed with an excitatory chandelier cell component demonstrated increased synchronous bursting in the model, as predicted from

  11. Neural substrates underlying stimulation-enhanced motor skill learning after stroke.

    Science.gov (United States)

    Lefebvre, Stéphanie; Dricot, Laurence; Laloux, Patrice; Gradkowski, Wojciech; Desfontaines, Philippe; Evrard, Frédéric; Peeters, André; Jamart, Jacques; Vandermeeren, Yves

    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

  12. Excessive Sensory Stimulation during Development Alters Neural Plasticity and Vulnerability to Cocaine in Mice.

    Science.gov (United States)

    Ravinder, Shilpa; Donckels, Elizabeth A; Ramirez, Julian S B; Christakis, Dimitri A; Ramirez, Jan-Marino; Ferguson, Susan M

    2016-01-01

    Early life experiences affect the formation of neuronal networks, which can have a profound impact on brain function and behavior later in life. Previous work has shown that mice exposed to excessive sensory stimulation during development are hyperactive and novelty seeking, and display impaired cognition compared with controls. In this study, we addressed the issue of whether excessive sensory stimulation during development could alter behaviors related to addiction and underlying circuitry in CD-1 mice. We found that the reinforcing properties of cocaine were significantly enhanced in mice exposed to excessive sensory stimulation. Moreover, although these mice displayed hyperactivity that became more pronounced over time, they showed impaired persistence of cocaine-induced locomotor sensitization. These behavioral effects were associated with alterations in glutamatergic transmission in the nucleus accumbens and amygdala. Together, these findings suggest that excessive sensory stimulation in early life significantly alters drug reward and the neural circuits that regulate addiction and attention deficit hyperactivity. These observations highlight the consequences of early life experiences and may have important implications for children growing up in today's complex technological environment.

  13. Neural mechanism of pupillary dilation elicited by electro-acupuncture stimulation in anesthetized rats.

    Science.gov (United States)

    Ohsawa, H; Yamaguchi, S; Ishimaru, H; Shimura, M; Sato, Y

    1997-06-06

    The neural mechanisms to reflex dilation elicited by electro-acupuncture stimulation were investigated in anesthetized rats. Two needles, with 160 microns diameter and about 5 mm apart, were inserted into the skin and underlying muscle of a hindpaw. Repetitive 20 Hz, 0.5 ms electrical pulses at various intensities were used for stimulation for 30s. The pupil size was magnified about 44 times via a microscope and was continuously recorded on a videotape. Electro-acupuncture stimulation at more than 0.5 up to 6 mA induced stimulus intensity-dependent pupil dilation. These responses were abolished by the severance of the sciatic and saphenous nerve of the stimulated hindlimb. Compound action potentials were recorded from the distal cut end of the tibial of a saphenous nerve following electro-acupuncture stimulation of the hindpaw. The mean threshold of the compound action potentials of the myelinated fibers in saphenous nerves was 0.18 mA, while that of unmyelinated fibers was 3.0 mA. The mean threshold of the compound action potentials of the myelinated fibers in the tibial nerve was 0.20 mA of unmyelinated fibers was 3.3 mA. Severance of bilateral trunks did not affect the response, while severance of the third cranial nerves abolished the responses. In conclusion, electro-acupuncture stimulation applied to the hindpaws of the anesthetized rats induced excitation of myelinated or of both myelinated and unmyelinated afferent fibers of the tibial and saphenous nerve, and involved a reflex response of pupil dilation through the third cranial parasympathetic efferent nerve.

  14. Neural stem cell differentiation by electrical stimulation using a cross-linked PEDOT substrate: Expanding the use of biocompatible conjugated conductive polymers for neural tissue engineering.

    Science.gov (United States)

    Pires, Filipa; Ferreira, Quirina; Rodrigues, Carlos A V; Morgado, Jorge; Ferreira, Frederico Castelo

    2015-06-01

    The use of conjugated polymers allows versatile interactions between cells and flexible processable materials, while providing a platform for electrical stimulation, which is particularly relevant when targeting differentiation of neural stem cells and further application for therapy or drug screening. Materials were tested for cytotoxicity following the ISO10993-5. PSS was cross-linked. ReNcellVM neural stem cells (NSC) were seeded in laminin coated surfaces, cultured for 4 days in the presence of EGF (20 ng/mL), FGF-2 (20 ng/mL) and B27 (20 μg/mL) and differentiated over eight additional days in the absence of those factors under 100Hz pulsed DC electrical stimulation, 1V with 10 ms pulses. NSC and neuron elongation aspect ratio as well as neurite length were assessed using ImageJ. Cells were immune-stained for Tuj1 and GFAP. F8T2, MEH-PPV, P3HT and cross-linked PSS (x PSS) were assessed as non-cytotoxic. L929 fibroblast population was 1.3 higher for x PSS than for glass control, while F8T2 presents moderate proliferation. The population of neurons (Tuj1) was 1.6 times higher with longer neurites (73 vs 108 μm) for cells cultured under electrical stimulus, with cultured NSC. Such stimulus led also to longer neurons. x PSS was, for the first time, used to elongate human NSC through the application of pulsed current, impacting on their differentiation towards neurons and contributing to longer neurites. The range of conductive conjugated polymers known as non-cytotoxic was expanded. x PSS was introduced as a stable material, easily processed from solution, to interface with biological systems, in particular NSC, without the need of in-situ polymerization. Copyright © 2015 Elsevier B.V. All rights reserved.

  15. Evaluation of Galvanic Vestibular Stimulation System

    Science.gov (United States)

    Kofman, I. S.; Warren, E.; DeSoto, R.; Moroney, G.; Chastain, J.; De Dios, Y. E.; Gadd, N.; Taylor, L.; Peters, B. T.; Allen, E.; hide

    2017-01-01

    ) similar to what astronauts experience during transitions to new gravitational environments. Stochastic electrical stimulation can be applied to the vestibular system through electrodes placed over the mastoid process behind the ears in the binaural configuration resulting in stimulation in the mediolateral (side-to-side) plane. An additional electrode can be placed over the bony landmark of the tip of the c7 spinous process for the double monaural configuration, which will cause stimulation in the anteroposterior (forward-backward) plane. A portable constant current bipolar stimulator with subject isolation was designed and built to deliver the stimulus. The unit is powered using a 3.7 V battery pack and designed to produce currents up to 5 mA. The stimulator, controlled by a Raspberry Pi 3 computer, offers several stimulus signal generation options including a standalone mode, which uses onboard signal files stored on the flash memory card. Stochastic stimulation signals will be generated in 0-30 Hz frequency bandwidth. Stimulation amplitude can be increased incrementally to a maximum amplitude of 5.0 mA (e.g., 0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0 mA). In control trials, subjects will be experiencing vestibular stimulation with 0-mA current applied through the electrodes. The system will be evaluated at various levels of stimulation and in both the binaural and double monaural electrode configurations. One of the objectives is to identify stimulation levels producing effects most comparable to the post-flight disturbances. This is a pilot study that will set the stage for a larger, more comprehensive study that will investigate wider aspects of post-flight sensorimotor dysfunction and set sensorimotor standards for crew health.

  16. The Artifical Neural Network as means for modeling Nonlinear Systems

    OpenAIRE

    Drábek Oldøich; Taufer Ivan

    1998-01-01

    The paper deals with nonlinear system identification based on neural network. The topic of this publication is simulation of training and testing a neural network. A contribution is assigned to technologists which are good at the clasical identification problems but their knowledges about identification based on neural network are only on the stage of theoretical bases.

  17. The Artifical Neural Network as means for modeling Nonlinear Systems

    Directory of Open Access Journals (Sweden)

    Drábek Oldøich

    1998-12-01

    Full Text Available The paper deals with nonlinear system identification based on neural network. The topic of this publication is simulation of training and testing a neural network. A contribution is assigned to technologists which are good at the clasical identification problems but their knowledges about identification based on neural network are only on the stage of theoretical bases.

  18. Selective metabolic stimulation of the subfornical organ and pituitary neural lobe by peripheral angiotensin II

    Energy Technology Data Exchange (ETDEWEB)

    Gross, P.M.; Kadekaro, M.; Andrews, D.W.; Sokoloff, L.; Saavedra, J.M.

    1985-01-01

    The subfornical organ is a major receptor area for one of the principal stimuli of thirst, the octapeptide, angiotensin II. In conscious water-sated rats, the authors examined the effects of intravenous infusion of angiotensin II on the rate of glucose utilization in the subfornical organ and in structures anatomically and functionally connected with it. Angiotensin II produced pressor and drinking responses and increased glucose utilization selectively in the subfornical organ and pituitary neural lobe and in no other brain structure. Treatment with the angiotensin II antagonist, sar1-leu8-angiotensin II, before intravenous administration of angiotensin II prevented metabolic stimulation of the subfornical organ and neural lobe. Captopril, an inhibitor of angiotensin-converting enzyme, reduced subfornical organ glucose metabolism to a level similar to that found in control animals. These results demonstrate that peripheral angiotensin II stimulates glucose metabolism in the subfornical organ under conditions in which it provokes drinking and pressor responses. The findings suggest that circulating angiotensin II is responsible for the high rate of glucose utilization observed in the subfornical organ of Brattleboro rats homozygous for diabetes insipidus.

  19. Infrared neural stimulation induces intracellular Ca2+ release mediated by phospholipase C.

    Science.gov (United States)

    Moreau, David; Lefort, Claire; Pas, Jolien; Bardet, Sylvia M; Leveque, Philippe; O'Connor, Rodney P

    2018-02-01

    The influence of infrared laser pulses on intracellular Ca2+ signaling was investigated in neural cell lines with fluorescent live cell imaging. The probe Fluo-4 was used to measure Ca2+ in HT22 mouse hippocampal neurons and nonelectrically excitable U87 human glioblastoma cells exposed to 50 to 500 ms infrared pulses at 1470 nm. Fluorescence recordings of Fluo-4 demonstrated that infrared stimulation induced an instantaneous intracellular Ca2+ transient with similar dose-response characteristics in hippocampal neurons and glioblastoma cells (half-maximal effective energy density EC50 of around 58 J.cm-2 ). For both type of cells, the source of the infrared-induced Ca2+ transients was found to originate from intracellular stores and to be mediated by phospholipase C and IP3 -induced Ca2+ release from the endoplasmic reticulum. The activation of phosphoinositide signaling by IR light is a new mechanism of interaction relevant to infrared neural stimulation that will also be widely applicable to nonexcitable cell types. The prospect of infrared optostimulation of the PLC/IP3 cell signaling cascade has many potential applications including the development of optoceutical therapeutics. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  20. Early life allergen-induced mucus overproduction requires augmented neural stimulation of pulmonary neuroendocrine cell secretion.

    Science.gov (United States)

    Barrios, Juliana; Patel, Kruti R; Aven, Linh; Achey, Rebecca; Minns, Martin S; Lee, Yoonjoo; Trinkaus-Randall, Vickery E; Ai, Xingbin

    2017-09-01

    Pulmonary neuroendocrine cells (PNECs) are the only innervated airway epithelial cells. To what extent neural innervation regulates PNEC secretion and function is unknown. Here, we discover that neurotrophin 4 (NT4) plays an essential role in mucus overproduction after early life allergen exposure by orchestrating PNEC innervation and secretion of GABA. We found that PNECs were the only cellular source of GABA in airways. In addition, PNECs expressed NT4 as a target-derived mechanism underlying PNEC innervation during development. Early life allergen exposure elevated the level of NT4 and caused PNEC hyperinnervation and nodose neuron hyperactivity. Associated with aberrant PNEC innervation, the authors discovered that GABA hypersecretion was required for the induction of mucin Muc5ac expression. In contrast, NT4-/- mice were protected from allergen-induced mucus overproduction and changes along the nerve-PNEC axis without any defects in inflammation. Last, GABA installation restored mucus overproduction in NT4-/- mice after early life allergen exposure. Together, our findings provide the first evidence for NT4-dependent neural regulation of PNEC secretion of GABA in a neonatal disease model. Targeting the nerve-PNEC axis may be a valid treatment strategy for mucus overproduction in airway diseases, such as childhood asthma.-Barrios, J., Patel, K. R., Aven, L., Achey, R., Minns, M. S., Lee, Y., Trinkaus-Randall, V. E., Ai, X. Early life allergen-induced mucus overproduction requires augmented neural stimulation of pulmonary neuroendocrine cell secretion. © FASEB.

  1. Central nervous system stimulants and sport practice.

    Science.gov (United States)

    Avois, L; Robinson, N; Saudan, C; Baume, N; Mangin, P; Saugy, M

    2006-07-01

    Central nervous system (CNS) stimulants may be used to reduce tiredness and increase alertness, competitiveness, and aggression. They are more likely to be used in competition but may be used during training to increase the intensity of the training session. There are several potential dangers involving their misuse in contact sports. This paper reviews the three main CNS stimulants, ephedrine, amfetamine, and cocaine, in relation to misuse in sport. Description of the pharmacology, actions, and side effects of amfetamine, cocaine, and ephedrine. CNS stimulants have psychotropic effects that may be perceived to be ergogenic. Some are prescription drugs, such as Ephedra alkaloids, and there are issues regarding their appropriate therapeutic use. Recently attention has been given to their widespread use by athletes, despite the lack of evidence regarding any ergogenic or real performance benefit, and their potentially serious side effects. Recreational drugs, some of which are illegal (cocaine, amfetamines), are commonly used by athletes and cause potential ergolytic effects. Overall, these drugs are important for their frequent use and mention in anti-doping laboratories statistics and the media, and their potentially serious adverse effects. Doping with CNS stimulants is a real public health problem and all sports authorities should participate in its prevention. Dissemination of information is essential to prevent doping in sport and to provide alternatives. Adequate training and education in this domain should be introduced.

  2. Neurophysiological aspects of musical auditory stimulation on the cardiovascular system

    Directory of Open Access Journals (Sweden)

    Lucas Lima Ferreira

    2013-12-01

    Full Text Available Introduction: The literature has shown that musical stimulation can influence the cardiovascular system, however, the neurophysiological aspects of this influence are not yet fully elucidated. Objective: This study describes the influence of music on the neurophysiological mechanisms in the human body, specifically the variable blood pressure, as well as the neural mechanisms of music processing. Methods: Searches were conducted in Medline, PEDro, Lilacs and SciELO using the intersection of the keyword “music” with the keyword descriptors “blood pressure” and “neurophysiology”. Results: There were selected 11 articles, which indicated that music interferes in some aspects of physiological variables. Conclusion: Studies have indicated that music interferes on the control of blood pressure, heart and respiratory rate, through possible involvement of limbic brain areas which modulate hypothalamic-pituitary functions. Further studies are needed in order to identify the mechanisms by which this influence occurs.

  3. Stability of Neural Firing in the Trigeminal Nuclei under Mechanical Whisker Stimulation

    Directory of Open Access Journals (Sweden)

    Valeri A. Makarov

    2010-01-01

    Full Text Available Sensory information handling is an essentially nonstationary process even under a periodic stimulation. We show how the time evolution of ridges in the wavelet spectrum of spike trains can be used for quantification of the dynamical stability of the neuronal responses to a stimulus. We employ this method to study neuronal responses in trigeminal nuclei of the rat provoked by tactile whisker stimulation. Neurons from principalis (Pr5 and interpolaris (Sp5i show the maximal stability at the intermediate (50 ms stimulus duration, whereas Sp5o cells “prefer” shorter (10 ms stimulation. We also show that neurons in all three nuclei can perform as stimulus frequency filters. The response stability of about 33% of cells exhibits low-pass frequency dynamics. About 57% of cells have band-pass dynamics with the optimal frequency at 5 Hz for Pr5 and Sp5i, and 4 Hz for Sp5o, and the remaining 10% show no prominent dependence on the stimulus frequency. This suggests that the neural coding scheme in trigeminal nuclei is not fixed, but instead it adapts to the stimulus characteristics.

  4. Near infrared laser stimulation of human neural stem cells into neurons on graphene nanomesh semiconductors.

    Science.gov (United States)

    Akhavan, Omid; Ghaderi, Elham; Shirazian, Soheil A

    2015-02-01

    Reduced graphene oxide nanomeshes (rGONMs), as p-type semiconductors with band-gap energy of ∼ 1 eV, were developed and applied in near infrared (NIR) laser stimulation of human neural stem cells (hNSCs) into neurons. The biocompatibility of the rGONMs in growth of hNSCs was found similar to that of the graphene oxide (GO) sheets. Proliferation of the hNSCs on the GONMs was assigned to the excess oxygen functional groups formed on edge defects of the GONMs, resulting in superhydrophilicity of the surface. Under NIR laser stimulation, the graphene layers (especially the rGONMs) exhibited significant cell differentiations, including more elongations of the cells and higher differentiation of neurons than glia. The higher hNSC differentiation on the rGONM than the reduced GO (rGO) was assigned to the stimulation effects of the low-energy photoexcited electrons injected from the rGONM semiconductors into the cells, while the high-energy photoelectrons of the rGO (as a zero band-gap semiconductor) could suppress the cell proliferation and/or even cause cell damages. Using conventional heating of the culture media up to ∼ 43 °C (the temperature typically reached under the laser irradiation), no significant differentiation was observed in dark. This further confirmed the role of photoelectrons in the hNSC differentiation. Copyright © 2014 Elsevier B.V. All rights reserved.

  5. Wireless distributed functional electrical stimulation system

    Directory of Open Access Journals (Sweden)

    Jovičić Nenad S

    2012-08-01

    Full Text Available Abstract Background The control of movement in humans is hierarchical and distributed and uses feedback. An assistive system could be best integrated into the therapy of a human with a central nervous system lesion if the system is controlled in a similar manner. Here, we present a novel wireless architecture and routing protocol for a distributed functional electrical stimulation system that enables control of movement. Methods The new system comprises a set of miniature battery-powered devices with stimulating and sensing functionality mounted on the body of the subject. The devices communicate wirelessly with one coordinator device, which is connected to a host computer. The control algorithm runs on the computer in open- or closed-loop form. A prototype of the system was designed using commercial, off-the-shelf components. The propagation characteristics of electromagnetic waves and the distributed nature of the system were considered during the development of a two-hop routing protocol, which was implemented in the prototype’s software. Results The outcomes of this research include a novel system architecture and routing protocol and a functional prototype based on commercial, off-the-shelf components. A proof-of-concept study was performed on a hemiplegic subject with paresis of the right arm. The subject was tasked with generating a fully functional palmar grasp (closing of the fingers. One node was used to provide this movement, while a second node controlled the activation of extensor muscles to eliminate undesired wrist flexion. The system was tested with the open- and closed-loop control algorithms. Conclusions The system fulfilled technical and application requirements. The novel communication protocol enabled reliable real-time use of the system in both closed- and open-loop forms. The testing on a patient showed that the multi-node system could operate effectively to generate functional movement.

  6. Wireless distributed functional electrical stimulation system.

    Science.gov (United States)

    Jovičić, Nenad S; Saranovac, Lazar V; Popović, Dejan B

    2012-08-09

    The control of movement in humans is hierarchical and distributed and uses feedback. An assistive system could be best integrated into the therapy of a human with a central nervous system lesion if the system is controlled in a similar manner. Here, we present a novel wireless architecture and routing protocol for a distributed functional electrical stimulation system that enables control of movement. The new system comprises a set of miniature battery-powered devices with stimulating and sensing functionality mounted on the body of the subject. The devices communicate wirelessly with one coordinator device, which is connected to a host computer. The control algorithm runs on the computer in open- or closed-loop form. A prototype of the system was designed using commercial, off-the-shelf components. The propagation characteristics of electromagnetic waves and the distributed nature of the system were considered during the development of a two-hop routing protocol, which was implemented in the prototype's software. The outcomes of this research include a novel system architecture and routing protocol and a functional prototype based on commercial, off-the-shelf components. A proof-of-concept study was performed on a hemiplegic subject with paresis of the right arm. The subject was tasked with generating a fully functional palmar grasp (closing of the fingers). One node was used to provide this movement, while a second node controlled the activation of extensor muscles to eliminate undesired wrist flexion. The system was tested with the open- and closed-loop control algorithms. The system fulfilled technical and application requirements. The novel communication protocol enabled reliable real-time use of the system in both closed- and open-loop forms. The testing on a patient showed that the multi-node system could operate effectively to generate functional movement.

  7. Convergent evolution of neural systems in ctenophores.

    Science.gov (United States)

    Moroz, Leonid L

    2015-02-15

    Neurons are defined as polarized secretory cells specializing in directional propagation of electrical signals leading to the release of extracellular messengers - features that enable them to transmit information, primarily chemical in nature, beyond their immediate neighbors without affecting all intervening cells en route. Multiple origins of neurons and synapses from different classes of ancestral secretory cells might have occurred more than once during ~600 million years of animal evolution with independent events of nervous system centralization from a common bilaterian/cnidarian ancestor without the bona fide central nervous system. Ctenophores, or comb jellies, represent an example of extensive parallel evolution in neural systems. First, recent genome analyses place ctenophores as a sister group to other animals. Second, ctenophores have a smaller complement of pan-animal genes controlling canonical neurogenic, synaptic, muscle and immune systems, and developmental pathways than most other metazoans. However, comb jellies are carnivorous marine animals with a complex neuromuscular organization and sophisticated patterns of behavior. To sustain these functions, they have evolved a number of unique molecular innovations supporting the hypothesis of massive homoplasies in the organization of integrative and locomotory systems. Third, many bilaterian/cnidarian neuron-specific genes and 'classical' neurotransmitter pathways are either absent or, if present, not expressed in ctenophore neurons (e.g. the bilaterian/cnidarian neurotransmitter, γ-amino butyric acid or GABA, is localized in muscles and presumed bilaterian neuron-specific RNA-binding protein Elav is found in non-neuronal cells). Finally, metabolomic and pharmacological data failed to detect either the presence or any physiological action of serotonin, dopamine, noradrenaline, adrenaline, octopamine, acetylcholine or histamine - consistent with the hypothesis that ctenophore neural systems evolved

  8. Thermally stimulated currents in glassy systems

    CERN Document Server

    Halpern, V

    2003-01-01

    A common interpretation of the results of thermally stimulated current experiments is in terms of a fixed set of states present in the system with a range of activation energies, of which specific subsets are selected and identified by thermal slicing (TS) (or fractional polarization) experiments at different polarization temperatures T sub p. On the other hand, such an interpretation is not consistent with many current theories of supercooled liquids and of the glass transition. The results are presented of our calculations on thermally activated Fredrickson-Andersen model systems, which indicate that the TS technique does identify the isothermal response of the system at T sub p. However, this response need not be associated with specific states present in the system, but can arise for instance from a competition between different relaxation paths for the dipole moments of the individual particles. In addition, the compensation law is found to be obeyed for systems with and without cooperative effects, so t...

  9. Body Position Influences Which Neural Structures Are Recruited by Lumbar Transcutaneous Spinal Cord Stimulation.

    Directory of Open Access Journals (Sweden)

    Simon M Danner

    Full Text Available Transcutaneous stimulation of the human lumbosacral spinal cord is used to evoke spinal reflexes and to neuromodulate altered sensorimotor function following spinal cord injury. Both applications require the reliable stimulation of afferent posterior root fibers. Yet under certain circumstances, efferent anterior root fibers can be co-activated. We hypothesized that body position influences the preferential stimulation of sensory or motor fibers. Stimulus-triggered responses to transcutaneous spinal cord stimulation were recorded using surface-electromyography from quadriceps, hamstrings, tibialis anterior, and triceps surae muscles in 10 individuals with intact nervous systems in the supine, standing and prone positions. Single and paired (30-ms inter-stimulus intervals biphasic stimulation pulses were applied through surface electrodes placed on the skin between the T11 and T12 inter-spinous processes referenced to electrodes on the abdomen. The paired stimulation was applied to evaluate the origin of the evoked electromyographic response; trans-synaptic responses would be suppressed whereas direct efferent responses would almost retain their amplitude. We found that responses to the second stimulus were decreased to 14%±5% of the amplitude of the response to the initial pulse in the supine position across muscles, to 30%±5% in the standing, and to only 80%±5% in the prone position. Response thresholds were lowest during standing and highest in the prone position and response amplitudes were largest in the supine and smallest in the prone position. The responses obtained in the supine and standing positions likely resulted from selective stimulation of sensory fibers while concomitant motor-fiber stimulation occurred in the prone position. We assume that changes of root-fiber paths within the generated electric field when in the prone position increase the stimulation thresholds of posterior above those of anterior root fibers. Thus, we

  10. Body Position Influences Which Neural Structures Are Recruited by Lumbar Transcutaneous Spinal Cord Stimulation.

    Science.gov (United States)

    Danner, Simon M; Krenn, Matthias; Hofstoetter, Ursula S; Toth, Andrea; Mayr, Winfried; Minassian, Karen

    2016-01-01

    Transcutaneous stimulation of the human lumbosacral spinal cord is used to evoke spinal reflexes and to neuromodulate altered sensorimotor function following spinal cord injury. Both applications require the reliable stimulation of afferent posterior root fibers. Yet under certain circumstances, efferent anterior root fibers can be co-activated. We hypothesized that body position influences the preferential stimulation of sensory or motor fibers. Stimulus-triggered responses to transcutaneous spinal cord stimulation were recorded using surface-electromyography from quadriceps, hamstrings, tibialis anterior, and triceps surae muscles in 10 individuals with intact nervous systems in the supine, standing and prone positions. Single and paired (30-ms inter-stimulus intervals) biphasic stimulation pulses were applied through surface electrodes placed on the skin between the T11 and T12 inter-spinous processes referenced to electrodes on the abdomen. The paired stimulation was applied to evaluate the origin of the evoked electromyographic response; trans-synaptic responses would be suppressed whereas direct efferent responses would almost retain their amplitude. We found that responses to the second stimulus were decreased to 14%±5% of the amplitude of the response to the initial pulse in the supine position across muscles, to 30%±5% in the standing, and to only 80%±5% in the prone position. Response thresholds were lowest during standing and highest in the prone position and response amplitudes were largest in the supine and smallest in the prone position. The responses obtained in the supine and standing positions likely resulted from selective stimulation of sensory fibers while concomitant motor-fiber stimulation occurred in the prone position. We assume that changes of root-fiber paths within the generated electric field when in the prone position increase the stimulation thresholds of posterior above those of anterior root fibers. Thus, we recommend conducting

  11. Spacecraft Neural Network Control System Design using FPGA

    OpenAIRE

    Hanaa T. El-Madany; Faten H. Fahmy; Ninet M. A. El-Rahman; Hassen T. Dorrah

    2011-01-01

    Designing and implementing intelligent systems has become a crucial factor for the innovation and development of better products of space technologies. A neural network is a parallel system, capable of resolving paradigms that linear computing cannot. Field programmable gate array (FPGA) is a digital device that owns reprogrammable properties and robust flexibility. For the neural network based instrument prototype in real time application, conventional specific VLSI neural chip design suffer...

  12. Immune system stimulation by probiotic microorganisms.

    Science.gov (United States)

    Ashraf, Rabia; Shah, Nagendra P

    2014-01-01

    Probiotic organisms are claimed to offer several functional properties including stimulation of immune system. This review is presented to provide detailed informations about how probiotics stimulate our immune system. Lactobacillus rhamnosus GG, Lactobacillus casei Shirota, Bifidobacterium animalis Bb-12, Lactobacillus johnsonii La1, Bifidobacterium lactis DR10, and Saccharomyces cerevisiae boulardii are the most investigated probiotic cultures for their immunomodulation properties. Probiotics can enhance nonspecific cellular immune response characterized by activation of macrophages, natural killer (NK) cells, antigen-specific cytotoxic T-lymphocytes, and the release of various cytokines in strain-specific and dose-dependent manner. Mixture and type (gram-positive and gram-negative) of probiotic organisms may induce different cytokine responses. Supplementation of probiotic organisms in infancy could help prevent immune-mediated diseases in childhood, whereas their intervention in pregnancy could affect fetal immune parameters, such as cord blood interferon (IFN)-γ levels, transforming growth factor (TGF)-β1 levels, and breast milk immunoglobulin (Ig)A. Probiotics that can be delivered via fermented milk or yogurt could improve the gut mucosal immune system by increasing the number of IgA(+) cells and cytokine-producing cells in the effector site of the intestine.

  13. Artificial Neural Network System for Thyroid Diagnosis

    Directory of Open Access Journals (Sweden)

    Mazin Abdulrasool Hameed

    2017-05-01

    Full Text Available Thyroid disease is one of major causes of severe medical problems for human beings. Therefore, proper diagnosis of thyroid disease is considered as an important issue to determine treatment for patients. This paper focuses on using Artificial Neural Network (ANN as a significant technique of artificial intelligence to diagnose thyroid diseases. The continuous values of three laboratory blood tests are used as input signals to the proposed system of ANN. All types of thyroid diseases that may occur in patients are taken into account in design of system, as well as the high accuracy of the detection and categorization of thyroid diseases are considered in the system. A multilayer feedforward architecture of ANN is adopted in the proposed design, and the back propagation is selected as learning algorithm to accomplish the training process. The result of this research shows that the proposed ANN system is able to precisely diagnose thyroid disease, and can be exploited in practical uses. The system is simulated via MATLAB software to evaluate its performance

  14. Neural network system for traffic flow management

    Science.gov (United States)

    Gilmore, John F.; Elibiary, Khalid J.; Petersson, L. E. Rickard

    1992-09-01

    Atlanta will be the home of several special events during the next five years ranging from the 1996 Olympics to the 1994 Super Bowl. When combined with the existing special events (Braves, Falcons, and Hawks games, concerts, festivals, etc.), the need to effectively manage traffic flow from surface streets to interstate highways is apparent. This paper describes a system for traffic event response and management for intelligent navigation utilizing signals (TERMINUS) developed at Georgia Tech for adaptively managing special event traffic flows in the Atlanta, Georgia area. TERMINUS (the original name given Atlanta, Georgia based upon its role as a rail line terminating center) is an intelligent surface street signal control system designed to manage traffic flow in Metro Atlanta. The system consists of three components. The first is a traffic simulation of the downtown Atlanta area around Fulton County Stadium that models the flow of traffic when a stadium event lets out. Parameters for the surrounding area include modeling for events during various times of day (such as rush hour). The second component is a computer graphics interface with the simulation that shows the traffic flows achieved based upon intelligent control system execution. The final component is the intelligent control system that manages surface street light signals based upon feedback from control sensors that dynamically adapt the intelligent controller's decision making process. The intelligent controller is a neural network model that allows TERMINUS to control the configuration of surface street signals to optimize the flow of traffic away from special events.

  15. 3K3A-activated protein C stimulates postischemic neuronal repair by human neural stem cells in mice

    DEFF Research Database (Denmark)

    Wang, Yaoming; Zhao, Zhen; Rege, Sanket V

    2016-01-01

    profile in humans, 3K3A-APC has advanced to clinical trials as a neuroprotectant in ischemic stroke. Recently, 3K3A-APC has been shown to stimulate neuronal production by human neural stem and progenitor cells (NSCs) in vitro via a PAR1-PAR3-sphingosine-1-phosphate-receptor 1-Akt pathway, which suggests...

  16. Neural Network for Optimization of Existing Control Systems

    DEFF Research Database (Denmark)

    Madsen, Per Printz

    1995-01-01

    The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems.......The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems....

  17. Transient muscarinic and glutamatergic stimulation of neural stem cells triggers acute and persistent changes in differentiation.

    Science.gov (United States)

    Samarasinghe, Ranmal A; Kanuparthi, Prasad S; Timothy Greenamyre, J; DeFranco, Donald B; Di Maio, Roberto

    2014-10-01

    While aberrant cell proliferation and differentiation may contribute to epileptogenesis, the mechanisms linking an initial epileptic insult to subsequent changes in cell fate remain elusive. Using both mouse and human iPSC-derived neural progenitor/stem cells (NPSCs), we found that a combined transient muscarinic and mGluR1 stimulation inhibited overall neurogenesis but enhanced NPSC differentiation into immature GABAergic cells. If treated NPSCs were further passaged, they retained a nearly identical phenotype upon differentiation. A similar profusion of immature GABAergic cells was seen in rats with pilocarpine-induced chronic epilepsy. Furthermore, live cell imaging revealed abnormal de-synchrony of Ca(++) transients and altered gap junction intercellular communication following combined muscarinic/glutamatergic stimulation, which was associated with either acute site-specific dephosphorylation of connexin 43 or a long-term enhancement of its degradation. Therefore, epileptogenic stimuli can trigger acute and persistent changes in cell fate by altering distinct mechanisms that function to maintain appropriate intercellular communication between coupled NPSCs. Copyright © 2014 Elsevier Inc. All rights reserved.

  18. Neural substrate for brain stimulation reward in the rat: cathodal and anodal strength-duration properties.

    Science.gov (United States)

    Matthews, G

    1977-08-01

    The trade-off between current strength and duration of a stimulating pulse was studied for the rewarding and priming effects of brain stimulation reward (BSR). With cathodal pulses, strenght-duration functions for BSR had chronaxies of .8-3 msec. No differences were observed between the results for rewarding and priming effects. With anodal pulses. strength-duration curves were parallel to the cathodal curves at pulse durations of .1-5 msec, but at pulse durations greater than 5 msec the anodal curves showed a greater drop in required current intensity than did the cathodal curves. The parallel portion of the anodal curves was interpreted as due to anode-make excitation, and the drop at longer pulse durations was interpreted as due to anode-break excitation. Cathodal strength-duration functions for the motor effect elicited through the BSR electrodes had chronaxies of .15-.48 msec. Measurements of the latency of the muscle twitch confirmed that anode-make and anode-break excitation occurred, the latter becoming evident at pulse durations as brief as .3-.4 msec. The results provide quantitative characterization of cathodal and anodal strength-duration properties of the neural substrate for BSR and are discussed in terms of their value in guiding electrophysiological investigation of that substrate.

  19. The Effects of GABAergic Polarity Changes on Episodic Neural Network Activity in Developing Neural Systems

    Directory of Open Access Journals (Sweden)

    Wilfredo Blanco

    2017-09-01

    Full Text Available Early in development, neural systems have primarily excitatory coupling, where even GABAergic synapses are excitatory. Many of these systems exhibit spontaneous episodes of activity that have been characterized through both experimental and computational studies. As development progress the neural system goes through many changes, including synaptic remodeling, intrinsic plasticity in the ion channel expression, and a transformation of GABAergic synapses from excitatory to inhibitory. What effect each of these, and other, changes have on the network behavior is hard to know from experimental studies since they all happen in parallel. One advantage of a computational approach is that one has the ability to study developmental changes in isolation. Here, we examine the effects of GABAergic synapse polarity change on the spontaneous activity of both a mean field and a neural network model that has both glutamatergic and GABAergic coupling, representative of a developing neural network. We find some intuitive behavioral changes as the GABAergic neurons go from excitatory to inhibitory, shared by both models, such as a decrease in the duration of episodes. We also find some paradoxical changes in the activity that are only present in the neural network model. In particular, we find that during early development the inter-episode durations become longer on average, while later in development they become shorter. In addressing this unexpected finding, we uncover a priming effect that is particularly important for a small subset of neurons, called the “intermediate neurons.” We characterize these neurons and demonstrate why they are crucial to episode initiation, and why the paradoxical behavioral change result from priming of these neurons. The study illustrates how even arguably the simplest of developmental changes that occurs in neural systems can present non-intuitive behaviors. It also makes predictions about neural network behavioral changes

  20. Neural Network Based Intelligent Sootblowing System

    Energy Technology Data Exchange (ETDEWEB)

    Mark Rhode

    2005-04-01

    . Due to the composition of coal, particulate matter is also a by-product of coal combustion. Modern day utility boilers are usually fitted with electrostatic precipitators to aid in the collection of particulate matter. Although extremely efficient, these devices are sensitive to rapid changes in inlet mass concentration as well as total mass loading. Traditionally, utility boilers are equipped with devices known as sootblowers, which use, steam, water or air to dislodge and clean the surfaces within the boiler and are operated based upon established rule or operator's judgment. Poor sootblowing regimes can influence particulate mass loading to the electrostatic precipitators. The project applied a neural network intelligent sootblowing system in conjunction with state-of-the-art controls and instruments to optimize the operation of a utility boiler and systematically control boiler slagging/fouling. This optimization process targeted reduction of NOx of 30%, improved efficiency of 2% and a reduction in opacity of 5%. The neural network system proved to be a non-invasive system which can readily be adapted to virtually any utility boiler. Specific conclusions from this neural network application are listed below. These conclusions should be used in conjunction with the specific details provided in the technical discussions of this report to develop a thorough understanding of the process.

  1. Analog neural network-based helicopter gearbox health monitoring system.

    Science.gov (United States)

    Monsen, P T; Dzwonczyk, M; Manolakos, E S

    1995-12-01

    The development of a reliable helicopter gearbox health monitoring system (HMS) has been the subject of considerable research over the past 15 years. The deployment of such a system could lead to a significant saving in lives and vehicles as well as dramatically reduce the cost of helicopter maintenance. Recent research results indicate that a neural network-based system could provide a viable solution to the problem. This paper presents two neural network-based realizations of an HMS system. A hybrid (digital/analog) neural system is proposed as an extremely accurate off-line monitoring tool used to reduce helicopter gearbox maintenance costs. In addition, an all analog neural network is proposed as a real-time helicopter gearbox fault monitor that can exploit the ability of an analog neural network to directly compute the discrete Fourier transform (DFT) as a sum of weighted samples. Hardware performance results are obtained using the Integrated Neural Computing Architecture (INCA/1) analog neural network platform that was designed and developed at The Charles Stark Draper Laboratory. The results indicate that it is possible to achieve a 100% fault detection rate with 0% false alarm rate by performing a DFT directly on the first layer of INCA/1 followed by a small-size two-layer feed-forward neural network and a simple post-processing majority voting stage.

  2. The ctenophore genome and the evolutionary origins of neural systems

    NARCIS (Netherlands)

    Moroz, Leonid L.; Kocot, Kevin M.; Citarella, Mathew R.; Dosung, Sohn; Norekian, Tigran P.; Povolotskaya, Inna S.; Grigorenko, Anastasia P.; Dailey, Christopher; Berezikov, Eugene; Buckley, Katherine M.; Ptitsyn, Andrey; Reshetov, Denis; Mukherjee, Krishanu; Moroz, Tatiana P.; Bobkova, Yelena; Yu, Fahong; Kapitonov, Vladimir V.; Jurka, Jerzy; Bobkov, Yuri V.; Swore, Joshua J.; Girardo, David O.; Fodor, Alexander; Gusev, Fedor; Sanford, Rachel; Bruders, Rebecca; Kittler, Ellen; Mills, Claudia E.; Rast, Jonathan P.; Derelle, Romain; Solovyev, Victor V.; Kondrashov, Fyodor A.; Swalla, Billie J.; Sweedler, Jonathan V.; Rogaev, Evgeny I.; Halanych, Kenneth M.; Kohn, Andrea B.

    2014-01-01

    The origins of neural systems remain unresolved. In contrast to other basal metazoans, ctenophores (comb jellies) have both complex nervous and mesoderm-derived muscular systems. These holoplanktonic predators also have sophisticated ciliated locomotion, behaviour and distinct development. Here we

  3. Neural network model of vestibular nuclei reaction to onset of vestibular prosthetic stimulation

    Directory of Open Access Journals (Sweden)

    Jack eDigiovanna

    2016-04-01

    Full Text Available The vestibular system incorporates multiple sensory pathways to provide crucial information about head and body motion. Damage to the semicircular canals, the peripheral vestibular organs that sense rotational velocities of the head, can severely degrade the ability to perform activities of daily life. Vestibular prosthetics address this problem by using stimulating electrodes that can trigger primary vestibular afferents to modulate their firing rates, thus encoding head movement. These prostheses have been demonstrated chronically in multiple animal models and acutely tested in short-duration trials within the clinic in humans. However, mainly due to limited opportunities to fully characterize stimulation parameters, there is a lack of understanding of ‘optimal’ stimulation configurations for humans. Here we model possible adaptive plasticity in the vestibular pathway. Specifically, this model highlights the influence of adaptation of synaptic strengths and offsets in the vestibular nuclei to compensate for the initial activation of the prosthetic. By changing the synaptic strengths, the model is able to replicate the clinical observation that erroneous eye movements are attenuated within 30 minutes without any change to the prosthetic stimulation rate. Although our model was only built to match this time-point, we further examined how it affected subsequent pulse rate and pulse amplitude modulation. Pulse amplitude modulation was more effective than pulse rate modulation for nearly all stimulation configurations during these acute tests. Two non-intuitive relationships highlighted by our model explain this performance discrepancy. Specifically the attenuation of synaptic strengths for afferents stimulated during baseline adaptation and the discontinuity between baseline and residual firing rates both disproportionally boost pulse amplitude modulation. Co-modulation of pulse rate and amplitude has been experimentally shown to induce both

  4. Spiking Neural P Systems with Communication on Request.

    Science.gov (United States)

    Pan, Linqiang; Păun, Gheorghe; Zhang, Gexiang; Neri, Ferrante

    2017-12-01

    Spiking Neural [Formula: see text] Systems are Neural System models characterized by the fact that each neuron mimics a biological cell and the communication between neurons is based on spikes. In the Spiking Neural [Formula: see text] systems investigated so far, the application of evolution rules depends on the contents of a neuron (checked by means of a regular expression). In these [Formula: see text] systems, a specified number of spikes are consumed and a specified number of spikes are produced, and then sent to each of the neurons linked by a synapse to the evolving neuron. [Formula: see text]In the present work, a novel communication strategy among neurons of Spiking Neural [Formula: see text] Systems is proposed. In the resulting models, called Spiking Neural [Formula: see text] Systems with Communication on Request, the spikes are requested from neighboring neurons, depending on the contents of the neuron (still checked by means of a regular expression). Unlike the traditional Spiking Neural [Formula: see text] systems, no spikes are consumed or created: the spikes are only moved along synapses and replicated (when two or more neurons request the contents of the same neuron). [Formula: see text]The Spiking Neural [Formula: see text] Systems with Communication on Request are proved to be computationally universal, that is, equivalent with Turing machines as long as two types of spikes are used. Following this work, further research questions are listed to be open problems.

  5. Neural Systems for Speech and Song in Autism

    Science.gov (United States)

    Lai, Grace; Pantazatos, Spiro P.; Schneider, Harry; Hirsch, Joy

    2012-01-01

    Despite language disabilities in autism, music abilities are frequently preserved. Paradoxically, brain regions associated with these functions typically overlap, enabling investigation of neural organization supporting speech and song in autism. Neural systems sensitive to speech and song were compared in low-functioning autistic and age-matched…

  6. Effect of anatomical variability on neural stimulation strength and focality in electroconvulsive therapy (ECT) and magnetic seizure therapy (MST).

    Science.gov (United States)

    Deng, Zhi-De; Lisanby, Sarah H; Peterchev, Angel V

    2009-01-01

    We present a quantitative comparison of two metrics-neural stimulation strength and focality-in electrocon-vulsive therapy (ECT) and magnetic seizure therapy (MST) using finite-element method (FEM) simulation in a spherical head model. Five stimulation modalities were modeled, including bilateral ECT, unilateral ECT, focal electrically administered seizure therapy (FEAST), and MST with circular and double-cone coils, with stimulation parameters identical to those applied in clinical practice. We further examine the effect on the stimulation metrics of individual-, sex- and age-related variability in tissue layer thickness and conductivity. Neural stimulation by MST is shown to be more focal and superficial than ECT. This result suggests that it may be advantageous to reduce the current used in ECT. The stimulation strength in MST is also less sensitive to variations in head geometry and tissue conductivity than in ECT. Individualization of pulse amplitude in both ECT and MST could compensate for anatomical variability, which could lead to more consistent clinical outcomes.

  7. The molecular evidence of neural plasticity induced by cerebellar repetitive transcranial magnetic stimulation in the rat brain: a preliminary report.

    Science.gov (United States)

    Lee, Seung Ah; Oh, Byung-Mo; Kim, Sang Jeong; Paik, Nam-Jong

    2014-07-11

    Cerebellar repetitive transcranial magnetic stimulation (rTMS) has been applied to treat several pathological conditions with insufficient evidence of molecular mechanism. Neural plasticity is proposed as one of mechanism. This study aimed to (1) confirm the feasibility of focal stimulation over cerebellar cortex and (2) investigate cerebellar rTMS effects on molecular changes associated with neural plasticity in the rat. For feasibility, six male Sprague-Dawley rats underwent (18)F-FDG positron emission tomography (PET) to confirm focal stimulation on the cerebellar cortex after rTMS. For molecular evidence, thirty rats underwent a single (N=15) or 10 sessions (N=15) of rTMS with low-, high-frequency, or sham stimulation. In cerebellar cortex, reverse-transcriptase polymerase chain reaction and western blotting were performed on mRNA and proteins associated with neural plasticity: metabotrophic glutamate receptor 1 (GluR1), 2-amino-5-methyl-4-isoxazole-propionatic acid (AMPA) receptor (GluR2) and protein kinase C (PKC). As a result, (18)F-FDG-PET showed an increase of glucose metabolism in the cerebellar cortex. The transcription of mGluR1 decreased following a single session of high-frequency rTMS. Synthesis of mGluR, PKC and GluR2 was reduced after rTMS, especially high frequency stimulation. It is suggested that rTMS could focus on the cerebellar cortex in the rat and induce neural plasticity associated with long-term depression. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  8. Short-term synaptic plasticity and heterogeneity in neural systems

    Science.gov (United States)

    Mejias, J. F.; Kappen, H. J.; Longtin, A.; Torres, J. J.

    2013-01-01

    We review some recent results on neural dynamics and information processing which arise when considering several biophysical factors of interest, in particular, short-term synaptic plasticity and neural heterogeneity. The inclusion of short-term synaptic plasticity leads to enhanced long-term memory capacities, a higher robustness of memory to noise, and irregularity in the duration of the so-called up cortical states. On the other hand, considering some level of neural heterogeneity in neuron models allows neural systems to optimize information transmission in rate coding and temporal coding, two strategies commonly used by neurons to codify information in many brain areas. In all these studies, analytical approximations can be made to explain the underlying dynamics of these neural systems.

  9. A distributed transducer system for functional electrical stimulation

    DEFF Research Database (Denmark)

    Gudnason, Gunnar; Nielsen, Jannik Hammel; Bruun, Erik

    2001-01-01

    Implanted transducers for functional electrical stimulation (FES) powered by inductive links are subject to conflicting requirements arising from low link efficiency, a low power budget and the need for protection of the weak signals against strong RF electromagnetic fields. We propose a solution...... to be affected by the inductive link. Neural stimulators are affected to a lesser degree, but still benefit from the partitioning. As a test case, we have designed a transceiver and a sensor chip which implement this partitioning policy. The transceiver is designed to operate in the 6.78 MHz ISM band...

  10. Sensitive and selective neural control using an intraneural multielectrode stimulation device in silicon technology

    NARCIS (Netherlands)

    Rutten, Wim; van Wier, H.J.; Put, J.H.M.; Meier, J.H.

    1988-01-01

    The ideal neural stimulatory information transducer must be able to activate individual neural fibers within a fascicular bundle, for example in a sensory or motor nerve. In a local approach using microelectrodes it is sufficient to apply current pulses to one neural node in order to reach the

  11. Dopamine system: Manager of neural pathways

    Directory of Open Access Journals (Sweden)

    Simon eHong

    2013-12-01

    Full Text Available There are a growing number of roles that midbrain dopamine (DA neurons assume, such as, reward, aversion, alerting and vigor. Here I propose a theory that may be able to explain why the suggested functions of DA came about. It has been suggested that largely parallel cortico-basal ganglia-thalamo-cortico loops exist to control different aspects of behavior. I propose that (1 the midbrain DA system is organized in a similar manner, with different groups of DA neurons corresponding to these parallel neural pathways (NPs. The DA system can be viewed as the manager of these parallel NPs in that it recruits and activates only the task-relevant NPs when they are needed. It is likely that the functions of those NPs that have been consistently activated by the corresponding DA groups are facilitated. I also propose that (2 there are two levels of DA roles: the How and What roles. The How role is encoded in tonic and phasic DA neuron firing patterns and gives a directive to its target NP: how vigorously its function needs to be carried out. The tonic DA firing is to maintain a certain level of DA in the target NPs to support their expected behavioral and mental functions; it is only when a sudden unexpected boost or suppression of activity is required by the relevant target NP that DA neurons in the corresponding NP act in a phasic manner. The What role is the implementational aspect of the role of DA in the target NP, such as binding to D1 receptors to boost working memory. This What aspect of DA explains why DA seems to assume different functions depending on the region of the brain in which it is involved. In terms of the role of the lateral habenula (LHb, the LHb is expected to suppress maladaptive behaviors and mental processes by controlling the DA system. The demand-based smart management by the DA system may have given animals an edge in evolution with adaptive behaviors and a better survival rate in resource-scarce situations.

  12. Neural correlates of transmeatal cochlear laser (TCL) stimulation in healthy human subjects.

    Science.gov (United States)

    Siedentopf, Christian M; Ischebeck, Anja; Haala, Ilka A; Mottaghy, Felix M; Schikora, Detlef; Verius, Michael; Koppelstaetter, Florian; Buchberger, Waltraud; Schlager, Andreas; Felber, Stephan R; Golaszewski, Stefan M

    2007-01-16

    Transmeatal cochlear laser (TCL) treatment has recently been proposed as a therapeutic procedure for cochlear dysfunction such as chronic cochlear tinnitus or sensorineural hearing loss. The aim of this study was to investigate whether TLC has any influence on the central nervous system using functional MRI with healthy young adults. The laser stimulation device was placed on the tympanic membrane of both ears. A laser stimulation run and a placebo run were performed in random order. The participants were unable to differentiate between verum and placebo stimulation. In the comparison of verum to placebo runs, we observed significant activations within the left superior frontal gyrus, the right middle and medial frontal gyrus, the right superior parietal lobule, the left superior occipital gyrus, the precuneus and cuneus bilaterally, the right anterior and the left and right middle and posterior cingulate gyrus and the left thalamus. This network of brain areas corresponds well to results from previous PET studies of patients with tinnitus. Though TCL seems to have a clinically measurable effect on the central nervous system the neurophysiological mechanism leading to the observed activated neuronal network remains unknown.

  13. Optical production systems using neural networks and symbolic substitution

    Science.gov (United States)

    Botha, Elizabeth; Casasent, David; Barnard, Etienne

    1988-01-01

    Two optical implementations of production systems are advanced. The production systems operate on a knowledge base where facts and rules are encoded as formulas in propositional calculus. The first implementation is a binary neural network. An analog neural network is used to include reasoning with uncertainties. The second implementation uses a new optical symbolic substitution correlator. This implementation is useful when a set of similar situations has to be handled in parallel on one processor.

  14. Genetic learning in rule-based and neural systems

    Science.gov (United States)

    Smith, Robert E.

    1993-01-01

    The design of neural networks and fuzzy systems can involve complex, nonlinear, and ill-conditioned optimization problems. Often, traditional optimization schemes are inadequate or inapplicable for such tasks. Genetic Algorithms (GA's) are a class of optimization procedures whose mechanics are based on those of natural genetics. Mathematical arguments show how GAs bring substantial computational leverage to search problems, without requiring the mathematical characteristics often necessary for traditional optimization schemes (e.g., modality, continuity, availability of derivative information, etc.). GA's have proven effective in a variety of search tasks that arise in neural networks and fuzzy systems. This presentation begins by introducing the mechanism and theoretical underpinnings of GA's. GA's are then related to a class of rule-based machine learning systems called learning classifier systems (LCS's). An LCS implements a low-level production-system that uses a GA as its primary rule discovery mechanism. This presentation illustrates how, despite its rule-based framework, an LCS can be thought of as a competitive neural network. Neural network simulator code for an LCS is presented. In this context, the GA is doing more than optimizing and objective function. It is searching for an ecology of hidden nodes with limited connectivity. The GA attempts to evolve this ecology such that effective neural network performance results. The GA is particularly well adapted to this task, given its naturally-inspired basis. The LCS/neural network analogy extends itself to other, more traditional neural networks. Conclusions to the presentation discuss the implications of using GA's in ecological search problems that arise in neural and fuzzy systems.

  15. Conductive nanogel-interfaced neural microelectrode arrays with electrically controlled in-situ delivery of manganese ions enabling high-resolution MEMRI for synchronous neural tracing with deep brain stimulation.

    Science.gov (United States)

    Huang, Wei-Chen; Lo, Yu-Chih; Chu, Chao-Yi; Lai, Hsin-Yi; Chen, You-Yin; Chen, San-Yuan

    2017-04-01

    Chronic brain stimulation has become a promising physical therapy with increased efficacy and efficiency in the treatment of neurodegenerative diseases. The application of deep brain electrical stimulation (DBS) combined with manganese-enhanced magnetic resonance imaging (MEMRI) provides an unbiased representation of the functional anatomy, which shows the communication between areas of the brain responding to the therapy. However, it is challenging for the current system to provide a real-time high-resolution image because the incorporated MnCl2 solution through microinjection usually results in image blurring or toxicity due to the uncontrollable diffusion of Mn2+. In this study, we developed a new type of conductive nanogel-based neural interface composed of amphiphilic chitosan-modified poly(3,4 -ethylenedioxythiophene) (PMSDT) that can exhibit biomimic structural/mechanical properties and ionic/electrical conductivity comparable to that of Au. More importantly, the PMSDT enables metal-ligand bonding with Mn2+ ions, so that the system can release Mn2+ ions rather than MnCl2 solution directly and precisely controlled by electrical stimulation (ES) to achieve real-time high-resolution MEMRI. With the integration of PMSDT nanogel-based coating in polyimide-based microelectrode arrays, the post-implantation DBS enables frequency-dependent MR imaging in vivo, as well as small focal imaging in response to channel site-specific stimulation on the implant. The MR imaging of the implanted brain treated with 5-min electrical stimulation showed a thalamocortical neuronal pathway after 36 h, confirming the effective activation of a downstream neuronal circuit following DBS. By eliminating the susceptibility to artifact and toxicity, this system, in combination with a MR-compatible implant and a bio-compliant neural interface, provides a harmless and synchronic functional anatomy for DBS. The study demonstrates a model of MEMRI-functionalized DBS based on functional

  16. Estrogen Stimulates Proliferation and Differentiation of Neural Stem/Progenitor Cells through Different Signal Transduction Pathways

    Directory of Open Access Journals (Sweden)

    Makiko Okada

    2010-10-01

    Full Text Available Our previous study indicated that both 17β-estradiol (E2, known to be an endogenous estrogen, and bisphenol A (BPA, known to be a xenoestrogen, could positively influence the proliferation or differentiation of neural stem/progenitor cells (NS/PCs. The aim of the present study was to identify the signal transduction pathways for estrogenic activities promoting proliferation and differentiation of NS/PCs via well known nuclear estrogen receptors (ERs or putative membrane-associated ERs. NS/PCs were cultured from the telencephalon of 15-day-old rat embryos. In order to confirm the involvement of nuclear ERs for estrogenic activities, their specific antagonist, ICI-182,780, was used. The presence of putative membrane-associated ER was functionally examined as to whether E2 can activate rapid intracellular signaling mechanism. In order to confirm the involvement of membrane-associated ERs for estrogenic activities, a cell-impermeable E2, bovine serum albumin-conjugated E2 (E2-BSA was used. We showed that E2 could rapidly activate extracellular signal-regulated kinases 1/2 (ERK 1/2, which was not inhibited by ICI-182,780. ICI-182,780 abrogated the stimulatory effect of these estrogens (E2 and BPA on the proliferation of NS/PCs, but not their effect on the differentiation of the NS/PCs into oligodendroglia. Furthermore, E2-BSA mimicked the activity of differentiation from NS/PCs into oligodendroglia, but not the activity of proliferation. Our study suggests that (1 the estrogen induced proliferation of NS/PCs is mediated via nuclear ERs; (2 the oligodendroglial generation from NS/PCs is likely to be stimulated via putative membrane‑associated ERs.

  17. Multiple neural network approaches to clinical expert systems

    Science.gov (United States)

    Stubbs, Derek F.

    1990-08-01

    We briefly review the concept of computer aided medical diagnosis and more extensively review the the existing literature on neural network applications in the field. Neural networks can function as simple expert systems for diagnosis or prognosis. Using a public database we develop a neural network for the diagnosis of a major presenting symptom while discussing the development process and possible approaches. MEDICAL EXPERTS SYSTEMS COMPUTER AIDED DIAGNOSIS Biomedicine is an incredibly diverse and multidisciplinary field and it is not surprising that neural networks with their many applications are finding more and more applications in the highly non-linear field of biomedicine. I want to concentrate on neural networks as medical expert systems for clinical diagnosis or prognosis. Expert Systems started out as a set of computerized " ifthen" rules. Everything was reduced to boolean logic and the promised land of computer experts was said to be in sight. It never came. Why? First the computer code explodes as the number of " ifs" increases. All the " ifs" have to interact. Second experts are not very good at reducing expertise to language. It turns out that experts recognize patterns and have non-verbal left-brain intuition decision processes. Third learning by example rather than learning by rule is the way natural brains works and making computers work by rule-learning is hideously labor intensive. Neural networks can learn from example. They learn the results

  18. System Identification, Prediction, Simulation and Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1997-01-01

    a Gauss-Newton search direction is applied. 3) Amongst numerous model types, often met in control applications, only the Non-linear ARMAX (NARMAX) model, representing input/output description, is examined. A simulated example confirms that a neural network has the potential to perform excellent System...... Identification, Prediction, Simulation and Control of a dynamic, non-linear and noisy process. Further, the difficulties to control a practical non-linear laboratory process in a satisfactory way by using a traditional controller are overcomed by using a trained neural network to perform non-linear System......The intention of this paper is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...

  19. The Criticality Hypothesis in Neural Systems

    Science.gov (United States)

    Karimipanah, Yahya

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

  20. A comparison between the neural correlates of laser and electric pain stimulation and their modulation by expectation.

    Science.gov (United States)

    Hird, E J; Jones, A K P; Talmi, D; El-Deredy, W

    2018-01-01

    Pain is modulated by expectation. Event-related potential (ERP) studies of the influence of expectation on pain typically utilise laser heat stimulation to provide a controllable nociceptive-specific stimulus. Painful electric stimulation has a number of practical advantages, but is less nociceptive-specific. We compared the modulation of electric versus laser-evoked pain by expectation, and their corresponding pain-evoked and anticipatory ERPs. We developed understanding of recognised methods of laser and electric stimulation. We tested whether pain perception and neural activity induced by electric stimulation was modulated by expectation, whether this expectation elicited anticipatory neural correlates, and how these measures compared to those associated with laser stimulation by eliciting cue-evoked expectations of high and low pain in a within-participant design. Despite sensory and affective differences between laser and electric pain, intensity ratings and pain-evoked potentials were modulated equivalently by expectation, though ERPs only correlated with pain ratings in the laser pain condition. Anticipatory correlates differentiated pain intensity expectation to laser but not electric pain. Previous studies show that laser-evoked potentials are modulated by expectation. We extend this by showing electric pain-evoked potentials are equally modulated by expectation, within the same participants. We also show a difference between the pain types in anticipation. Though laser-evoked potentials express a stronger relationship with pain perception, both laser and electric stimulation may be used to study the modulation of pain-evoked potentials by expectation. Anticipatory-evoked potentials are elicited by both pain types, but they may reflect different processes. Copyright © 2017. Published by Elsevier B.V.

  1. GABAA receptors in visual and auditory cortex and neural activity changes during basic visual stimulation

    Science.gov (United States)

    Qin, Pengmin; Duncan, Niall W.; Wiebking, Christine; Gravel, Paul; Lyttelton, Oliver; Hayes, Dave J.; Verhaeghe, Jeroen; Kostikov, Alexey; Schirrmacher, Ralf; Reader, Andrew J.; Northoff, Georg

    2012-01-01

    Recent imaging studies have demonstrated that levels of resting γ-aminobutyric acid (GABA) in the visual cortex predict the degree of stimulus-induced activity in the same region. These studies have used the presentation of discrete visual stimulus; the change from closed eyes to open also represents a simple visual stimulus, however, and has been shown to induce changes in local brain activity and in functional connectivity between regions. We thus aimed to investigate the role of the GABA system, specifically GABAA receptors, in the changes in brain activity between the eyes closed (EC) and eyes open (EO) state in order to provide detail at the receptor level to complement previous studies of GABA concentrations. We conducted an fMRI study involving two different modes of the change from EC to EO: an EO and EC block design, allowing the modeling of the haemodynamic response, followed by longer periods of EC and EO to allow the measuring of functional connectivity. The same subjects also underwent [18F]Flumazenil PET to measure GABAA receptor binding potentials. It was demonstrated that the local-to-global ratio of GABAA receptor binding potential in the visual cortex predicted the degree of changes in neural activity from EC to EO. This same relationship was also shown in the auditory cortex. Furthermore, the local-to-global ratio of GABAA receptor binding potential in the visual cortex also predicted the change in functional connectivity between the visual and auditory cortex from EC to EO. These findings contribute to our understanding of the role of GABAA receptors in stimulus-induced neural activity in local regions and in inter-regional functional connectivity. PMID:23293594

  2. GABAA receptors in visual and auditory cortex and neural activity changes during basic visual stimulation

    Directory of Open Access Journals (Sweden)

    Pengmin eQin

    2012-12-01

    Full Text Available Recent imaging studies have demonstrated that levels of resting GABA in the visual cortex predict the degree of stimulus-induced activity in the same region. These studies have used the presentation of discrete visual stimulus; the change from closed eyes to open also represents a simple visual stimulus, however, and has been shown to induce changes in local brain activity and in functional connectivity between regions. We thus aimed to investigate the role of the GABA system, specifically GABAA receptors, in the changes in brain activity between the eyes closed (EC and eyes open (EO state in order to provide detail at the receptor level to complement previous studies of GABA concentrations. We conducted an fMRI study involving two different modes of the change from EC to EO: An EO and EC block design, allowing the modelling of the haemodynamic response, followed by longer periods of EC and EO to allow the measuring of functional connectivity. The same subjects also underwent [18F]Flumazenil PET measure GABAA receptor binding potentials. It was demonstrated that the local-to-global ratio of GABAA receptor binding potential in the visual cortex predicted the degree of changes in neural activity from EC to EO. This same relationship was also shown in the auditory cortex. Furthermore, the local-to-global ratio of GABAA receptor binding potential in the visual cortex also predicts the change of functional connectivity between visual and auditory cortex from EC to EO. These findings contribute to our understanding of the role of GABAA receptors in stimulus-induced neural activity in local regions and in inter-regional functional connectivity.

  3. Conducting Polymers for Neural Prosthetic and Neural Interface Applications

    Science.gov (United States)

    2015-01-01

    Neural interfacing devices are an artificial mechanism for restoring or supplementing the function of the nervous system lost as a result of injury or disease. Conducting polymers (CPs) are gaining significant attention due to their capacity to meet the performance criteria of a number of neuronal therapies including recording and stimulating neural activity, the regeneration of neural tissue and the delivery of bioactive molecules for mediating device-tissue interactions. CPs form a flexible platform technology that enables the development of tailored materials for a range of neuronal diagnostic and treatment therapies. In this review the application of CPs for neural prostheses and other neural interfacing devices are discussed, with a specific focus on neural recording, neural stimulation, neural regeneration, and therapeutic drug delivery. PMID:26414302

  4. Investigating Tactile Stimulation in Symbiotic Systems

    DEFF Research Database (Denmark)

    Orso, Valeria; Mazza, Renato; Gamberini, Luciano

    2017-01-01

    the user’s perspective. Here we present the process of selecting the most adequate tactile stimulation delivered by a tactile vest while users were engaged in an absorbing activity, namely playing a video-game. A total of 20 participants (mean age 24.78; SD= 1.57) were involved. Among the eight tactile...

  5. Stimulant Treatment Trajectories Are Associated With Neural Reward Processing in Attention-Deficit/Hyperactivity Disorder

    NARCIS (Netherlands)

    Schweren, Lizanne J. S.; Groenman, Annabeth; von Rhein, Daniel; Weeda, Wouter; Faraone, Stephen F.; Luman, Marjolein; van Ewijk, Hanneke; Heslenfeld, Dirk J.; Franke, Barbara; Buitelaar, Jan K.; Oosterlaan, Jaap; Hoekstra, Pieter J.; Hartman, Catharina A.

    2017-01-01

    Objective: The past decades have seen a surge in stimulant prescriptions for the treatment of attention-deficit/hyperactivity disorder (ADHD). Stimulants acutely alleviate symptoms and cognitive deficits associated with ADHD by modulating striatal dopamine neurotransmission and induce therapeutic

  6. Stimulant Treatment Trajectories Are Associated With Neural Reward Processing in Attention-Deficit/Hyperactivity Disorder

    NARCIS (Netherlands)

    Schweren, L.J.; Groenman, A.; Rhein, D.T. von; Weeda, W.; Faraone, S.F.; Luman, M.; Ewijk, H. van; Heslenfeld, D.J.; Franke, B.; Buitelaar, J.K.; Oosterlaan, Jaap; Hoekstra, P.J.; Hartman, C.A.

    2017-01-01

    OBJECTIVE: The past decades have seen a surge in stimulant prescriptions for the treatment of attention-deficit/hyperactivity disorder (ADHD). Stimulants acutely alleviate symptoms and cognitive deficits associated with ADHD by modulating striatal dopamine neurotransmission and induce therapeutic

  7. EDITORIAL: Deep brain stimulation, deontology and duty: the moral obligation of non-abandonment at the neural interface Deep brain stimulation, deontology and duty: the moral obligation of non-abandonment at the neural interface

    Science.gov (United States)

    Fins, Joseph J.; MD; FACP

    2009-10-01

    intrusions on their bodies and their selves. Previously, I suggested that stimulation parameters for the treatment of neuropsychiatric disorders might be manipulated by patients one day. I envisioned a degree of patient discretion, within a pre-set safe range determined by physicians, much like patient-controlled analgesia (PCA) pumps give patients control over the dosing of opioid analgesia [3]. I am glad that such an advance is evolving as a means to preserve batteries in the treatment of motor disorders [16]. I would encourage the neural engineers to embrace the ethical mandate to develop additional platforms that might enhance patient self-determination and foster a greater degree of functional independence. While the neuromodulation community has every reason to celebrate its accomplishments, it would be better served by appreciating that the insertion of a device into the human brain comes with, if not the penumbra of sacrilege, a moral obligation to step out of the shadows and remain clearly available to patients and families over the long haul. Although neuromodulation has liberated many patients from the shackles of disease, we need to appreciate that the hardware that has made this possible can remain tethering. The challenge for the next generation of innovators is to minimize these burdens at this neural interface. By reducing barriers to care that exist in an unprepared health care system and developing more user-friendly technology, the neuromodulation community can expand its reach and broaden the relief provided by these neuro-palliative interventions [17]. Acknowledgements and Disclosures Dr Fins is the recipient of an Investigator Award in Health Policy Research (Minds Apart: Severe Brain Injury and Health Policy) from The Robert Wood Johnson Foundation. He also gratefully acknowledges grant support from the Buster Foundation (Neuroethics and Disorders of Consciousness). He is an unfunded co-investigator of a study of deep brain stimulation in the minimally

  8. An artificial neural network controller for intelligent transportation systems applications

    Energy Technology Data Exchange (ETDEWEB)

    Vitela, J.E.; Hanebutte, U.R.; Reifman, J. [Argonne National Lab., IL (United States). Reactor Analysis Div.

    1996-04-01

    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 applications. The AICC is based on a simple nonlinear model of the vehicle dynamics. A Neural Network Controller (NNC) code developed at Argonne National Laboratory to control discrete dynamical systems was used for this purpose. In order to test the NNC, an AICC-simulator containing graphical displays was developed for a system of two vehicles driving in a single lane. Two simulation cases are shown, one involving a lead vehicle with constant velocity and the other a lead vehicle with varying acceleration. More realistic vehicle dynamic models will be considered in future work.

  9. Neural signal sampling via the low power wireless pico system.

    Science.gov (United States)

    Cieslewski, Grzegorz; Cheney, David; Gugel, Karl; Sanchez, Justin C; Principe, Jose C

    2006-01-01

    This paper presents a powerful new low power wireless system for sampling multiple channels of neural activity based on Texas Instruments MSP430 microprocessors and Nordic Semiconductor's ultra low power high bandwidth RF transmitters and receivers. The system's development process, component selection, features and test methodology are presented.

  10. Role of neural network models for developing speech systems

    Indian Academy of Sciences (India)

    These prosody models are further examined for applications such as text to speech synthesis, speech recognition, speaker recognition and language identification. Neural network models in voice conversion system are explored for capturing the mapping functions between source and target speakers at source, system and ...

  11. NNSYSID - toolbox for system identification with neural networks

    DEFF Research Database (Denmark)

    Norgaard, M.; Ravn, Ole; Poulsen, Niels Kjølstad

    2002-01-01

    The NNSYSID toolset for System Identification has been developed as an add on to MATLAB(R). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains a number of nonlinear model structures based on neural networks, effective training algorithms...

  12. Neural expert decision support system for stroke diagnosis

    Science.gov (United States)

    Kupershtein, Leonid M.; Martyniuk, Tatiana B.; Krencin, Myhail D.; Kozhemiako, Andriy V.; Bezsmertnyi, Yurii; Bezsmertna, Halyna; Kolimoldayev, Maksat; Smolarz, Andrzej; Weryńska-Bieniasz, RóŻa; Uvaysova, Svetlana

    2017-08-01

    In the work the hybrid expert system for stroke diagnosis was presented. The base of expert system consists of neural network and production rules. This program can quickly and accurately set to the patient preliminary and final diagnoses, get examination and treatment plans, print data of patient, analyze statistics data and perform parameterized search for patients.

  13. Common neural structures activated by epidural and transcutaneous lumbar spinal cord stimulation: Elicitation of posterior root-muscle reflexes.

    Science.gov (United States)

    Hofstoetter, Ursula S; Freundl, Brigitta; Binder, Heinrich; Minassian, Karen

    2018-01-01

    Epidural electrical stimulation of the lumbar spinal cord is currently regaining momentum as a neuromodulation intervention in spinal cord injury (SCI) to modify dysregulated sensorimotor functions and augment residual motor capacity. There is ample evidence that it engages spinal circuits through the electrical stimulation of large-to-medium diameter afferent fibers within lumbar and upper sacral posterior roots. Recent pilot studies suggested that the surface electrode-based method of transcutaneous spinal cord stimulation (SCS) may produce similar neuromodulatory effects as caused by epidural SCS. Neurophysiological and computer modeling studies proposed that this noninvasive technique stimulates posterior-root fibers as well, likely activating similar input structures to the spinal cord as epidural stimulation. Here, we add a yet missing piece of evidence substantiating this assumption. We conducted in-depth analyses and direct comparisons of the electromyographic (EMG) characteristics of short-latency responses in multiple leg muscles to both stimulation techniques derived from ten individuals with SCI each. Post-activation depression of responses evoked by paired pulses applied either epidurally or transcutaneously confirmed the reflex nature of the responses. The muscle responses to both techniques had the same latencies, EMG peak-to-peak amplitudes, and waveforms, except for smaller responses with shorter onset latencies in the triceps surae muscle group and shorter offsets of the responses in the biceps femoris muscle during epidural stimulation. Responses obtained in three subjects tested with both methods at different time points had near-identical waveforms per muscle group as well as same onset latencies. The present results strongly corroborate the activation of common neural input structures to the lumbar spinal cord-predominantly primary afferent fibers within multiple posterior roots-by both techniques and add to unraveling the basic mechanisms

  14. Reject mechanisms for massively parallel neural network character recognition systems

    Science.gov (United States)

    Garris, Michael D.; Wilson, Charles L.

    1992-12-01

    Two reject mechanisms are compared using a massively parallel character recognition system implemented at NIST. The recognition system was designed to study the feasibility of automatically recognizing hand-printed text in a loosely constrained environment. The first method is a simple scalar threshold on the output activation of the winning neurode from the character classifier network. The second method uses an additional neural network trained on all outputs from the character classifier network to accept or reject assigned classifications. The neural network rejection method was expected to perform with greater accuracy than the scalar threshold method, but this was not supported by the test results presented. The scalar threshold method, even though arbitrary, is shown to be a viable reject mechanism for use with neural network character classifiers. Upon studying the performance of the neural network rejection method, analyses show that the two neural networks, the character classifier network and the rejection network, perform very similarly. This can be explained by the strong non-linear function of the character classifier network which effectively removes most of the correlation between character accuracy and all activations other than the winning activation. This suggests that any effective rejection network must receive information from the system which has not been filtered through the non-linear classifier.

  15. Neural-network-based fuzzy logic decision systems

    Science.gov (United States)

    Kulkarni, Arun D.; Giridhar, G. B.; Coca, Praveen

    1994-10-01

    During the last few years there has been a large and energetic upswing in research efforts aimed at synthesizing fuzzy logic with neural networks. This combination of neural networks and fuzzy logic seems natural because the two approaches generally attack the design of `intelligent' system from quite different angles. Neural networks provide algorithms for learning, classification, and optimization whereas fuzzy logic often deals with issues such as reasoning in a high (semantic or linguistic) level. Consequently the two technologies complement each other. In this paper, we combine neural networks with fuzzy logic techniques. We propose an artificial neural network (ANN) model for a fuzzy logic decision system. The model consists of six layers. The first three layers map the input variables to fuzzy set membership functions. The last three layers implement the decision rules. The model learns the decision rules using a supervised gradient descent procedure. As an illustration we considered two examples. The first example deals with pixel classification in multispectral satellite images. In our second example we used the fuzzy decision system to analyze data from magnetic resonance imaging (MRI) scans for tissue classification.

  16. Elimination of the geomagnetic field stimulates the proliferation of mouse neural progenitor and stem cells

    Directory of Open Access Journals (Sweden)

    Jing-Peng Fu

    2016-08-01

    Full Text Available Abstract Living organisms are exposed to the geomagnetic field (GMF throughout their lifespan. Elimination of the GMF, resulting in a hypogeomagnetic field (HMF, leads to central nervous system dysfunction and abnormal development in animals. However, the cellular mechanisms underlying these effects have not been identified so far. Here, we show that exposure to an HMF (<200 nT, produced by a magnetic field shielding chamber, promotes the proliferation of neural progenitor/stem cells (NPCs/NSCs from C57BL/6 mice. Following seven-day HMF-exposure, the primary neurospheres (NSs were significantly larger in size, and twice more NPCs/NSCs were harvested from neonatal NSs, when compared to the GMF controls. The self-renewal capacity and multipotency of the NSs were maintained, as HMF-exposed NSs were positive for NSC markers (Nestin and Sox2, and could differentiate into neurons and astrocyte/glial cells and be passaged continuously. In addition, adult mice exposed to the HMF for one month were observed to have a greater number of proliferative cells in the subventricular zone. These findings indicate that continuous HMF-exposure increases the proliferation of NPCs/NSCs, in vitro and in vivo. HMF-disturbed NPCs/NSCs production probably affects brain development and function, which provides a novel clue for elucidating the cellular mechanisms of the bio-HMF response.

  17. Adaptive Synchronization of Memristor-based Chaotic Neural Systems

    Directory of Open Access Journals (Sweden)

    Xiaofang Hu

    2014-11-01

    Full Text Available Chaotic neural networks consisting of a great number of chaotic neurons are able to reproduce the rich dynamics observed in biological nervous systems. In recent years, the memristor has attracted much interest in the efficient implementation of artificial synapses and neurons. This work addresses adaptive synchronization of a class of memristor-based neural chaotic systems using a novel adaptive backstepping approach. A systematic design procedure is presented. Simulation results have demonstrated the effectiveness of the proposed adaptive synchronization method and its potential in practical application of memristive chaotic oscillators in secure communication.

  18. Reliability Modeling of Microelectromechanical Systems Using Neural Networks

    Science.gov (United States)

    Perera. J. Sebastian

    2000-01-01

    Microelectromechanical systems (MEMS) are a broad and rapidly expanding field that is currently receiving a great deal of attention because of the potential to significantly improve the ability to sense, analyze, and control a variety of processes, such as heating and ventilation systems, automobiles, medicine, aeronautical flight, military surveillance, weather forecasting, and space exploration. MEMS are very small and are a blend of electrical and mechanical components, with electrical and mechanical systems on one chip. This research establishes reliability estimation and prediction for MEMS devices at the conceptual design phase using neural networks. At the conceptual design phase, before devices are built and tested, traditional methods of quantifying reliability are inadequate because the device is not in existence and cannot be tested to establish the reliability distributions. A novel approach using neural networks is created to predict the overall reliability of a MEMS device based on its components and each component's attributes. The methodology begins with collecting attribute data (fabrication process, physical specifications, operating environment, property characteristics, packaging, etc.) and reliability data for many types of microengines. The data are partitioned into training data (the majority) and validation data (the remainder). A neural network is applied to the training data (both attribute and reliability); the attributes become the system inputs and reliability data (cycles to failure), the system output. After the neural network is trained with sufficient data. the validation data are used to verify the neural networks provided accurate reliability estimates. Now, the reliability of a new proposed MEMS device can be estimated by using the appropriate trained neural networks developed in this work.

  19. Optical and electrochemical methods for determining the effective area and charge density of conducting polymer modified electrodes for neural stimulation.

    Science.gov (United States)

    Harris, Alexander R; Molino, Paul J; Kapsa, Robert M I; Clark, Graeme M; Paolini, Antonio G; Wallace, Gordon G

    2015-01-06

    Neural stimulation is used in the cochlear implant, bionic eye, and deep brain stimulation, which involves implantation of an array of electrodes into a patient's brain. The current passed through the electrodes is used to provide sensory queues or reduce symptoms associated with movement disorders and increasingly for psychological and pain therapies. Poor control of electrode properties can lead to suboptimal performance; however, there are currently no standard methods to assess them, including the electrode area and charge density. Here we demonstrate optical and electrochemical methods for measuring these electrode properties and show the charge density is dependent on electrode geometry. This technique highlights that materials can have widely different charge densities but also large variation in performance. Measurement of charge density from an electroactive area may result in new materials and electrode geometries that improve patient outcomes and reduce side effects.

  20. Towards multifocal ultrasonic neural stimulation II: design considerations for an acoustic retinal prosthesis

    Science.gov (United States)

    Naor, Omer; Hertzberg, Yoni; Zemel, Esther; Kimmel, Eitan; Shoham, Shy

    2012-04-01

    Ultrasound waves, widely used as a non-invasive diagnostic modality, were recently shown to stimulate neuronal activity. Functionally meaningful stimulation, as is required in order to form a unified percept, requires the dynamic generation of simultaneous stimulation patterns. In this paper, we examine the general feasibility and properties of an acoustic retinal prosthesis, a new vision restoration strategy that will combine ultrasonic neuro-stimulation and ultrasonic field sculpting technology towards non-invasive artificial stimulation of surviving neurons in a degenerating retina. We explain the conceptual framework for such a device, study its feasibility in an in vivo ultrasonic retinal stimulation study and discuss the associated design considerations and tradeoffs. Finally, we simulate and experimentally validate a new holographic method—the angular spectrum-GSW—for efficient generation of uniform and accurate continuous ultrasound patterns. This method provides a powerful, flexible solution to the problem of projecting complex acoustic images onto structures like the retina.

  1. Space-time system architecture for the neural optical computing

    Science.gov (United States)

    Lo, Yee-Man V.

    1991-02-01

    The brain can perform the tasks of associative recall detection recognition and optimization. In this paper space-time system field models of the brain are introduced. They are called the space-time maximum likelihood associative memory system (ST-ML-AMS) and the space-time adaptive learning system (ST-ALS). Performance of the system is analyzed using the probability of error in memory recall (PEMR) and the space-time neural capacity (ST-NC). 1.

  2. Central nervous system stimulants and drugs that suppress appetite

    DEFF Research Database (Denmark)

    Aagaard, Lise

    2014-01-01

    of the January 2012 to June 2013 publications on central nervous system stimulants and drugs that suppress appetite covers amphetamines (including metamfetamine, paramethoxyamfetamine and paramethoxymetamfetamine), fenfluramine and benfluorex, atomoxetine, methylphenidate, modafinil and armodafinil...

  3. EGF–FGF{sub 2} stimulates the proliferation and improves the neuronal commitment of mouse epidermal neural crest stem cells (EPI-NCSCs)

    Energy Technology Data Exchange (ETDEWEB)

    Bressan, Raul Bardini; Melo, Fernanda Rosene; Almeida, Patricia Alves; Bittencourt, Denise Avani; Visoni, Silvia; Jeremias, Talita Silva [Departamento de Biologia Celular, Embriologia e Genética, Centro de Ciências Biológicas, Universidade Federal de Santa Catarina, Campus Universitário – Trindade, 88040-900 Florianópolis SC (Brazil); Costa, Ana Paula; Leal, Rodrigo Bainy [Departamento de Bioquímica, Centro de Ciências Biológicas, Universidade Federal de Santa Catarina, Campus Universitário – Trindade, 88040-900 Florianópolis SC (Brazil); Trentin, Andrea Gonçalves, E-mail: andrea.trentin@ufsc.br [Departamento de Biologia Celular, Embriologia e Genética, Centro de Ciências Biológicas, Universidade Federal de Santa Catarina, Campus Universitário – Trindade, 88040-900 Florianópolis SC (Brazil)

    2014-09-10

    Epidermal neural crest stem cells (EPI-NCSCs), which reside in the bulge of hair follicles, are attractive candidates for several applications in cell therapy, drug screening and tissue engineering. As suggested remnants of the embryonic neural crest (NC) in an adult location, EPI-NCSCs are able to generate a wide variety of cell types and are readily accessible by a minimally invasive procedure. Since the combination of epidermal growth factor (EGF) and fibroblast growth factor type 2 (FGF{sub 2}) is mitogenic and promotes the neuronal commitment of various stem cell populations, we examined its effects in the proliferation and neuronal potential of mouse EPI-NCSCs. By using a recognized culture protocol of bulge whiskers follicles, we were able to isolate a population of EPI-NCSCs, characterized by the migratory potential, cell morphology and expression of phenotypic markers of NC cells. EPI-NCSCs expressed neuronal, glial and smooth muscle markers and exhibited the NC-like fibroblastic morphology. The treatment with the combination EGF and FGF{sub 2}, however, increased their proliferation rate and promoted the acquisition of a neuronal-like morphology accompanied by reorganization of neural cytoskeletal proteins βIII-tubulin and nestin, as well as upregulation of the pan neuronal marker βIII-tubulin and down regulation of the undifferentiated NC, glial and smooth muscle cell markers. Moreover, the treatment enhanced the response of EPI-NCSCs to neurogenic stimulation, as evidenced by induction of GAP43, and increased expression of Mash-1 in neuron-like cell, both neuronal-specific proteins. Together, the results suggest that the combination of EGF–FGF2 stimulates the proliferation and improves the neuronal potential of EPI-NCSCs similarly to embryonic NC cells, ES cells and neural progenitor/stem cells of the central nervous system and highlights the advantage of using EGF–FGF{sub 2} in neuronal differentiation protocols. - Highlights: • EPI

  4. A dynamical systems view of motor preparation: Implications for neural prosthetic system design

    Science.gov (United States)

    Shenoy, Krishna V.; Kaufman, Matthew T.; Sahani, Maneesh; Churchland, Mark M.

    2013-01-01

    Neural prosthetic systems aim to help disabled patients suffering from a range of neurological injuries and disease by using neural activity from the brain to directly control assistive devices. This approach in effect bypasses the dysfunctional neural circuitry, such as an injured spinal cord. To do so, neural prostheses depend critically on a scientific understanding of the neural activity that drives them. We review here several recent studies aimed at understanding the neural processes in premotor cortex that precede arm movements and lead to the initiation of movement. These studies were motivated by hypotheses and predictions conceived of within a dynamical systems perspective. This perspective concentrates on describing the neural state using as few degrees of freedom as possible and on inferring the rules that govern the motion of that neural state. Although quite general, this perspective has led to a number of specific predictions that have been addressed experimentally. It is hoped that the resulting picture of the dynamical role of preparatory and movement-related neural activity will be particularly helpful to the development of neural prostheses, which can themselves be viewed as dynamical systems under the control of the larger dynamical system to which they are attached. PMID:21763517

  5. Enhancement of multitasking performance and neural oscillations by transcranial alternating current stimulation

    OpenAIRE

    Hsu, Wan-Yu; Zanto, Theodore P.; van Schouwenburg, Martine R.; Gazzaley, Adam

    2017-01-01

    Multitasking is associated with the generation of stimulus-locked theta (4?7 Hz) oscillations arising from prefrontal cortex (PFC). Transcranial alternating current stimulation (tACS) is a non-invasive brain stimulation technique that influences endogenous brain oscillations. Here, we investigate whether applying alternating current stimulation within the theta frequency band would affect multitasking performance, and explore tACS effects on neurophysiological measures. Brief runs of bilatera...

  6. A NEURAL NETWORK BASED IRIS RECOGNITION SYSTEM FOR PERSONAL IDENTIFICATION

    Directory of Open Access Journals (Sweden)

    Usham Dias

    2010-10-01

    Full Text Available This paper presents biometric personal identification based on iris recognition using artificial neural networks. Personal identification system consists of localization of the iris region, normalization, enhancement and then iris pattern recognition using neural network. In this paper, through results obtained, we have shown that a person’s left and right eye are unique. In this paper, we also show that the network is sensitive to the initial weights and that over-training gives bad results. We also propose a fast algorithm for the localization of the inner and outer boundaries of the iris region. Results of simulations illustrate the effectiveness of the neural system in personal identification. Finally a hardware iris recognition model is proposed and implementation aspects are discussed.

  7. Enhancement of multitasking performance and neural oscillations by transcranial alternating current stimulation

    National Research Council Canada - National Science Library

    Wan-Yu Hsu; Theodore P Zanto; Martine R van Schouwenburg; Adam Gazzaley

    2017-01-01

    .... Here, we investigate whether applying alternating current stimulation within the theta frequency band would affect multitasking performance, and explore tACS effects on neurophysiological measures...

  8. Optical neural network system for pose determination of spinning satellites

    Science.gov (United States)

    Lee, Andrew; Casasent, David

    1990-01-01

    An optical neural network architecture and algorithm based on a Hopfield optimization network are presented for multitarget tracking. This tracker utilizes a neuron for every possible target track, and a quadratic energy function of neural activities which is minimized using gradient descent neural evolution. The neural net tracker is demonstrated as part of a system for determining position and orientation (pose) of spinning satellites with respect to a robotic spacecraft. The input to the system is time sequence video from a single camera. Novelty detection and filtering are utilized to locate and segment novel regions from the input images. The neural net multitarget tracker determines the correspondences (or tracks) of the novel regions as a function of time, and hence the paths of object (satellite) parts. The path traced out by a given part or region is approximately elliptical in image space, and the position, shape and orientation of the ellipse are functions of the satellite geometry and its pose. Having a geometric model of the satellite, and the elliptical path of a part in image space, the three-dimensional pose of the satellite is determined. Digital simulation results using this algorithm are presented for various satellite poses and lighting conditions.

  9. Design of a Closed-Loop, Bidirectional Brain Machine Interface System With Energy Efficient Neural Feature Extraction and PID Control.

    Science.gov (United States)

    Liu, Xilin; Zhang, Milin; Richardson, Andrew G; Lucas, Timothy H; Van der Spiegel, Jan

    2017-08-01

    This paper presents a bidirectional brain machine interface (BMI) microsystem designed for closed-loop neuroscience research, especially experiments in freely behaving animals. The system-on-chip (SoC) consists of 16-channel neural recording front-ends, neural feature extraction units, 16-channel programmable neural stimulator back-ends, in-channel programmable closed-loop controllers, global analog-digital converters (ADC), and peripheral circuits. The proposed neural feature extraction units includes 1) an ultra low-power neural energy extraction unit enabling a 64-step natural logarithmic domain frequency tuning, and 2) a current-mode action potential (AP) detection unit with time-amplitude window discriminator. A programmable proportional-integral-derivative (PID) controller has been integrated in each channel enabling a various of closed-loop operations. The implemented ADCs include a 10-bit voltage-mode successive approximation register (SAR) ADC for the digitization of the neural feature outputs and/or local field potential (LFP) outputs, and an 8-bit current-mode SAR ADC for the digitization of the action potential outputs. The multi-mode stimulator can be programmed to perform monopolar or bipolar, symmetrical or asymmetrical charge balanced stimulation with a maximum current of 4 mA in an arbitrary channel configuration. The chip has been fabricated in 0.18 μ m CMOS technology, occupying a silicon area of 3.7 mm 2 . The chip dissipates 56 μW/ch on average. General purpose low-power microcontroller with Bluetooth module are integrated in the system to provide wireless link and SoC configuration. Methods, circuit techniques and system topology proposed in this work can be used in a wide range of relevant neurophysiology research, especially closed-loop BMI experiments.

  10. Neural correlates of heterotopic facilitation induced after high frequency electrical stimulation of nociceptive pathways

    NARCIS (Netherlands)

    Broeke, E.N. van den; Heck, C.H. van; Rijn, C.M. van; Wilder-Smith, O.H.G.

    2011-01-01

    Background High frequency electrical stimulation (HFS) of primary nociceptive afferents in humans induce a heightened sensitivity in the surrounding non-stimulated skin area. Several studies suggest that this heterotopic effect is the result of central (spinal) plasticity. The aim of this study is

  11. Neural correlates of heterotopic facilitation induced after high frequency electrical stimulation of nociceptive pathways

    NARCIS (Netherlands)

    Broeke, E.N. van den; Heck, C.H. van; Rijn, C.M. van; Wilder-Smith, O.H.G.

    2011-01-01

    BACKGROUND: High frequency electrical stimulation (HFS) of primary nociceptive afferents in humans induce a heightened sensitivity in the surrounding non-stimulated skin area. Several studies suggest that this heterotopic effect is the result of central (spinal) plasticity. The aim of this study is

  12. Influence of repetitive peripheral magnetic stimulation on neural plasticity in the motor cortex related to swallowing.

    Science.gov (United States)

    Momosaki, Ryo; Kakuda, Wataru; Yamada, Naoki; Abo, Masahiro

    2016-09-01

    The aim of this study was to evaluate the effect of repetitive peripheral magnetic stimulation at two different frequencies (20 and 30 Hz) on cortical excitability in motor areas related to swallowing in healthy individuals. The study participants were 10 healthy normal volunteers (two women and eight men, age range 25-36 years). Repetitive peripheral magnetic stimulation was applied to the submandibular muscle using a parabolic coil at the site where contraction of the suprahyoid muscles was elicited. Stimulation was continued for 10 min (total 1200 pulses) at 20 Hz on 1 day and at 30 Hz on another day, with the stimulation strength set at 90% of the intensity that elicited pain. The motor-evoked potential amplitude of suprahyoid muscles was assessed before, immediately after, and 30 min after stimulation. Stimulations at both 20 and 30 Hz significantly increased motor-evoked potential amplitude (Pstimulation. The motor-evoked potential amplitude immediately after stimulation was not significantly different between the 20 and 30 Hz frequencies. The results indicated that repetitive magnetic stimulation increased motor-evoked potential amplitude of swallowing muscles, suggesting facilitation of the motor cortex related to swallowing in healthy individuals.

  13. Neural reflex regulation of systemic inflammation: potential new targets for sepsis therapy.

    Directory of Open Access Journals (Sweden)

    Ricardo eFernandez

    2014-12-01

    Full Text Available Sepsis progresses to multiple organ dysfunction due to the uncontrolled release of inflammatory mediators, and a growing body of evidence shows that neural signals play a significant role in modulating the immune response. Thus, similar toall other physiological systems, the immune system is both connected to and regulated by the central nervous system. The efferent arc consists of the activation of the hypothalamic–pituitary–adrenal axis, sympathetic activation, the cholinergic anti-inflammatory reflex, and the local release of physiological neuromodulators. Immunosensory activity is centered on the production of pro-inflammatory cytokines, signals that are conveyed to the brain through different pathways. The activation of peripheral sensory nerves, i.e., vagal paraganglia by the vagus nerve, and carotid body (CB chemoreceptors by the carotid/sinus nerve are broadly discussed here. Despite cytokine receptor expression in vagal afferent fibers, pro-inflammatory cytokines have no significant effect on vagus nerve activity. Thus, the CB may be the source of immunosensory inputs and incoming neural signals and, in fact, sense inflammatory mediators, playing a protective role during sepsis. Considering that CB stimulation increases sympathetic activity and adrenal glucocorticoids release, the electrical stimulation of arterial chemoreceptors may be suitable therapeutic approach for regulating systemic inflammation.

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

    Directory of Open Access Journals (Sweden)

    Yongcheng Li

    Full Text Available 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.

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

    Science.gov (United States)

    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

  16. Electrical stimulation superimposed on voluntary training can limit sensory integration in neural adaptations.

    Science.gov (United States)

    Paillard, Thierry

    2012-01-01

    P. Bezerra, S. Zhou, Z. Crowley, A. Davie, and R. Baglin (2011) suggested that the neural mechanisms responsible for steadiness improvement relate in particular to the discharge behavior of motor units and the practice and learning of skills rather than the strength gain after electromyostimulation superimposed over voluntary training. However, the afferent inputs are determining in control of the force level produced and thus contribute to ensure muscle steadiness. Hence, it is possible that electromyostimulation interferes in neurophysiological afference integration and prevents neural adaptations that enable improvement of the control of force (and then muscle steadiness) to occur. Therefore, the neural adaptations induced by electromyostimulation superimposed onto voluntary training should also be researched in relation to the sensory pathways.

  17. Using fuzzy logic to integrate neural networks and knowledge-based systems

    Science.gov (United States)

    Yen, John

    1991-01-01

    Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems.

  18. Classical Conditioning with Pulsed Integrated Neural Networks: Circuits and System

    DEFF Research Database (Denmark)

    Lehmann, Torsten

    1998-01-01

    In this paper we investigate on-chip learning for pulsed, integrated neural networks. We discuss the implementational problems the technology imposes on learning systems and we find that abiologically inspired approach using simple circuit structures is most likely to bring success. We develop a ...... chip to solve simple classical conditioning tasks, thus verifying the design methodologies put forward in the paper....

  19. Dynamic causal models of neural system dynamics: current state ...

    Indian Academy of Sciences (India)

    2006-09-28

    Sep 28, 2006 ... Keywords. Dynamic causal modelling; EEG; effective connectivity; event-related potentials; fMRI; neural system ... In this article, we review the conceptual and mathematical basis of DCM and its implementation for functional magnetic resonance imaging data and event-related potentials. After introducing ...

  20. Neural network based system for script identification in Indian ...

    Indian Academy of Sciences (India)

    R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

    environments. The system developed includes a feature extractor and a modular neural network. The feature extractor consists of two stages. In the first stage ... environments is script/language identification (Muthusamy et al 1994; Hochberg et al 1997). ... In order to take advantage of the learning and generalization abilities ...

  1. A breathing circuit alarm system based on neural networks.

    Science.gov (United States)

    Orr, J A; Westenskow, D R

    1994-03-01

    The objectives of our study were (1) to implement intelligent respiratory alarms with a neural network; and (2) to increase alarm specificity and decrease false-alarm rates compared with current alarms. We trained a neural network to recognize 13 faults in an anesthesia breathing circuit. The system extracted 30 breath-to-breath features from the airway CO2, flow, and pressure signals. We created training data for the network by introducing 13 faults repeatedly in 5 dogs (616 total faults). We used the data to train the neural network using the backward error propagation algorithm. In animals, the trained network reported the alarms correctly for 95.0% of the faults when tested during controlled ventilation, and for 86.9% of the faults during spontaneous breathing. When tested in the operating room, the system found and correctly reported 54 of 57 faults that occurred during 43.6 hr of use. The alarm system produced a total of 74 false alarms during 43.6 hr of monitoring. Neural networks may be useful in creating intelligent anesthesia alarm systems.

  2. Tissue heterogeneity as a mechanism for localized neural stimulation by applied electric fields

    Energy Technology Data Exchange (ETDEWEB)

    Miranda, P C [Institute of Biophysics and Biomedical Engineering, Faculty of Sciences, University of Lisbon, 1749-016 Lisbon (Portugal); Correia, L [Institute of Biophysics and Biomedical Engineering, Faculty of Sciences, University of Lisbon, 1749-016 Lisbon (Portugal); Salvador, R [Institute of Biophysics and Biomedical Engineering, Faculty of Sciences, University of Lisbon, 1749-016 Lisbon (Portugal); Basser, P J [Section on Tissue Biophysics and Biomimetics, NICHD, National Institutes of Health, Bethesda, MD 20892-1428 (United States)

    2007-09-21

    We investigate the heterogeneity of electrical conductivity as a new mechanism to stimulate excitable tissues via applied electric fields. In particular, we show that stimulation of axons crossing internal boundaries can occur at boundaries where the electric conductivity of the volume conductor changes abruptly. The effectiveness of this and other stimulation mechanisms was compared by means of models and computer simulations in the context of transcranial magnetic stimulation. While, for a given stimulation intensity, the largest membrane depolarization occurred where an axon terminates or bends sharply in a high electric field region, a slightly smaller membrane depolarization, still sufficient to generate action potentials, also occurred at an internal boundary where the conductivity jumped from 0.143 S m{sup -1} to 0.333 S m{sup -1}, simulating a white-matter-grey-matter interface. Tissue heterogeneity can also give rise to local electric field gradients that are considerably stronger and more focal than those impressed by the stimulation coil and that can affect the membrane potential, albeit to a lesser extent than the two mechanisms mentioned above. Tissue heterogeneity may play an important role in electric and magnetic 'far-field' stimulation.

  3. Frequency-difference-dependent stochastic resonance in neural systems

    Science.gov (United States)

    Guo, Daqing; Perc, Matjaž; Zhang, Yangsong; Xu, Peng; Yao, Dezhong

    2017-08-01

    Biological neurons receive multiple noisy oscillatory signals, and their dynamical response to the superposition of these signals is of fundamental importance for information processing in the brain. Here we study the response of neural systems to the weak envelope modulation signal, which is superimposed by two periodic signals with different frequencies. We show that stochastic resonance occurs at the beat frequency in neural systems at the single-neuron as well as the population level. The performance of this frequency-difference-dependent stochastic resonance is influenced by both the beat frequency and the two forcing frequencies. Compared to a single neuron, a population of neurons is more efficient in detecting the information carried by the weak envelope modulation signal at the beat frequency. Furthermore, an appropriate fine-tuning of the excitation-inhibition balance can further optimize the response of a neural ensemble to the superimposed signal. Our results thus introduce and provide insights into the generation and modulation mechanism of the frequency-difference-dependent stochastic resonance in neural systems.

  4. Internal models and neural computation in the vestibular system.

    Science.gov (United States)

    Green, Andrea M; Angelaki, Dora E

    2010-01-01

    The vestibular system is vital for motor control and spatial self-motion perception. Afferents from the otolith organs and the semicircular canals converge with optokinetic, somatosensory and motor-related signals in the vestibular nuclei, which are reciprocally interconnected with the vestibulocerebellar cortex and deep cerebellar nuclei. Here, we review the properties of the many cell types in the vestibular nuclei, as well as some fundamental computations implemented within this brainstem-cerebellar circuitry. These include the sensorimotor transformations for reflex generation, the neural computations for inertial motion estimation, the distinction between active and passive head movements, as well as the integration of vestibular and proprioceptive information for body motion estimation. A common theme in the solution to such computational problems is the concept of internal models and their neural implementation. Recent studies have shed new insights into important organizational principles that closely resemble those proposed for other sensorimotor systems, where their neural basis has often been more difficult to identify. As such, the vestibular system provides an excellent model to explore common neural processing strategies relevant both for reflexive and for goal-directed, voluntary movement as well as perception.

  5. Gold nanoparticles functionalized with a fragment of the neural cell adhesion molecule L1 stimulate L1-mediated functions

    Science.gov (United States)

    Schulz, Florian; Lutz, David; Rusche, Norman; Bastús, Neus G.; Stieben, Martin; Höltig, Michael; Grüner, Florian; Weller, Horst; Schachner, Melitta; Vossmeyer, Tobias; Loers, Gabriele

    2013-10-01

    The neural cell adhesion molecule L1 is involved in nervous system development and promotes regeneration in animal models of acute and chronic injury of the adult nervous system. To translate these conducive functions into therapeutic approaches, a 22-mer peptide that encompasses a minimal and functional L1 sequence of the third fibronectin type III domain of murine L1 was identified and conjugated to gold nanoparticles (AuNPs) to obtain constructs that interact homophilically with the extracellular domain of L1 and trigger the cognate beneficial L1-mediated functions. Covalent conjugation was achieved by reacting mixtures of two cysteine-terminated forms of this L1 peptide and thiolated poly(ethylene) glycol (PEG) ligands (~2.1 kDa) with citrate stabilized AuNPs of two different sizes (~14 and 40 nm in diameter). By varying the ratio of the L1 peptide-PEG mixtures, an optimized layer composition was achieved that resulted in the expected homophilic interaction of the AuNPs. These AuNPs were stable as tested over a time period of 30 days in artificial cerebrospinal fluid and interacted with the extracellular domain of L1 on neurons and Schwann cells, as could be shown by using cells from wild-type and L1-deficient mice. In vitro, the L1-derivatized particles promoted neurite outgrowth and survival of neurons from the central and peripheral nervous system and stimulated Schwann cell process formation and proliferation. These observations raise the hope that, in combination with other therapeutic approaches, L1 peptide-functionalized AuNPs may become a useful tool to ameliorate the deficits resulting from acute and chronic injuries of the mammalian nervous system.The neural cell adhesion molecule L1 is involved in nervous system development and promotes regeneration in animal models of acute and chronic injury of the adult nervous system. To translate these conducive functions into therapeutic approaches, a 22-mer peptide that encompasses a minimal and functional L1

  6. Remote control of microcontroller-based infant stimulating system.

    Science.gov (United States)

    Burunkaya, M; Güler, I

    2000-04-01

    In this paper, a remote-controlled and microcontroller-based cradle is designed and constructed. This system is also called Remote Control of Microcontroller-Based Infant Stimulation System or the RECOMBIS System. Cradle is an infant stimulating system that provides relaxation and sleeping for the baby. RECOMBIS system is designed for healthy full-term newborns to provide safe infant care and provide relaxation and sleeping for the baby. A microcontroller-based electronic circuit was designed and implemented for RECOMBIS system. Electromagnets were controlled by 8-bit PIC16F84 microcontroller, which is programmed using MPASM package. The system works by entering preset values from the keyboard, or pulse code modulated radio frequency remote control system. The control of the system and the motion range were tested. The test results showed that the system provided a good performance.

  7. Synthesis of recurrent neural networks for dynamical system simulation.

    Science.gov (United States)

    Trischler, Adam P; D'Eleuterio, Gabriele M T

    2016-08-01

    We review several of the most widely used techniques for training recurrent neural networks to approximate dynamical systems, then describe a novel algorithm for this task. The algorithm is based on an earlier theoretical result that guarantees the quality of the network approximation. We show that a feedforward neural network can be trained on the vector-field representation of a given dynamical system using backpropagation, then recast it as a recurrent network that replicates the original system's dynamics. After detailing this algorithm and its relation to earlier approaches, we present numerical examples that demonstrate its capabilities. One of the distinguishing features of our approach is that both the original dynamical systems and the recurrent networks that simulate them operate in continuous time. Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. Tension stimulation drives tissue formation in scaffold-free systems

    Science.gov (United States)

    Lee, Jennifer K.; Huwe, Le W.; Paschos, Nikolaos; Aryaei, Ashkan; Gegg, Courtney A.; Hu, Jerry C.; Athanasiou, Kyriacos A.

    2017-08-01

    Scaffold-free systems have emerged as viable approaches for engineering load-bearing tissues. However, the tensile properties of engineered tissues have remained far below the values for native tissue. Here, by using self-assembled articular cartilage as a model to examine the effects of intermittent and continuous tension stimulation on tissue formation, we show that the application of tension alone, or in combination with matrix remodelling and synthesis agents, leads to neocartilage with tensile properties approaching those of native tissue. Implantation of tension-stimulated tissues results in neotissues that are morphologically reminiscent of native cartilage. We also show that tension stimulation can be translated to a human cell source to generate anisotropic human neocartilage with enhanced tensile properties. Tension stimulation, which results in nearly sixfold improvements in tensile properties over unstimulated controls, may allow the engineering of mechanically robust biological replacements of native tissue.

  9. Stimulation of unidirectional pulses in excitable systems

    Science.gov (United States)

    Friedman, M.; Ovsyshcher, I. E.; Fleidervish, I.; Crystal, E.; Rabinovitch, A.

    2004-10-01

    Using a judicious spatial shape of input current pulses (and electrodes), responses of an excitable system (FitzHugh-Nagumo) appear as unidirectional pulses (UDP’s) instead of bidirectional ones (in one dimension) or circular ones (in two dimensions). The importance of the UDP’s for a possible mechanism for pinpointing the reentry cycle position and for a possible use in tachycardia suppression is discussed.

  10. Neural - fuzzy approach for system identification

    NARCIS (Netherlands)

    Tien, B.T.

    1997-01-01

    Most real-world processes have nonlinear and complex dynamics. Conventional methods of constructing nonlinear models from first principles are time consuming and require a level of knowledge about the internal functioning of the system that is often not available. Consequently, in such

  11. Neural Computations in a Dynamical System with Multiple Time Scales

    Science.gov (United States)

    Mi, Yuanyuan; Lin, Xiaohan; Wu, Si

    2016-01-01

    Neural systems display rich short-term dynamics at various levels, e.g., spike-frequency adaptation (SFA) at the single-neuron level, and short-term facilitation (STF) and depression (STD) at the synapse level. These dynamical features typically cover a broad range of time scales and exhibit large diversity in different brain regions. It remains unclear what is the computational benefit for the brain to have such variability in short-term dynamics. In this study, we propose that the brain can exploit such dynamical features to implement multiple seemingly contradictory computations in a single neural circuit. To demonstrate this idea, we use continuous attractor neural network (CANN) as a working model and include STF, SFA and STD with increasing time constants in its dynamics. Three computational tasks are considered, which are persistent activity, adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, and hence cannot be implemented by a single dynamical feature or any combination with similar time constants. However, with properly coordinated STF, SFA and STD, we show that the network is able to implement the three computational tasks concurrently. We hope this study will shed light on the understanding of how the brain orchestrates its rich dynamics at various levels to realize diverse cognitive functions. PMID:27679569

  12. Neural Plasticity in Human Brain Connectivity: The Effects of Long Term Deep Brain Stimulation of the Subthalamic Nucleus in Parkinson’s Disease

    Science.gov (United States)

    van Hartevelt, Tim J.; Cabral, Joana; Deco, Gustavo; Møller, Arne; Green, Alexander L.; Aziz, Tipu Z.; Kringelbach, Morten L.

    2014-01-01

    Background Positive clinical outcomes are now well established for deep brain stimulation, but little is known about the effects of long-term deep brain stimulation on brain structural and functional connectivity. Here, we used the rare opportunity to acquire pre- and postoperative diffusion tensor imaging in a patient undergoing deep brain stimulation in bilateral subthalamic nuclei for Parkinson’s Disease. This allowed us to analyse the differences in structural connectivity before and after deep brain stimulation. Further, a computational model of spontaneous brain activity was used to estimate the changes in functional connectivity arising from the specific changes in structural connectivity. Results We found significant localised structural changes as a result of long-term deep brain stimulation. These changes were found in sensory-motor, prefrontal/limbic, and olfactory brain regions which are known to be affected in Parkinson’s Disease. The nature of these changes was an increase of nodal efficiency in most areas and a decrease of nodal efficiency in the precentral sensory-motor area. Importantly, the computational model clearly shows the impact of deep brain stimulation-induced structural alterations on functional brain changes, which is to shift the neural dynamics back towards a healthy regime. The results demonstrate that deep brain stimulation in Parkinson’s Disease leads to a topological reorganisation towards healthy bifurcation of the functional networks measured in controls, which suggests a potential neural mechanism for the alleviation of symptoms. Conclusions The findings suggest that long-term deep brain stimulation has not only restorative effects on the structural connectivity, but also affects the functional connectivity at a global level. Overall, our results support causal changes in human neural plasticity after long-term deep brain stimulation and may help to identify the underlying mechanisms of deep brain stimulation. PMID

  13. Neural plasticity in human brain connectivity: the effects of long term deep brain stimulation of the subthalamic nucleus in Parkinson's disease.

    Science.gov (United States)

    van Hartevelt, Tim J; Cabral, Joana; Deco, Gustavo; Møller, Arne; Green, Alexander L; Aziz, Tipu Z; Kringelbach, Morten L

    2014-01-01

    Positive clinical outcomes are now well established for deep brain stimulation, but little is known about the effects of long-term deep brain stimulation on brain structural and functional connectivity. Here, we used the rare opportunity to acquire pre- and postoperative diffusion tensor imaging in a patient undergoing deep brain stimulation in bilateral subthalamic nuclei for Parkinson's Disease. This allowed us to analyse the differences in structural connectivity before and after deep brain stimulation. Further, a computational model of spontaneous brain activity was used to estimate the changes in functional connectivity arising from the specific changes in structural connectivity. We found significant localised structural changes as a result of long-term deep brain stimulation. These changes were found in sensory-motor, prefrontal/limbic, and olfactory brain regions which are known to be affected in Parkinson's Disease. The nature of these changes was an increase of nodal efficiency in most areas and a decrease of nodal efficiency in the precentral sensory-motor area. Importantly, the computational model clearly shows the impact of deep brain stimulation-induced structural alterations on functional brain changes, which is to shift the neural dynamics back towards a healthy regime. The results demonstrate that deep brain stimulation in Parkinson's Disease leads to a topological reorganisation towards healthy bifurcation of the functional networks measured in controls, which suggests a potential neural mechanism for the alleviation of symptoms. The findings suggest that long-term deep brain stimulation has not only restorative effects on the structural connectivity, but also affects the functional connectivity at a global level. Overall, our results support causal changes in human neural plasticity after long-term deep brain stimulation and may help to identify the underlying mechanisms of deep brain stimulation.

  14. New acid systems for sandstone stimulation. [Oil wells

    Energy Technology Data Exchange (ETDEWEB)

    Clark, G.J.; Wong, T.C.T.; Mungan, N.

    1982-01-01

    A new series of prepackaged acid systems have been developed for stimulation of sandstone formations. The original system, containing phosphoric acid and other additives (P.P.A.S.) was specifically formulated to overcome several limitations of existing acid systems. The scope of applications for P.P.A.S. has since been expanded by combining HCl (Hydrochloric acid) or HF (Hydrofluoric acid) with the P.P.A.S. to form hybrid systems that have unique properties. These new systems have been successfully used for stimulating sandstone formations that have been difficult to treat with existing acid systems. The problems associated with currently used acids and their limitations are compared to the P.P.A.S. to illustrate the advantages of these new systems. 10 refs.

  15. Evolutionary Computation and Its Applications in Neural and Fuzzy Systems

    Directory of Open Access Journals (Sweden)

    Biaobiao Zhang

    2011-01-01

    Full Text Available Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neo-Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.

  16. Neural Mechanisms and Information Processing in Recognition Systems

    Directory of Open Access Journals (Sweden)

    Mamiko Ozaki

    2014-10-01

    Full Text Available Nestmate recognition is a hallmark of social insects. It is based on the match/mismatch of an identity signal carried by members of the society with that of the perceiving individual. While the behavioral response, amicable or aggressive, is very clear, the neural systems underlying recognition are not fully understood. Here we contrast two alternative hypotheses for the neural mechanisms that are responsible for the perception and information processing in recognition. We focus on recognition via chemical signals, as the common modality in social insects. The first, classical, hypothesis states that upon perception of recognition cues by the sensory system the information is passed as is to the antennal lobes and to higher brain centers where the information is deciphered and compared to a neural template. Match or mismatch information is then transferred to some behavior-generating centers where the appropriate response is elicited. An alternative hypothesis, that of “pre-filter mechanism”, posits that the decision as to whether to pass on the information to the central nervous system takes place in the peripheral sensory system. We suggest that, through sensory adaptation, only alien signals are passed on to the brain, specifically to an “aggressive-behavior-switching center”, where the response is generated if the signal is above a certain threshold.

  17. Enhancement of multitasking performance and neural oscillations by transcranial alternating current stimulation.

    Science.gov (United States)

    Hsu, Wan-Yu; Zanto, Theodore P; van Schouwenburg, Martine R; Gazzaley, Adam

    2017-01-01

    Multitasking is associated with the generation of stimulus-locked theta (4-7 Hz) oscillations arising from prefrontal cortex (PFC). Transcranial alternating current stimulation (tACS) is a non-invasive brain stimulation technique that influences endogenous brain oscillations. Here, we investigate whether applying alternating current stimulation within the theta frequency band would affect multitasking performance, and explore tACS effects on neurophysiological measures. Brief runs of bilateral PFC theta-tACS were applied while participants were engaged in a multitasking paradigm accompanied by electroencephalography (EEG) data collection. Unlike an active control group, a tACS stimulation group showed enhancement of multitasking performance after a 90-minute session (F1,35 = 6.63, p = 0.01, ηp2 = 0.16; effect size = 0.96), coupled with significant modulation of posterior beta (13-30 Hz) activities (F1,32 = 7.66, p = 0.009, ηp2 = 0.19; effect size = 0.96). Across participant regression analyses indicated that those participants with greater increases in frontal theta, alpha and beta oscillations exhibited greater multitasking performance improvements. These results indicate frontal theta-tACS generates benefits on multitasking performance accompanied by widespread neuronal oscillatory changes, and suggests that future tACS studies with extended treatments are worth exploring as promising tools for cognitive enhancement.

  18. Variability in neural excitability and plasticity induction in the human cortex: A brain stimulation study.

    Science.gov (United States)

    Hordacre, Brenton; Goldsworthy, Mitchell R; Vallence, Ann-Maree; Darvishi, Sam; Moezzi, Bahar; Hamada, Masashi; Rothwell, John C; Ridding, Michael C

    The potential of non-invasive brain stimulation (NIBS) for both probing human neuroplasticity and the induction of functionally relevant neuroplastic change has received significant interest. However, at present the utility of NIBS is limited due to high response variability. One reason for this response variability is that NIBS targets a diffuse cortical population and the net outcome to stimulation depends on the relative levels of excitability in each population. There is evidence that the relative excitability of complex oligosynaptic circuits (late I-wave circuits) as assessed by transcranial magnetic stimulation (TMS) is useful in predicting NIBS response. Here we examined whether an additional marker of cortical excitability, MEP amplitude variability, could provide additional insights into response variability following application of the continuous theta burst stimulation (cTBS) NIBS protocol. Additionally we investigated whether I-wave recruitment was associated with MEP variability. Thirty-four healthy subjects (15 male, aged 18-35 years) participated in two experiments. Experiment 1 investigated baseline MEP variability and cTBS response. Experiment 2 determined if I-wave recruitment was associated with MEP variability. Data show that both baseline MEP variability and late I-wave recruitment are associated with cTBS response, but were independent of each other; together, these variables predict 31% of the variability in cTBS response. This study provides insight into the physiological mechanisms underpinning NIBS plasticity responses and may facilitate development of more reliable NIBS protocols. Copyright © 2016 Elsevier Inc. All rights reserved.

  19. Rhythmic entrainment source separation: Optimizing analyses of neural responses to rhythmic sensory stimulation

    NARCIS (Netherlands)

    Cohen, M.S.; Gulbinaite, R.

    2017-01-01

    Steady-state evoked potentials (SSEPs) are rhythmic brain responses to rhythmic sensory stimulation, and are often used to study perceptual and attentional processes. We present a data analysis method for maximizing the signal-to-noise ratio of the narrow-band steady-state response in the frequency

  20. Mechanisms of magnetic stimulation of central nervous system neurons.

    Directory of Open Access Journals (Sweden)

    Tamar Pashut

    2011-03-01

    Full Text Available Transcranial magnetic stimulation (TMS is a stimulation method in which a magnetic coil generates a magnetic field in an area of interest in the brain. This magnetic field induces an electric field that modulates neuronal activity. The spatial distribution of the induced electric field is determined by the geometry and location of the coil relative to the brain. Although TMS has been used for several decades, the biophysical basis underlying the stimulation of neurons in the central nervous system (CNS is still unknown. To address this problem we developed a numerical scheme enabling us to combine realistic magnetic stimulation (MS with compartmental modeling of neurons with arbitrary morphology. The induced electric field for each location in space was combined with standard compartmental modeling software to calculate the membrane current generated by the electromagnetic field for each segment of the neuron. In agreement with previous studies, the simulations suggested that peripheral axons were excited by the spatial gradients of the induced electric field. In both peripheral and central neurons, MS amplitude required for action potential generation was inversely proportional to the square of the diameter of the stimulated compartment. Due to the importance of the fiber's diameter, magnetic stimulation of CNS neurons depolarized the soma followed by initiation of an action potential in the initial segment of the axon. Passive dendrites affect this process primarily as current sinks, not sources. The simulations predict that neurons with low current threshold are more susceptible to magnetic stimulation. Moreover, they suggest that MS does not directly trigger dendritic regenerative mechanisms. These insights into the mechanism of MS may be relevant for the design of multi-intensity TMS protocols, may facilitate the construction of magnetic stimulators, and may aid the interpretation of results of TMS of the CNS.

  1. STIMULATING ENTREPRENEURSHIP IN THE ROMANIAN EDUCATION SYSTEM

    Directory of Open Access Journals (Sweden)

    DORINA NIȚĂ

    2014-12-01

    Full Text Available Romania is on its way to find and apply the most effective solutions in order to ensure a sustainable economic growth and stable and well paid employment. It is in fact a requirement often set out at European Union level which resulted in a series of strategies and programs among which one can highlight the support of entrepreneurship. One of the areas of intervention in this direction is represented by the adaptation of education systems to the new challenges by introducing elements of entrepreneurial culture in the curriculum of the European States, including Romania, the concept of “training firm” or initiatives through which students can accumulate work experience before completing University studies. The development of entrepreneurship among younger generations suppresses the fear of becoming unemployed and gives them the opportunity not only to start up their own business, but also to obtain benefits that exceed their wages. At the local/ regional/ national economy it is similar with a diversification of economic activities, increase employment, income and growing living standards.

  2. Engineering neural systems for high-level problem solving.

    Science.gov (United States)

    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

  3. Neural feedback linearization adaptive control for affine nonlinear systems based on neural network estimator

    Directory of Open Access Journals (Sweden)

    Bahita Mohamed

    2011-01-01

    Full Text Available In this work, we introduce an adaptive neural network controller for a class of nonlinear systems. The approach uses two Radial Basis Functions, RBF networks. The first RBF network is used to approximate the ideal control law which cannot be implemented since the dynamics of the system are unknown. The second RBF network is used for on-line estimating the control gain which is a nonlinear and unknown function of the states. The updating laws for the combined estimator and controller are derived through Lyapunov analysis. Asymptotic stability is established with the tracking errors converging to a neighborhood of the origin. Finally, the proposed method is applied to control and stabilize the inverted pendulum system.

  4. Anodal transcranial direct current stimulation of the motor cortex increases cortical voluntary activation and neural plasticity.

    Science.gov (United States)

    Frazer, Ashlyn; Williams, Jacqueline; Spittles, Michael; Rantalainen, Timo; Kidgell, Dawson

    2016-11-01

    We examined the cumulative effect of 4 consecutive bouts of noninvasive brain stimulation on corticospinal plasticity and motor performance, and whether these responses were influenced by the brain-derived neurotrophic factor (BDNF) polymorphism. In a randomized double-blinded cross-over design, changes in strength and indices of corticospinal plasticity were analyzed in 14 adults who were exposed to 4 consecutive sessions of anodal and sham transcranial direct current stimulation (tDCS). Participants also undertook a blood sample for BDNF genotyping (N = 13). We observed a significant increase in isometric wrist flexor strength with transcranial magnetic stimulation revealing increased corticospinal excitability, decreased silent period duration, and increased cortical voluntary activation compared with sham tDCS. The results show that 4 consecutive sessions of anodal tDCS increased cortical voluntary activation manifested as an improvement in strength. Induction of corticospinal plasticity appears to be influenced by the BDNF polymorphism. Muscle Nerve 54: 903-913, 2016. © 2016 Wiley Periodicals, Inc.

  5. 21 CFR 862.1690 - Thyroid stimulating hormone test system.

    Science.gov (United States)

    2010-04-01

    ... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Thyroid stimulating hormone test system. 862.1690 Section 862.1690 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES... produced by the anterior pituitary are used in the diagnosis of thyroid or pituitary disorders. (b...

  6. Deep brain transcranial magnetic stimulation using variable "Halo coil" system

    Science.gov (United States)

    Meng, Y.; Hadimani, R. L.; Crowther, L. J.; Xu, Z.; Qu, J.; Jiles, D. C.

    2015-05-01

    Transcranial Magnetic Stimulation has the potential to treat various neurological disorders non-invasively and safely. The "Halo coil" configuration can stimulate deeper regions of the brain with lower surface to deep-brain field ratio compared to other coil configurations. The existing "Halo coil" configuration is fixed and is limited in varying the site of stimulation in the brain. We have developed a new system based on the current "Halo coil" design along with a graphical user interface system that enables the larger coil to rotate along the transverse plane. The new system can also enable vertical movement of larger coil. Thus, this adjustable "Halo coil" configuration can stimulate different regions of the brain by adjusting the position and orientation of the larger coil on the head. We have calculated magnetic and electric fields inside a MRI-derived heterogeneous head model for various positions and orientations of the coil. We have also investigated the mechanical and thermal stability of the adjustable "Halo coil" configuration for various positions and orientations of the coil to ensure safe operation of the system.

  7. First permanent human implant of the Stimulus Router System, a novel neuroprosthesis: preliminary testing of a polarity reversing stimulation technique.

    Science.gov (United States)

    Gan, Liu Shi; Ravid, Einat N; Kowalczewski, Jan; Gauthier, Michel; Olson, Jaret; Morhart, Michael; Prochazka, Arthur

    2011-01-01

    Neuroprostheses (NPs) are electrical stimulators that help to restore sensory or motor functions lost as a result of neural damage. The Stimulus Router System (SRS) is a new type of NP developed in our laboratory. The system uses fully implanted, passive leads to "capture" and "route" some of the current flowing between pairs of surface electrodes to the vicinity of the target nerves, hence eliminating the need for an implanted stimulator. In June 2008, 3 SRS leads were implanted in a tetraplegic man for restoration of grasp and release. To reduce the size of the external wristlet and thereby optimize usability, we recently implemented a polarity reversing stimulation technique that allowed us to eliminate a reference electrode. Selective activation of three target muscles was achieved by switching the polarities of the stimulus current delivered between pairs of surface electrodes located over the pick-up terminals of the implanted leads and reducing the amplitude of the secondary phases of the stimulus pulses.

  8. Predictive and Neural Predictive Control of Uncertain Systems

    Science.gov (United States)

    Kelkar, Atul G.

    2000-01-01

    Accomplishments and future work are:(1) Stability analysis: the work completed includes characterization of stability of receding horizon-based MPC in the setting of LQ paradigm. The current work-in-progress includes analyzing local as well as global stability of the closed-loop system under various nonlinearities; for example, actuator nonlinearities; sensor nonlinearities, and other plant nonlinearities. Actuator nonlinearities include three major types of nonlineaxities: saturation, dead-zone, and (0, 00) sector. (2) Robustness analysis: It is shown that receding horizon parameters such as input and output horizon lengths have direct effect on the robustness of the system. (3) Code development: A matlab code has been developed which can simulate various MPC formulations. The current effort is to generalize the code to include ability to handle all plant types and all MPC types. (4) Improved predictor: It is shown that MPC design using better predictors that can minimize prediction errors. It is shown analytically and numerically that Smith predictor can provide closed-loop stability under GPC operation for plants with dead times where standard optimal predictor fails. (5) Neural network predictors: When neural network is used as predictor it can be shown that neural network predicts the plant output within some finite error bound under certain conditions. Our preliminary study shows that with proper choice of update laws and network architectures such bound can be obtained. However, much work needs to be done to obtain a similar result in general case.

  9. Intelligent systems II complete approximation by neural network operators

    CERN Document Server

    Anastassiou, George A

    2016-01-01

    This monograph is the continuation and completion of the monograph, “Intelligent Systems: Approximation by Artificial Neural Networks” written by the same author and published 2011 by Springer. The book you hold in hand presents the complete recent and original work of the author in approximation by neural networks. Chapters are written in a self-contained style and can be read independently. Advanced courses and seminars can be taught out of this brief book. All necessary background and motivations are given per chapter. A related list of references is given also per chapter. The book’s results are expected to find applications in many areas of applied mathematics, computer science and engineering. As such this monograph is suitable for researchers, graduate students, and seminars of the above subjects, also for all science and engineering libraries.  .

  10. Neural systems for choice and valuation with counterfactual learning signals.

    Science.gov (United States)

    Tobia, M J; Guo, R; Schwarze, U; Boehmer, W; Gläscher, J; Finckh, B; Marschner, A; Büchel, C; Obermayer, K; Sommer, T

    2014-04-01

    The purpose of this experiment was to test a computational model of reinforcement learning with and without fictive prediction error (FPE) signals to investigate how counterfactual consequences contribute to acquired representations of action-specific expected value, and to determine the functional neuroanatomy and neuromodulator systems that are involved. 80 male participants underwent dietary depletion of either tryptophan or tyrosine/phenylalanine to manipulate serotonin (5HT) and dopamine (DA), respectively. They completed 80 rounds (240 trials) of a strategic sequential investment task that required accepting interim losses in order to access a lucrative state and maximize long-term gains, while being scanned. We extended the standard Q-learning model by incorporating both counterfactual gains and losses into separate error signals. The FPE model explained the participants' data significantly better than a model that did not include counterfactual learning signals. Expected value from the FPE model was significantly correlated with BOLD signal change in the ventromedial prefrontal cortex (vmPFC) and posterior orbitofrontal cortex (OFC), whereas expected value from the standard model did not predict changes in neural activity. The depletion procedure revealed significantly different neural responses to expected value in the vmPFC, caudate, and dopaminergic midbrain in the vicinity of the substantia nigra (SN). Differences in neural activity were not evident in the standard Q-learning computational model. These findings demonstrate that FPE signals are an important component of valuation for decision making, and that the neural representation of expected value incorporates cortical and subcortical structures via interactions among serotonergic and dopaminergic modulator systems. Copyright © 2013 Elsevier Inc. All rights reserved.

  11. Central-peripheral neural network interactions evoked by vagus nerve stimulation: functional consequences on control of cardiac function.

    Science.gov (United States)

    Ardell, Jeffrey L; Rajendran, Pradeep S; Nier, Heath A; KenKnight, Bruce H; Armour, J Andrew

    2015-11-15

    Using vagus nerve stimulation (VNS), we sought to determine the contribution of vagal afferents to efferent control of cardiac function. In anesthetized dogs, the right and left cervical vagosympathetic trunks were stimulated in the intact state, following ipsilateral or contralateral vagus nerve transection (VNTx), and then following bilateral VNTx. Stimulations were performed at currents from 0.25 to 4.0 mA, frequencies from 2 to 30 Hz, and a 500-μs pulse width. Right or left VNS evoked significantly greater current- and frequency-dependent suppression of chronotropic, inotropic, and lusitropic function subsequent to sequential VNTx. Bradycardia threshold was defined as the current first required for a 5% decrease in heart rate. The threshold for the right vs. left vagus-induced bradycardia in the intact state (2.91 ± 0.18 and 3.47 ± 0.20 mA, respectively) decreased significantly with right VNTx (1.69 ± 0.17 mA for right and 3.04 ± 0.27 mA for left) and decreased further following bilateral VNTx (1.29 ± 0.16 mA for right and 1.74 ± 0.19 mA for left). Similar effects were observed following left VNTx. The thresholds for afferent-mediated effects on cardiac parameters were 0.62 ± 0.04 and 0.65 ± 0.06 mA with right and left VNS, respectively, and were reflected primarily as augmentation. Afferent-mediated tachycardias were maintained following β-blockade but were eliminated by VNTx. The increased effectiveness and decrease in bradycardia threshold with sequential VNTx suggest that 1) vagal afferents inhibit centrally mediated parasympathetic efferent outflow and 2) the ipsilateral and contralateral vagi exert a substantial buffering capacity. The intact threshold reflects the interaction between multiple levels of the cardiac neural hierarchy. Copyright © 2015 the American Physiological Society.

  12. A 128-Channel FPGA-Based Real-Time Spike-Sorting Bidirectional Closed-Loop Neural Interface System.

    Science.gov (United States)

    Park, Jongkil; Kim, Gookhwa; Jung, Sang-Don

    2017-12-01

    A multichannel neural interface system is an important tool for various types of neuroscientific studies. For the electrical interface with a biological system, high-precision high-speed data recording and various types of stimulation capability are required. In addition, real-time signal processing is an important feature in the implementation of a real-time closed-loop system without unwanted substantial delay for feedback stimulation. Online spike sorting, the process of assigning neural spikes to an identified group of neurons or clusters, is a necessary step to make a closed-loop path in real time, but massive memory-space requirements commonly limit hardware implementations. Here, we present a 128-channel field-programmable gate array (FPGA)-based real-time closed-loop bidirectional neural interface system. The system supports 128 channels for simultaneous signal recording and eight selectable channels for stimulation. A modular 64-channel analog front-end (AFE) provides scalability and a parameterized specification of the AFE supports the recording of various electrophysiological signal types with 1.59 ± 0.76 root-mean-square noise. The stimulator supports both voltage-controlled and current-controlled arbitrarily shaped waveforms with the programmable amplitude and duration of pulse. An empirical algorithm for online real-time spike sorting is implemented in an FPGA. The spike-sorting is performed by template matching, and templates are created by an online real-time unsupervised learning process. A memory saving technique, called dynamic cache organizing, is proposed to reduce the memory requirement down to 6 kbit per channel and modular implementation improves the scalability for further extensions.

  13. Gastric Electrical Stimulation with the Enterra System: A Systematic Review.

    Science.gov (United States)

    Lal, Nikhil; Livemore, Sam; Dunne, Declan; Khan, Iftikhar

    2015-01-01

    Background. Gastric electrical stimulation (GES) is a surgically implanted treatment option for refractory gastroparesis. Aim. To systematically appraise the current evidence for the use of gastric electrical stimulation and suggest a method of standardisation of assessment and follow-up in these patients. Methods. A systematic review of PubMed, Web of Science, DISCOVER, and Cochrane Library was conducted using the keywords including gastric electrical stimulation, gastroparesis, nausea, and vomiting and neuromodulation, stomach, central nervous system, gastric pacing, electrical stimulation, and gastrointestinal. Results. 1139 potentially relevant articles were identified, of which 21 met the inclusion criteria and were included. The quality of studies was variable. There was a variation in outcome measures and follow-up methodology. Included studies suggested significant reductions in symptom severity reporting over the study period, but improvements in gastric emptying time were variable and rarely correlated with symptom improvement. Conclusion. The evidence in support of gastric electrical stimulation is limited and heterogeneous in quality. While current evidence has shown a degree of efficacy in these patients, high-quality, large clinical trials are needed to establish the efficacy of this therapy and to identify the patients for whom this therapy is inappropriate. A consensus view on essential preoperative assessment and postoperative measurement is needed.

  14. Gastric Electrical Stimulation with the Enterra System: A Systematic Review

    Directory of Open Access Journals (Sweden)

    Nikhil Lal

    2015-01-01

    Full Text Available Background. Gastric electrical stimulation (GES is a surgically implanted treatment option for refractory gastroparesis. Aim. To systematically appraise the current evidence for the use of gastric electrical stimulation and suggest a method of standardisation of assessment and follow-up in these patients. Methods. A systematic review of PubMed, Web of Science, DISCOVER, and Cochrane Library was conducted using the keywords including gastric electrical stimulation, gastroparesis, nausea, and vomiting and neuromodulation, stomach, central nervous system, gastric pacing, electrical stimulation, and gastrointestinal. Results. 1139 potentially relevant articles were identified, of which 21 met the inclusion criteria and were included. The quality of studies was variable. There was a variation in outcome measures and follow-up methodology. Included studies suggested significant reductions in symptom severity reporting over the study period, but improvements in gastric emptying time were variable and rarely correlated with symptom improvement. Conclusion. The evidence in support of gastric electrical stimulation is limited and heterogeneous in quality. While current evidence has shown a degree of efficacy in these patients, high-quality, large clinical trials are needed to establish the efficacy of this therapy and to identify the patients for whom this therapy is inappropriate. A consensus view on essential preoperative assessment and postoperative measurement is needed.

  15. [A telemetery system for neural signal acquiring and processing].

    Science.gov (United States)

    Wang, Min; Song, Yongji; Suen, Jiantao; Zhao, Yiliang; Jia, Aibin; Zhu, Jianping

    2011-02-01

    Recording and extracting characteristic brain signals in freely moving animals is the basic and significant requirement in the study of brain-computer interface (BCI). To record animal's behaving and extract characteristic brain signals simultaneously could help understand the complex behavior of neural ensembles. Here, a system was established to record and analyse extracellular discharge in freely moving rats for the study of BCI. It comprised microelectrode and micro-driver assembly, analog front end (AFE), programmer system on chip (PSoC), wireless communication and the LabVIEW used as the platform for the graphic user interface.

  16. A simple mechanical system for studying adaptive oscillatory neural networks

    DEFF Research Database (Denmark)

    Jouffroy, Guillaume; Jouffroy, Jerome

    model, etc.) might be too complex to study. In this paper, we use a comparatively simple mechanical system, the nonholonomic vehicle referred to as the Roller-Racer, as a means towards testing different learning strategies for an Recurrent Neural Network-based (RNN) controller/guidance system. After...... a brief description of the Roller-Racer, we present as a preliminary study an RNN-based feed-forward controller whose parameters are obtained through the well-known teacher forcing learning algorithm, extended to learn signals with a continuous component....

  17. Radial basis function (RBF) neural network control for mechanical systems design, analysis and Matlab simulation

    CERN Document Server

    Liu, Jinkun

    2013-01-01

    Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with the fundamentals of neural network control system design.   This book is intended for the researchers in the fields of neural adaptive control, mechanical systems, Matlab simulation, engineering design, robotics and automation. Jinkun Liu is a professor at Beijing University of Aeronautics and Astronauti...

  18. Neural Network Based Intrusion Detection System for Critical Infrastructures

    Energy Technology Data Exchange (ETDEWEB)

    Todd Vollmer; Ondrej Linda; Milos Manic

    2009-07-01

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

  19. Neural Network Target Identification System for False Alarm Reduction

    Science.gov (United States)

    Ye, David; Edens, Weston; Lu, Thomas T.; Chao, Tien-Hsin

    2009-01-01

    A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feed forward back propagation neural network (NN) is then trained to classify each feature vector and remove false positives. This paper discusses the test of the system performance and parameter optimizations process which adapts the system to various targets and datasets. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar image dataset.

  20. System identification of an unmanned quadcopter system using MRAN neural

    Science.gov (United States)

    Pairan, M. F.; Shamsudin, S. S.

    2017-12-01

    This project presents the performance analysis of the radial basis function neural network (RBF) trained with Minimal Resource Allocating Network (MRAN) algorithm for real-time identification of quadcopter. MRAN’s performance is compared with the RBF with Constant Trace algorithm for 2500 input-output pair data sampling. MRAN utilizes adding and pruning hidden neuron strategy to obtain optimum RBF structure, increase prediction accuracy and reduce training time. The results indicate that MRAN algorithm produces fast training time and more accurate prediction compared with standard RBF. The model proposed in this paper is capable of identifying and modelling a nonlinear representation of the quadcopter flight dynamics.

  1. Neural system modeling and simulation using Hybrid Functional Petri Net.

    Science.gov (United States)

    Tang, Yin; Wang, Fei

    2012-02-01

    The Petri net formalism has been proved to be powerful in biological modeling. It not only boasts of a most intuitive graphical presentation but also combines the methods of classical systems biology with the discrete modeling technique. Hybrid Functional Petri Net (HFPN) was proposed specially for biological system modeling. An array of well-constructed biological models using HFPN yielded very interesting results. In this paper, we propose a method to represent neural system behavior, where biochemistry and electrical chemistry are both included using the Petri net formalism. We built a model for the adrenergic system using HFPN and employed quantitative analysis. Our simulation results match the biological data well, showing that the model is very effective. Predictions made on our model further manifest the modeling power of HFPN and improve the understanding of the adrenergic system. The file of our model and more results with their analysis are available in our supplementary material.

  2. Intelligent reservoir operation system based on evolving artificial neural networks

    Science.gov (United States)

    Chaves, Paulo; Chang, Fi-John

    2008-06-01

    We propose a novel intelligent reservoir operation system based on an evolving artificial neural network (ANN). Evolving means the parameters of the ANN model are identified by the GA evolutionary optimization technique. Accordingly, the ANN model should represent the operational strategies of reservoir operation. The main advantages of the Evolving ANN Intelligent System (ENNIS) are as follows: (i) only a small number of parameters to be optimized even for long optimization horizons, (ii) easy to handle multiple decision variables, and (iii) the straightforward combination of the operation model with other prediction models. The developed intelligent system was applied to the operation of the Shihmen Reservoir in North Taiwan, to investigate its applicability and practicability. The proposed method is first built to a simple formulation for the operation of the Shihmen Reservoir, with single objective and single decision. Its results were compared to those obtained by dynamic programming. The constructed network proved to be a good operational strategy. The method was then built and applied to the reservoir with multiple (five) decision variables. The results demonstrated that the developed evolving neural networks improved the operation performance of the reservoir when compared to its current operational strategy. The system was capable of successfully simultaneously handling various decision variables and provided reasonable and suitable decisions.

  3. The ctenophore genome and the evolutionary origins of neural systems.

    Science.gov (United States)

    Moroz, Leonid L; Kocot, Kevin M; Citarella, Mathew R; Dosung, Sohn; Norekian, Tigran P; Povolotskaya, Inna S; Grigorenko, Anastasia P; Dailey, Christopher; Berezikov, Eugene; Buckley, Katherine M; Ptitsyn, Andrey; Reshetov, Denis; Mukherjee, Krishanu; Moroz, Tatiana P; Bobkova, Yelena; Yu, Fahong; Kapitonov, Vladimir V; Jurka, Jerzy; Bobkov, Yuri V; Swore, Joshua J; Girardo, David O; Fodor, Alexander; Gusev, Fedor; Sanford, Rachel; Bruders, Rebecca; Kittler, Ellen; Mills, Claudia E; Rast, Jonathan P; Derelle, Romain; Solovyev, Victor V; Kondrashov, Fyodor A; Swalla, Billie J; Sweedler, Jonathan V; Rogaev, Evgeny I; Halanych, Kenneth M; Kohn, Andrea B

    2014-06-05

    The origins of neural systems remain unresolved. In contrast to other basal metazoans, ctenophores (comb jellies) have both complex nervous and mesoderm-derived muscular systems. These holoplanktonic predators also have sophisticated ciliated locomotion, behaviour and distinct development. Here we present the draft genome of Pleurobrachia bachei, Pacific sea gooseberry, together with ten other ctenophore transcriptomes, and show that they are remarkably distinct from other animal genomes in their content of neurogenic, immune and developmental genes. Our integrative analyses place Ctenophora as the earliest lineage within Metazoa. This hypothesis is supported by comparative analysis of multiple gene families, including the apparent absence of HOX genes, canonical microRNA machinery, and reduced immune complement in ctenophores. Although two distinct nervous systems are well recognized in ctenophores, many bilaterian neuron-specific genes and genes of 'classical' neurotransmitter pathways either are absent or, if present, are not expressed in neurons. Our metabolomic and physiological data are consistent with the hypothesis that ctenophore neural systems, and possibly muscle specification, evolved independently from those in other animals.

  4. Deep brain stimulation improves behavior and modulates neural circuits in a rodent model of schizophrenia.

    Science.gov (United States)

    Bikovsky, Lior; Hadar, Ravit; Soto-Montenegro, María Luisa; Klein, Julia; Weiner, Ina; Desco, Manuel; Pascau, Javier; Winter, Christine; Hamani, Clement

    2016-09-01

    Schizophrenia is a debilitating psychiatric disorder with a significant number of patients not adequately responding to treatment. Deep brain stimulation (DBS) is a surgical technique currently investigated for medically-refractory psychiatric disorders. Here, we use the poly I:C rat model of schizophrenia to study the effects of medial prefrontal cortex (mPFC) and nucleus accumbens (Nacc) DBS on two behavioral schizophrenia-like deficits, i.e. sensorimotor gating, as reflected by disrupted prepulse inhibition (PPI), and attentional selectivity, as reflected by disrupted latent inhibition (LI). In addition, the neurocircuitry influenced by DBS was studied using FDG PET. We found that mPFC- and Nacc-DBS alleviated PPI and LI abnormalities in poly I:C offspring, whereas Nacc- but not mPFC-DBS disrupted PPI and LI in saline offspring. In saline offspring, mPFC-DBS increased metabolism in the parietal cortex, striatum, ventral hippocampus and Nacc, while reducing it in the brainstem, cerebellum, hypothalamus and periaqueductal gray. Nacc-DBS, on the other hand, increased activity in the ventral hippocampus and olfactory bulb and reduced it in the septal area, brainstem, periaqueductal gray and hypothalamus. In poly I:C offspring changes in metabolism following mPFC-DBS were similar to those recorded in saline offspring, except for a reduced activity in the brainstem and hypothalamus. In contrast, Nacc-DBS did not induce any statistical changes in brain metabolism in poly I:C offspring. Our study shows that mPFC- or Nacc-DBS delivered to the adult progeny of poly I:C treated dams improves deficits in PPI and LI. Despite common behavioral responses, stimulation in the two targets induced different metabolic effects. Copyright © 2016. Published by Elsevier Inc.

  5. Fault Tolerant Neural Network for ECG Signal Classification Systems

    Directory of Open Access Journals (Sweden)

    MERAH, M.

    2011-08-01

    Full Text Available The aim of this paper is to apply a new robust hardware Artificial Neural Network (ANN for ECG classification systems. This ANN includes a penalization criterion which makes the performances in terms of robustness. Specifically, in this method, the ANN weights are normalized using the auto-prune method. Simulations performed on the MIT ? BIH ECG signals, have shown that significant robustness improvements are obtained regarding potential hardware artificial neuron failures. Moreover, we show that the proposed design achieves better generalization performances, compared to the standard back-propagation algorithm.

  6. Separate neural systems value immediate and delayed monetary rewards.

    Science.gov (United States)

    McClure, Samuel M; Laibson, David I; Loewenstein, George; Cohen, Jonathan D

    2004-10-15

    When humans are offered the choice between rewards available at different points in time, the relative values of the options are discounted according to their expected delays until delivery. Using functional magnetic resonance imaging, we examined the neural correlates of time discounting while subjects made a series of choices between monetary reward options that varied by delay to delivery. We demonstrate that two separate systems are involved in such decisions. Parts of the limbic system associated with the midbrain dopamine system, including paralimbic cortex, are preferentially activated by decisions involving immediately available rewards. In contrast, regions of the lateral prefrontal cortex and posterior parietal cortex are engaged uniformly by intertemporal choices irrespective of delay. Furthermore, the relative engagement of the two systems is directly associated with subjects' choices, with greater relative fronto-parietal activity when subjects choose longer term options.

  7. Examination of neural systems sub-serving facebook "addiction".

    Science.gov (United States)

    Turel, Ofir; He, Qinghua; Xue, Gui; Xiao, Lin; Bechara, Antoine

    2014-12-01

    Because addictive behaviors typically result from violated homeostasis of the impulsive (amygdala-striatal) and inhibitory (prefrontal cortex) brain systems, this study examined whether these systems sub-serve a specific case of technology-related addiction, namely Facebook "addiction." Using a go/no-go paradigm in functional MRI settings, the study examined how these brain systems in 20 Facebook users (M age = 20.3 yr., SD = 1.3, range = 18-23) who completed a Facebook addiction questionnaire, responded to Facebook and less potent (traffic sign) stimuli. The findings indicated that at least at the examined levels of addiction-like symptoms, technology-related "addictions" share some neural features with substance and gambling addictions, but more importantly they also differ from such addictions in their brain etiology and possibly pathogenesis, as related to abnormal functioning of the inhibitory-control brain system.

  8. Modulation of Autonomous Nervous System activity by gyrosonic stimulation

    CERN Document Server

    Ghatak, S K; Choudhuri, R; Bandopadhaya, S

    2010-01-01

    A novel audio binaural stimulus that generates rotational perceptions of sound movement in brain at a particular predetermined frequency is referred as gyrosonics. The influence of gyrosonics on autonomic nervous system of healthy subjects has been examined by analyzing heart rate variability (HRV) in time- and frequency- domain. The M-lagged Poincare plot shows that the parameters SD1, SD2 and ratio SD12 (SD1/SD2) increases with lagged number M, and M-dependence is well described by Pade' approximant $\\chi \\frac{1+\\beta M}{1+\\gamma M}$ where values of $\\chi$, $\\beta$ and $ \\gamma$ depend on parameters SD1,SD2 and SD12. The values of these parameters for different M are augmented after gyrosonic stimulation. The slope and magnitude of curvature of SD1 and SD12 vs M plot increase considerably due to stimulation. The DFA analysis exhibits decrease in value of exponent $\\alpha$ due to stimulation. This stimulation results slower Heart rate, higher values of the standard deviation SD and the root-mean squared suc...

  9. Neural systems supporting and affecting economically relevant behavior

    Directory of Open Access Journals (Sweden)

    Braeutigam S

    2012-05-01

    Full Text Available Sven BraeutigamOxford Centre for Human Brain Activity, University of Oxford, Oxford, United KingdomAbstract: For about a hundred years, theorists and traders alike have tried to unravel and understand the mechanisms and hidden rules underlying and perhaps determining economically relevant behavior. This review focuses on recent developments in neuroeconomics, where the emphasis is placed on two directions of research: first, research exploiting common experiences of urban inhabitants in industrialized societies to provide experimental paradigms with a broader real-life content; second, research based on behavioral genetics, which provides an additional dimension for experimental control and manipulation. In addition, possible limitations of state-of-the-art neuroeconomics research are addressed. It is argued that observations of neuronal systems involved in economic behavior converge to some extent across the technologies and paradigms used. Conceptually, the data available as of today raise the possibility that neuroeconomic research might provide evidence at the neuronal level for the existence of multiple systems of thought and for the importance of conflict. Methodologically, Bayesian approaches in particular may play an important role in identifying mechanisms and establishing causality between patterns of neural activity and economic behavior.Keywords: neuroeconomics, behavioral genetics, decision-making, consumer behavior, neural system

  10. Power amplifier circuits for functional electrical stimulation systems

    OpenAIRE

    Souza, Delmar Carvalho de; Gaiotto, Marcelo do Carmo; Nogueira Neto,Guilherme Nunes; Castro,Maria Claudia Ferrari de; Nohama, Percy

    2017-01-01

    Abstract Introduction: Functional electrical stimulation (FES) is a technique that has been successfully employed in rehabilitation treatment to mitigate problems after spinal cord injury (SCI). One of the most relevant modules in a typical FES system is the power or output amplifier stage, which is responsible for the application of voltage or current pulses of proper intensity to the biological tissue, applied noninvasively via electrodes, placed on the skin surface or inside the muscular ...

  11. MOTIVATION AND STIMULATION SYSTEM OF THE PERSONNEL OF INDUSTRIAL ENTERPRISES

    Directory of Open Access Journals (Sweden)

    Aliona Klymchuk

    2016-03-01

    Full Text Available This article is devoted to the problems of studying the system of motivation and stimulation of personnel at the industrial enterprises, which are important in today's conditions. The purpose of the study is a detailed analysis of system characteristics and motivation of personnel at the enterprises. It is proved that the enterprise's management must constantly deal with the improvement of the system of motivation and stimulation of personnel, that should be adapted to the new conditions for the market functioning. The author notes that the system of motivation and stimulation at the industrial enterprises is effectively connected with the problem of production activities at the enterprise and the final results of its operations increasing and the living standards of workers improving. In the article the basic functions (attracting qualified specialists, preservation groups of professionals within the required time limit, which must perform a system of motivation and stimulation of the personnel at the enterprise are determined. The main tasks that are necessary to ensure the desired level of labor activity of personnel are formed: determining a set of values that should form the basis of personnel motivation; needs formation of each employee, his interests and capabilities of pleasure; specification of the types of work that require the enterprise and that it is advisable to motivate; labour organization so as to convince the employee's ability to satisfy its interests; coordination of certain types of worker with a set of values and preferences; clarify the motives, interests and values when hiring an employee at the enterprise. In order, to form the control system of motivation and stimulation, the need for the following conditions are stressed: availability of complete and accurate information about equipment management; the need to have permanent representation on the status and dynamics of the motivational orientation of personnel

  12. Electric field stimulation through a biodegradable polypyrrole-co-polycaprolactone substrate enhances neural cell growth.

    Science.gov (United States)

    Nguyen, Hieu T; Sapp, Shawn; Wei, Claudia; Chow, Jacqueline K; Nguyen, Alvin; Coursen, Jeff; Luebben, Silvia; Chang, Emily; Ross, Robert; Schmidt, Christine E

    2014-08-01

    Nerve guidance conduits (NGCs) are FDA-approved devices used to bridge gaps across severed nerve cables and help direct axons sprouting from the proximal end toward the distal stump. In this article, we present the development of a novel electrically conductive, biodegradable NGC made from a polypyrrole-block-polycaprolactone (PPy-PCL) copolymer material laminated with poly(lactic-co-glycolic acid) (PLGA). The PPy-PCL has a bulk conductivity ranging 10-20 S/cm and loses 40 wt % after 7 months under physiologic conditions. Dorsal root ganglia (DRG) grown on flat PPy-PCL/PLGA material exposed to direct current electric fields (EF) of 100 mV/cm for 2 h increased axon growth by 13% (± 2%) toward either electrode of a 2-electrode setup, compared with control grown on identical substrates without EF exposure. Alternating current increased axon growth by 21% (±3%) without an observable directional preference, compared with the same control group. The results from this study demonstrate PLGA-coated PPy-PCL is a unique biodegradable material that can deliver substrate EF stimulation to improve axon growth for peripheral nerve repair. © 2013 Wiley Periodicals, Inc.

  13. Normalization of Intrinsic Neural Circuits Governing Tourette's Syndrome Using Cranial Electrotherapy Stimulation.

    Science.gov (United States)

    Qiao, Jianping; Weng, Shenhong; Wang, Pengwei; Long, Jun; Wang, Zhishun

    2015-05-01

    The aim of this study was to investigate the normalization of the intrinsic functional activity and connectivity of TS adolescents before and after the cranial electrotherapy stimulation (CES) with alpha stim device. We performed resting-state functional magnetic resonance imaging on eight adolescents before and after CES with mean age of about nine-years old who had Tourette's syndrome with moderate to severe tics symptom. Independent component analysis (ICA) with hierarchical partner matching method was used to examine the functional connectivity between regions within cortico-striato-thalamo-cortical (CSTC) circuit. Granger causality was used to investigate effective connectivity among these regions detected by ICA. We then performed pattern classification on independent components with significant group differences that served as endophenotype markers to distinguish the adolescents between TS and the normalized ones after CES. Results showed that TS adolescents after CES treatment had stronger functional activity and connectivity in anterior cingulate cortex (ACC), caudate and posterior cingulate cortex while had weaker activity in supplementary motor area within the motor pathway compared with TS before CES. The results suggest that the functional activity and connectivity in motor pathway was suppressed while activities in the control portions within CSTC loop including ACC and caudate were increased in TS adolescents after CES compared with adolescents before CES. The normalization of the balance between motor and control portions of the CSTC circuit may result in the recovery of TS adolescents.

  14. Using pulse width modulation for wireless transmission of neural signals in multichannel neural recording systems.

    Science.gov (United States)

    Yin, Ming; Ghovanloo, Maysam

    2009-08-01

    We have used a well-known technique in wireless communication, pulse width modulation (PWM) of time division multiplexed (TDM) signals, within the architecture of a novel wireless integrated neural recording (WINeR) system. We have evaluated the performance of the PWM-based architecture and indicated its accuracy and potential sources of error through detailed theoretical analysis, simulations, and measurements on a setup consisting of a 15-channel WINeR prototype as the transmitter and two types of receivers; an Agilent 89600 vector signal analyzer and a custom wideband receiver, with 36 and 75 MHz of maximum bandwidth, respectively. Furthermore, we present simulation results from a realistic MATLAB-Simulink model of the entire WINeR system to observe the system behavior in response to changes in various parameters. We have concluded that the 15-ch WINeR prototype, which is fabricated in a 0.5- mum standard CMOS process and consumes 4.5 mW from +/-1.5 V supplies, can acquire and wirelessly transmit up to 320 k-samples/s to a 75-MHz receiver with 8.4 bits of resolution, which is equivalent to a wireless data rate of approximately 2.56 Mb/s.

  15. A Chinese Named Entity Recognition System with Neural Networks

    Directory of Open Access Journals (Sweden)

    Yi Hui-Kang

    2017-01-01

    Full Text Available Named entity recognition (NER is a typical sequential labeling problem that plays an important role in natural language processing (NLP systems. In this paper, we discussed the details of applying a comprehensive model aggregating neural networks and conditional random field (CRF on Chinese NER tasks, and how to discovery character level features when implement a NER system in word level. We compared the difference between Chinese and English when modeling the character embeddings. We developed a NER system based on our analysis, it works well on the ACE 2004 and SIGHAN bakeoff 2006 MSRA dataset, and doesn’t rely on any gazetteers or handcraft features. We obtained F1 score of 82.3% on MSRA 2006.

  16. Power amplifier circuits for functional electrical stimulation systems

    Directory of Open Access Journals (Sweden)

    Delmar Carvalho de Souza

    Full Text Available Abstract Introduction: Functional electrical stimulation (FES is a technique that has been successfully employed in rehabilitation treatment to mitigate problems after spinal cord injury (SCI. One of the most relevant modules in a typical FES system is the power or output amplifier stage, which is responsible for the application of voltage or current pulses of proper intensity to the biological tissue, applied noninvasively via electrodes, placed on the skin surface or inside the muscular tissue, closer to the nervous fibers. The goals of this paper are to describe and discuss about the main power output designs usually employed in transcutaneous functional electrical stimulators as well as safety precautions taken to protect patients. Methods A systematic review investigated the circuits of papers published in IEEE Xplore and ScienceDirect databases from 2000 to 2016. The query terms were “((FES or Functional electric stimulator and (circuit or design” with 274 papers retrieved from IEEE Xplore and 29 from ScienceDirect. After the application of exclusion criteria the amount of papers decreased to 9 and 2 from IEEE Xplore and ScienceDirect, respectively. One paper was inserted in the results as a technological contribution to the field. Therefore, 12 papers presented power stage circuits suitable to stimulate great muscles. Discussion The retrieved results presented relevant circuits with different electronic strategies and circuit components. Some of them considered patient safety strategies or aimed to preserve muscle homeostasis such as biphasic current application, which prevents charge accumulation in stimulated tissues as well as circuits that dealt with electrical impedance variation to keep the electrode-tissue interface within an electrochemical safe regime. The investigation revealed a predominance of design strategies using operational amplifiers in power circuits, current outputs, and safety methods to reduce risks of electrical

  17. Repeated anodal transcranial direct current stimulation induces neural plasticity-associated gene expression in the rat cortex and hippocampus.

    Science.gov (United States)

    Kim, Min Sun; Koo, Ho; Han, Sang Who; Paulus, Walter; Nitsche, Michael A; Kim, Yun-Hee; Yoon, Jin A; Shin, Yong-Il

    2017-01-01

    Anodal transcranial direct current stimulation (A-tDCS) induces a long-lasting increase in cortical excitability that can increase gene transcription in the brain. The purpose of this study was to evaluate the expression of genes related to activity-dependent neuronal plasticity in the sensorimotor cortex and hippocampus of young Sprague-Dawley rats following A-tDCS. We applied A-tDCS over the right sensorimotor cortex epicranially with a circular electrode (3 mm diameter) at 250 μA for 20 min per day for 7 consecutive days. Levels of mRNA for brain-derived neurotrophic factor (BDNF), cAMP response element-binding protein (CREB), synapsin I, Ca2+/calmodulin-dependent protein kinase II (CaMKII), activity-regulated cytoskeleton-associated protein (Arc), and c-Fos were analyzed using SYBR Green quantitative real-time polymerase chain reaction (PCR). We found that 7 days of unilateral A-tDCS resulted in significant increases in transcription of all plasticity-related genes tested in the ipsilateral cortex. Daily A-tDCS also resulted in a significant increase in c-Fos mRNA in the ipsilateral hippocampus. These results indicate that altered expression of plasticity-associated genes in the cortex and hippocampus is a molecular substrate of A-tDCS-induced neural plasticity.

  18. Electron transfer processes occurring on platinum neural stimulating electrodes: a tutorial on the i(V e) profile

    Science.gov (United States)

    Kumsa, Doe W.; Bhadra, Narendra; Hudak, Eric M.; Kelley, Shawn C.; Untereker, Darrel F.; Mortimer, J. Thomas

    2016-10-01

    The aim of this tutorial is to encourage members of the neuroprosthesis community to incorporate electron transfer processes into their thinking and provide them with the tools to do so when they design and work with neurostimulating devices. The focus of this article is on platinum because it is the most used electrode metal for devices in commercial use. The i(V e) profile or cyclic voltammogram contains information about electron transfer processes that can occur when the electrode-electrolyte interface, V e, is at a specific potential, and assumed to be near steady-state conditions. For the engineer/designer this means that if the potential is not in the range of a specific electron transfer process, that process cannot occur. An i(V e) profile, recorded at sweep rates greater than 0.1 mVs-1, approximates steady-state conditions. Rapid transient potential excursions, like that seen with neural stimulation pulses, may be too fast for the reaction to occur, however, this means that if the potential is in the range of a specific electron transfer process it may occur and should be considered. The approach described here can be used to describe the thermodynamic electron transfer processes on other candidate electrode metals, e.g. stainless steel, iridium, carbon-based, etc.

  19. Fully Implantable Deep Brain Stimulation System with Wireless Power Transmission for Long-term Use in Rodent Models of Parkinson's Disease.

    Science.gov (United States)

    Heo, Man Seung; Moon, Hyun Seok; Kim, Hee Chan; Park, Hyung Woo; Lim, Young Hoon; Paek, Sun Ha

    2015-03-01

    The purpose of this study to develop new deep-brain stimulation system for long-term use in animals, in order to develop a variety of neural prostheses. Our system has two distinguished features, which are the fully implanted system having wearable wireless power transfer and ability to change the parameter of stimulus parameter. It is useful for obtaining a variety of data from a long-term experiment. To validate our system, we performed pre-clinical test in Parkinson's disease-rat models for 4 weeks. Through the in vivo test, we observed the possibility of not only long-term implantation and stability, but also free movement of animals. We confirmed that the electrical stimulation neither caused any side effect nor damaged the electrodes. We proved possibility of our system to conduct the long-term pre-clinical test in variety of parameter, which is available for development of neural prostheses.

  20. Vertical electric field stimulated neural cell functionality on porous amorphous carbon electrodes.

    Science.gov (United States)

    Jain, Shilpee; Sharma, Ashutosh; Basu, Bikramjit

    2013-12-01

    We demonstrate the efficacy of amorphous macroporous carbon substrates as electrodes to support neuronal cell proliferation and differentiation in electric field mediated culture conditions. The electric field was applied perpendicular to carbon substrate electrode, while growing mouse neuroblastoma (N2a) cells in vitro. The placement of the second electrode outside of the cell culture medium allows the investigation of cell response to electric field without the concurrent complexities of submerged electrodes such as potentially toxic electrode reactions, electro-kinetic flows and charge transfer (electrical current) in the cell medium. The macroporous carbon electrodes are uniquely characterized by a higher specific charge storage capacity (0.2 mC/cm(2)) and low impedance (3.3 kΩ at 1 kHz). The optimal window of electric field stimulation for better cell viability and neurite outgrowth is established. When a uniform or a gradient electric field was applied perpendicular to the amorphous carbon substrate, it was found that the N2a cell viability and neurite length were higher at low electric field strengths (≤ 2.5 V/cm) compared to that measured without an applied field (0 V/cm). While the cell viability was assessed by two complementary biochemical assays (MTT and LDH), the differentiation was studied by indirect immunostaining. Overall, the results of the present study unambiguously establish the uniform/gradient vertical electric field based culture protocol to either enhance or to restrict neurite outgrowth respectively at lower or higher field strengths, when neuroblastoma cells are cultured on porous glassy carbon electrodes having a desired combination of electrochemical properties. Copyright © 2013 Elsevier Ltd. All rights reserved.

  1. Repetitive magnetic stimulation promotes neural stem cells proliferation by upregulating MiR-106b in vitro.

    Science.gov (United States)

    Liu, Hua; Han, Xiao-hua; Chen, Hong; Zheng, Cai-xia; Yang, Yi; Huang, Xiao-lin

    2015-10-01

    Neural stem cells (NSCs) proliferation can be influenced by repetitive transcranial magnetic stimulation (rTMS) in vivo via microRNA-106b-25 cluster, but the underlying mechanisms are poorly understood. This study investigated the involvement of microRNA-106b-25 cluster in the proliferation of NSCs after repetitive magnetic stimulation (rMS) in vitro. NSCs were stimulated by rMS (200/400/600/800/1000 pulses per day, with 10 Hz frequency and 50% maximum machine output) over a 3-day period. NSCs proliferation was detected by using ki-67 and EdU staining. Ki-67, p21, p57, cyclinD1, cyclinE, cyclinA, cdk2, cdk4 proteins and miR-106b, miR-93, miR-25 mRNAs were detected by Western blotting and qRT-PCR, respectively. The results showed that rMS could promote NSCs proliferation in a dose-dependent manner. The proportions of ki-67+ and Edu+ cells in 1000 pulses group were 20.65% and 4.00%, respectively, significantly higher than those in control group (9.25%, 2.05%). The expression levels of miR-106b and miR-93 were significantly upregulated in 600-1000 pulses groups compared with control group (P<0.05 or 0.01 for all). The expression levels of p21 protein were decreased significantly in 800/1000 pulses groups, and those of cyclinD1, cyclinA, cyclinE, cdk2 and cdk4 were obviously increased after rMS as compared with control group (P<0.05 or 0.01 for all). In conclusion, our findings suggested that rMS enhances the NSCs proliferation in vitro in a dose-dependent manner and miR-106b/p21/cdks/cyclins pathway was involved in the process.

  2. A four-channel microelectronic system for neural signal regeneration

    Energy Technology Data Exchange (ETDEWEB)

    Xie Shushan; Wang Zhigong; Li Wenyuan [Institute of RF- and OE-ICs, Southeast University, Nanjing 210096 (China); Lue Xiaoying; Pan Haixian, E-mail: zgwang@seu.edu.c [State Key Laboratory of Bio-Electronics, Southeast University, Nanjing 210096 (China)

    2009-12-15

    This paper presents a microelectronic system which is capable of making a signal record and functional electric stimulation of an injured spinal cord. As a requirement of implantable engineering for the regeneration microelectronic system, the system is of low noise, low power, small size and high performance. A front-end circuit and two high performance OPAs (operational amplifiers) have been designed for the system with different functions, and the two OPAs are a low-noise low-power two-stage OPA and a constant-g{sub m} RTR input and output OPA. The system has been realized in CSMC 0.5-{mu}m CMOS technology. The test results show that the system satisfies the demands of neuron signal regeneration. (semiconductor integrated circuits)

  3. Neural Network Enhanced Structure Determination of Osteoporosis, Immune System, and Radiation Repair Proteins Project

    Data.gov (United States)

    National Aeronautics and Space Administration — The proposed innovation will utilize self learning neural network technology to determine the structure of osteoporosis, immune system disease, and excess radiation...

  4. Hybrid fault diagnosis of nonlinear systems using neural parameter estimators.

    Science.gov (United States)

    Sobhani-Tehrani, E; Talebi, H A; Khorasani, K

    2014-02-01

    This paper presents a novel integrated hybrid approach for fault diagnosis (FD) of nonlinear systems taking advantage of both the system's mathematical model and the adaptive nonlinear approximation capability of computational intelligence techniques. Unlike most FD techniques, the proposed solution simultaneously accomplishes fault detection, isolation, and identification (FDII) within a unified diagnostic module. At the core of this solution is a bank of adaptive neural parameter estimators (NPEs) associated with a set of single-parameter fault models. The NPEs continuously estimate unknown fault parameters (FPs) that are indicators of faults in the system. Two NPE structures, series-parallel and parallel, are developed with their exclusive set of desirable attributes. The parallel scheme is extremely robust to measurement noise and possesses a simpler, yet more solid, fault isolation logic. In contrast, the series-parallel scheme displays short FD delays and is robust to closed-loop system transients due to changes in control commands. Finally, a fault tolerant observer (FTO) is designed to extend the capability of the two NPEs that originally assumes full state measurements for systems that have only partial state measurements. The proposed FTO is a neural state estimator that can estimate unmeasured states even in the presence of faults. The estimated and the measured states then comprise the inputs to the two proposed FDII schemes. Simulation results for FDII of reaction wheels of a three-axis stabilized satellite in the presence of disturbances and noise demonstrate the effectiveness of the proposed FDII solutions under partial state measurements. Copyright © 2013 Elsevier Ltd. All rights reserved.

  5. Noise exposure alters long-term neural firing rates and synchrony in primary auditory and rostral belt cortices following bimodal stimulation.

    Science.gov (United States)

    Takacs, Joseph D; Forrest, Taylor J; Basura, Gregory J

    2017-12-01

    We previously demonstrated that bimodal stimulation (spinal trigeminal nucleus [Sp5] paired with best frequency tone) altered neural tone-evoked and spontaneous firing rates (SFRs) in primary auditory cortex (A1) 15 min after pairing in guinea pigs with and without noise-induced tinnitus. Neural responses were enhanced (+10 ms) or suppressed (0 ms) based on the bimodal pairing interval. Here we investigated whether bimodal stimulation leads to long-term (up to 2 h) changes in tone-evoked and SFRs and neural synchrony (correlate of tinnitus) and if the long-term bimodal effects are altered following noise exposure. To obviate the effects of permanent hearing loss on the results, firing rates and neural synchrony were measured three weeks following unilateral (left ear) noise exposure and a temporary threshold shift. Simultaneous extra-cellular single-unit recordings were made from contralateral (to noise) A1 and dorsal rostral belt (RB); an associative auditory cortical region thought to influence A1, before and after bimodal stimulation (pairing intervals of 0 ms; simultaneous Sp5-tone and +10 ms; Sp5 precedes tone). Sixty and 120 min after 0 ms pairing tone-evoked and SFRs were suppressed in sham A1; an effect only preserved 120 min following pairing in noise. Stimulation at +10 ms only affected SFRs 120 min after pairing in sham and noise-exposed A1. Within sham RB, pairing at 0 and +10 ms persistently suppressed tone-evoked and SFRs, while 0 ms pairing in noise markedly enhanced tone-evoked and SFRs up to 2 h. Together, these findings suggest that bimodal stimulation has long-lasting effects in A1 that also extend to the associative RB that is altered by noise and may have persistent implications for how noise damaged brains process multi-sensory information. Moreover, prior to bimodal stimulation, noise damage increased neural synchrony in A1, RB and between A1 and RB neurons. Bimodal stimulation led to persistent changes in neural synchrony in

  6. Low-cost wireless neural recording system and software.

    Science.gov (United States)

    Gregory, Jeffrey A; Borna, Amir; Roy, Sabyasachi; Wang, Xiaoqin; Lewandowski, Brian; Schmidt, Marc; Najafi, Khalil

    2009-01-01

    We describe a flexible wireless neural recording system, which is comprised of a 15-channel analog FM transmitter, digital receiver and custom user interface software for data acquisition. The analog front-end is constructed from commercial off the shelf (COTS) components and weighs 6.3g (including batteries) and is capable of transmitting over 24 hours up to a range over 3m with a 25microV(rms) in-vivo noise floor. The Software Defined Radio (SDR) and the acquisition software provide a data acquisition platform with real time data display and can be customized based on the specifications of various experiments. The described system was characterized with in-vitro and in-vivo experiments and the results are presented.

  7. Stability Analysis of Neural Networks-Based System Identification

    Directory of Open Access Journals (Sweden)

    Talel Korkobi

    2008-01-01

    Full Text Available This paper treats some problems related to nonlinear systems identification. A stability analysis neural network model for identifying nonlinear dynamic systems is presented. A constrained adaptive stable backpropagation updating law is presented and used in the proposed identification approach. The proposed backpropagation training algorithm is modified to obtain an adaptive learning rate guarantying convergence stability. The proposed learning rule is the backpropagation algorithm under the condition that the learning rate belongs to a specified range defining the stability domain. Satisfying such condition, unstable phenomena during the learning process are avoided. A Lyapunov analysis leads to the computation of the expression of a convenient adaptive learning rate verifying the convergence stability criteria. Finally, the elaborated training algorithm is applied in several simulations. The results confirm the effectiveness of the CSBP algorithm.

  8. A direct-to-drive neural data acquisition system

    Directory of Open Access Journals (Sweden)

    Justin P Kinney

    2015-09-01

    Full Text Available Driven by the increasing channel count of neural probes, there is much effort being directed to creating increasingly scalable electrophysiology data acquisition systems. However, all such systems still rely on personal computers for data storage, and thus are limited by the bandwidth and cost of the computers, especially as the scale of recording increases. Here we present a novel architecture in which a digital processor receives data from an analog-to-digital converter, and writes that data directly to hard drives, without the need for a personal computer to serve as an intermediary in the data acquisition process. This minimalist architecture may support exceptionally high data throughput, without incurring costs to support unnecessary hardware and overhead associated with personal computers, thus facilitating scaling of electrophysiological recording in the future.

  9. Fuzzy stochastic neural network model for structural system identification

    Science.gov (United States)

    Jiang, Xiaomo; Mahadevan, Sankaran; Yuan, Yong

    2017-01-01

    This paper presents a dynamic fuzzy stochastic neural network model for nonparametric system identification using ambient vibration data. The model is developed to handle two types of imprecision in the sensed data: fuzzy information and measurement uncertainties. The dimension of the input vector is determined by using the false nearest neighbor approach. A Bayesian information criterion is applied to obtain the optimum number of stochastic neurons in the model. A fuzzy C-means clustering algorithm is employed as a data mining tool to divide the sensed data into clusters with common features. The fuzzy stochastic model is created by combining the fuzzy clusters of input vectors with the radial basis activation functions in the stochastic neural network. A natural gradient method is developed based on the Kullback-Leibler distance criterion for quick convergence of the model training. The model is validated using a power density pseudospectrum approach and a Bayesian hypothesis testing-based metric. The proposed methodology is investigated with numerically simulated data from a Markov Chain model and a two-story planar frame, and experimentally sensed data from ambient vibration data of a benchmark structure.

  10. Multiagent Intrusion Detection Based on Neural Network Detectors and Artificial Immune System

    OpenAIRE

    Vaitsekhovich, L.; Golovko, V; Rubanau, V.

    2009-01-01

    In this article the artificial immune system and neural network techniques for intrusion detection have been addressed. The AIS allows detecting unknown samples of computer attacks. The integration of AIS and neural networks as detectors permits to increase performance of the system security. The detector structure is based on the integration of the different neural networks namely RNN and MLP. The KDD-99 dataset was used for experiments performing. The experimental results show that such int...

  11. Identification of the non-linear systems using internal recurrent neural networks

    Directory of Open Access Journals (Sweden)

    Bogdan CODRES

    2006-12-01

    Full Text Available In the past years utilization of neural networks took a distinct ampleness because of the following properties: distributed representation of information, capacity of generalization in case of uncontained situation in training data set, tolerance to noise, resistance to partial destruction, parallel processing. Another major advantage of neural networks is that they allow us to obtain the model of the investigated system, systems that is not necessarily to be linear. In fact, the true value of neural networks is seen in the case of identification and control of nonlinear systems. In this paper there are presented some identification techniques using neural networks.

  12. Super-resolution for scanning light stimulation systems

    Energy Technology Data Exchange (ETDEWEB)

    Bitzer, L. A.; Neumann, K.; Benson, N., E-mail: niels.benson@uni-due.de; Schmechel, R. [Faculty of Engineering, NST and CENIDE, University of Duisburg-Essen, Bismarckstr. 81, 47057 Duisburg (Germany)

    2016-09-15

    Super-resolution (SR) is a technique used in digital image processing to overcome the resolution limitation of imaging systems. In this process, a single high resolution image is reconstructed from multiple low resolution images. SR is commonly used for CCD and CMOS (Complementary Metal-Oxide-Semiconductor) sensor images, as well as for medical applications, e.g., magnetic resonance imaging. Here, we demonstrate that super-resolution can be applied with scanning light stimulation (LS) systems, which are common to obtain space-resolved electro-optical parameters of a sample. For our purposes, the Projection Onto Convex Sets (POCS) was chosen and modified to suit the needs of LS systems. To demonstrate the SR adaption, an Optical Beam Induced Current (OBIC) LS system was used. The POCS algorithm was optimized by means of OBIC short circuit current measurements on a multicrystalline solar cell, resulting in a mean square error reduction of up to 61% and improved image quality.

  13. BOOK REVIEW: Theory of Neural Information Processing Systems

    Science.gov (United States)

    Galla, Tobias

    2006-04-01

    It is difficult not to be amazed by the ability of the human brain to process, to structure and to memorize information. Even by the toughest standards the behaviour of this network of about 1011 neurons qualifies as complex, and both the scientific community and the public take great interest in the growing field of neuroscience. The scientific endeavour to learn more about the function of the brain as an information processing system is here a truly interdisciplinary one, with important contributions from biology, computer science, physics, engineering and mathematics as the authors quite rightly point out in the introduction of their book. The role of the theoretical disciplines here is to provide mathematical models of information processing systems and the tools to study them. These models and tools are at the centre of the material covered in the book by Coolen, Kühn and Sollich. The book is divided into five parts, providing basic introductory material on neural network models as well as the details of advanced techniques to study them. A mathematical appendix complements the main text. The range of topics is extremely broad, still the presentation is concise and the book well arranged. To stress the breadth of the book let me just mention a few keywords here: the material ranges from the basics of perceptrons and recurrent network architectures to more advanced aspects such as Bayesian learning and support vector machines; Shannon's theory of information and the definition of entropy are discussed, and a chapter on Amari's information geometry is not missing either. Finally the statistical mechanics chapters cover Gardner theory and the replica analysis of the Hopfield model, not without being preceded by a brief introduction of the basic concepts of equilibrium statistical physics. The book also contains a part on effective theories of the macroscopic dynamics of neural networks. Many dynamical aspects of neural networks are usually hard to find in the

  14. Neural mechanism of facilitation system during physical fatigue.

    Directory of Open Access Journals (Sweden)

    Masaaki Tanaka

    Full Text Available An enhanced facilitation system caused by motivational input plays an important role in supporting performance during physical fatigue. We tried to clarify the neural mechanisms of the facilitation system during physical fatigue using magnetoencephalography (MEG and a classical conditioning technique. Twelve right-handed volunteers participated in this study. Participants underwent MEG recording during the imagery of maximum grips of the right hand guided by metronome sounds for 10 min. Thereafter, fatigue-inducing maximum handgrip trials were performed for 10 min; the metronome sounds were started 5 min after the beginning of the handgrip trials. The metronome sounds were used as conditioned stimuli and maximum handgrip trials as unconditioned stimuli. The next day, they were randomly assigned to two groups in a single-blinded, two-crossover fashion to undergo two types of MEG recordings, that is, for the control and motivation sessions, during the imagery of maximum grips of the right hand guided by metronome sounds for 10 min. The alpha-band event-related desynchronizations (ERDs of the motivation session relative to the control session within the time windows of 500 to 700 and 800 to 900 ms after the onset of handgrip cue sounds were identified in the sensorimotor areas. In addition, the alpha-band ERD within the time window of 400 to 500 ms was identified in the right dorsolateral prefrontal cortex (Brodmann's area 46. The ERD level in the right dorsolateral prefrontal cortex was positively associated with that in the sensorimotor areas within the time window of 500 to 700 ms. These results suggest that the right dorsolateral prefrontal cortex is involved in the neural substrates of the facilitation system and activates the sensorimotor areas during physical fatigue.

  15. Optimal Workflow Scheduling in Critical Infrastructure Systems with Neural Networks

    Directory of Open Access Journals (Sweden)

    S. Vukmirović

    2012-04-01

    Full Text Available Critical infrastructure systems (CISs, such as power grids, transportation systems, communication networks and water systems are the backbone of a country’s national security and industrial prosperity. These CISs execute large numbers of workflows with very high resource requirements that can span through different systems and last for a long time. The proper functioning and synchronization of these workflows is essential since humanity’s well-being is connected to it. Because of this, the challenge of ensuring availability and reliability of these services in the face of a broad range of operating conditions is very complicated. This paper proposes an architecture which dynamically executes a scheduling algorithm using feedback about the current status of CIS nodes. Different artificial neural networks (ANNs were created in order to solve the scheduling problem. Their performances were compared and as the main result of this paper, an optimal ANN architecture for workflow scheduling in CISs is proposed. A case study is shown for a meter data management system with measurements from a power distribution management system in Serbia. Performance tests show that significant improvement of the overall execution time can be achieved by ANNs.

  16. Case report: Percutaneous electrical neural field stimulation in two cases of sympathetically-mediated pain [version 1; referees: 1 approved, 2 approved with reservations

    Directory of Open Access Journals (Sweden)

    Lynn Fraser

    2017-06-01

    Full Text Available Background: Fibromyalgia and complex regional pain syndrome (CRPS are both chronic pain syndromes with pathophysiologic mechanisms related to autonomic nervous system dysregulation and central sensitization.  Both syndromes are considered difficult to treat with conventional pain therapies. Case presentations: Here we describe a female veteran with fibromyalgia and a male veteran with CRPS, both of whom failed multiple pharmacologic, physical and psychological therapies for pain, but responded to percutaneous electrical neural field stimulation (PENFS targeted at the auricular branches of the cranial nerves. Discussion: While PENFS applied to the body has been previously described for treatment of localized pain, PENFS effects on cranial nerve branches of the ear is not well-known, particularly when used for regional and full-body pain syndromes such as those described here. PENFS of the ear is a minimally-invasive, non-pharmacologic therapy that could lead to improved quality of life and decreased reliance on medication. However, further research is needed to guide clinical application, particularly in complex pain patients.

  17. Stochastic Neural Field Theory and the System-Size Expansion

    KAUST Repository

    Bressloff, Paul C.

    2010-01-01

    We analyze a master equation formulation of stochastic neurodynamics for a network of synaptically coupled homogeneous neuronal populations each consisting of N identical neurons. The state of the network is specified by the fraction of active or spiking neurons in each population, and transition rates are chosen so that in the thermodynamic or deterministic limit (N → ∞) we recover standard activity-based or voltage-based rate models. We derive the lowest order corrections to these rate equations for large but finite N using two different approximation schemes, one based on the Van Kampen system-size expansion and the other based on path integral methods. Both methods yield the same series expansion of the moment equations, which at O(1/N) can be truncated to form a closed system of equations for the first-and second-order moments. Taking a continuum limit of the moment equations while keeping the system size N fixed generates a system of integrodifferential equations for the mean and covariance of the corresponding stochastic neural field model. We also show how the path integral approach can be used to study large deviation or rare event statistics underlying escape from the basin of attraction of a stable fixed point of the mean-field dynamics; such an analysis is not possible using the system-size expansion since the latter cannot accurately determine exponentially small transitions. © by SIAM.

  18. Neural systems language: a formal modeling language for the systematic description, unambiguous communication, and automated digital curation of neural connectivity.

    Science.gov (United States)

    Brown, Ramsay A; Swanson, Larry W

    2013-09-01

    Systematic description and the unambiguous communication of findings and models remain among the unresolved fundamental challenges in systems neuroscience. No common descriptive frameworks exist to describe systematically the connective architecture of the nervous system, even at the grossest level of observation. Furthermore, the accelerating volume of novel data generated on neural connectivity outpaces the rate at which this data is curated into neuroinformatics databases to synthesize digitally systems-level insights from disjointed reports and observations. To help address these challenges, we propose the Neural Systems Language (NSyL). NSyL is a modeling language to be used by investigators to encode and communicate systematically reports of neural connectivity from neuroanatomy and brain imaging. NSyL engenders systematic description and communication of connectivity irrespective of the animal taxon described, experimental or observational technique implemented, or nomenclature referenced. As a language, NSyL is internally consistent, concise, and comprehensible to both humans and computers. NSyL is a promising development for systematizing the representation of neural architecture, effectively managing the increasing volume of data on neural connectivity and streamlining systems neuroscience research. Here we present similar precedent systems, how NSyL extends existing frameworks, and the reasoning behind NSyL's development. We explore NSyL's potential for balancing robustness and consistency in representation by encoding previously reported assertions of connectivity from the literature as examples. Finally, we propose and discuss the implications of a framework for how NSyL will be digitally implemented in the future to streamline curation of experimental results and bridge the gaps among anatomists, imagers, and neuroinformatics databases. Copyright © 2013 Wiley Periodicals, Inc.

  19. A neural network architecture for implementation of expert systems for real time monitoring

    Science.gov (United States)

    Ramamoorthy, P. A.

    1991-01-01

    Since neural networks have the advantages of massive parallelism and simple architecture, they are good tools for implementing real time expert systems. In a rule based expert system, the antecedents of rules are in the conjunctive or disjunctive form. We constructed a multilayer feedforward type network in which neurons represent AND or OR operations of rules. Further, we developed a translator which can automatically map a given rule base into the network. Also, we proposed a new and powerful yet flexible architecture that combines the advantages of both fuzzy expert systems and neural networks. This architecture uses the fuzzy logic concepts to separate input data domains into several smaller and overlapped regions. Rule-based expert systems for time critical applications using neural networks, the automated implementation of rule-based expert systems with neural nets, and fuzzy expert systems vs. neural nets are covered.

  20. Research on Full Data Planning Stimulation State of Smart Distribution Automation and Stimulation Evaluating System

    Directory of Open Access Journals (Sweden)

    Qiang Chang

    2014-05-01

    Full Text Available Smart distribution automation is an important part of smart grids. In our country, power distribution network is open looped and radial. To optimize the power distribution network, this paper aims to conduct full data planning in smart distribution automation from the perspective of annual total cost to explore efficient and practical planning algorithm, and provide fundamental basis for development and completion of smart power distribution system. In this paper, firstly, we analyze the proposal background and features of smart distribution automation to prepare for limits and targets in the process of distribution network planning. Then, according to regional characteristics of smart distribution automation, we provide with appliance strategies of multi ant colony algorithm in planning of smart distribution automation and figure out the stimulation branch circuit data of full data planning in certain power supply region after 500 iterations. It turns out that the convergence of algorithm and the result of optimization are good. At last, after evaluating the result of distribution network planning, we make out an index evaluating system and come up with evaluating process of the planning results as well as algorithm principle of AHP, and finally provide fundamental basis for self-cure of smart distribution automation.

  1. Platforms for artificial neural networks : neurosimulators and performance prediction of MIMD-parallel systems

    NARCIS (Netherlands)

    Vuurpijl, L.G.

    1998-01-01

    In this thesis, two platforms for simulating artificial neural networks are discussed: MIMD-parallel processor systems as an execution platform and neurosimulators as a research and development platform. Because of the parallelism encountered in neural networks, distributed processor systems seem to

  2. Analysis of the developing neural system using an in vitro model by Raman spectroscopy.

    Science.gov (United States)

    Hashimoto, Kosuke; Kudoh, Suguru N; Sato, Hidetoshi

    2015-04-07

    We developed an in vitro model of early neural cell development. The maturation of a normal neural cell was studied in vitro using Raman spectroscopy for 120 days. The Raman spectra datasets were analyzed by principal component analysis (PCA) to investigate the relationship between maturation stages and molecular composition changes in neural cells. According to the PCA, the Raman spectra datasets can be classified into four larger groups. Previous electrophysiological studies have suggested that a normal neural cell goes through three maturation states. The groups we observed by Raman analysis showed good agreement with the electrophysiological results, except with the addition of a fourth state. The results demonstrated that Raman analysis was powerful to investigate the daily changes in molecular composition of the growing neural cell. This in vitro model system may be useful for future studies of the effects of endocrine disrupters in the developing early neural system.

  3. NNETS - NEURAL NETWORK ENVIRONMENT ON A TRANSPUTER SYSTEM

    Science.gov (United States)

    Villarreal, J.

    1994-01-01

    The primary purpose of NNETS (Neural Network Environment on a Transputer System) is to provide users a high degree of flexibility in creating and manipulating a wide variety of neural network topologies at processing speeds not found in conventional computing environments. To accomplish this purpose, NNETS supports back propagation and back propagation related algorithms. The back propagation algorithm used is an implementation of Rumelhart's Generalized Delta Rule. NNETS was developed on the INMOS Transputer. NNETS predefines a Back Propagation Network, a Jordan Network, and a Reinforcement Network to assist users in learning and defining their own networks. The program also allows users to configure other neural network paradigms from the NNETS basic architecture. The Jordan network is basically a feed forward network that has the outputs connected to a pseudo input layer. The state of the network is dependent on the inputs from the environment plus the state of the network. The Reinforcement network learns via a scalar feedback signal called reinforcement. The network propagates forward randomly. The environment looks at the outputs of the network to produce a reinforcement signal that is fed back to the network. NNETS was written for the INMOS C compiler D711B version 1.3 or later (MS-DOS version). A small portion of the software was written in the OCCAM language to perform the communications routing between processors. NNETS is configured to operate on a 4 X 10 array of Transputers in sequence with a Transputer based graphics processor controlled by a master IBM PC 286 (or better) Transputer. A RGB monitor is required which must be capable of 512 X 512 resolution. It must be able to receive red, green, and blue signals via BNC connectors. NNETS is meant for experienced Transputer users only. The program is distributed on 5.25 inch 1.2Mb MS-DOS format diskettes. NNETS was developed in 1991. Transputer and OCCAM are registered trademarks of Inmos Corporation. MS

  4. Adaptive Neural Control for a Class of Outputs Time-Delay Nonlinear Systems

    Directory of Open Access Journals (Sweden)

    Ruliang Wang

    2012-01-01

    Full Text Available This paper considers an adaptive neural control for a class of outputs time-delay nonlinear systems with perturbed or no. Based on RBF neural networks, the radius basis function (RBF neural networks is employed to estimate the unknown continuous functions. The proposed control guarantees that all closed-loop signals remain bounded. The simulation results demonstrate the effectiveness of the proposed control scheme.

  5. GABA(A) receptors in visual and auditory cortex and neural activity changes during basic visual stimulation.

    Science.gov (United States)

    Qin, Pengmin; Duncan, Niall W; Wiebking, Christine; Gravel, Paul; Lyttelton, Oliver; Hayes, Dave J; Verhaeghe, Jeroen; Kostikov, Alexey; Schirrmacher, Ralf; Reader, Andrew J; Northoff, Georg

    2012-01-01

    Recent imaging studies have demonstrated that levels of resting γ-aminobutyric acid (GABA) in the visual cortex predict the degree of stimulus-induced activity in the same region. These studies have used the presentation of discrete visual stimulus; the change from closed eyes to open also represents a simple visual stimulus, however, and has been shown to induce changes in local brain activity and in functional connectivity between regions. We thus aimed to investigate the role of the GABA system, specifically GABA(A) receptors, in the changes in brain activity between the eyes closed (EC) and eyes open (EO) state in order to provide detail at the receptor level to complement previous studies of GABA concentrations. We conducted an fMRI study involving two different modes of the change from EC to EO: an EO and EC block design, allowing the modeling of the haemodynamic response, followed by longer periods of EC and EO to allow the measuring of functional connectivity. The same subjects also underwent [(18)F]Flumazenil PET to measure GABA(A) receptor binding potentials. It was demonstrated that the local-to-global ratio of GABA(A) receptor binding potential in the visual cortex predicted the degree of changes in neural activity from EC to EO. This same relationship was also shown in the auditory cortex. Furthermore, the local-to-global ratio of GABA(A) receptor binding potential in the visual cortex also predicted the change in functional connectivity between the visual and auditory cortex from EC to EO. These findings contribute to our understanding of the role of GABA(A) receptors in stimulus-induced neural activity in local regions and in inter-regional functional connectivity.

  6. Computer-controlled stimulation for functional magnetic resonance imaging studies of the neonatal olfactory system.

    Science.gov (United States)

    Arichi, T; Gordon-Williams, R; Allievi, A; Groves, A M; Burdet, E; Edwards, A D

    2013-09-01

    Olfactory sensation is highly functional early in human neonatal life, with studies suggesting that odours can influence behaviour and infant-mother bonding. Due to its good spatial properties, blood oxygen level-dependent (BOLD) contrast functional magnetic resonance imaging (fMRI) has the potential to rapidly advance our understanding of the neural activity which underlies the development of olfactory perception in this key period. We aimed to design an 'olfactometer' specifically for use with neonatal subjects for fMRI studies of odour perception. We describe a fully automated and programmable, fMRI compatible system capable of presenting odorant liquids. To prevent contamination of the system and minimize between-subject infective risk, the majority of the olfactometer is constructed from single-use, readily available clinical equipment. The system was used to present the odour of infant formula milk in a validation group of seven neonatal subjects at term equivalent postmenstrual age (median age 40 weeks). A safe, reliable and reproducible pattern of stimulation was delivered leading to well-localized positive BOLD functional responses in the piriform cortex, amygdala, thalamus, insular cortex and cerebellum. The described system is therefore suitable for detailed studies of the ontology of olfactory sensation and perception during early human brain development. ©2013 Foundation Acta Paediatrica. Published by John Wiley & Sons Ltd.

  7. Non-invasive neuromuscular electrical stimulation in patients with central nervous system lesions: an educational review.

    Science.gov (United States)

    Schuhfried, Othmar; Crevenna, Richard; Fialka-Moser, Veronika; Paternostro-Sluga, Tatjana

    2012-02-01

    The aim of this educational review is to provide an overview of the clinical application of transcutaneous electrical stimulation of the extremities in patients with upper motor neurone lesions. In general two methods of electrical stimulation can be distinguished: (i) therapeutic electrical stimulation, and (ii) functional electrical stimulation. Therapeutic electrical stimulation improves neuromuscular functional condition by strengthening muscles, increasing motor control, reducing spasticity, decreasing pain and increasing range of motion. Transcutaneous electrical stimulation may be used for neuromuscular electrical stimulation inducing repetitive muscle contraction, electromyography-triggered neuromuscular electrical stimulation, position-triggered electrical stimulation and subsensory or sensory transcutaneous electric stimulation. Functional electrical stimulation provokes muscle contraction and thereby produces a functionally useful movement during stimulation. In patients with spinal cord injuries or stroke, electrical upper limb neuroprostheses are applied to enhance upper limb and hand function, and electrical lower limb neuroprostheses are applied for restoration of standing and walking. For example, a dropped foot stimulator is used to trigger ankle dorsiflexion to restore gait function. A review of the literature and clinical experience of the use of therapeutic electrical stimulation as well as of functional electrical stimulation in combination with botulinum toxin, exercise therapy and/or splinting are presented. Although the evidence is limited we conclude that neuromuscular electrical stimulation in patients with central nervous system lesions can be an effective modality to improve function, and that combination with other treatments has an additive therapeutic effect.

  8. NNSYSID and NNCTRL Tools for system identification and control with neural networks

    DEFF Research Database (Denmark)

    Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

    2001-01-01

    a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...... choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview...

  9. NNSYSID and NNCTRL Tools for system identification and control with neural networks

    DEFF Research Database (Denmark)

    Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

    2001-01-01

    choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...... a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...

  10. Hybrid energy system evaluation in water supply system energy production: neural network approach

    Energy Technology Data Exchange (ETDEWEB)

    Goncalves, Fabio V.; Ramos, Helena M. [Civil Engineering Department, Instituto Superior Tecnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001, Lisbon (Portugal); Reis, Luisa Fernanda R. [Universidade de Sao Paulo, EESC/USP, Departamento de Hidraulica e Saneamento., Avenida do Trabalhador Saocarlense, 400, Sao Carlos-SP (Brazil)

    2010-07-01

    Water supply systems are large consumers of energy and the use of hybrid systems for green energy production is this new proposal. This work presents a computational model based on neural networks to determine the best configuration of a hybrid system to generate energy in water supply systems. In this study the energy sources to make this hybrid system can be the national power grid, micro-hydro and wind turbines. The artificial neural network is composed of six layers, trained to use data generated by a model of hybrid configuration and an economic simulator - CES. The reason for the development of an advanced model of forecasting based on neural networks is to allow rapid simulation and proper interaction with hydraulic and power model simulator - HPS. The results show that this computational model is useful as advanced decision support system in the design of configurations of hybrid power systems applied to water supply systems, improving the solutions in the development of its global energy efficiency.

  11. An Arbitrary Waveform Wearable Neuro-stimulator System for Neurophysiology Research on Freely Behaving Animals

    OpenAIRE

    Samani, Mohsen Mosayebi; Mahnam, Amin; HOSSEINI, Nasrin

    2014-01-01

    Portable wireless neuro-stimulators have been developed to facilitate long-term cognitive and behavioral studies on the central nervous system in freely moving animals. These stimulators can provide precisely controllable input(s) to the nervous system, without distracting the animal attention with cables connected to its body. In this study, a low power backpack neuro-stimulator was developed for animal brain researches that can provides arbitrary stimulus waveforms for the stimulation, whil...

  12. Using repetitive transcranial magnetic stimulation to study the underlying neural mechanisms of human motor learning and memory

    National Research Council Canada - National Science Library

    Censor, Nitzan; Cohen, Leonardo G

    2011-01-01

    .... Repetitive transcranial magnetic stimulation (rTMS), a safe non‐invasive brain stimulation technique, enables the study of the functional role of specific cortical areas by evaluating the behavioural consequences of selective modulation of activity...

  13. On the Computational Power of Spiking Neural P Systems with Self-Organization

    Science.gov (United States)

    Wang, Xun; Song, Tao; Gong, Faming; Zheng, Pan

    2016-06-01

    Neural-like computing models are versatile computing mechanisms in the field of artificial intelligence. Spiking neural P systems (SN P systems for short) are one of the recently developed spiking neural network models inspired by the way neurons communicate. The communications among neurons are essentially achieved by spikes, i. e. short electrical pulses. In terms of motivation, SN P systems fall into the third generation of neural network models. In this study, a novel variant of SN P systems, namely SN P systems with self-organization, is introduced, and the computational power of the system is investigated and evaluated. It is proved that SN P systems with self-organization are capable of computing and accept the family of sets of Turing computable natural numbers. Moreover, with 87 neurons the system can compute any Turing computable recursive function, thus achieves Turing universality. These results demonstrate promising initiatives to solve an open problem arisen by Gh Păun.

  14. ANOMALY NETWORK INTRUSION DETECTION SYSTEM BASED ON DISTRIBUTED TIME-DELAY NEURAL NETWORK (DTDNN

    Directory of Open Access Journals (Sweden)

    LAHEEB MOHAMMAD IBRAHIM

    2010-12-01

    Full Text Available In this research, a hierarchical off-line anomaly network intrusion detection system based on Distributed Time-Delay Artificial Neural Network is introduced. This research aims to solve a hierarchical multi class problem in which the type of attack (DoS, U2R, R2L and Probe attack detected by dynamic neural network. The results indicate that dynamic neural nets (Distributed Time-Delay Artificial Neural Network can achieve a high detection rate, where the overall accuracy classification rate average is equal to 97.24%.

  15. An alternative respiratory sounds classification system utilizing artificial neural networks

    Directory of Open Access Journals (Sweden)

    Rami J Oweis

    2015-04-01

    Full Text Available Background: Computerized lung sound analysis involves recording lung sound via an electronic device, followed by computer analysis and classification based on specific signal characteristics as non-linearity and nonstationarity caused by air turbulence. An automatic analysis is necessary to avoid dependence on expert skills. Methods: This work revolves around exploiting autocorrelation in the feature extraction stage. All process stages were implemented in MATLAB. The classification process was performed comparatively using both artificial neural networks (ANNs and adaptive neuro-fuzzy inference systems (ANFIS toolboxes. The methods have been applied to 10 different respiratory sounds for classification. Results: The ANN was superior to the ANFIS system and returned superior performance parameters. Its accuracy, specificity, and sensitivity were 98.6%, 100%, and 97.8%, respectively. The obtained parameters showed superiority to many recent approaches. Conclusions: The promising proposed method is an efficient fast tool for the intended purpose as manifested in the performance parameters, specifically, accuracy, specificity, and sensitivity. Furthermore, it may be added that utilizing the autocorrelation function in the feature extraction in such applications results in enhanced performance and avoids undesired computation complexities compared to other techniques.

  16. An alternative respiratory sounds classification system utilizing artificial neural networks.

    Science.gov (United States)

    Oweis, Rami J; Abdulhay, Enas W; Khayal, Amer; Awad, Areen

    2015-01-01

    Computerized lung sound analysis involves recording lung sound via an electronic device, followed by computer analysis and classification based on specific signal characteristics as non-linearity and nonstationarity caused by air turbulence. An automatic analysis is necessary to avoid dependence on expert skills. This work revolves around exploiting autocorrelation in the feature extraction stage. All process stages were implemented in MATLAB. The classification process was performed comparatively using both artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) toolboxes. The methods have been applied to 10 different respiratory sounds for classification. The ANN was superior to the ANFIS system and returned superior performance parameters. Its accuracy, specificity, and sensitivity were 98.6%, 100%, and 97.8%, respectively. The obtained parameters showed superiority to many recent approaches. The promising proposed method is an efficient fast tool for the intended purpose as manifested in the performance parameters, specifically, accuracy, specificity, and sensitivity. Furthermore, it may be added that utilizing the autocorrelation function in the feature extraction in such applications results in enhanced performance and avoids undesired computation complexities compared to other techniques.

  17. Neural network based optimal control of HVAC&R systems

    Science.gov (United States)

    Ning, Min

    Heating, Ventilation, Air-Conditioning and Refrigeration (HVAC&R) systems have wide applications in providing a desired indoor environment for different types of buildings. It is well acknowledged that 30%-40% of the total energy generated is consumed by buildings and HVAC&R systems alone account for more than 50% of the building energy consumption. Low operational efficiency especially under partial load conditions and poor control are part of reasons for such high energy consumption. To improve energy efficiency, HVAC&R systems should be properly operated to maintain a comfortable and healthy indoor environment under dynamic ambient and indoor conditions with the least energy consumption. This research focuses on the optimal operation of HVAC&R systems. The optimization problem is formulated and solved to find the optimal set points for the chilled water supply temperature, discharge air temperature and AHU (air handling unit) fan static pressure such that the indoor environment is maintained with the least chiller and fan energy consumption. To achieve this objective, a dynamic system model is developed first to simulate the system behavior under different control schemes and operating conditions. The system model is modular in structure, which includes a water-cooled vapor compression chiller model and a two-zone VAV system model. A fuzzy-set based extended transformation approach is then applied to investigate the uncertainties of this model caused by uncertain parameters and the sensitivities of the control inputs with respect to the interested model outputs. A multi-layer feed forward neural network is constructed and trained in unsupervised mode to minimize the cost function which is comprised of overall energy cost and penalty cost when one or more constraints are violated. After training, the network is implemented as a supervisory controller to compute the optimal settings for the system. In order to implement the optimal set points predicted by the

  18. Skin-Derived Precursor Cells Promote Angiogenesis and Stimulate Proliferation of Endogenous Neural Stem Cells after Cerebral Infarction

    Directory of Open Access Journals (Sweden)

    Duo Mao

    2015-01-01

    Full Text Available Stroke is one of the most common diseases that caused high mortality and has become burden to the health care systems. Stem cell transplantation has shown therapeutic effect in ameliorating ischemic damage after cerebral artery occlusion mainly due to their neurogenesis, immune regulation, or effects on the plasticity, proliferation, and survival of host cells. Recent studies demonstrated that skin-derived precursor cells (SKPs could promote central nervous system regeneration in spinal cord injury model or the neonatal peripheral neuron. Here, we investigated the therapeutic potential of SKPs in a rat model of cerebral ischemia. SKPs were isolated, expanded, and transplanted into rat cortex and striatum after transient middle cerebral artery occlusion. Our results revealed that SKPs transplantation could improve the behavioral measures of neurological deficit. Moreover, immunohistology confirmed that SKPs could secrete basic FGF and VEGF in the ischemic region and further markedly increase the proliferation of endogenous nestin+ and βIII-tubulin+ neural stem cells. Furthermore, increased angiogenesis induced by SKPs was observed by vWF and α-SMA staining. These data suggest that SKPs induced endogenous neurogenesis and angiogenesis and protected neuron from hypoxic-ischemic environment. In conclusion, SKPs transplantation may be a promising approach in treatment of stroke.

  19. RehaMovePro: A versatile mobile stimulation system for transcutaneous FES applications

    Directory of Open Access Journals (Sweden)

    Markus Valtin

    2016-06-01

    Full Text Available Functional Electrical Stimulation is a commonly used method in clinical rehabilitation and research to trigger useful muscle contractions by electrical stimuli. In this work, we present a stimulation system for transcutaneous electrical stimulation that gives extensive control over the stimulation waveform and the stimulation timing. The system supports electrode arrays, which have been suggested to achieve better selectivity and to simplify electrode placement. Electromyography (EMG measurements are obtained from the active stimulation electrodes (between the stimulation pulses or from separate surface EMG electrodes. The modular design enables the implementation of sophisticated stimulation control systems including external triggers or wireless sensors. This is demonstrated by the standalone implementation of a feedback-controlled drop foot neuroprosthesis, which uses a wireless inertial sensor for real-time gait phase detection and foot orientation measurement.

  20. PERFORMANCE COMPARISON FOR INTRUSION DETECTION SYSTEM USING NEURAL NETWORK WITH KDD DATASET

    Directory of Open Access Journals (Sweden)

    S. Devaraju

    2014-04-01

    Full Text Available Intrusion Detection Systems are challenging task for finding the user as normal user or attack user in any organizational information systems or IT Industry. The Intrusion Detection System is an effective method to deal with the kinds of problem in networks. Different classifiers are used to detect the different kinds of attacks in networks. In this paper, the performance of intrusion detection is compared with various neural network classifiers. In the proposed research the four types of classifiers used are Feed Forward Neural Network (FFNN, Generalized Regression Neural Network (GRNN, Probabilistic Neural Network (PNN and Radial Basis Neural Network (RBNN. The performance of the full featured KDD Cup 1999 dataset is compared with that of the reduced featured KDD Cup 1999 dataset. The MATLAB software is used to train and test the dataset and the efficiency and False Alarm Rate is measured. It is proved that the reduced dataset is performing better than the full featured dataset.

  1. Review: the role of neural crest cells in the endocrine system.

    Science.gov (United States)

    Adams, Meghan Sara; Bronner-Fraser, Marianne

    2009-01-01

    The neural crest is a pluripotent population of cells that arises at the junction of the neural tube and the dorsal ectoderm. These highly migratory cells form diverse derivatives including neurons and glia of the sensory, sympathetic, and enteric nervous systems, melanocytes, and the bones, cartilage, and connective tissues of the face. The neural crest has long been associated with the endocrine system, although not always correctly. According to current understanding, neural crest cells give rise to the chromaffin cells of the adrenal medulla, chief cells of the extra-adrenal paraganglia, and thyroid C cells. The endocrine tumors that correspond to these cell types are pheochromocytomas, extra-adrenal paragangliomas, and medullary thyroid carcinomas. Although controversies concerning embryological origin appear to have mostly been resolved, questions persist concerning the pathobiology of each tumor type and its basis in neural crest embryology. Here we present a brief history of the work on neural crest development, both in general and in application to the endocrine system. In particular, we present findings related to the plasticity and pluripotency of neural crest cells as well as a discussion of several different neural crest tumors in the endocrine system.

  2. Neural Control System in Obstacle Avoidance in Mobile Robots Using Ultrasonic Sensors

    Directory of Open Access Journals (Sweden)

    A. Medina-Santiago

    2014-02-01

    Full Text Available This paper presents the development and implementation of neural control systems in mobile robots in obstacle avoidance in real time using ultrasonic sensors with complex strategies of decision-making in development (Matlab and Processing. An Arduino embedded platform is used to implement the neural control for field results.

  3. Modeling of the height control system using artificial neural networks

    Directory of Open Access Journals (Sweden)

    A. R Tahavvor

    2016-09-01

    Full Text Available Introduction Automation of agricultural and machinery construction has generally been enhanced by intelligent control systems due to utility and efficiency rising, ease of use, profitability and upgrading according to market demand. A broad variety of industrial merchandise are now supplied with computerized control systems of earth moving processes to be performed by construction and agriculture field vehicle such as grader, backhoe, tractor and scraper machines. A height control machine which is used in measuring base thickness is consisted of two mechanical and electronic parts. The mechanical part is consisted of conveyor belt, main body, electrical engine and invertors while the electronic part is consisted of ultrasonic, wave transmitter and receiver sensor, electronic board, control set, and microcontroller. The main job of these controlling devices consists of the topographic surveying, cutting and filling of elevated and spotted low area, and these actions fundamentally dependent onthe machine's ability in elevation and thickness measurement and control. In this study, machine was first tested and then some experiments were conducted for data collection. Study of system modeling in artificial neural networks (ANN was done for measuring, controlling the height for bases by input variable input vectors such as sampling time, probe speed, conveyer speed, sound wave speed and speed sensor are finally the maximum and minimum probe output vector on various conditions. The result reveals the capability of this procedure for experimental recognition of sensors' behavior and improvement of field machine control systems. Inspection, calibration and response, diagnosis of the elevation control system in combination with machine function can also be evaluated by some extra development of this system. Materials and Methods Designing and manufacture of the planned apparatus classified in three dissimilar, mechanical and electronic module, courses of

  4. Fault diagnosis in satellite attitude control systems using artificial neural networkk

    Science.gov (United States)

    Ayodele I., Olanipekun

    The nonlinear behavior exhibited by altitude control system processes and also the presence of external constraints on the operating conditions causes hitch in the dynamics of system processes. This research work proposes a fault detection/tolerant prediction in an altitude control system. This is done through the artificial neural network fault detection by deploying the neural network approach. A fault detection and isolation module is developed in the actuator system of the Altitude Control System, thereby achieving the goal of this thesis. This can be done by two basic classification stages: Neural Residual Generator (Neural Observer)- This stage is responsible for generating residual errors that can reflect the real behavior of the entire process as against its normal conditions. Adaptive Neural Classifier - This stage is responsible for managing the isolation task of the fault detected by evaluating the generated residual errors from the neural estimator which gives detailed information about faults detected e.g., fault location and time. These two stages can be implemented by executing the tasks listed below: 1. Study and develop a generic three axis stabilized altitude control model based on the reaction wheels. This is established with three separate PD controllers designed for each reaction wheel of the satellite axis using the Matlab - SIMULINK. 2. Develop a dynamic neural network residual generator based on Dynamic Multilayer Perceptron Network (DMLP) which is then applied to the reaction wheel model designed commonly called the actuator in the altitude control system of a satellite 3. Develop a neural network adaptive classifier based on the Learning Vector Quantization (LVQ) model which is used for the isolation concept. The advantages of the proposed dynamic neural network and neural adaptive classifier approach are showcased.

  5. Modulation of Neural Activity in the Temporoparietal Junction with Transcranial Direct Current Stimulation Changes the Role of Beliefs in Moral Judgment.

    Science.gov (United States)

    Ye, Hang; Chen, Shu; Huang, Daqiang; Zheng, Haoli; Jia, Yongmin; Luo, Jun

    2015-01-01

    Judgments about whether an action is morally right or wrong typically depend on our capacity to infer the actor's beliefs and the outcomes of the action. Prior neuroimaging studies have found that mental state (e.g., beliefs, intentions) attribution for moral judgment involves a complex neural network that includes the temporoparietal junction (TPJ). However, neuroimaging studies cannot demonstrate a direct causal relationship between the activity of this brain region and mental state attribution for moral judgment. In the current study, we used transcranial direct current stimulation (tDCS) to transiently alter neural activity in the TPJ. The participants were randomly assigned to one of three stimulation treatments (right anodal/left cathodal tDCS, left anodal/right cathodal tDCS, or sham stimulation). Each participant was required to complete two similar tasks of moral judgment before receiving tDCS and after receiving tDCS. We studied whether tDCS to the TPJ altered mental state attribution for moral judgment. The results indicated that restraining the activity of the right temporoparietal junction (RTPJ) or the left the temporoparietal junction (LTPJ) decreased the role of beliefs in moral judgments and led to an increase in the dependance of the participants' moral judgments on the action's consequences. We also found that the participants exhibited reduced reaction times both in the cases of intentional harms and attempted harms after receiving right cathodal/left anodal tDCS to the TPJ. These findings inform and extend the current neural models of moral judgment and moral development in typically developing people and in individuals with neurodevelopmental disorders such as autism.

  6. Modulation of neural activity in the temporoparietal junction with transcranial direct current stimulation changes the role of beliefs in moral judgment

    Directory of Open Access Journals (Sweden)

    Hang eYe

    2015-12-01

    Full Text Available Judgments about whether an action is morally right or wrong typically depend on our capacity to infer the actor’s beliefs and the outcomes of the action. Prior neuroimaging studies have found that mental state (e.g., beliefs, intentions attribution for moral judgment involves a complex neural network that includes the temporoparietal junction (TPJ. However, neuroimaging studies cannot demonstrate a direct causal relationship between the activity of this brain region and mental state attribution for moral judgment. In the current study, we used transcranial direct current stimulation (tDCS to transiently alter neural activity in the TPJ. The participants were randomly assigned to one of three stimulation treatments (right anodal/left cathodal tDCS, left anodal/right cathodal tDCS, or sham stimulation. Each participant was required to complete two similar tasks of moral judgment before receiving tDCS and after receiving tDCS. We studied whether tDCS to the TPJ altered mental state attribution for moral judgment. The results indicated that restraining the activity of the right temporoparietal junction (RTPJ or the left the temporoparietal junction (LTPJ decreased the role of beliefs in moral judgments and led to an increase in the dependence of the participants’ moral judgments on the action’s consequences. We also found that the participants exhibited reduced reaction times both in the cases of intentional harms and attempted harms after receiving right cathodal/left anodal tDCS to the TPJ. These findings inform and extend the current neural models of moral judgment and moral development in typically developing people and in individuals with neurodevelopmental disorders such as autism.

  7. Artificial Neural Network-Based System for PET Volume Segmentation

    Directory of Open Access Journals (Sweden)

    Mhd Saeed Sharif

    2010-01-01

    Full Text Available Tumour detection, classification, and quantification in positron emission tomography (PET imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs, as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results.

  8. Honey characterization using computer vision system and artificial neural networks.

    Science.gov (United States)

    Shafiee, Sahameh; Minaei, Saeid; Moghaddam-Charkari, Nasrollah; Barzegar, Mohsen

    2014-09-15

    This paper reports the development of a computer vision system (CVS) for non-destructive characterization of honey based on colour and its correlated chemical attributes including ash content (AC), antioxidant activity (AA), and total phenolic content (TPC). Artificial neural network (ANN) models were applied to transform RGB values of images to CIE L*a*b* colourimetric measurements and to predict AC, TPC and AA from colour features of images. The developed ANN models were able to convert RGB values to CIE L*a*b* colourimetric parameters with low generalization error of 1.01±0.99. In addition, the developed models for prediction of AC, TPC and AA showed high performance based on colour parameters of honey images, as the R(2) values for prediction were 0.99, 0.98, and 0.87, for AC, AA and TPC, respectively. The experimental results show the effectiveness and possibility of applying CVS for non-destructive honey characterization by the industry. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

    2014-11-01

    grid, using an Advanced Brain Monitoring (ABM) ×24 system configured with the single-trial event - related potential (ERP) sensor strip and operating...ROC curve BCI brain-computer interface EEG electroencephalogram ERP event - related potential EVUS estimated volume under the surface FOV field of...stations. 15. SUBJECT TERMS rapid serial visual presentation, RSVP, EEG, neural classification, P300 , brain-computer interface 16. SECURITY

  10. Predictive Control of Hydronic Floor Heating Systems using Neural Networks and Genetic Algorithms

    DEFF Research Database (Denmark)

    Vinther, Kasper; Green, Torben; Østergaard, Søren

    2017-01-01

    This paper presents the use a neural network and a micro genetic algorithm to optimize future set-points in existing hydronic floor heating systems for improved energy efficiency. The neural network can be trained to predict the impact of changes in set-points on future room temperatures. Additio...... space is not guaranteed. Evaluation of the performance of multiple neural networks is performed, using different levels of information, and optimization results are presented on a detailed house simulation model.......This paper presents the use a neural network and a micro genetic algorithm to optimize future set-points in existing hydronic floor heating systems for improved energy efficiency. The neural network can be trained to predict the impact of changes in set-points on future room temperatures...

  11. Compensating for Channel Fading in DS-CDMA Communication Systems Employing ICA Neural Network Detectors

    Directory of Open Access Journals (Sweden)

    David Overbye

    2005-06-01

    Full Text Available In this paper we examine the impact of channel fading on the bit error rate of a DS-CDMA communication system. The system employs detectors that incorporate neural networks effecting methods of independent component analysis (ICA, subspace estimation of channel noise, and Hopfield type neural networks. The Rayleigh fading channel model is used. When employed in a Rayleigh fading environment, the ICA neural network detectors that give superior performance in a flat fading channel did not retain this superior performance. We then present a new method of compensating for channel fading based on the incorporation of priors in the ICA neural network learning algorithms. When the ICA neural network detectors were compensated using the incorporation of priors, they give significantly better performance than the traditional detectors and the uncompensated ICA detectors. Keywords: CDMA, Multi-user Detection, Rayleigh Fading, Multipath Detection, Independent Component Analysis, Prior Probability Hebbian Learning, Natural Gradient

  12. Artificial Neural Systems Application to the Simulation of Air Combat Decision Making

    Science.gov (United States)

    1992-04-01

    unit, the CPU , whereas neural networks utilize the effects of many, simple processing elements. Traditional computing is done in a step-by-step, serial...Nielsen Neurocomputers (HNC). The ANZA-Plus coprocessor is part of an 80386 -based computer system which is optimized for training and executing neural...host computer for this program is a Zenith 386/16 system running under the DOS 3.31 operating system. The 80386 microprocessor in this machine operates

  13. Neural Network Expert System in the Application of Tower Fault Diagnosis

    Science.gov (United States)

    Liu, Xiaoyang; Xia, Zhongwu; Tao, Zhiyong; Zhao, Zhenlian

    For the corresponding fuzzy relationship between the fault symptoms and the fault causes in the process of tower crane operation, this paper puts forward a kind of rapid new method of fast detection and diagnosis for common fault based on neural network expert system. This paper makes full use of expert system and neural network advantages, and briefly introduces the structure, function, algorithm and realization of the adopted system. Results show that the new algorithm is feasible and can achieve rapid faults diagnosis.

  14. Research on architecture of intelligent design platform for artificial neural network expert system

    Science.gov (United States)

    Gu, Honghong

    2017-09-01

    Based on the review of the development and current situation of CAD technology, the necessity of combination of artificial neural network and expert system, and then present an intelligent design system based on artificial neural network. Moreover, it discussed the feasibility of realization of a design-oriented expert system development tools on the basis of above combination. In addition, knowledge representation strategy and method and the solving process are given in this paper.

  15. A case for spiking neural network simulation based on configurable multiple-FPGA systems.

    Science.gov (United States)

    Yang, Shufan; Wu, Qiang; Li, Renfa

    2011-09-01

    Recent neuropsychological research has begun to reveal that neurons encode information in the timing of spikes. Spiking neural network simulations are a flexible and powerful method for investigating the behaviour of neuronal systems. Simulation of the spiking neural networks in software is unable to rapidly generate output spikes in large-scale of neural network. An alternative approach, hardware implementation of such system, provides the possibility to generate independent spikes precisely and simultaneously output spike waves in real time, under the premise that spiking neural network can take full advantage of hardware inherent parallelism. We introduce a configurable FPGA-oriented hardware platform for spiking neural network simulation in this work. We aim to use this platform to combine the speed of dedicated hardware with the programmability of software so that it might allow neuroscientists to put together sophisticated computation experiments of their own model. A feed-forward hierarchy network is developed as a case study to describe the operation of biological neural systems (such as orientation selectivity of visual cortex) and computational models of such systems. This model demonstrates how a feed-forward neural network constructs the circuitry required for orientation selectivity and provides platform for reaching a deeper understanding of the primate visual system. In the future, larger scale models based on this framework can be used to replicate the actual architecture in visual cortex, leading to more detailed predictions and insights into visual perception phenomenon.

  16. Closed-loop stimulation of a delayed neural fields model of parkinsonian STN-GPe network: a theoretical and computational study

    Directory of Open Access Journals (Sweden)

    Georgios Is. Detorakis

    2015-07-01

    Full Text Available Several disorders are related to pathological brain oscillations. In the case of Parkinson's disease, sustained low-frequency oscillations (especially in the beta-band, 13-30Hz correlate with motor symptoms. It is still under debate whether these oscillations are the cause of parkinsonian motor symptoms. The development of techniques enabling selectivedisruption of these beta-oscillations could contribute to the understanding of the underlying mechanisms, and could be exploited for treatments. A particularly appealing technique is Deep Brain Stimulation (DBS. With clinicalelectrical DBS, electrical currents are delivered at high frequency to a region made of potentially heterogeneous neurons(the subthalamic nucleus (STN in the case of Parkinson's disease. Even more appealing is DBS with optogenetics, which is until now a preclinical method using both gene transfer and deep brain light delivery and enabling neuromodulation at the scale of one given neural network. In this work, we rely on delayed neural fields models of STN and the external Globus Pallidus (GPe to develop , theoretically validate and test in silico a closed-loop stimulation strategy to disrupt these sustained oscillations with optogenetics. First, we rely on tools from control theory to provide theoretical conditions under which sustained oscillations can be attenuated by a closed-loop stimulation proportional to the measured activityof STN. Second, based on this theoretical framework, we show numerically that the proposed closed-loop stimulation efficiently attenuates sustained oscillations, even in the case when the photosensitization effectively affects only 50$%$ of STN neurons. We also show through simulations that oscillations disruption can be achieved when the same light source is used for the whole STN population. We finally test the robustness of the proposed strategy to possible acquisition and processing delays, as well as parameters uncertainty.

  17. Feedback error learning controller for functional electrical stimulation assistance in a hybrid robotic system for reaching rehabilitation

    Directory of Open Access Journals (Sweden)

    Francisco Resquín

    2016-07-01

    Full Text Available Hybrid robotic systems represent a novel research field, where functional electrical stimulation (FES is combined with a robotic device for rehabilitation of motor impairment. Under this approach, the design of robust FES controllers still remains an open challenge. In this work, we aimed at developing a learning FES controller to assist in the performance of reaching movements in a simple hybrid robotic system setting. We implemented a Feedback Error Learning (FEL control strategy consisting of a feedback PID controller and a feedforward controller based on a neural network. A passive exoskeleton complemented the FES controller by compensating the effects of gravity. We carried out experiments with healthy subjects to validate the performance of the system. Results show that the FEL control strategy is able to adjust the FES intensity to track the desired trajectory accurately without the need of a previous mathematical model.

  18. Neutron spectrometry and dosimetry by means of Bonner spheres system and artificial neural networks applying robust design of artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Martinez B, M.R.; Ortiz R, J.M.; Vega C, H.R. [UAZ, Av. Ramon Lopez Velarde No. 801, 98000 Zacatecas (Mexico)

    2006-07-01

    An Artificial Neural Network has been designed, trained and tested to unfold neutron spectra and simultaneously to calculate equivalent doses. A set of 187 neutron spectra compiled by the International Atomic Energy Agency and 13 equivalent doses were used in the artificial neural network designed, trained and tested. In order to design the neural network was used the robust design of artificial neural networks methodology, which assures that the quality of the neural networks takes into account from the design stage. Unless previous works, here, for first time a group of neural networks were designed and trained to unfold 187 neutron spectra and at the same time to calculate 13 equivalent doses, starting from the count rates coming from the Bonner spheres system by using a systematic and experimental strategy. (Author)

  19. Simulation of Missile Autopilot with Two-Rate Hybrid Neural Network System

    Directory of Open Access Journals (Sweden)

    ASTROV, I.

    2007-04-01

    Full Text Available This paper proposes a two-rate hybrid neural network system, which consists of two artificial neural network subsystems. These neural network subsystems are used as the dynamic subsystems controllers.1 This is because such neuromorphic controllers are especially suitable to control complex systems. An illustrative example - two-rate neural network hybrid control of decomposed stochastic model of a rigid guided missile over different operating conditions - was carried out using the proposed two-rate state-space decomposition technique. This example demonstrates that this research technique results in simplified low-order autonomous control subsystems with various speeds of actuation, and shows the quality of the proposed technique. The obtained results show that the control tasks for the autonomous subsystems can be solved more qualitatively than for the original system. The simulation and animation results with use of software package Simulink demonstrate that this research technique would work for real-time stochastic systems.

  20. Identification of Complex Dynamical Systems with Neural Networks (2/2)

    CERN Multimedia

    CERN. Geneva

    2016-01-01

    The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...

  1. Identification of Complex Dynamical Systems with Neural Networks (1/2)

    CERN Multimedia

    CERN. Geneva

    2016-01-01

    The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...

  2. Adaptive Wavelet Neural Network Backstepping Sliding Mode Tracking Control for PMSM Drive System

    OpenAIRE

    Liu, Da; Li, Muguo

    2015-01-01

    This paper presents a wavelet neural network backstepping sliding mode controller (WNNBSSM) for permanent-magnet synchronous motor (PMSM) position servo control system. Backstepping sliding mode (BSSM) is utilized to guarantee favorable tracking performance and stability of the whole system, meanwhile, wavelet neural network (WNN) is used for approximating nonlinear uncertainties. The designed controller combined the merits of the backstepping sliding mode control with robust characteristics ...

  3. Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning.

    Science.gov (United States)

    Xu, Tao; Xiao, Na; Zhai, Xiaolong; Kwan Chan, Pak; Tin, Chung

    2018-02-01

    Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

  4. Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning

    Science.gov (United States)

    Xu, Tao; Xiao, Na; Zhai, Xiaolong; Chan, Pak Kwan; Tin, Chung

    2018-02-01

    Objective. Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). Approach. The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. Main results. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. Significance. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

  5. Cadherin-6B stimulates an epithelial mesenchymal transition and the delamination of cells from the neural ectoderm via LIMK/cofilin mediated non-canonical BMP receptor signaling

    Science.gov (United States)

    Park, Ki-Sook; Gumbiner, Barry M.

    2012-01-01

    We previously provided evidence that cadherin-6B induces de-epithelialization of the neural crest prior to delamination and is required for the overall epithelial mesenchymal transition (EMT). Furthermore, de-epithelialization induced by cadherin-6B was found to be mediated by BMP receptor signaling independent of BMP. We now find that de-epithelialization is mediated by non-canonical BMP signaling through the BMP type II receptor (BMPRII) and not by canonical Smad dependent signaling through BMP Type I receptor. The LIM kinase/cofilin pathway mediates non-canonical BMPRII induced de-epithelialization, in response to either cadherin-6B or BMP. LIMK1 induces de-epithelialization in the neural tube and dominant negative LIMK1 decreases de-epithelialization induced by either cadherin-6B or BMP. Cofilin is the major known LIMK1 target and a S3A phosphorylation deficient mutated cofilin inhibits de-epithelialization induced by cadherin-6B as well as LIMK1. Importantly, LIMK1 as well as cadherin-6B can trigger ectopic delamination when co-expressed with the competence factor SOX9, showing that this cadherin-6B stimulated signaling pathway can mediate the full EMT in the appropriate context. These findings suggest that the de-epithelialization step of the neural crest EMT by cadherin-6B/BMPRII involves regulation of actin dynamics via LIMK/cofilin. PMID:22537493

  6. Transient stability analysis of electric energy systems via a fuzzy ART-ARTMAP neural network

    Energy Technology Data Exchange (ETDEWEB)

    Ferreira, Wagner Peron; Silveira, Maria do Carmo G.; Lotufo, AnnaDiva P.; Minussi, Carlos. R. [Department of Electrical Engineering, Sao Paulo State University (UNESP), P.O. Box 31, 15385-000, Ilha Solteira, SP (Brazil)

    2006-04-15

    This work presents a methodology to analyze transient stability (first oscillation) of electric energy systems, using a neural network based on ART architecture (adaptive resonance theory), named fuzzy ART-ARTMAP neural network for real time applications. The security margin is used as a stability analysis criterion, considering three-phase short circuit faults with a transmission line outage. The neural network operation consists of two fundamental phases: the training and the analysis. The training phase needs a great quantity of processing for the realization, while the analysis phase is effectuated almost without computation effort. This is, therefore the principal purpose to use neural networks for solving complex problems that need fast solutions, as the applications in real time. The ART neural networks have as primordial characteristics the plasticity and the stability, which are essential qualities to the training execution and to an efficient analysis. The fuzzy ART-ARTMAP neural network is proposed seeking a superior performance, in terms of precision and speed, when compared to conventional ARTMAP, and much more when compared to the neural networks that use the training by backpropagation algorithm, which is a benchmark in neural network area. (author)

  7. NMTPY: A Flexible Toolkit for Advanced Neural Machine Translation Systems

    Directory of Open Access Journals (Sweden)

    Caglayan Ozan

    2017-10-01

    Full Text Available In this paper, we present nmtpy, a flexible Python toolkit based on Theano for training Neural Machine Translation and other neural sequence-to-sequence architectures. nmtpy decouples the specification of a network from the training and inference utilities to simplify the addition of a new architecture and reduce the amount of boilerplate code to be written. nmtpy has been used for LIUM’s top-ranked submissions to WMT Multimodal Machine Translation and News Translation tasks in 2016 and 2017.

  8. Counteracting Muscle Atrophy using Galvanic Stimulation of the Vestibular System

    Science.gov (United States)

    Fox, Robert A.; Polyakov, Igor

    1999-01-01

    The unloading of weight bearing from antigravity muscles during space flight produces significant muscle atrophy and is one of the most serious health problems facing the space program. Various exercise regimens have been developed and used either alone or in combination with pharmacological techniques to ameliorate this atrophy, but no effective countermeasure exists for this problem. The research in this project was conducted to evaluate the potential use of vestibular galvanic stimulation (VGS) to prevent muscle atrophy resulting from unloading of weight bearing from antigravity muscles. This approach was developed based on two concepts related to the process of maintaining the status of the anti-gravity neuromuscular system. These two premises are: (1) The "tone," or bias on spinal motorneurons is affected by vestibular projections that contribute importantly to maintaining muscle health and status. (2) VGS can be used to modify the excitability, or 'tone' of motorneuron of antigravity muscles. Thus, the strategy is to use VGS to modify the gain of vestibular projections to antigravity muscles and thereby change the general status of these muscles.

  9. A Power-Efficient Wireless System With Adaptive Supply Control for Deep Brain Stimulation.

    Science.gov (United States)

    Lee, Hyung-Min; Park, Hangue; Ghovanloo, Maysam

    2013-09-01

    A power-efficient wireless stimulating system for a head-mounted deep brain stimulator (DBS) is presented. A new adaptive rectifier generates a variable DC supply voltage from a constant AC power carrier utilizing phase control feedback, while achieving high AC-DC power conversion efficiency (PCE) through active synchronous switching. A current-controlled stimulator adopts closed-loop supply control to automatically adjust the stimulation compliance voltage by detecting stimulation site potentials through a voltage readout channel, and improve the stimulation efficiency. The stimulator also utilizes closed-loop active charge balancing to maintain the residual charge at each site within a safe limit, while receiving the stimulation parameters wirelessly from the amplitude-shift-keyed power carrier. A 4-ch wireless stimulating system prototype was fabricated in a 0.5-μm 3M2P standard CMOS process, occupying 2.25 mm². With 5 V peak AC input at 2 MHz, the adaptive rectifier provides an adjustable DC output between 2.5 V and 4.6 V at 2.8 mA loading, resulting in measured PCE of 72 ~ 87%. The adaptive supply control increases the stimulation efficiency up to 30% higher than a fixed supply voltage to 58 ~ 68%. The prototype wireless stimulating system was verified in vitro.

  10. Fundamentals of computational intelligence neural networks, fuzzy systems, and evolutionary computation

    CERN Document Server

    Keller, James M; Fogel, David B

    2016-01-01

    This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation. Discusses single-layer and multilayer neural networks, radial-basi function networks, and recurrent neural networks Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzz...

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

    Science.gov (United States)

    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.

  12. Software implementation of artificial neural networks in automated intelligent systems

    Directory of Open Access Journals (Sweden)

    В.П. Харченко

    2009-02-01

    Full Text Available  Application of neural networks technologies effectively decides the task of synthesis of origin of accident risk and gives out the vector of managing signals of network on incomplete and distorted information about the phenomena, events and processes which influence on safety flights.

  13. Credit Risk Evaluation System: An Artificial Neural Network Approach

    African Journals Online (AJOL)

    In this paper, we proposed an architecture which uses the theory of artificial neural networks and business rules to correctly determine whether a customer is good or bad. In the first step, by using clustering algorithm, clients are segmented into groups with similar features. In the second step, decision trees are built based ...

  14. Integrating resource defence theory with a neural nonapeptide pathway to explain territory-based mating systems.

    Science.gov (United States)

    Oldfield, Ronald G; Harris, Rayna M; Hofmann, Hans A

    2015-01-01

    The ultimate-level factors that drive the evolution of mating systems have been well studied, but an evolutionarily conserved neural mechanism involved in shaping behaviour and social organization across species has remained elusive. Here, we review studies that have investigated the role of neural arginine vasopressin (AVP), vasotocin (AVT), and their receptor V1a in mediating variation in territorial behaviour. First, we discuss how aggression and territoriality are a function of population density in an inverted-U relationship according to resource defence theory, and how territoriality influences some mating systems. Next, we find that neural AVP, AVT, and V1a expression, especially in one particular neural circuit involving the lateral septum of the forebrain, are associated with territorial behaviour in males of diverse species, most likely due to their role in enhancing social cognition. Then we review studies that examined multiple species and find that neural AVP, AVT, and V1a expression is associated with territory size in mammals and fishes. Because territoriality plays an important role in shaping mating systems in many species, we present the idea that neural AVP, AVT, and V1a expression that is selected to mediate territory size may also influence the evolution of different mating systems. Future research that interprets proximate-level neuro-molecular mechanisms in the context of ultimate-level ecological theory may provide deep insight into the brain-behaviour relationships that underlie the diversity of social organization and mating systems seen across the animal kingdom.

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

    Science.gov (United States)

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

    1991-01-01

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

  16. An Arbitrary Waveform Wearable Neuro-stimulator System for Neurophysiology Research on Freely Behaving Animals.

    Science.gov (United States)

    Samani, Mohsen Mosayebi; Mahnam, Amin; Hosseini, Nasrin

    2014-04-01

    Portable wireless neuro-stimulators have been developed to facilitate long-term cognitive and behavioral studies on the central nervous system in freely moving animals. These stimulators can provide precisely controllable input(s) to the nervous system, without distracting the animal attention with cables connected to its body. In this study, a low power backpack neuro-stimulator was developed for animal brain researches that can provides arbitrary stimulus waveforms for the stimulation, while it is small and light weight to be used for small animals including rats. The system consists of a controller that uses an RF link to program and activate a small and light microprocessor-based stimulator. A Howland current source was implemented to produce precise current controlled arbitrary waveform stimulations. The system was optimized for ultra-low power consumption and small size. The stimulator was first tested for its electrical specifications. Then its performance was evaluated in a rat experiment when electrical stimulation of medial longitudinal fasciculus induced circling behavior. The stimulator is capable of delivering programmed stimulations up to ± 2 mA with adjusting steps of 1 μA, accuracy of 0.7% and compliance of 6 V. The stimulator is 15 mm × 20 mm × 40 mm in size, weights 13.5 g without battery and consumes a total power of only 5.l mW. In the experiment, the rat could easily carry the stimulator and demonstrated the circling behavior for 0.1 ms current pulses of above 400 μA. The developed system has a competitive size and weight, whereas providing a wide range of operation and the flexibility of generating arbitrary stimulation patterns ideal for long-term experiments in the field of cognitive and neuroscience research.

  17. Neural mechanisms underlying stop-and-restart difficulties: involvement of the motor and perceptual systems.

    Directory of Open Access Journals (Sweden)

    Kentaro Yamanaka

    Full Text Available The ability to suddenly stop a planned movement or a movement being performed and restart it after a short interval is an important mechanism that allows appropriate behavior in response to contextual or environmental changes. However, performing such stop-and-restart movements smoothly is difficult at times. We investigated performance (response time of stop-and-restart movements using a go/stop/re-go task and found consistent stop-and-restart difficulties after short (~100 ms stop-to-restart intervals (SRSI, and an increased probability of difficulties after longer (>200 ms SRSIs, suggesting that two different mechanisms underlie stop-and-restart difficulties. Next, we investigated motor evoked potentials (MEPs in a moving muscle induced by transcranial magnetic stimulation during a go/stop/re-go task. In re-go trials with a short SRSI (100 ms, the MEP amplitude continued to decrease after the re-go-signal onset, indicating that stop-and-restart difficulties with short SRSIs might be associated with a neural mechanism in the human motor system, namely, stop-related suppression of corticomotor (CM excitability. Finally, we recorded electroencephalogram (EEG activity during a go/stop/re-go task and performed a single-trial-based EEG power and phase time-frequency analysis. Alpha-band EEG phase locking to re-go-signal, which was only observed in re-go trials with long SRSI (250 ms, weakened in the delayed re-go response trials. These EEG phase dynamics indicate an association between stop-and-restart difficulties with long SRSIs and a neural mechanism in the human perception system, namely, decreased probability of EEG phase locking to visual stimuli. In contrast, smooth stop-and-restart human movement can be achieved in re-go trials with sufficient SRSI (150-200 ms, because release of stop-related suppression and simultaneous counter-activation of CM excitability may occur as a single task without second re-go-signal perception. These results

  18. Neural mechanisms underlying stop-and-restart difficulties: involvement of the motor and perceptual systems.

    Science.gov (United States)

    Yamanaka, Kentaro; Nozaki, Daichi

    2013-01-01

    The ability to suddenly stop a planned movement or a movement being performed and restart it after a short interval is an important mechanism that allows appropriate behavior in response to contextual or environmental changes. However, performing such stop-and-restart movements smoothly is difficult at times. We investigated performance (response time) of stop-and-restart movements using a go/stop/re-go task and found consistent stop-and-restart difficulties after short (~100 ms) stop-to-restart intervals (SRSI), and an increased probability of difficulties after longer (>200 ms) SRSIs, suggesting that two different mechanisms underlie stop-and-restart difficulties. Next, we investigated motor evoked potentials (MEPs) in a moving muscle induced by transcranial magnetic stimulation during a go/stop/re-go task. In re-go trials with a short SRSI (100 ms), the MEP amplitude continued to decrease after the re-go-signal onset, indicating that stop-and-restart difficulties with short SRSIs might be associated with a neural mechanism in the human motor system, namely, stop-related suppression of corticomotor (CM) excitability. Finally, we recorded electroencephalogram (EEG) activity during a go/stop/re-go task and performed a single-trial-based EEG power and phase time-frequency analysis. Alpha-band EEG phase locking to re-go-signal, which was only observed in re-go trials with long SRSI (250 ms), weakened in the delayed re-go response trials. These EEG phase dynamics indicate an association between stop-and-restart difficulties with long SRSIs and a neural mechanism in the human perception system, namely, decreased probability of EEG phase locking to visual stimuli. In contrast, smooth stop-and-restart human movement can be achieved in re-go trials with sufficient SRSI (150-200 ms), because release of stop-related suppression and simultaneous counter-activation of CM excitability may occur as a single task without second re-go-signal perception. These results suggest that

  19. Design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization

    CERN Document Server

    Castillo, Oscar; Kacprzyk, Janusz

    2015-01-01

    This book presents recent advances on the design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization and their application in areas such as, intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. The book is organized in eight main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theoretical aspects of fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on fuzzy systems. The second part contains papers with the main theme of neural networks theory, which are basically papers dealing with new concepts and algorithms in neural networks. The third part contains papers describing applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The fourth part contains papers describing new nature-inspired optimization algorithms. The fifth part presents div...

  20. Developing and using expert systems and neural networks in medicine: a review on benefits and challenges.

    Science.gov (United States)

    Sheikhtaheri, Abbas; Sadoughi, Farahnaz; Hashemi Dehaghi, Zahra

    2014-09-01

    Complicacy of clinical decisions justifies utilization of information systems such as artificial intelligence (e.g. expert systems and neural networks) to achieve better decisions, however, application of these systems in the medical domain faces some challenges. We aimed at to review the applications of these systems in the medical domain and discuss about such challenges. Following a brief introduction of expert systems and neural networks by representing few examples, the challenges of these systems in the medical domain are discussed. We found that the applications of expert systems and artificial neural networks have been increased in the medical domain. These systems have shown many advantages such as utilization of experts' knowledge, gaining rare knowledge, more time for assessment of the decision, more consistent decisions, and shorter decision-making process. In spite of all these advantages, there are challenges ahead of developing and using such systems including maintenance, required experts, inputting patients' data into the system, problems for knowledge acquisition, problems in modeling medical knowledge, evaluation and validation of system performance, wrong recommendations and responsibility, limited domains of such systems and necessity of integrating such systems into the routine work flows. We concluded that expert systems and neural networks can be successfully used in medicine; however, there are many concerns and questions to be answered through future studies and discussions.

  1. Case Study of Ecstatic Meditation: fMRI and EEG Evidence of Self-Stimulating a Reward System

    Directory of Open Access Journals (Sweden)

    Michael R. Hagerty

    2013-01-01

    Full Text Available We report the first neural recording during ecstatic meditations called jhanas and test whether a brain reward system plays a role in the joy reported. Jhanas are Altered States of Consciousness (ASC that imply major brain changes based on subjective reports: (1 external awareness dims, (2 internal verbalizations fade, (3 the sense of personal boundaries is altered, (4 attention is highly focused on the object of meditation, and (5 joy increases to high levels. The fMRI and EEG results from an experienced meditator show changes in brain activity in 11 regions shown to be associated with the subjective reports, and these changes occur promptly after jhana is entered. In particular, the extreme joy is associated not only with activation of cortical processes but also with activation of the nucleus accumbens (NAc in the dopamine/opioid reward system. We test three mechanisms by which the subject might stimulate his own reward system by external means and reject all three. Taken together, these results demonstrate an apparently novel method of self-stimulating a brain reward system using only internal mental processes in a highly trained subject.

  2. Soft computing integrating evolutionary, neural, and fuzzy systems

    CERN Document Server

    Tettamanzi, Andrea

    2001-01-01

    Soft computing encompasses various computational methodologies, which, unlike conventional algorithms, are tolerant of imprecision, uncertainty, and partial truth. Soft computing technologies offer adaptability as a characteristic feature and thus permit the tracking of a problem through a changing environment. Besides some recent developments in areas like rough sets and probabilistic networks, fuzzy logic, evolutionary algorithms, and artificial neural networks are core ingredients of soft computing, which are all bio-inspired and can easily be combined synergetically. This book presents a well-balanced integration of fuzzy logic, evolutionary computing, and neural information processing. The three constituents are introduced to the reader systematically and brought together in differentiated combinations step by step. The text was developed from courses given by the authors and offers numerous illustrations as

  3. Psychological Processing in Chronic Pain: A Neural Systems Approach

    Science.gov (United States)

    Simons, Laura; Elman, Igor; Borsook, David

    2014-01-01

    Our understanding of chronic pain involves complex brain circuits that include sensory, emotional, cognitive and interoceptive processing. The feed-forward interactions between physical (e.g., trauma) and emotional pain and the consequences of altered psychological status on the expression of pain have made the evaluation and treatment of chronic pain a challenge in the clinic. By understanding the neural circuits involved in psychological processes, a mechanistic approach to the implementation of psychology-based treatments may be better understood. In this review we evaluate some of the principle processes that may be altered as a consequence of chronic pain in the context of localized and integrated neural networks. These changes are ongoing, vary in their magnitude, and their hierarchical manifestations, and may be temporally and sequentially altered by treatments, and all contribute to an overall pain phenotype. Furthermore, we link altered psychological processes to specific evidence-based treatments to put forth a model of pain neuroscience psychology. PMID:24374383

  4. A low-cost multielectrode system for data acquisition enabling real-time closed-loop processing with rapid recovery from stimulation artifacts

    Directory of Open Access Journals (Sweden)

    John D Rolston

    2009-07-01

    Full Text Available Commercially available data acquisition systems for multielectrode recording from freely moving animals are expensive, often rely on proprietary software, and do not provide detailed, modifiable circuit schematics. When used in conjunction with electrical stimulation, they are prone to prolonged, saturating stimulation artifacts that prevent the recording of short-latency evoked responses. Yet electrical stimulation is integral to many experimental designs, and critical for emerging brain-computer interfacing and neuroprosthetic applications. To address these issues, we developed an easy-to-use, modifiable, and inexpensive system for multielectrode neural recording and stimulation. Setup costs are less than US$10,000 for 64 channels, an order of magnitude lower than comparable commercial systems. Unlike commercial equipment, the system recovers rapidly from stimulation and allows short-latency action potentials (<1 ms post-stimulus to be detected, facilitating closed-loop applications and exposing neural activity that would otherwise remain hidden. To illustrate this capability, evoked activity from microstimulation of the rodent hippocampus is presented. The system is modular, in banks of 16 channels, and flexible in usage: while primarily designed for in vivo use, it can be combined with commercial preamplifiers to record from in vitro multielectrode arrays. The system’s open-source control software, NeuroRighter, is implemented in C#, with an easy-to-use graphical interface. As C# functions in a managed code environment, which may impact performance, analysis was conducted to ensure comparable speed to C++ for this application. Hardware schematics, layout files, and software are freely available. Since maintaining wired headstage connections with freely moving animals is difficult, we describe a new method of electrode-headstage coupling using neodymium magnets.

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

    Science.gov (United States)

    Peng, Jinzhu; Dubay, Rickey

    2011-10-01

    In this paper, an adaptive control approach based on the neural networks is presented to control a DC motor system with dead-zone characteristics (DZC), where two neural networks are proposed to formulate the traditional identification and control approaches. First, a Wiener-type neural network (WNN) is proposed to identify the motor DZC, which formulates the Wiener model with a linear dynamic block in cascade with a nonlinear static gain. Second, a feedforward neural network is proposed to formulate the traditional PID controller, termed as PID-type neural network (PIDNN), which is then used to control and compensate for the DZC. In this way, the DC motor system with DZC is identified by the WNN identifier, which provides model information to the PIDNN controller in order to make it adaptive. Back-propagation algorithms are used to train both neural networks. Also, stability and convergence analysis are conducted using the Lyapunov theorem. Finally, experiments on the DC motor system demonstrated accurate identification and good compensation for dead-zone with improved control performance over the conventional PID control. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

  6. Study on the idity fuzzy neural network controller based on improved genetic algorithm of intelligent temperature control system in vegetable greenhouse

    Science.gov (United States)

    Zhang, Su; Yuan, Hongbo; Zhou, Yuhong; Wang, Nan

    2009-07-01

    In order to create the environment that the suitable crop grows, direct against the characteristic of the system of the greenhouse. The aim of the research was to study the intelligent temperature control system in vegetable greenhouse. Based on computer automatic control ,a kind of intelligent temperature control system in vegetable greenhouse was designed. The design thought of systematic hardwares such as temperature collection system, temperature display, control system, heater control circuit in the heater were expounded in detail The control algorithm of the system was improved and system simulation was made by using MATLAB finally. The control algorithm of the system was improved by a new fuzzy neural network controller. The stimulation curve showed that the system had better controlling and tracking performances ,higher accuracy of controlling the temperature. And this system and host epigyny computer could constitute the secondary computer control system which was favorable for realizing the centralized management of the production.

  7. Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems

    Science.gov (United States)

    Broccard, Frédéric D.; Joshi, Siddharth; Wang, Jun; Cauwenberghs, Gert

    2017-08-01

    Objective. Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks. Mixed analog/digital neuromorphic VLSI systems are compact, consume little power and operate in real time independently of the size and complexity of the model. Approach. This article highlights the current efforts to interface neuromorphic systems with neural systems at multiple levels of biological organization, from the synaptic to the system level, and discusses the prospects for future biohybrid systems with neuromorphic circuits of greater complexity. Main results. Single silicon neurons have been interfaced successfully with invertebrate and vertebrate neural networks. This approach allowed the investigation of neural properties that are inaccessible with traditional techniques while providing a realistic biological context not achievable with traditional numerical modeling methods. At the network level, populations of neurons are envisioned to communicate bidirectionally with neuromorphic processors of hundreds or thousands of silicon neurons. Recent work on brain-machine interfaces suggests that this is feasible with current neuromorphic technology. Significance. Biohybrid interfaces between biological neurons and VLSI neuromorphic systems of varying complexity have started to emerge in the literature. Primarily intended as a

  8. Dual small-molecule targeting of SMAD signaling stimulates human induced pluripotent stem cells toward neural lineages.

    Directory of Open Access Journals (Sweden)

    Methichit Wattanapanitch

    Full Text Available Incurable neurological disorders such as Parkinson's disease (PD, Huntington's disease (HD, and Alzheimer's disease (AD are very common and can be life-threatening because of their progressive disease symptoms with limited treatment options. To provide an alternative renewable cell source for cell-based transplantation and as study models for neurological diseases, we generated induced pluripotent stem cells (iPSCs from human dermal fibroblasts (HDFs and then differentiated them into neural progenitor cells (NPCs and mature neurons by dual SMAD signaling inhibitors. Reprogramming efficiency was improved by supplementing the histone deacethylase inhibitor, valproic acid (VPA, and inhibitor of p160-Rho associated coiled-coil kinase (ROCK, Y-27632, after retroviral transduction. We obtained a number of iPS colonies that shared similar characteristics with human embryonic stem cells in terms of their morphology, cell surface antigens, pluripotency-associated gene and protein expressions as well as their in vitro and in vivo differentiation potentials. After treatment with Noggin and SB431542, inhibitors of the SMAD signaling pathway, HDF-iPSCs demonstrated rapid and efficient differentiation into neural lineages. Six days after neural induction, neuroepithelial cells (NEPCs were observed in the adherent monolayer culture, which had the ability to differentiate further into NPCs and neurons, as characterized by their morphology and the expression of neuron-specific transcripts and proteins. We propose that our study may be applied to generate neurological disease patient-specific iPSCs allowing better understanding of disease pathogenesis and drug sensitivity assays.

  9. A Novel In Vitro System for Comparative Analyses of Bone Cells and Bacteria under Electrical Stimulation

    Directory of Open Access Journals (Sweden)

    Thomas Josef Dauben

    2016-01-01

    Full Text Available Electrical stimulation is a promising approach to enhance bone regeneration while having potential to inhibit bacterial growth. To investigate effects of alternating electric field stimulation on both human osteoblasts and bacteria, a novel in vitro system was designed. Electric field distribution was simulated numerically and proved by experimental validation. Cells were stimulated on Ti6Al4V electrodes and in short distance to electrodes. Bacterial growth was enumerated in supernatant and on the electrode surface and biofilm formation was quantified. Electrical stimulation modulated gene expression of osteoblastic differentiation markers in a voltage-dependent manner, resulting in significantly enhanced osteocalcin mRNA synthesis rate on electrodes after stimulation with 1.4VRMS. While collagen type I synthesis increased when stimulated with 0.2VRMS, it decreased after stimulation with 1.4VRMS. Only slight and infrequent influence on bacterial growth was observed following stimulations with 0.2VRMS and 1.4VRMS after 48 and 72 h, respectively. In summary this novel test system is applicable for extended in vitro studies concerning definition of appropriate stimulation parameters for bone cell growth and differentiation, bacterial growth suppression, and investigation of general effects of electrical stimulation.

  10. A Novel In Vitro System for Comparative Analyses of Bone Cells and Bacteria under Electrical Stimulation

    Science.gov (United States)

    Zaatreh, Sarah; Kreikemeyer, Bernd

    2016-01-01

    Electrical stimulation is a promising approach to enhance bone regeneration while having potential to inhibit bacterial growth. To investigate effects of alternating electric field stimulation on both human osteoblasts and bacteria, a novel in vitro system was designed. Electric field distribution was simulated numerically and proved by experimental validation. Cells were stimulated on Ti6Al4V electrodes and in short distance to electrodes. Bacterial growth was enumerated in supernatant and on the electrode surface and biofilm formation was quantified. Electrical stimulation modulated gene expression of osteoblastic differentiation markers in a voltage-dependent manner, resulting in significantly enhanced osteocalcin mRNA synthesis rate on electrodes after stimulation with 1.4V RMS. While collagen type I synthesis increased when stimulated with 0.2V RMS, it decreased after stimulation with 1.4V RMS. Only slight and infrequent influence on bacterial growth was observed following stimulations with 0.2V RMS and 1.4V RMS after 48 and 72 h, respectively. In summary this novel test system is applicable for extended in vitro studies concerning definition of appropriate stimulation parameters for bone cell growth and differentiation, bacterial growth suppression, and investigation of general effects of electrical stimulation. PMID:28044132

  11. Modulation of the Endocannabinoid System: Vulnerability Factor and New Treatment Target for Stimulant Addiction

    OpenAIRE

    Oli?re, St?phanie; Jolette-Riopel, Antoine; Potvin, St?phane; Jutras-Aswad, Didier

    2013-01-01

    Cannabis is one of the most widely used illicit substance among users of stimulants such as cocaine and amphetamine. Interestingly, recent accumulating evidence points toward the involvement of the endocannabinoid system (ECBS) in the neurobiological processes related to stimulant addiction. This article presents an up-to-date review with deep-insights into the pivotal role of the ECBS in the neurobiology of stimulant addiction and the effects of its modulation on addictive behaviors. The aim...

  12. A Comparative Study of Neural Networks and Fuzzy Systems in Modeling of a Nonlinear Dynamic System

    Directory of Open Access Journals (Sweden)

    Metin Demirtas

    2011-07-01

    Full Text Available The aim of this paper is to compare the neural networks and fuzzy modeling approaches on a nonlinear system. We have taken Permanent Magnet Brushless Direct Current (PMBDC motor data and have generated models using both approaches. The predictive performance of both methods was compared on the data set for model configurations. The paper describes the results of these tests and discusses the effects of changing model parameters on predictive and practical performance. Modeling sensitivity was used to compare for two methods.

  13. Fault detection and classification in electrical power transmission system using artificial neural network.

    Science.gov (United States)

    Jamil, Majid; Sharma, Sanjeev Kumar; Singh, Rajveer

    2015-01-01

    This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB(®) environment.

  14. Nonoptimal component placement, but short processing paths, due to long-distance projections in neural systems.

    Directory of Open Access Journals (Sweden)

    Marcus Kaiser

    2006-07-01

    Full Text Available It has been suggested that neural systems across several scales of organization show optimal component placement, in which any spatial rearrangement of the components would lead to an increase of total wiring. Using extensive connectivity datasets for diverse neural networks combined with spatial coordinates for network nodes, we applied an optimization algorithm to the network layouts, in order to search for wire-saving component rearrangements. We found that optimized component rearrangements could substantially reduce total wiring length in all tested neural networks. Specifically, total wiring among 95 primate (Macaque cortical areas could be decreased by 32%, and wiring of neuronal networks in the nematode Caenorhabditis elegans could be reduced by 48% on the global level, and by 49% for neurons within frontal ganglia. Wiring length reductions were possible due to the existence of long-distance projections in neural networks. We explored the role of these projections by comparing the original networks with minimally rewired networks of the same size, which possessed only the shortest possible connections. In the minimally rewired networks, the number of processing steps along the shortest paths between components was significantly increased compared to the original networks. Additional benchmark comparisons also indicated that neural networks are more similar to network layouts that minimize the length of processing paths, rather than wiring length. These findings suggest that neural systems are not exclusively optimized for minimal global wiring, but for a variety of factors including the minimization of processing steps.

  15. Adaptive Control of Nonlinear Discrete-Time Systems by Using OS-ELM Neural Networks

    Directory of Open Access Journals (Sweden)

    Xiao-Li Li

    2014-01-01

    Full Text Available As a kind of novel feedforward neural network with single hidden layer, ELM (extreme learning machine neural networks are studied for the identification and control of nonlinear dynamic systems. The property of simple structure and fast convergence of ELM can be shown clearly. In this paper, we are interested in adaptive control of nonlinear dynamic plants by using OS-ELM (online sequential extreme learning machine neural networks. Based on data scope division, the problem that training process of ELM neural network is sensitive to the initial training data is also solved. According to the output range of the controlled plant, the data corresponding to this range will be used to initialize ELM. Furthermore, due to the drawback of conventional adaptive control, when the OS-ELM neural network is used for adaptive control of the system with jumping parameters, the topological structure of the neural network can be adjusted dynamically by using multiple model switching strategy, and an MMAC (multiple model adaptive control will be used to improve the control performance. Simulation results are included to complement the theoretical results.

  16. A Unique Electrical Thermal Stimulation System Comparable to Moxibustion of Subcutaneous Tissue

    Directory of Open Access Journals (Sweden)

    Hyoun-Seok Myoung

    2014-01-01

    Full Text Available Moxibustion strengthens immunity and it is an effective treatment modality, but, depending on the material quantity, shape, and composition, the thermal strength and intensity can be difficult to control, which may cause pain or epidermal burns. To overcome these limitations, a heat stimulating system which is able to control the thermal intensity was developed. The temperature distributions on epidermis, at 5 mm and 10 mm of depth, in rabbit femoral tissue were compared between moxibustion and the electric thermal stimulation system. The stimulation system consists of a high radio frequency dielectric heating equipment (2 MHz frequency, maximum power 200 W, isolation probe, isolation plate, negative pressure generator, and a temperature assessment system. The temperature was modulated by controlling the stimulation pulse duty ratio, repetition number, and output. There were 95% and 91% temperature distribution correlations between moxibustion and the thermal stimulus at 5 mm and 10 mm of depth in tissue, respectively. Moreover, the epidermal temperature in thermal stimulation was lower than that in moxibustion. These results showed that heat loss by the electric thermal stimulation system is less than that by the traditional moxibustion method. Furthermore, the proposed electric thermal stimulation did not cause adverse effects, such as suppuration or blisters, and also provided subcutaneous stimulation comparable to moxibustion.

  17. A Modular Neural Network Scheme Applied to Fault Diagnosis in Electric Power Systems

    Science.gov (United States)

    Flores, Agustín; Morant, Francisco

    2014-01-01

    This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system. PMID:25610897

  18. A Modular Neural Network Scheme Applied to Fault Diagnosis in Electric Power Systems

    Directory of Open Access Journals (Sweden)

    Agustín Flores

    2014-01-01

    Full Text Available This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.

  19. Fuzzy wavelet plus a quantum neural network as a design base for power system stability enhancement.

    Science.gov (United States)

    Ganjefar, Soheil; Tofighi, Morteza; Karami, Hamidreza

    2015-11-01

    In this study, we introduce an indirect adaptive fuzzy wavelet neural controller (IAFWNC) as a power system stabilizer to damp inter-area modes of oscillations in a multi-machine power system. Quantum computing is an efficient method for improving the computational efficiency of neural networks, so we developed an identifier based on a quantum neural network (QNN) to train the IAFWNC in the proposed scheme. All of the controller parameters are tuned online based on the Lyapunov stability theory to guarantee the closed-loop stability. A two-machine, two-area power system equipped with a static synchronous series compensator as a series flexible ac transmission system was used to demonstrate the effectiveness of the proposed controller. The simulation and experimental results demonstrated that the proposed IAFWNC scheme can achieve favorable control performance. Copyright © 2015 Elsevier Ltd. All rights reserved.

  20. Astrocyte glycogen metabolism is required for neural activity during aglycemia or intense stimulation in mouse white matter

    DEFF Research Database (Denmark)

    Brown, Angus M; Sickmann, Helle M; Fosgerau, Keld

    2005-01-01

    We tested the hypothesis that inhibiting glycogen degradation accelerates compound action potential (CAP) failure in mouse optic nerve (MON) during aglycemia or high-intensity stimulation. Axon function was assessed as the evoked CAP, and glycogen content was measured biochemically. Isofagomine, ...

  1. Child Maltreatment and Neural Systems Underlying Emotion Regulation.

    Science.gov (United States)

    McLaughlin, Katie A; Peverill, Matthew; Gold, Andrea L; Alves, Sonia; Sheridan, Margaret A

    2015-09-01

    The strong associations between child maltreatment and psychopathology have generated interest in identifying neurodevelopmental processes that are disrupted following maltreatment. Previous research has focused largely on neural response to negative facial emotion. We determined whether child maltreatment was associated with neural responses during passive viewing of negative and positive emotional stimuli and effortful attempts to regulate emotional responses. A total of 42 adolescents aged 13 to 19 years, half with exposure to physical and/or sexual abuse, participated. Blood oxygen level-dependent (BOLD) response was measured during passive viewing of negative and positive emotional stimuli and attempts to modulate emotional responses using cognitive reappraisal. Maltreated adolescents exhibited heightened response in multiple nodes of the salience network, including amygdala, putamen, and anterior insula, to negative relative to neutral stimuli. During attempts to decrease responses to negative stimuli relative to passive viewing, maltreatment was associated with greater recruitment of superior frontal gyrus, dorsal anterior cingulate cortex, and frontal pole; adolescents with and without maltreatment down-regulated amygdala response to a similar degree. No associations were observed between maltreatment and neural response to positive emotional stimuli during passive viewing or effortful regulation. Child maltreatment heightens the salience of negative emotional stimuli. Although maltreated adolescents modulate amygdala responses to negative cues to a degree similar to that of non-maltreated youths, they use regions involved in effortful control to a greater degree to do so, potentially because greater effort is required to modulate heightened amygdala responses. These findings are promising, given the centrality of cognitive restructuring in trauma-focused treatments for children. Copyright © 2015 American Academy of Child and Adolescent Psychiatry

  2. Neural Systems Underlying Individual Differences in Intertemporal Decision-making.

    Science.gov (United States)

    Elton, Amanda; Smith, Christopher T; Parrish, Michael H; Boettiger, Charlotte A

    2017-03-01

    Excessively choosing immediate over larger future rewards, or delay discounting (DD), associates with multiple clinical conditions. Individual differences in DD likely depend on variations in the activation of and functional interactions between networks, representing possible endophenotypes for associated disorders, including alcohol use disorders (AUDs). Numerous fMRI studies have probed the neural bases of DD, but investigations of large-scale networks remain scant. We addressed this gap by testing whether activation within large-scale networks during Now/Later decision-making predicts individual differences in DD. To do so, we scanned 95 social drinkers (18-40 years old; 50 women) using fMRI during hypothetical choices between small monetary amounts available "today" or larger amounts available later. We identified neural networks engaged during Now/Later choice using independent component analysis and tested the relationship between component activation and degree of DD. The activity of two components during Now/Later choice correlated with individual DD rates: A temporal lobe network positively correlated with DD, whereas a frontoparietal-striatal network negatively correlated with DD. Activation differences between these networks predicted individual differences in DD, and their negative correlation during Now/Later choice suggests functional competition. A generalized psychophysiological interactions analysis confirmed a decrease in their functional connectivity during decision-making. The functional connectivity of these two networks negatively correlates with alcohol-related harm, potentially implicating these networks in AUDs. These findings provide novel insight into the neural underpinnings of individual differences in impulsive decision-making with potential implications for addiction and related disorders in which impulsivity is a defining feature.

  3. Study of the neural dynamics for understanding communication in terms of complex hetero systems.

    Science.gov (United States)

    Tsuda, Ichiro; Yamaguchi, Yoko; Hashimoto, Takashi; Okuda, Jiro; Kawasaki, Masahiro; Nagasaka, Yasuo

    2015-01-01

    The purpose of the research project was to establish a new research area named "neural information science for communication" by elucidating its neural mechanism. The research was performed in collaboration with applied mathematicians in complex-systems science and experimental researchers in neuroscience. The project included measurements of brain activity during communication with or without languages and analyses performed with the help of extended theories for dynamical systems and stochastic systems. The communication paradigm was extended to the interactions between human and human, human and animal, human and robot, human and materials, and even animal and animal. Copyright © 2014 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

  4. Sign Language Recognition System using Neural Network for Digital Hardware Implementation

    Energy Technology Data Exchange (ETDEWEB)

    Vargas, Lorena P [Lorena Vargas Quintero, Optic and Computer Science Group - Universidad Popular del Cesar (Colombia); Barba, Leiner; Torres, C O; Mattos, L, E-mail: vargas.lorena@yahoo.com [Optic and Computer Science Group - Popular of Cesar University, Km 12, Valledupar (Colombia)

    2011-01-01

    This work presents an image pattern recognition system using neural network for the identification of sign language to deaf people. The system has several stored image that show the specific symbol in this kind of language, which is employed to teach a multilayer neural network using a back propagation algorithm. Initially, the images are processed to adapt them and to improve the performance of discriminating of the network, including in this process of filtering, reduction and elimination noise algorithms as well as edge detection. The system is evaluated using the signs without including movement in their representation.

  5. Tissue engineering bioreactor systems for applying physical and electrical stimulations to cells.

    Science.gov (United States)

    Jin, GyuHyun; Yang, Gi-Hoon; Kim, GeunHyung

    2015-05-01

    Bioreactor systems in tissue engineering applications provide various types of stimulation to mimic the tissues in vitro and in vivo. Various bioreactors have been designed to induce high cellular activities, including initial cell attachment, cell growth, and differentiation. Although cell-stimulation processes exert mostly positive effects on cellular responses, in some cases such stimulation can also have a negative effect on cultured cells. In this review, we discuss various types of bioreactor and the positive and negative effects of stimulation (physical, chemical, and electrical) on various cultured cell types. © 2014 Wiley Periodicals, Inc.

  6. Teaching artificial neural systems to drive: Manual training techniques for autonomous systems

    Science.gov (United States)

    Shepanski, J. F.; Macy, S. A.

    1987-01-01

    A methodology was developed for manually training autonomous control systems based on artificial neural systems (ANS). In applications where the rule set governing an expert's decisions is difficult to formulate, ANS can be used to extract rules by associating the information an expert receives with the actions taken. Properly constructed networks imitate rules of behavior that permits them to function autonomously when they are trained on the spanning set of possible situations. This training can be provided manually, either under the direct supervision of a system trainer, or indirectly using a background mode where the networks assimilates training data as the expert performs its day-to-day tasks. To demonstrate these methods, an ANS network was trained to drive a vehicle through simulated freeway traffic.

  7. Feasibility of Using Neural Network Models to Accelerate the Testing of Mechanical Systems

    Science.gov (United States)

    Fusaro, Robert L.

    1998-01-01

    Verification testing is an important aspect of the design process for mechanical mechanisms, and full-scale, full-length life testing is typically used to qualify any new component for use in space. However, as the required life specification is increased, full-length life tests become more costly and lengthen the development time. At the NASA Lewis Research Center, we theorized that neural network systems may be able to model the operation of a mechanical device. If so, the resulting neural network models could simulate long-term mechanical testing with data from a short-term test. This combination of computer modeling and short-term mechanical testing could then be used to verify the reliability of mechanical systems, thereby eliminating the costs associated with long-term testing. Neural network models could also enable designers to predict the performance of mechanisms at the conceptual design stage by entering the critical parameters as input and running the model to predict performance. The purpose of this study was to assess the potential of using neural networks to predict the performance and life of mechanical systems. To do this, we generated a neural network system to model wear obtained from three accelerated testing devices: 1) A pin-on-disk tribometer; 2) A line-contact rub-shoe tribometer; 3) A four-ball tribometer.

  8. Electronically switchable sham transcranial magnetic stimulation (TMS system.

    Directory of Open Access Journals (Sweden)

    Fumiko Hoeft

    Full Text Available Transcranial magnetic stimulation (TMS is increasingly being used to demonstrate the causal links between brain and behavior in humans. Further, extensive clinical trials are being conducted to investigate the therapeutic role of TMS in disorders such as depression. Because TMS causes strong peripheral effects such as auditory clicks and muscle twitches, experimental artifacts such as subject bias and placebo effect are clear concerns. Several sham TMS methods have been developed, but none of the techniques allows one to intermix real and sham TMS on a trial-by-trial basis in a double-blind manner. We have developed an attachment that allows fast, automated switching between Standard TMS and two types of control TMS (Sham and Reverse without movement of the coil or reconfiguration of the setup. We validate the setup by performing mathematical modeling, search-coil and physiological measurements. To see if the stimulus conditions can be blinded, we conduct perceptual discrimination and sensory perception studies. We verify that the physical properties of the stimulus are appropriate, and that successive stimuli do not contaminate each other. We find that the threshold for motor activation is significantly higher for Reversed than for Standard stimulation, and that Sham stimulation entirely fails to activate muscle potentials. Subjects and experimenters perform poorly at discriminating between Sham and Standard TMS with a figure-of-eight coil, and between Reverse and Standard TMS with a circular coil. Our results raise the possibility of utilizing this technique for a wide range of applications.

  9. Distributed neural system for emotional intelligence revealed by lesion mapping.

    Science.gov (United States)

    Barbey, Aron K; Colom, Roberto; Grafman, Jordan

    2014-03-01

    Cognitive neuroscience has made considerable progress in understanding the neural architecture of human intelligence, identifying a broadly distributed network of frontal and parietal regions that support goal-directed, intelligent behavior. However, the contributions of this network to social and emotional aspects of intellectual function remain to be well characterized. Here we investigated the neural basis of emotional intelligence in 152 patients with focal brain injuries using voxel-based lesion-symptom mapping. Latent variable modeling was applied to obtain measures of emotional intelligence, general intelligence and personality from the Mayer, Salovey, Caruso Emotional Intelligence Test (MSCEIT), the Wechsler Adult Intelligence Scale and the Neuroticism-Extroversion-Openness Inventory, respectively. Regression analyses revealed that latent scores for measures of general intelligence and personality reliably predicted latent scores for emotional intelligence. Lesion mapping results further indicated that these convergent processes depend on a shared network of frontal, temporal and parietal brain regions. The results support an integrative framework for understanding the architecture of executive, social and emotional processes and make specific recommendations for the interpretation and application of the MSCEIT to the study of emotional intelligence in health and disease.

  10. Distributed neural system for emotional intelligence revealed by lesion mapping

    Science.gov (United States)

    Colom, Roberto; Grafman, Jordan

    2014-01-01

    Cognitive neuroscience has made considerable progress in understanding the neural architecture of human intelligence, identifying a broadly distributed network of frontal and parietal regions that support goal-directed, intelligent behavior. However, the contributions of this network to social and emotional aspects of intellectual function remain to be well characterized. Here we investigated the neural basis of emotional intelligence in 152 patients with focal brain injuries using voxel-based lesion-symptom mapping. Latent variable modeling was applied to obtain measures of emotional intelligence, general intelligence and personality from the Mayer, Salovey, Caruso Emotional Intelligence Test (MSCEIT), the Wechsler Adult Intelligence Scale and the Neuroticism-Extroversion-Openness Inventory, respectively. Regression analyses revealed that latent scores for measures of general intelligence and personality reliably predicted latent scores for emotional intelligence. Lesion mapping results further indicated that these convergent processes depend on a shared network of frontal, temporal and parietal brain regions. The results support an integrative framework for understanding the architecture of executive, social and emotional processes and make specific recommendations for the interpretation and application of the MSCEIT to the study of emotional intelligence in health and disease. PMID:23171618

  11. WeAidU-a decision support system for myocardial perfusion images using artificial neural networks.

    Science.gov (United States)

    Ohlsson, Mattias

    2004-01-01

    This paper presents a computer-based decision support system for automated interpretation of diagnostic heart images (called WeAidU), which is made available via the Internet. The system is based on image processing techniques, artificial neural networks (ANNs) and large well-validated medical databases. We present results using artificial neural networks, and compare with two other classification methods, on a retrospective data set containing 1320 images from the clinical routine. The performance of the artificial neural networks detecting infarction and ischemia in different parts of the heart, measured as areas under the receiver operating characteristic curves, is in the range 0.83-0.96. These results indicate a high potential for the tool as a clinical decision support system.

  12. Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition

    CERN Document Server

    Melin, Patricia

    2012-01-01

    This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural ne...

  13. On-line identification of hybrid systems using an adaptive growing and pruning RBF neural network

    DEFF Research Database (Denmark)

    Alizadeh, Tohid

    2008-01-01

    This paper introduces an adaptive growing and pruning radial basis function (GAP-RBF) neural network for on-line identification of hybrid systems. The main idea is to identify a global nonlinear model that can predict the continuous outputs of hybrid systems. In the proposed approach, GAP-RBF neu...

  14. Absolute stability of nonlinear systems with time delays and applications to neural networks

    Directory of Open Access Journals (Sweden)

    Xinzhi Liu

    2001-01-01

    Full Text Available In this paper, absolute stability of nonlinear systems with time delays is investigated. Sufficient conditions on absolute stability are derived by using the comparison principle and differential inequalities. These conditions are simple and easy to check. In addition, exponential stability conditions for some special cases of nonlinear delay systems are discussed. Applications of those results to cellular neural networks are presented.

  15. Neural Effects of Transcranial Direct Current Stimulation in Schizophrenia: A Case Study using Functional Near-infrared Spectroscopy

    OpenAIRE

    Taylor, S. Trevor; Chhabra, Harleen; Vanteemar S Sreeraj; Shivakumar, Venkataram; Sunil V Kalmady; Venkatasubramanian, Ganesan

    2017-01-01

    Schizophrenia is a severe neuropsychiatric disorder characterized by delusions, hallucinations, behavioral symptoms, and cognitive deficits. Roughly, 70%–80% of schizophrenia patients experience auditory verbal hallucinations (AVHs), with 25%–30% demonstrating resistance to conventional antipsychotic medications. Studies suggest a promising role for add-on transcranial direct current stimulation (tDCS) in the treatment of medication-refractory AVHs. The mechanisms through which tDCS could be ...

  16. An electric stimulation system for electrokinetic particle manipulation in microfluidic devices.

    Science.gov (United States)

    Lopez-de la Fuente, M S; Moncada-Hernandez, H; Perez-Gonzalez, V H; Lapizco-Encinas, B H; Martinez-Chapa, S O

    2013-03-01

    Microfluidic devices have grown significantly in the number of applications. Microfabrication techniques have evolved considerably; however, electric stimulation systems for microdevices have not advanced at the same pace. Electric stimulation of micro-fluidic devices is an important element in particle manipulation research. A flexible stimulation instrument is desired to perform configurable, repeatable, automated, and reliable experiments by allowing users to select the stimulation parameters. The instrument presented here is a configurable and programmable stimulation system for electrokinetic-driven microfluidic devices; it consists of a processor, a memory system, and a user interface to deliver several types of waveforms and stimulation patterns. It has been designed to be a flexible, highly configurable, low power instrument capable of delivering sine, triangle, and sawtooth waveforms with one single frequency or two superimposed frequencies ranging from 0.01 Hz to 40 kHz, and an output voltage of up to 30 Vpp. A specific stimulation pattern can be delivered over a single time period or as a sequence of different signals for different time periods. This stimulation system can be applied as a research tool where manipulation of particles suspended in liquid media is involved, such as biology, medicine, environment, embryology, and genetics. This system has the potential to lead to new schemes for laboratory procedures by allowing application specific and user defined electric stimulation. The development of this device is a step towards portable and programmable instrumentation for electric stimulation on electrokinetic-based microfluidic devices, which are meant to be integrated with lab-on-a-chip devices.

  17. Medial preoptic area interactions with dopamine neural systems in the control of the onset and maintenance of maternal behavior in rats.

    Science.gov (United States)

    Numan, Michael; Stolzenberg, Danielle S

    2009-01-01

    The medial preoptic area (MPOA) and dopamine (DA) neural systems interact to regulate maternal behavior in rats. Two DA systems are involved: the mesolimbic DA system and the incerto-hypothalamic DA system. The hormonally primed MPOA regulates the appetitive aspects of maternal behavior by activating mesolimbic DA input to the shell region of the nucleus accumbens (NAs). DA action on MPOA via the incerto-hypothalamic system may interact with steroid and peptide hormone effects so that MPOA output to the mesolimbic DA system is facilitated. Neural oxytocin facilitates the onset of maternal behavior by actions at critical nodes in this circuitry. DA-D1 receptor agonist action on either the MPOA or NAs can substitute for the effects of estradiol in stimulating the onset of maternal behavior, suggesting an overlap in underlying cellular mechanisms between estradiol and DA. Maternal memory involves the neural plasticity effects of mesolimbic DA activity. Finally, early life stressors may affect the development of MPOA-DA interactions and maternal behavior.

  18. Emergency Department Visits Involving Nonmedical Use of Central Nervous System Stimulants among Adults Aged 18 to 34 ...

    Science.gov (United States)

    ... Emergency Department Visits Involving Nonmedical Use of Central Nervous System Stimulants among Adults Aged 18 to 34 Increased between 2005 and 2011 Central nervous system (CNS) stimulants include prescription drugs, like those used ...

  19. EDITORIAL: Focus on the neural interface Focus on the neural interface

    Science.gov (United States)

    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

  20. Prediction of Phase Behavior in Microemulsion Systems Using Artificial Neural Networks

    Science.gov (United States)

    Richardson; Mbanefo; Aboofazeli; Lawrence; Barlow

    1997-03-15

    Preliminary investigations have been conducted to assess the potential for using (back-propagation, feed-forward) artificial neural networks to predict the phase behavior of quaternary microemulsion-forming systems, with a view to employing this type of methodology in the evaluation of novel cosurfactants for the formulation of pharmaceutically acceptable drug-delivery systems. The data employed in training the neural networks related to microemulsion systems containing lecithin, isopropyl myristate, and water, together with different types of cosurfactants, including short- and medium-chain alcohols, amines, acids, and ethylene glycol monoalkyl ethers. Previously unpublished phase diagrams are presented for four systems involving the cosurfactants 2-methyl-2-butanol, 2-methyl-1-propanol, 2-methyl-1-butanol, and isopropanol, which, along with eight other published sets of data, are used to test the predictive ability of the trained networks. The pseudo-ternary phase diagrams for these systems are predicted using only four computed physicochemical properties for the cosurfactants involved. The artificial neural networks are shown to be highly successful in predicting phase behavior for these systems, achieving mean success rates of 96.7 and 91.6% for training and test data, respectively. The conclusion is reached that artificial neural networks can provide useful tools for the development of microemulsion-based drug-delivery systems.

  1. Laser stimulation of the auditory system at 1.94 μm and microsecond pulse durations

    Science.gov (United States)

    Izzo, Agnella D.; Walsh, Joseph T., Jr.; Ralph, Heather; Webb, Jim; Wells, Jonathon; Bendett, Mark; Richter, Claus-Peter

    2008-02-01

    Light can artificially stimulate nerve activity in vivo. A significant advantage of optical neural stimulation is the potential for higher spatial selectivity when compared with electrical stimulation. An increased spatial selectivity of stimulation could improve significantly the function of neuroprosthetics, such as cochlear implants. Cochlear implants restore a sense of hearing and communication to deaf individuals by directly electrically stimulating the remaining neural cells in the cochlea. However, performance is limited by overlapping electric fields from neighboring electrodes. Here, we report on experiments with a new laser, offering a previously unavailable wavelength, 1.94μm, and pulse durations down to 5μs, to stimulate cochlear neurons. Compound action potentials (CAP) were evoked from the gerbil cochlea with pulse durations as short as 1μs. Data show that water absorption of light is a significant factor in optical stimulation, as evidenced by the required distance between the optical fiber and the neurons during stimulation. CAP threshold measurements indicate that there is an optimal range of pulse durations over which to deposit the laser energy, less than ~100μs. The implications of these data could direct further research and design of an optical cochlear implant.

  2. Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Wang, L [School of Aeronautics and Astronautics, Tongji University, Shanghai (China); Zhang, Y Y [Chinese-German School of Postgraduate Studies, Tongji University (China); Ding, L [Chinese-German School of Postgraduate Studies, Tongji University (China)

    2006-10-15

    The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module.

  3. Gain Scheduling Control of Nonlinear Systems Based on Neural State Space Models

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon; Stoustrup, Jakob

    2003-01-01

    This paper presents a novel method for gain scheduling control of nonlinear systems based on extraction of local linear state space models from neural networks with direct application to robust control. A neural state space model of the system is first trained based on in- and output training sam...... control can be achieved by interpolating between each controller.In this paper, we propose to use the Youla-Jabr-Bongiorno-Kucera parameterization to achieve a smooth scheduling between the operating points with certain stability guarantees....

  4. Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

    Science.gov (United States)

    Wang, L.; Zhang, Y. Y.; Ding, L.

    2006-10-01

    The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module.

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

    Science.gov (United States)

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

    2017-03-01

    A new online detection strategy is developed to detect faults in sensors and actuators of unmanned aerial vehicle (UAV) systems. In this design, the weighting parameters of the Neural Network (NN) are updated by using the Extended Kalman Filter (EKF). Online adaptation of these weighting parameters helps to detect abrupt, intermittent, and incipient faults accurately. We apply the proposed fault detection system to a nonlinear dynamic model of the WVU YF-22 unmanned aircraft for its evaluation. The simulation results show that the new method has better performance in comparison with conventional recurrent neural network-based fault detection strategies. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  6. Neural maps in insect versus vertebrate auditory systems.

    Science.gov (United States)

    Hildebrandt, K Jannis

    2014-02-01

    The convergent evolution of hearing in insects and vertebrates raises the question about similarity of the central representation of sound in these distant animal groups. Topographic representations of spectral, spatial and temporal cues have been widely described in mammals, but evidence for such maps is scarce in insects. Recent data on insect sound encoding provides evidence for an early integration of sound parameters to form highly-specific representation that predict behavioral output. In mammals, new studies investigating neural representation of perceptual features in behaving animals allow asking similar questions. A comparative approach may help in understanding principles underlying the formation of perceptual categories and behavioral plasticity. Copyright © 2013 Elsevier Ltd. All rights reserved.

  7. System Identification Using Multilayer Differential Neural Networks: A New Result

    Directory of Open Access Journals (Sweden)

    J. Humberto Pérez-Cruz

    2012-01-01

    Full Text Available In previous works, a learning law with a dead zone function was developed for multilayer differential neural networks. This scheme requires strictly a priori knowledge of an upper bound for the unmodeled dynamics. In this paper, the learning law is modified in such a way that this condition is relaxed. By this modification, the tuning process is simpler and the dead-zone function is not required anymore. On the basis of this modification and by using a Lyapunov-like analysis, a stronger result is here demonstrated: the exponential convergence of the identification error to a bounded zone. Besides, a value for upper bound of such zone is provided. The workability of this approach is tested by a simulation example.

  8. A review of spinal cord stimulation systems for chronic pain

    Directory of Open Access Journals (Sweden)

    Verrills P

    2016-07-01

    Full Text Available Paul Verrills,1 Chantelle Sinclair,2 Adele Barnard2 1Metro Pain Group, 2Monash Clinical Research, Monash House, Clayton, Victoria, Australia Abstract: Spinal cord stimulation (SCS applications and technologies are fast advancing. New SCS technologies are being used increasingly in the clinical environment, but often there is a lag period between the clinical application and the publishing of high-quality evidence on safety and efficacy. Recent developments will undoubtedly expand the applicability of SCS, allowing more effective and individualized treatment for patients, and may have the potential to salvage patients who have previously failed neuromodulation. Already, high-level evidence exists for the safety, efficacy, and cost-effectiveness (Level I–II of traditional SCS therapies in the treatment of chronic refractory low back with predominant limb pain (regardless of surgical history. More than half of all patients with chronic painful conditions experience sustained and significant levels of pain reduction following SCS treatment. Although only limited evidence exists for burst stimulation, there is now Level I evidence for both dorsal root ganglion SCS and high-frequency SCS that demonstrates compelling results compared with traditional therapies. The body of evidence built on traditional SCS research may be redundant, with newer iterations of SCS therapies such as dorsal root ganglion SCS, high-frequency SCS, and burst SCS. A number of variables have been identified that can affect SCS efficacy: implanter experience, appropriate patient selection, etiologies of patient pain, existence of comorbidities, including psychiatric illness, smoking status, and delay to SCS implant following pain onset. Overall, scientific literature demonstrates SCS to be a safe, effective, and drug-free treatment option for many chronic pain etiologies. Keywords: spinal cord stimulator, neuromodulation, chronic pain, low back pain

  9. Neural systems for evaluating speaker (Un)believability.

    Science.gov (United States)

    Jiang, Xiaoming; Sanford, Ryan; Pell, Marc D

    2017-04-30

    Our voice provides salient cues about how confident we sound, which promotes inferences about how believable we are. However, the neural mechanisms involved in these social inferences are largely unknown. Employing functional magnetic resonance imaging, we examined the brain networks and individual differences underlying the evaluation of speaker believability from vocal expressions. Participants (n = 26) listened to statements produced in a confident, unconfident, or "prosodically unmarked" (neutral) voice, and judged how believable the speaker was on a 4-point scale. We found frontal-temporal networks were activated for different levels of confidence, with the left superior and inferior frontal gyrus more activated for confident statements, the right superior temporal gyrus for unconfident expressions, and bilateral cerebellum for statements in a neutral voice. Based on listener's believability judgment, we observed increased activation in the right superior parietal lobule (SPL) associated with higher believability, while increased left posterior central gyrus (PoCG) was associated with less believability. A psychophysiological interaction analysis found that the anterior cingulate cortex and bilateral caudate were connected to the right SPL when higher believability judgments were made, while supplementary motor area was connected with the left PoCG when lower believability judgments were made. Personal characteristics, such as interpersonal reactivity and the individual tendency to trust others, modulated the brain activations and the functional connectivity when making believability judgments. In sum, our data pinpoint neural mechanisms that are involved when inferring one's believability from a speaker's voice and establish ways that these mechanisms are modulated by individual characteristics of a listener. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  10. Chemical stimulation of adherent cells by localized application of acetylcholine from a microfluidic system

    Directory of Open Access Journals (Sweden)

    Susanne Zibek

    2010-11-01

    Full Text Available Chemical stimulation of cells is inherently cell type selective in contrast to electro-stimulation. The availability of a system for localized application of minute amounts of chemical stimulants could be useful for dose related response studies to test new compounds. It could also bring forward the development of a novel type of neuroprostheses.In an experimental setup micro-droplets of an acetylcholine solution were ejected from a fluidic microsystem and applied to the bottom of a nanoporous membrane. The solution travelled through the pores to the top of the membrane on which TE671 cells were cultivated. Calcium imaging was used to visualize cellular response with temporal and spatial resolution. Experimental demonstration of chemical stimulation for both threshold gated stimulation as well as accumulated dose response was achieved by either employing acetylcholine as chemical stimulant or applying calcein uptake, respectively.Numerical modelling and simulation of transport mechanisms involved were employed to gain a theoretical understanding of the influence of pore size, concentration of stimulant and droplet volume on the spatial-temporal distribution of stimulant and on the cellular response. Diffusion, pressure driven flow and evaporation effects were taken into account. Fast stimulation kinetic is achieved with pores of 0.82 µm diameter, whereas sustained substance delivery is obtained with nanoporous membranes. In all cases threshold concentrations ranging from 0.01 to 0.015 µM acetylcholine independent of pore size were determined.

  11. RBF Neural Network of Sliding Mode Control for Time-Varying 2-DOF Parallel Manipulator System

    Directory of Open Access Journals (Sweden)

    Haizhong Chen

    2013-01-01

    Full Text Available This paper presents a radial basis function (RBF neural network control scheme for manipulators with actuator nonlinearities. The control scheme consists of a time-varying sliding mode control (TVSMC and an RBF neural network compensator. Since the actuator nonlinearities are usually included in the manipulator driving motor, a compensator using RBF network is proposed to estimate the actuator nonlinearities and their upper boundaries. Subsequently, an RBF neural network controller that requires neither the evaluation of off-line dynamical model nor the time-consuming training process is given. In addition, Barbalat Lemma is introduced to help prove the stability of the closed control system. Considering the SMC controller and the RBF network compensator as the whole control scheme, the closed-loop system is proved to be uniformly ultimately bounded. The whole scheme provides a general procedure to control the manipulators with actuator nonlinearities. Simulation results verify the effectiveness of the designed scheme and the theoretical discussion.

  12. High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide

    Science.gov (United States)

    Artrith, Nongnuch; Morawietz, Tobias; Behler, Jörg

    2011-04-01

    Artificial neural networks represent an accurate and efficient tool to construct high-dimensional potential-energy surfaces based on first-principles data. However, so far the main drawback of this method has been the limitation to a single atomic species. We present a generalization to compounds of arbitrary chemical composition, which now enables simulations of a wide range of systems containing large numbers of atoms. The required incorporation of long-range interactions is achieved by combining the numerical accuracy of neural networks with an electrostatic term based on environment-dependent charges. Using zinc oxide as a benchmark system we show that the neural network potential-energy surface is in excellent agreement with density-functional theory reference calculations, while the evaluation is many orders of magnitude faster.

  13. Global neural dynamic surface tracking control of strict-feedback systems with application to hypersonic flight vehicle.

    Science.gov (United States)

    Xu, Bin; Yang, Chenguang; Pan, Yongping

    2015-10-01

    This paper studies both indirect and direct global neural control of strict-feedback systems in the presence of unknown dynamics, using the dynamic surface control (DSC) technique in a novel manner. A new switching mechanism is designed to combine an adaptive neural controller in the neural approximation domain, together with the robust controller that pulls the transient states back into the neural approximation domain from the outside. In comparison with the conventional control techniques, which could only achieve semiglobally uniformly ultimately bounded stability, the proposed control scheme guarantees all the signals in the closed-loop system are globally uniformly ultimately bounded, such that the conventional constraints on initial conditions of the neural control system can be relaxed. The simulation studies of hypersonic flight vehicle (HFV) are performed to demonstrate the effectiveness of the proposed global neural DSC design.

  14. Neural mirroring and social interaction: Motor system involvement during action observation relates to early peer cooperation

    Directory of Open Access Journals (Sweden)

    H.M. Endedijk

    2017-04-01

    Full Text Available Whether we hand over objects to someone, play a team sport, or make music together, social interaction often involves interpersonal action coordination, both during instances of cooperation and entrainment. Neural mirroring is thought to play a crucial role in processing other’s actions and is therefore considered important for social interaction. Still, to date, it is unknown whether interindividual differences in neural mirroring play a role in interpersonal coordination during different instances of social interaction. A relation between neural mirroring and interpersonal coordination has particularly relevant implications for early childhood, since successful early interaction with peers is predictive of a more favorable social development. We examined the relation between neural mirroring and children’s interpersonal coordination during peer interaction using EEG and longitudinal behavioral data. Results showed that 4-year-old children with higher levels of motor system involvement during action observation (as indicated by lower beta-power were more successful in early peer cooperation. This is the first evidence for a relation between motor system involvement during action observation and interpersonal coordination during other instances of social interaction. The findings suggest that interindividual differences in neural mirroring are related to interpersonal coordination and thus successful social interaction.

  15. Neural mirroring and social interaction: Motor system involvement during action observation relates to early peer cooperation.

    Science.gov (United States)

    Endedijk, H M; Meyer, M; Bekkering, H; Cillessen, A H N; Hunnius, S

    2017-04-01

    Whether we hand over objects to someone, play a team sport, or make music together, social interaction often involves interpersonal action coordination, both during instances of cooperation and entrainment. Neural mirroring is thought to play a crucial role in processing other's actions and is therefore considered important for social interaction. Still, to date, it is unknown whether interindividual differences in neural mirroring play a role in interpersonal coordination during different instances of social interaction. A relation between neural mirroring and interpersonal coordination has particularly relevant implications for early childhood, since successful early interaction with peers is predictive of a more favorable social development. We examined the relation between neural mirroring and children's interpersonal coordination during peer interaction using EEG and longitudinal behavioral data. Results showed that 4-year-old children with higher levels of motor system involvement during action observation (as indicated by lower beta-power) were more successful in early peer cooperation. This is the first evidence for a relation between motor system involvement during action observation and interpersonal coordination during other instances of social interaction. The findings suggest that interindividual differences in neural mirroring are related to interpersonal coordination and thus successful social interaction. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  16. Biological neural networks as model systems for designing future parallel processing computers

    Science.gov (United States)

    Ross, Muriel D.

    1991-01-01

    One of the more interesting debates of the present day centers on whether human intelligence can be simulated by computer. The author works under the premise that neurons individually are not smart at all. Rather, they are physical units which are impinged upon continuously by other matter that influences the direction of voltage shifts across the units membranes. It is only the action of a great many neurons, billions in the case of the human nervous system, that intelligent behavior emerges. What is required to understand even the simplest neural system is painstaking analysis, bit by bit, of the architecture and the physiological functioning of its various parts. The biological neural network studied, the vestibular utricular and saccular maculas of the inner ear, are among the most simple of the mammalian neural networks to understand and model. While there is still a long way to go to understand even this most simple neural network in sufficient detail for extrapolation to computers and robots, a start was made. Moreover, the insights obtained and the technologies developed help advance the understanding of the more complex neural networks that underlie human intelligence.

  17. Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

    Science.gov (United States)

    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.

  18. Genetic manipulation of specific neural circuits by use of a viral vector system.

    Science.gov (United States)

    Kobayashi, Kenta; Kato, Shigeki; Kobayashi, Kazuto

    2017-01-05

    To understand the mechanisms underlying higher brain functions, we need to analyze the roles of specific neuronal pathways or cell types forming the complex neural networks. In the neuroscience field, the transgenic approach has provided a useful gene engineering tool for experimental studies of neural functions. The conventional transgenic technique requires the appropriate promoter regions that drive a neuronal type-specific gene expression, but the promoter sequences specifically functioning in each neuronal type are limited. Previously, we developed novel types of lentiviral vectors showing high efficiency of retrograde gene transfer in the central nervous system, termed highly efficient retrograde gene transfer (HiRet) vector and neuron-specific retrograde gene transfer (NeuRet) vector. The HiRet and NeuRet vectors enable genetical manipulation of specific neural pathways in diverse model animals in combination with conditional cell targeting, synaptic transmission silencing, and gene expression systems. These newly developed vectors provide powerful experimental strategies to investigate, more precisely, the machineries exerting various neural functions. In this review, we give an outline of the HiRet and NeuRet vectors and describe recent representative applications of these viral vectors for studies on neural circuits.

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

    Science.gov (United States)

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

    2000-01-01

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

  20. Networked neural spheroid by neuro-bundle mimicking nervous system created by topology effect.

    Science.gov (United States)

    Jeong, Gi Seok; Chang, Joon Young; Park, Ji Soo; Lee, Seung-A; Park, DoYeun; Woo, Junsung; An, Heeyoung; Lee, C Justin; Lee, Sang-Hoon

    2015-03-22

    In most animals, the nervous system consists of the central nervous system (CNS) and the peripheral nervous system (PNS), the latter of which connects the CNS to all parts of the body. Damage and/or malfunction of the nervous system causes serious pathologies, including neurodegenerative disorders, spinal cord injury, and Alzheimer's disease. Thus, not surprising, considerable research effort, both in vivo and in vitro, has been devoted to studying the nervous system and signal transmission through it. However, conventional in vitro cell culture systems do not enable control over diverse aspects of the neural microenvironment. Moreover, formation of certain nervous system growth patterns in vitro remains a challenge. In this study, we developed a deep hemispherical, microchannel-networked, concave array system and applied it to generate three-dimensional nerve-like neural bundles. The deep hemicylindrical channel network was easily fabricated by exploiting the meniscus induced by the surface tension of a liquid poly(dimethylsiloxane) (PDMS) prepolymer. Neurospheroids spontaneously aggregated in each deep concave microwell and were networked to neighboring spheroids through the deep hemicylindrical channel. Notably, two types of satellite spheroids also formed in deep hemispherical microchannels through self-aggregation and acted as an anchoring point to enhance formation of nerve-like networks with neighboring spheroids. During neural-network formation, neural progenitor cells successfully differentiated into glial and neuronal cells. These cells secreted laminin, forming an extracellular matrix around the host and satellite spheroids. Electrical stimuli were transmitted between networked neurospheroids in the resulting nerve-like neural bundle, as detected by imaging Ca(2+) signals in responding cells.

  1. Development of wireless, chipless neural stimulator by using one-port surface acoustic wave delay line and diode-capacitor interface

    Science.gov (United States)

    Kim, Jisung; Kim, Saehan; Lee, Keekeun

    2017-06-01

    For the first time, a wireless and chipless neuron stimulator was developed by utilizing a surface acoustic wave (SAW) delay line, a diode-capacitor interface, a sharp metal tip, and antennas for the stimulation of neurons in the brain. The SAW delay line supersedes presently existing complex wireless transmission systems composed of a few thousands of transistors, enabling the fabrication of wireless and chipless transceiver systems. The diode-capacitor interface was used to convert AC signals to DC signals and induce stimulus pulses at a sharp metal probe. A 400 MHz RF energy was wirelessly radiated from antennas and then stimulation pulses were observed at a sharp gold probe. A ˜5 m reading distance was obtained using a 1 mW power from a network analyzer. The cycles of electromagnetic (EM) radiation from an antenna were controlled by shielding the antenna with an EM absorber. Stimulation pulses with different amplitudes and durations were successfully observed at the probe. The obtained pulses were ˜0.08 mV in amplitude and 3-10 Hz in frequency. Coupling-of-mode (COM) and SPICE modeling simulations were also used to determine the optimal structural parameters for SAW delay line and the values of passive elements. On the basis of the extracted parameters, the entire system was experimentally implemented and characterized.

  2. Learning Efficiency of Consciousness System for Robot Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Osama Shoubaky

    2014-12-01

    Full Text Available This paper presents learning efficiency of a consciousness system for robot using artificial neural network. The proposed conscious system consists of reason system, feeling system and association system. The three systems are modeled using Module of Nerves for Advanced Dynamics (ModNAD. Artificial neural network of the type of supervised learning with the back propagation is used to train the ModNAD. The reason system imitates behaviour and represents self-condition and other-condition. The feeling system represents sensation and emotion. The association system represents behaviour of self and determines whether self is comfortable or not. A robot is asked to perform cognition and tasks using the consciousness system. Learning converges to about 0.01 within about 900 orders for imitation, pain, solitude and the association modules. It converges to about 0.01 within about 400 orders for the comfort and discomfort modules. It can be concluded that learning in the ModNAD completed after a relatively small number of times because the learning efficiency of the ModNAD artificial neural network is good. The results also show that each ModNAD has a function to imitate and cognize emotion. The consciousness system presented in this paper may be considered as a fundamental step for developing a robot having consciousness and feelings similar to humans.

  3. Modulation of the Endocannabinoid System: Vulnerability Factor and New Treatment Target for Stimulant Addiction.

    Directory of Open Access Journals (Sweden)

    Stéphanie eOlière

    2013-09-01

    Full Text Available Cannabis is one of the most widely used illicit substance among users of stimulants such as cocaine and amphetamine. Interestingly, recent accumulating evidence points toward the involvement of the endocannabinoid system (ECBS in the neurobiological processes related to stimulant addiction. This article presents an up-to-date review with deep-insights into the pivotal role of the ECBS in the neurobiology of stimulant addiction and the effects of its modulation on addictive behaviors. The aims of this article are to: 1 review the role of cannabis use and ECBS modulation in the neurobiological substrates of psychostimulant addiction and 2 evaluate the potential of cannabinoid-based pharmacological strategies to treat stimulant addiction. A growing number of studies support a critical role of the ECBS and its modulation by synthetic or natural cannabinoid in various neurobiological and behavioral aspects of stimulants addiction. Thus, cannabinoids modulate brain reward systems closely involved in stimulants addiction, and provide further evidence that the cannabinoid system could be explored as a potential drug discovery target for treating addiction across different classes of stimulants.

  4. Modulation of the endocannabinoid system: vulnerability factor and new treatment target for stimulant addiction.

    Science.gov (United States)

    Olière, Stéphanie; Joliette-Riopel, Antoine; Potvin, Stéphane; Jutras-Aswad, Didier

    2013-09-23

    Cannabis is one of the most widely used illicit substance among users of stimulants such as cocaine and amphetamines. Interestingly, increasing recent evidence points toward the involvement of the endocannabinoid system (ECBS) in the neurobiological processes related to stimulant addiction. This article presents an up-to-date review with deep insights into the pivotal role of the ECBS in the neurobiology of stimulant addiction and the effects of its modulation on addictive behaviors. This article aims to: (1) review the role of cannabis use and ECBS modulation in the neurobiological substrates of psychostimulant addiction and (2) evaluate the potential of cannabinoid-based pharmacological strategies to treat stimulant addiction. A growing number of studies support a critical role of the ECBS and its modulation by synthetic or natural cannabinoids in various neurobiological and behavioral aspects of stimulants addiction. Thus, cannabinoids modulate brain reward systems closely involved in stimulants addiction, and provide further evidence that the cannabinoid system could be explored as a potential drug discovery target for treating addiction across different classes of stimulants.

  5. Modified Neural Network for Dynamic Control and Operation of a Hybrid Generation Systems

    Directory of Open Access Journals (Sweden)

    Cong-Hui Huang

    2014-12-01

    Full Text Available This paper presents modified neural network for dynamic control and operation of a hybrid generation systems. PV and wind power are the primary power sources of the system to take full advantages of renewable energy, and the diesel-engine is used as a backup system. The simulation model of the hybrid system was developed using MATLAB Simulink. To achieve a fast and stable response for the real power control, the intelligent controller consists of a Radial Basis Function Network (RBFN and an modified Elman Neural Network (ENN for maximum power point tracking (MPPT. The pitch angle of wind turbine is controlled by ENN, and the PV system uses RBFN, where the output signal is used to control the DC I DC boost converters to achieve the MPPT. And the results show the hybrid generation system can effectively extract the maximum power from the PV and wind energy sources.

  6. Engineering applications of fpgas chaotic systems, artificial neural networks, random number generators, and secure communication systems

    CERN Document Server

    Tlelo-Cuautle, Esteban; de la Fraga, Luis Gerardo

    2016-01-01

    This book offers readers a clear guide to implementing engineering applications with FPGAs, from the mathematical description to the hardware synthesis, including discussion of VHDL programming and co-simulation issues. Coverage includes FPGA realizations such as: chaos generators that are described from their mathematical models; artificial neural networks (ANNs) to predict chaotic time series, for which a discussion of different ANN topologies is included, with different learning techniques and activation functions; random number generators (RNGs) that are realized using different chaos generators, and discussions of their maximum Lyapunov exponent values and entropies. Finally, optimized chaotic oscillators are synchronized and realized to implement a secure communication system that processes black and white and grey-scale images. In each application, readers will find VHDL programming guidelines and computer arithmetic issues, along with co-simulation examples with Active-HDL and Simulink. Readers will b...

  7. How instructed knowledge modulates the neural systems of reward learning.

    Science.gov (United States)

    Li, Jian; Delgado, Mauricio R; Phelps, Elizabeth A

    2011-01-04

    Recent research in neuroeconomics has demonstrated that the reinforcement learning model of reward learning captures the patterns of both behavioral performance and neural responses during a range of economic decision-making tasks. However, this powerful theoretical model has its limits. Trial-and-error is only one of the means by which individuals can learn the value associated with different decision options. Humans have also developed efficient, symbolic means of communication for learning without the necessity for committing multiple errors across trials. In the present study, we observed that instructed knowledge of cue-reward probabilities improves behavioral performance and diminishes reinforcement learning-related blood-oxygen level-dependent (BOLD) responses to feedback in the nucleus accumbens, ventromedial prefrontal cortex, and hippocampal complex. The decrease in BOLD responses in these brain regions to reward-feedback signals was functionally correlated with activation of the dorsolateral prefrontal cortex (DLPFC). These results suggest that when learning action values, participants use the DLPFC to dynamically adjust outcome responses in valuation regions depending on the usefulness of action-outcome information.

  8. Animal Recognition System Based on Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Tibor Trnovszky

    2017-01-01

    Full Text Available In this paper, the performances of well-known image recognition methods such as Principal Component Analysis (PCA, Linear Discriminant Analysis (LDA, Local Binary Patterns Histograms (LBPH and Support Vector Machine (SVM are tested and compared with proposed convolutional neural network (CNN for the recognition rate of the input animal images. In our experiments, the overall recognition accuracy of PCA, LDA, LBPH and SVM is demonstrated. Next, the time execution for animal recognition process is evaluated. The all experimental results on created animal database were conducted. This created animal database consist of 500 different subjects (5 classes/ 100 images for each class. The experimental result shows that the PCA features provide better results as LDA and LBPH for large training set. On the other hand, LBPH is better than PCA and LDA for small training data set. For proposed CNN we have obtained a recognition accuracy of 98%. The proposed method based on CNN outperforms the state of the art methods.

  9. Neural systemic impairment from whole-body vibration.

    Science.gov (United States)

    Yan, Ji-Geng; Zhang, Lin-ling; Agresti, Michael; LoGiudice, John; Sanger, James R; Matloub, Hani S; Havlik, Robert

    2015-05-01

    Insidious brain microinjury from motor vehicle-induced whole-body vibration (WBV) has not yet been investigated. For a long time we have believed that WBV would cause cumulative brain microinjury and impair cerebral function, which suggests an important risk factor for motor vehicle accidents and secondary cerebral vascular diseases. Fifty-six Sprague-Dawley rats were divided into seven groups (n = 8): 1) 2-week normal control group, 2) 2-week sham control group (restrained in the tube without vibration), 3) 2-week vibration group (exposed to whole-body vibration at 30 Hz and 0.5g acceleration for 4 hr/day, 5 days/week, for 2 weeks), 4) 4-week sham control group, 5) 4-week vibration group, 6) 8-week sham control group, and 7) 8-week vibration group. At the end point, all rats were evaluated in behavior, physiological, and brain histopathological studies. The cerebral injury from WBV is a cumulative process starting with vasospasm squeezing of the endothelial cells, followed by constriction of the cerebral arteries. After the 4-week vibration, brain neuron apoptosis started. After the 8-week vibration, vacuoles increased further in the brain arteries. Brain capillary walls thickened, mean neuron size was obviously reduced, neuron necrosis became prominent, and wide-ranging chronic cerebral edema was seen. These pathological findings are strongly correlated with neural functional impairments. © 2014 Wiley Periodicals, Inc.

  10. A novel implantable vagus nerve stimulation system (ADNS-300) for combined stimulation and recording of the vagus nerve: Pilot trial at Ghent University Hospital

    NARCIS (Netherlands)

    El Tahry, R.; Raedt, R.; Mollet, L.; de Herdt, V.; Wyckuys, T.; Van Dycke, A.; Meurs, A.; Dewaele, F.; van Roost, D.; Doguet, P.; Delbeke, J.; Wadman, W.; Vonck, K.; Boon, P.

    2010-01-01

    Purpose: Vagus nerve stimulation (VNS) is an established treatment for refractory epilepsy. The ADNS-300 is a new system for VNS that includes a rechargeable stimulus generator and an electrode for combined stimulation and recording. In this feasibility study, three patients were implanted with

  11. Vestibular stimulation-induced facilitation of cervical premotoneuronal systems in humans

    Science.gov (United States)

    Irie, Shun; Ariyasu, Ryohei; Komiyama, Tomoyoshi; Ohki, Yukari

    2017-01-01

    It is unclear how descending inputs from the vestibular system affect the excitability of cervical interneurons in humans. To elucidate this, we investigated the effects of galvanic vestibular stimulation (GVS) on the spatial facilitation of motor-evoked potentials (MEPs) induced by combined pyramidal tract and peripheral nerve stimulation. To assess the spatial facilitation, electromyograms were recorded from the biceps brachii muscles (BB) of healthy subjects. Transcranial magnetic stimulation (TMS) over the contralateral primary motor cortex and electrical stimulation of the ipsilateral ulnar nerve at the wrist were delivered either separately or together, with interstimulus intervals of 10 ms (TMS behind). Anodal/cathodal GVS was randomly delivered with TMS and/or ulnar nerve stimulation. The combination of TMS and ulnar nerve stimulation facilitated BB MEPs significantly more than the algebraic summation of responses induced separately by TMS and ulnar nerve stimulation (i.e., spatial facilitation). MEP facilitation significantly increased when combined stimulation was delivered with GVS (p < 0.01). No significant differences were found between anodal and cathodal GVS. Furthermore, single motor unit recordings showed that the short-latency excitatory peak in peri-stimulus time histograms during combined stimulation increased significantly with GVS. The spatial facilitatory effects of combined stimulation with short interstimulus intervals (i.e., 10 ms) indicate that facilitation occurred at the premotoneuronal level in the cervical cord. The present findings therefore suggest that GVS facilitates the cervical interneuron system that integrates inputs from the pyramidal tract and peripheral nerves and excites motoneurons innervating the arm muscles. PMID:28388686

  12. A New Method for Studying the Periodic System Based on a Kohonen Neural Network

    Science.gov (United States)

    Chen, David Zhekai

    2010-01-01

    A new method for studying the periodic system is described based on the combination of a Kohonen neural network and a set of chemical and physical properties. The classification results are directly shown in a two-dimensional map and easy to interpret. This is one of the major advantages of this approach over other methods reported in the…

  13. Artificial neural networks and support vector machine in banking computer systems

    Directory of Open Access Journals (Sweden)

    Jerzy Balicki

    2013-12-01

    Full Text Available In this paper, some artificial neural networks as well as a support vector machines have been studied due to bank computer system development. These approaches with the contact-less microprocessor technologies can upsurge the bank competitiveness by adding new functionalities. Moreover, some financial crisis influences can be declines.

  14. Neural Network Based Model of an Industrial Oil-Fired Boiler System ...

    African Journals Online (AJOL)

    In this study, an oil-fired boiler system is modeled as a multivariable plant with two inputs (feed water rate and oil-fired flow rate) and two outputs (steam temperature and pressure). The plant parameters are modeled using artificial neural network, based on experimental data collected directly from the physical plant.

  15. Image Classification System Based on Cortical Representations and Unsupervised Neural Network Learning

    NARCIS (Netherlands)

    Petkov, Nikolay

    1995-01-01

    A preprocessor based on a computational model of simple cells in the mammalian primary visual cortex is combined with a self-organising artificial neural network classifier. After learning with a sequence of input images, the output units of the system turn out to correspond to classes of input

  16. Comparable mechanisms for action and language: Neural systems behind intentions, goals and means

    NARCIS (Netherlands)

    Schie, H.T. van; Toni, I.; Bekkering, H.

    2006-01-01

    In this position paper we explore correspondence between neural systems for language and action starting from recent electrophysiological findings on the roles of posterior and frontal areas in goal-directed grasping actions. The paper compares the perceptual and motor organization for action and

  17. Comparison of different computer models of the neural control system of the lower urinary tract

    NARCIS (Netherlands)

    van Duin, F.; Rosier, P. F.; Bemelmans, B. L.; Wijkstra, H.; Debruyne, F. M.; van Oosterom, A.

    2000-01-01

    This paper presents a series of five models that were formulated for describing the neural control of the lower urinary tract in humans. A parsimonious formulation of the effect of the sympathetic system, the pre-optic area, and urethral afferents on the simulated behavior are included. In spite of

  18. A Drone Remote Sensing for Virtual Reality Simulation System for Forest Fires: Semantic Neural Network Approach

    Science.gov (United States)

    Narasimha Rao, Gudikandhula; Jagadeeswara Rao, Peddada; Duvvuru, Rajesh

    2016-09-01

    Wild fires have significant impact on atmosphere and lives. The demand of predicting exact fire area in forest may help fire management team by using drone as a robot. These are flexible, inexpensive and elevated-motion remote sensing systems that use drones as platforms are important for substantial data gaps and supplementing the capabilities of manned aircraft and satellite remote sensing systems. In addition, powerful computational tools are essential for predicting certain burned area in the duration of a forest fire. The reason of this study is to built up a smart system based on semantic neural networking for the forecast of burned areas. The usage of virtual reality simulator is used to support the instruction process of fire fighters and all users for saving of surrounded wild lives by using a naive method Semantic Neural Network System (SNNS). Semantics are valuable initially to have a enhanced representation of the burned area prediction and better alteration of simulation situation to the users. In meticulous, consequences obtained with geometric semantic neural networking is extensively superior to other methods. This learning suggests that deeper investigation of neural networking in the field of forest fires prediction could be productive.

  19. Predictive Control of Hydronic Floor Heating Systems using Neural Networks and Genetic Algorithms

    DEFF Research Database (Denmark)

    Vinther, Kasper; Green, Torben; Østergaard, Søren

    2017-01-01

    . Additionally, weather disturbances such as solar heat gain can be anticipated and compensated for, while taking into account the slow dynamics of the floor. Together with a genetic algorithm, they provide a way to search for optimal future set-point sequences, when convexity and continuity in the solution......This paper presents the use a neural network and a micro genetic algorithm to optimize future set-points in existing hydronic floor heating systems for improved energy efficiency. The neural network can be trained to predict the impact of changes in set-points on future room temperatures...

  20. OptoZIF Drive: a 3D printed implant and assembly tool package for neural recording and optical stimulation in freely moving mice

    Science.gov (United States)

    Freedman, David S.; Schroeder, Joseph B.; Telian, Gregory I.; Zhang, Zhengyang; Sunil, Smrithi; Ritt, Jason T.

    2016-12-01

    Objective. Behavioral neuroscience studies in freely moving rodents require small, light-weight implants to facilitate neural recording and stimulation. Our goal was to develop an integrated package of 3D printed parts and assembly aids for labs to rapidly fabricate, with minimal training, an implant that combines individually positionable microelectrodes, an optical fiber, zero insertion force (ZIF-clip) headstage connection, and secondary recording electrodes, e.g. for electromyography (EMG). Approach. Starting from previous implant designs that position recording electrodes using a control screw, we developed an implant where the main drive body, protective shell, and non-metal components of the microdrives are 3D printed in parallel. We compared alternative shapes and orientations of circuit boards for electrode connection to the headstage, in terms of their size, weight, and ease of wire insertion. We iteratively refined assembly methods, and integrated additional assembly aids into the 3D printed casing. Main results. We demonstrate the effectiveness of the OptoZIF Drive by performing real time optogenetic feedback in behaving mice. A novel feature of the OptoZIF Drive is its vertical circuit board, which facilities direct ZIF-clip connection. This feature requires angled insertion of an optical fiber that still can exit the drive from the center of a ring of recording electrodes. We designed an innovative 2-part protective shell that can be installed during the implant surgery to facilitate making additional connections to the circuit board. We use this feature to show that facial EMG in mice can be used as a control signal to lock stimulation to the animal’s motion, with stable EMG signal over several months. To decrease assembly time, reduce assembly errors, and improve repeatability, we fabricate assembly aids including a drive holder, a drill guide, an implant fixture for microelectode ‘pinning’, and a gold plating fixture. Significance. The

  1. Building an Early Warning System for Crude Oil Price Using Neural Network

    Directory of Open Access Journals (Sweden)

    Wonho Song

    2010-12-01

    Full Text Available In this paper, a crisis index for the oil price shock is defined and a neural network model is specified for the prediction of the crisis index. This paper contributes to the literature in three ways. First, we build an early warning system for crude oil price. Although the oil price became one of the most important price index recently, no research efforts have been made to build an early warning system for crude oil price. Second, the neural network (NN model is used to construct the early warning sysIn this paper, a crisis index for the oil price shock is defined and a neural network model is specified for the prediction of the crisis index. This paper contributes to the literature in three ways. First, we build an early warning system for crude oil price. Although the oil price became one of the most important price index recently, no research efforts have been made to build an early warning system for crude oil price. Second, the neural network (NN model is used to construct the early warning system. Most early warning systems are built based on the signaling approach. In this paper, we show that the neural network models are more flexible and have greater potential as EWS than the signaling approach. Third, we allow the multi-level crisis index. Previous models allowed only a zero/one crisis index whereas our model permits as many levels as possible. With this new model, we try to answer whether the oil price collapse following the historical peak in 2008 was predictable. We compare the results from the NN model with those from the ordered probit (OP model, and show that the oil price crisis and the following crash were predictable by the NN model, but not by the OP model.

  2. A neural network approach to fault detection in spacecraft attitude determination and control systems

    Science.gov (United States)

    Schreiner, John N.

    This thesis proposes a method of performing fault detection and isolation in spacecraft attitude determination and control systems. The proposed method works by deploying a trained neural network to analyze a set of residuals that are defined such that they encompass the attitude control, guidance, and attitude determination subsystems. Eight neural networks were trained using either the resilient backpropagation, Levenberg-Marquardt, or Levenberg-Marquardt with Bayesian regularization training algorithms. The results of each of the neural networks were analyzed to determine the accuracy of the networks with respect to isolating the faulty component or faulty subsystem within the ADCS. The performance of the proposed neural network-based fault detection and isolation method was compared and contrasted with other ADCS FDI methods. The results obtained via simulation showed that the best neural networks employing this method successfully detected the presence of a fault 79% of the time. The faulty subsystem was successfully isolated 75% of the time and the faulty components within the faulty subsystem were isolated 37% of the time.

  3. Time Series Forecasting of Daily Reference Evapotranspiration by Neural Network Ensemble Learning for Irrigation System

    Science.gov (United States)

    Manikumari, N.; Murugappan, A.; Vinodhini, G.

    2017-07-01

    Time series forecasting has gained remarkable interest of researchers in the last few decades. Neural networks based time series forecasting have been employed in various application areas. Reference Evapotranspiration (ETO) is one of the most important components of the hydrologic cycle and its precise assessment is vital in water balance and crop yield estimation, water resources system design and management. This work aimed at achieving accurate time series forecast of ETO using a combination of neural network approaches. This work was carried out using data collected in the command area of VEERANAM Tank during the period 2004 – 2014 in India. In this work, the Neural Network (NN) models were combined by ensemble learning in order to improve the accuracy for forecasting Daily ETO (for the year 2015). Bagged Neural Network (Bagged-NN) and Boosted Neural Network (Boosted-NN) ensemble learning were employed. It has been proved that Bagged-NN and Boosted-NN ensemble models are better than individual NN models in terms of accuracy. Among the ensemble models, Boosted-NN reduces the forecasting errors compared to Bagged-NN and individual NNs. Regression co-efficient, Mean Absolute Deviation, Mean Absolute Percentage error and Root Mean Square Error also ascertain that Boosted-NN lead to improved ETO forecasting performance.

  4. Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2014-11-01

    Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.

  5. A configurable realtime DWT-based neural data compression and communication VLSI system for wireless implants.

    Science.gov (United States)

    Yang, Yuning; Kamboh, Awais M; Mason, Andrew J

    2014-04-30

    This paper presents the design of a complete multi-channel neural recording compression and communication system for wireless implants that addresses the challenging simultaneous requirements for low power, high bandwidth and error-free communication. The compression engine implements discrete wavelet transform (DWT) and run length encoding schemes and offers a practical data compression solution that faithfully preserves neural information. The communication engine encodes data and commands separately into custom-designed packet structures utilizing a protocol capable of error handling. VLSI hardware implementation of these functions, within the design constraints of a 32-channel neural compression implant, is presented. Designed in 0.13μm CMOS, the core of the neural compression and communication chip occupies only 1.21mm(2) and consumes 800μW of power (25μW per channel at 26KS/s) demonstrating an effective solution for intra-cortical neural interfaces. Copyright © 2014 Elsevier B.V. All rights reserved.

  6. Neural Effects of Transcranial Direct Current Stimulation in Schizophrenia: A Case Study using Functional Near-infrared Spectroscopy.

    Science.gov (United States)

    Taylor, S Trevor; Chhabra, Harleen; Sreeraj, Vanteemar S; Shivakumar, Venkataram; Kalmady, Sunil V; Venkatasubramanian, Ganesan

    2017-01-01

    Schizophrenia is a severe neuropsychiatric disorder characterized by delusions, hallucinations, behavioral symptoms, and cognitive deficits. Roughly, 70%-80% of schizophrenia patients experience auditory verbal hallucinations (AVHs), with 25%-30% demonstrating resistance to conventional antipsychotic medications. Studies suggest a promising role for add-on transcranial direct current stimulation (tDCS) in the treatment of medication-refractory AVHs. The mechanisms through which tDCS could be therapeutic in such cases are unclear, but possibly involve neuroplastic effects. In recent years, functional near-infrared spectroscopy (fNIRS) has been used successfully to study tDCS-induced neuroplastic changes. In a double-blind, sham-controlled design, we applied fNIRS to measure task-dependent cerebral blood flow (CBF) changes as a surrogate outcome of single session tDCS-induced effects on neuroplasticity in a schizophrenia patient with persistent auditory hallucinations. The observations are discussed in this case report.

  7. Insights on the neural basis of motor plasticity induced by theta burst stimulation from TMS-EEG.

    Science.gov (United States)

    Vernet, Marine; Bashir, Shahid; Yoo, Woo-Kyoung; Perez, Jennifer M; Najib, Umer; Pascual-Leone, Alvaro

    2013-02-01

    Transcranial magnetic stimulation (TMS) is a useful tool to induce and measure plasticity in the human brain. However, the cortical effects are generally indirectly evaluated with motor-evoked potentials (MEPs) reflective of modulation of cortico-spinal excitability. In this study, we aim to provide direct measures of cortical plasticity by combining TMS with electroencephalography (EEG). Continuous theta-burst stimulation (cTBS) was applied over the primary motor cortex (M1) of young healthy adults, and we measured modulation of (i) MEPs, (ii) TMS-induced EEG evoked potentials (TEPs), (iii) TMS-induced EEG synchronization and (iv) eyes-closed resting EEG. Our results show the expected cTBS-induced decrease in MEP size, which we found to be paralleled by a modulation of a combination of TEPs. Furthermore, we found that cTBS increased the power in the theta band of eyes-closed resting EEG, whereas it decreased single-pulse TMS-induced power in the theta and alpha bands. In addition, cTBS decreased the power in the beta band of eyes-closed resting EEG, whereas it increased single-pulse TMS-induced power in the beta band. We suggest that cTBS acts by modulating the phase alignment between already active oscillators; it synchronizes low-frequency (theta and/or alpha) oscillators and desynchronizes high-frequency (beta) oscillators. These results provide novel insight into the cortical effects of cTBS and could be useful for exploring cTBS-induced plasticity outside of the motor cortex. © 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

  8. Design and Implementation of Behavior Recognition System Based on Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Yu Bo

    2017-01-01

    Full Text Available We build a set of human behavior recognition system based on the convolution neural network constructed for the specific human behavior in public places. Firstly, video of human behavior data set will be segmented into images, then we process the images by the method of background subtraction to extract moving foreground characters of body. Secondly, the training data sets are trained into the designed convolution neural network, and the depth learning network is constructed by stochastic gradient descent. Finally, the various behaviors of samples are classified and identified with the obtained network model, and the recognition results are compared with the current mainstream methods. The result show that the convolution neural network can study human behavior model automatically and identify human’s behaviors without any manually annotated trainings.

  9. VAGUS NERVE STIMULATION REGULATES HEMOSTASIS IN SWINE

    OpenAIRE

    Czura, Christopher J; Schultz, Arthur; Kaipel, Martin; Khadem, Anna; Huston, Jared M.; Pavlov, Valentin A.; Redl, Heinz; Tracey, Kevin J.

    2010-01-01

    The central nervous system regulates peripheral immune responses via the vagus nerve, the primary neural component of the cholinergic anti-inflammatory pathway. Electrical stimulation of the vagus nerve suppresses pro-inflammatory cytokine release in response to endotoxin, I/R injury, and hypovolemic shock and protects against lethal hypotension. To determine the effect of vagus nerve stimulation on coagulation pathways, anesthetized pigs were subjected to partial ear resection before and aft...

  10. Targeted drug delivery system to neural cells utilizes the nicotinic acetylcholine receptor.

    Science.gov (United States)

    Huey, Rachel; O'Hagan, Barry; McCarron, Paul; Hawthorne, Susan

    2017-06-15

    Drug delivery to the brain is still a major challenge in the field of therapeutics, especially for large and hydrophilic compounds. In order to achieve drug delivery of therapeutic concentration in the central nervous system, the problematic blood brain barrier (BBB) must be overcome. This work presents the formulation of a targeted nanoparticle-based drug delivery system using a specific neural cell targeting ligand, rabies virus derived peptide (RDP). Characterization studies revealed that RDP could be conjugated to drug-loaded PLGA nanoparticles of average diameter 257.10±22.39nm and zeta potential of -5.51±0.73mV. In vitro studies showed that addition of RDP to nanoparticles enhanced drug accumulation in a neural cell line specifically as opposed to non-neural cell lines. It was revealed that this drug delivery system is reliant upon nicotinic acetylcholine receptor (nAChR) function for RDP-facilitated effects, supporting a cellular uptake mechanism of action. The specific neural cell targeting capabilities of RDP via the nAChR offers a non-toxic, non-invasive and promising approach to the delivery of therapeutics to the brain. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

  11. CITY TRANSPORT SYSTEM ECOLOGICAL STATE FORECASTING WITH THE USE OF NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Andrey Lyamzin

    2017-11-01

    Full Text Available Purpose: The purpose of this work is to develop an effective model for city transport system ecological state assessment using neural networks general concept. Methods: The proposed model is based on two neural networks work, taking into account the traffic density effect and the transit capacity level on urban areas. Results: Based on the synthesis of the fuzzy sets theory and neural networks basic principles, the city transport system ecological state assessing model is developed. The graphical representation of the model is given. A forecast reliability high degree is provided even at low learning rates and high dynamics of changing statistical data in the city transit traffic conditions. Conclusions: The use of fuzzy neural networks makes it possible to state a complete correspondence between fuzzy inference procedure mathematical representation and the urban transport system structure. The proposed model allows to formulate well-defined environmental guidelines when making decisions in the transit traffic field, taking into account the interests of enterprises, transport and the urban population, with the subsequent distribution of traffic flows in time and geographical space of the city industrial areas.

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

    Science.gov (United States)

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

    2016-01-01

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

  13. An Intelligent Active Video Surveillance System Based on the Integration of Virtual Neural Sensors and BDI Agents

    Science.gov (United States)

    Gregorio, Massimo De

    In this paper we present an intelligent active video surveillance system currently adopted in two different application domains: railway tunnels and outdoor storage areas. The system takes advantages of the integration of Artificial Neural Networks (ANN) and symbolic Artificial Intelligence (AI). This hybrid system is formed by virtual neural sensors (implemented as WiSARD-like systems) and BDI agents. The coupling of virtual neural sensors with symbolic reasoning for interpreting their outputs, makes this approach both very light from a computational and hardware point of view, and rather robust in performances. The system works on different scenarios and in difficult light conditions.

  14. A programmable closed-loop recording and stimulating wireless system for behaving small laboratory animals

    Science.gov (United States)

    Angotzi, Gian Nicola; Boi, Fabio; Zordan, Stefano; Bonfanti, Andrea; Vato, Alessandro

    2014-08-01

    A portable 16-channels microcontroller-based wireless system for a bi-directional interaction with the central nervous system is presented in this work. The device is designed to be used with freely behaving small laboratory animals and allows recording of spontaneous and evoked neural activity wirelessly transmitted and stored on a personal computer. Biphasic current stimuli with programmable duration, frequency and amplitude may be triggered in real-time on the basis of the recorded neural activity as well as by the animal behavior within a specifically designed experimental setup. An intuitive graphical user interface was developed to configure and to monitor the whole system. The system was successfully tested through bench tests and in vivo measurements on behaving rats chronically implanted with multi-channels microwire arrays.

  15. Transcutaneous electrical neural stimulation for the treatment of urinary urgency or urge-incontinence in children and adolescents: a Phase II clinica.

    Science.gov (United States)

    Alcantara, Amanda Carolina Almeida de; Mello, Maria Júlia Gonçalves de; Costa e Silva, Eduardo Just da; Silva, Bárbara Bernardo Rinaldo da; Ribeiro Neto, José Pacheco Martins

    2015-01-01

    To determine the effectiveness of 20 twice-weekly sessions of parasacral transcutaneous electrical neural stimulation (TENS) for treatment of urinary urgency and urge-incontinence in children and adolescents. A Phase II clinical trial was carried out with patients with urinary urgency or urge-incontinence aged between 5 and 14 years. Twenty TENS sessions were conducted, twice weekly, using a Quark® Dualpex 961 apparatus. The variables analyzed were daily micturition, dynamics ultrasonography of the lower urinary tract pre-and post-treatment and responses to a questionnaire on urinary leakage applied during each session. The mean age of the 25 children participating in the study was 7.80 ± 2.22 years, most were female (92%) and had urge-incontinence (92%). The difference in urinary leakage pre- and post-treatment was statistically significant ( p = 0.04); a decline in the symptom of urinary leakage was reported by all caregivers in children who completed the 20th session; the ultrasound parameters, although not statistically significant, showed a reduction in the percentage of children with detrusor contractions (from 62.5% to 43.5%); and a more adequate pre-micturition bladder volume of 4.2% post-treatment compared with 19.0% prior to treatment. The electro-stimulation carried out during the twice weekly sessions appeared to be effective and urinary incontinence declined in half of the patients from the 12th session onwards. However, there is a need for a study involving a larger number of patients to confirm the results obtained.

  16. Complement System in Neural Synapse Elimination in Development and Disease.

    Science.gov (United States)

    Presumey, Jessy; Bialas, Allison R; Carroll, Michael C

    2017-01-01

    Recent discoveries implicate the classical complement cascade in normal brain development and in disease. Complement proteins C1q, C3, and C4 participate in synapse elimination, tagging inappropriate synaptic connections between neurons for removal by phagocytic microglia that exist in a special, highly phagocytic state during the synaptic pruning period. Several neurodevelopmental disorders, such as schizophrenia and autism, are thought to be caused by an imbalance in synaptic pruning, and recent studies suggest that dysregulation of complement could promote this synaptic pruning imbalance. Moreover, in the mature brain, complement can be aberrantly activated in early stages of neurodegenerative diseases to stimulate synapse loss. Similar pathways can also be activated in response to inflammation, as in West Nile Virus infection or in lupus, where peripheral inflammation can promote microglia-mediated synapse loss. Whether synapse loss in disease is a true reactivation of developmental synaptic pruning programs remains unclear; nonetheless, complement proteins represent potential therapeutic targets for both neurodevelopmental and neurodegenerative diseases. © 2017 Elsevier Inc. All rights reserved.

  17. A functional electrical stimulation system for human walking inspired by reflexive control principles

    OpenAIRE

    Meng, Lin; Porr, Bernd; Macleod, Catherine A.; Gollee, Henrik

    2017-01-01

    This study presents an innovative multichannel functional electrical stimulation gait-assist system which employs a well-established purely reflexive control algorithm, previously tested in a series of bipedal walking robots. In these robots, ground contact information was used to activate motors in the legs, generating a gait cycle similar to that of humans. Rather than developing a sophisticated closed-loop functional electrical stimulation control strategy for stepping, we have instead uti...

  18. DEVELOPMENT OF A COMPUTER SYSTEM FOR IDENTITY AUTHENTICATION USING ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Timur Kartbayev

    2017-03-01

    Full Text Available The aim of the study is to increase the effectiveness of automated face recognition to authenticate identity, considering features of change of the face parameters over time. The improvement of the recognition accuracy, as well as consideration of the features of temporal changes in a human face can be based on the methodology of artificial neural networks. Hybrid neural networks, combining the advantages of classical neural networks and fuzzy logic systems, allow using the network learnability along with the explanation of the findings. The structural scheme of intelligent system for identification based on artificial neural networks is proposed in this work. It realizes the principles of digital information processing and identity recognition taking into account the forecast of key characteristics’ changes over time (e.g., due to aging. The structural scheme has a three-tier architecture and implements preliminary processing, recognition and identification of images obtained as a result of monitoring. On the basis of expert knowledge, the fuzzy base of products is designed. It allows assessing possible changes in key characteristics, used to authenticate identity based on the image. To take this possibility into consideration, a neuro-fuzzy network of ANFIS type was used, which implements the algorithm of Tagaki-Sugeno. The conducted experiments showed high efficiency of the developed neural network and a low value of learning errors, which allows recommending this approach for practical implementation. Application of the developed system of fuzzy production rules that allow predicting changes in individuals over time, will improve the recognition accuracy, reduce the number of authentication failures and improve the efficiency of information processing and decision-making in applications, such as authentication of bank customers, users of mobile applications, or in video monitoring systems of sensitive sites.

  19. Prediction of a model enzymatic acidolysis system using neural networks

    Directory of Open Access Journals (Sweden)

    Güven, Aytaç

    2008-12-01

    Full Text Available A model for the acidolysis of trinolein and palmitic acid under the catalysis of immobilized sn-1,3 specific lipase was presented in this study. A neural networks (NN based model was developed for the prediction of the concentrations of the major reaction products of this reaction (1-palmitoyl-2,3-oleoyl-glycerol (POO 1,3-dipalmitoyl-2-oleoyl-glycerol (POP and triolein (OOO. Substrate ratio (SR, reaction temperature (T and reaction time (t were used as input parameters. The optimal architecture of the proposed NN model, which consists of one input layer with three inputs, one hidden layer with seven neurons and one output layer with three outputs, wass able to predict the reaction products concentration with a mean square error (MSE of less than 1.5 and R2 of 0.999. and explicit formulation of the proposed NN is presented. Considerable good performance is achieved in modeling the acidolysis reaction using neuronal networks.En este estudio se presenta un modelo para la acidólisis de la trilinoleina y el ácido palmítico mediante la catálisis con una lipasa específica sn-1,3 inmovilizada. Un modelo basado en redes neuronales (NN ha sido desarrollado para la predicción de la concentración de los principales productos de esta reacción (1-palmitoil-2,3-oleoil-glicerol (POO, 1,3-dipalmitoil-2-oleoil-glicerol (POP y trioleina (OOO. Se han usado como parámetros de entrada: la proporción del sustrato (SR, la temperatura de reacción (T y el tiempo de reacción (t. La arquitectura óptima del modelo de NN propuesto, que consiste en una capa de entrada con tres entradas, una capa oculta con siete neuronas y una capa de salida con tres salidas, fue capaz de predecir la concentración de los productos de reacción con un error cuadrático medio (MSE de menos de 1.5 y una R2 de 0.999 . Se presenta una formulación explícita del modelo NN propuesto. Se obtienen muy buenos resultados en la predicción de la reacciones de acidólisis mediante el uso de

  20. Functional electrical stimulation cycling improves body composition, metabolic and neural factors in persons with spinal cord injury.

    Science.gov (United States)

    Griffin, L; Decker, M J; Hwang, J Y; Wang, B; Kitchen, K; Ding, Z; Ivy, J L

    2009-08-01

    Persons with spinal cord injury (SCI) are at a heightened risk of developing type II diabetes and cardiovascular disease. The purpose of this investigation was to conduct an analysis of metabolic, body composition, and neurological factors before and after 10 weeks of functional electrical stimulation (FES) cycling in persons with SCI. Eighteen individuals with SCI received FES cycling 2-3 times per week for 10 weeks. Body composition was analyzed by dual X-ray absorptiometry. The American Spinal Injury Association (ASIA) neurological classification of SCI test battery was used to assess motor and sensory function. An oral glucose tolerance (OGTT) and insulin-response test was performed to assess blood glucose control. Additional metabolic variables including plasma cholesterol (total-C, HDL-C, LDL-C), triglyceride, and inflammatory markers (IL-6, TNF-alpha, and CRP) were also measured. Total FES cycling power and work done increased with training. Lean muscle mass also increased, whereas, bone and adipose mass did not change. The ASIA motor and sensory scores for the lower extremity significantly increased with training. Blood glucose and insulin levels were lower following the OGTT after 10 weeks of training. Triglyceride levels did not change following training. However, levels of IL-6, TNF-alpha, and CRP were all significantly reduced.

  1. Robust Fault Detection of Wind Energy Conversion Systems Based on Dynamic Neural Networks

    Directory of Open Access Journals (Sweden)

    Nasser Talebi

    2014-01-01

    Full Text Available Occurrence of faults in wind energy conversion systems (WECSs is inevitable. In order to detect the occurred faults at the appropriate time, avoid heavy economic losses, ensure safe system operation, prevent damage to adjacent relevant systems, and facilitate timely repair of failed components; a fault detection system (FDS is required. Recurrent neural networks (RNNs have gained a noticeable position in FDSs and they have been widely used for modeling of complex dynamical systems. One method for designing an FDS is to prepare a dynamic neural model emulating the normal system behavior. By comparing the outputs of the real system and neural model, incidence of the faults can be identified. In this paper, by utilizing a comprehensive dynamic model which contains both mechanical and electrical components of the WECS, an FDS is suggested using dynamic RNNs. The presented FDS detects faults of the generator's angular velocity sensor, pitch angle sensors, and pitch actuators. Robustness of the FDS is achieved by employing an adaptive threshold. Simulation results show that the proposed scheme is capable to detect the faults shortly and it has very low false and missed alarms rate.

  2. Robust fault detection of wind energy conversion systems based on dynamic neural networks.

    Science.gov (United States)

    Talebi, Nasser; Sadrnia, Mohammad Ali; Darabi, Ahmad

    2014-01-01

    Occurrence of faults in wind energy conversion systems (WECSs) is inevitable. In order to detect the occurred faults at the appropriate time, avoid heavy economic losses, ensure safe system operation, prevent damage to adjacent relevant systems, and facilitate timely repair of failed components; a fault detection system (FDS) is required. Recurrent neural networks (RNNs) have gained a noticeable position in FDSs and they have been widely used for modeling of complex dynamical systems. One method for designing an FDS is to prepare a dynamic neural model emulating the normal system behavior. By comparing the outputs of the real system and neural model, incidence of the faults can be identified. In this paper, by utilizing a comprehensive dynamic model which contains both mechanical and electrical components of the WECS, an FDS is suggested using dynamic RNNs. The presented FDS detects faults of the generator's angular velocity sensor, pitch angle sensors, and pitch actuators. Robustness of the FDS is achieved by employing an adaptive threshold. Simulation results show that the proposed scheme is capable to detect the faults shortly and it has very low false and missed alarms rate.

  3. Constrained adaptive neural network control of an MIMO aeroelastic system with input nonlinearities

    Directory of Open Access Journals (Sweden)

    Yiyong Gou

    2017-04-01

    Full Text Available A constrained adaptive neural network control scheme is proposed for a multi-input and multi-output (MIMO aeroelastic system in the presence of wind gust, system uncertainties, and input nonlinearities consisting of input saturation and dead-zone. In regard to the input nonlinearities, the right inverse function block of the dead-zone is added before the input nonlinearities, which simplifies the input nonlinearities into an equivalent input saturation. To deal with the equivalent input saturation, an auxiliary error system is designed to compensate for the impact of the input saturation. Meanwhile, uncertainties in pitch stiffness, plunge stiffness, and pitch damping are all considered, and radial basis function neural networks (RBFNNs are applied to approximate the system uncertainties. In combination with the designed auxiliary error system and the backstepping control technique, a constrained adaptive neural network controller is designed, and it is proven that all the signals in the closed-loop system are semi-globally uniformly bounded via the Lyapunov stability analysis method. Finally, extensive digital simulation results demonstrate the effectiveness of the proposed control scheme towards flutter suppression in spite of the integrated effects of wind gust, system uncertainties, and input nonlinearities.

  4. Command Filtered Adaptive Fuzzy Neural Network Backstepping Control for Marine Power System

    Directory of Open Access Journals (Sweden)

    Xin Zhang

    2014-01-01

    Full Text Available In order to retrain chaotic oscillation of marine power system which is excited by periodic electromagnetism perturbation, a novel command-filtered adaptive fuzzy neural network backstepping control method is designed. First, the mathematical model of marine power system is established based on the two parallel nonlinear model. Then, main results of command-filtered adaptive fuzzy neural network backstepping control law are given. And the Lyapunov stability theory is applied to prove that the system can remain closed-loop asymptotically stable with this controller. Finally, simulation results indicate that the designed controller can suppress chaotic oscillation with fast convergence speed that makes the system return to the equilibrium point quickly; meanwhile, the parameter which induces chaotic oscillation can also be discriminated.

  5. Silicon synaptic transistor for hardware-based spiking neural network and neuromorphic system

    Science.gov (United States)

    Kim, Hyungjin; Hwang, Sungmin; Park, Jungjin; Park, Byung-Gook

    2017-10-01

    Brain-inspired neuromorphic systems have attracted much attention as new computing paradigms for power-efficient computation. Here, we report a silicon synaptic transistor with two electrically independent gates to realize a hardware-based neural network system without any switching components. The spike-timing dependent plasticity characteristics of the synaptic devices are measured and analyzed. With the help of the device model based on the measured data, the pattern recognition capability of the hardware-based spiking neural network systems is demonstrated using the modified national institute of standards and technology handwritten dataset. By comparing systems with and without inhibitory synapse part, it is confirmed that the inhibitory synapse part is an essential element in obtaining effective and high pattern classification capability.

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

    Science.gov (United States)

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

    2014-01-01

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

  7. Adaptive Neural Tracking Control for Discrete-Time Switched Nonlinear Systems with Dead Zone Inputs

    Directory of Open Access Journals (Sweden)

    Jidong Wang

    2017-01-01

    Full Text Available In this paper, the adaptive neural controllers of subsystems are proposed for a class of discrete-time switched nonlinear systems with dead zone inputs under arbitrary switching signals. Due to the complicated framework of the discrete-time switched nonlinear systems and the existence of the dead zone, it brings about difficulties for controlling such a class of systems. In addition, the radial basis function neural networks are employed to approximate the unknown terms of each subsystem. Switched update laws are designed while the parameter estimation is invariable until its corresponding subsystem is active. Then, the closed-loop system is stable and all the signals are bounded. Finally, to illustrate the effectiveness of the proposed method, an example is employed.

  8. Review of Data Preprocessing Methods for Sign Language Recognition Systems based on Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Zorins Aleksejs

    2016-12-01

    Full Text Available The article presents an introductory analysis of relevant research topic for Latvian deaf society, which is the development of the Latvian Sign Language Recognition System. More specifically the data preprocessing methods are discussed in the paper and several approaches are shown with a focus on systems based on artificial neural networks, which are one of the most successful solutions for sign language recognition task.

  9. Central Neural Control of the Cardiovascular System: Current Perspectives

    Science.gov (United States)

    Dampney, Roger A. L.

    2016-01-01

    This brief review, which is based on a lecture presented at the American Physiological Society Teaching Refresher Course on the Brain and Systems Control as part of the Experimental Biology meeting in 2015, aims to summarize current concepts of the principal mechanisms in the brain that regulate the autonomic outflow to the cardiovascular system.…

  10. Plasticity and Neural Stem Cells in the Enteric Nervous System

    NARCIS (Netherlands)

    Schaefer, Karl-Herbert; Van Ginneken, Chris; Copray, Sjef

    2009-01-01

    The enteric nervous system (ENS) is a highly organized part of the autonomic nervous system, which innervates the whole gastrointestinal tract by several interconnected neuronal networks. The ENS changes during development and keeps throughout its lifespan a significant capacity to adapt to

  11. Point-and-Click Cursor Control With an Intracortical Neural Interface System by Humans With Tetraplegia

    Science.gov (United States)

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

    2012-01-01

    We present a point-and-click intracortical neural interface system (NIS) that enables humans with tetraplegia to volitionally move a 2-D computer cursor in any desired direction on a computer screen, hold it still, and click on the area of interest. This direct brain–computer interface extracts both discrete (click) and continuous (cursor velocity) signals from a single small population of neurons in human motor cortex. A key component of this system is a multi-state probabilistic decoding algorithm that simultaneously decodes neural spiking activity of a small population of neurons and outputs either a click signal or the velocity of the cursor. The algorithm combines a linear classifier, which determines whether the user is intending to click or move the cursor, with a Kalman filter that translates the neural population activity into cursor velocity. We present a paradigm for training the multi-state decoding algorithm using neural activity observed during imagined actions. Two human participants with tetraplegia (paralysis of the four limbs) performed a closed-loop radial target acquisition task using the point-and-click NIS over multiple sessions. We quantified point-and-click performance using various human-computer interaction measurements for pointing devices. We found that participants could control the cursor motion and click on specified targets with a small error rate (click 2-D cursor control of a personal computer. PMID:21278024

  12. Neural correlates of clinical improvement after deep transcranial magnetic stimulation (DTMS) for treatment-resistant depression: a case report using functional magnetic resonance imaging.

    Science.gov (United States)

    Harvey, Philippe-Olivier; Van den Eynde, Frederique; Zangen, Abraham; Berlim, Marcelo T

    2015-02-01

    We report the effects of a 4-week trial of deep transcranial magnetic stimulation (DTMS) on depressive and anxious symptoms and brain activity in a patient (Mrs A) with treatment-resistant depression (TRD). The protocol involved a pre- and a post-functional magnetic resonance imaging (fMRI) scan during which Mrs A had to perform a working memory task (i.e., n-back). Her baseline score on the 21-item Hamilton Depression Rating Scale (HAM-D21) was 24, indicating severe depressive symptoms. Immediately after 4 weeks of daily DTMS treatment applied over the left dorsolateral prefrontal cortex (DLPFC), her HAM-D21 score decreased to 13 (a 46% reduction), and 1 month later, it was 12 (a 50% reduction). Moreover, Mrs A's accuracy scores on the n-back task (i.e., 2-back condition) improved from 79% (baseline) to 96% (after DTMS treatment). At the neural level, Mrs A showed significantly increased brain activity in the working memory network (e.g., DLPFC, parietal cortex) during the execution of the 2-back condition after DTMS treatment compared to baseline.

  13. A wireless transmission neural interface system for unconstrained non-human primates

    Science.gov (United States)

    Fernandez-Leon, Jose A.; Parajuli, Arun; Franklin, Robert; Sorenson, Michael; Felleman, Daniel J.; Hansen, Bryan J.; Hu, Ming; Dragoi, Valentin

    2015-10-01

    Objective. Studying the brain in large animal models in a restrained laboratory rig severely limits our capacity to examine brain circuits in experimental and clinical applications. Approach. To overcome these limitations, we developed a high-fidelity 96-channel wireless system to record extracellular spikes and local field potentials from the neocortex. A removable, external case of the wireless device is attached to a titanium pedestal placed in the animal skull. Broadband neural signals are amplified, multiplexed, and continuously transmitted as TCP/IP data at a sustained rate of 24 Mbps. A Xilinx Spartan 6 FPGA assembles the digital signals into serial data frames for transmission at 20 kHz though an 802.11n wireless data link on a frequency-shift key-modulated signal at 5.7-5.8 GHz to a receiver up to 10 m away. The system is powered by two CR123A, 3 V batteries for 2 h of operation. Main results. We implanted a multi-electrode array in visual area V4 of one anesthetized monkey (Macaca fascicularis) and in the dorsolateral prefrontal cortex (dlPFC) of a freely moving monkey (Macaca mulatta). The implanted recording arrays were electrically stable and delivered broadband neural data over a year of testing. For the first time, we compared dlPFC neuronal responses to the same set of stimuli (food reward) in restrained and freely moving conditions. Although we did not find differences in neuronal responses as a function of reward type in the restrained and unrestrained conditions, there were significant differences in correlated activity. This demonstrates that measuring neural responses in freely moving animals can capture phenomena that are absent in the traditional head-fixed paradigm. Significance. We implemented a wireless neural interface for multi-electrode recordings in freely moving non-human primates, which can potentially move systems neuroscience to a new direction by allowing one to record neural signals while animals interact with their environment.

  14. New photic stimulating system with white light-emitting diodes to elicit electroretinograms from zebrafish larvae.

    Science.gov (United States)

    Matsubara, Hisashi; Matsui, Yoshitsugu; Miyata, Ryohei; Nishimura, Yuhei; Yamamoto, Tetsuro; Tanaka, Toshio; Kondo, Mineo

    2017-10-01

    The zebrafish is an established animal model commonly used in biological, neuroscience, and genetic research. We have developed a new light stimulating system using white light-emitting diodes (LEDs) to elicit ERGs from zebrafish larvae. The purpose of this study was to record full-field ERGs and to evaluate the inter-trial reliability of the ERGs recorded with our system from zebrafish larvae. The stimulating device used white LEDs that were attached to a stereomicroscope, and the location of the recording electrode on the cornea could be monitored while the eye was being stimulated. Full-field scotopic and photopic ERGs were recorded from larvae at the age of 5-7 days post-fertilization (dpf). Intensity-response curves were constructed from the ERGs. Inter-trial reliability of the ERGs recorded by our system was evaluated. This stimulating system could be used for efficient and reliable ERG recordings from 5-7 dpf larvae. The amplitudes, implicit times, and the waveforms of the scotopic and photopic ERGs were similar to those reported in earlier studies. Inter-trial reliability of the amplitudes of the photopic ERG b-waves was excellent with an intra-class correlation coefficient of 0.98. We conclude that this new light stimulation system using white LEDs attached to a stereomicroscope will be helpful in recording reliable ERGs from zebrafish larvae.

  15. High-dimensional neural network potentials for multicomponent systems: First applications to zinc oxide

    Energy Technology Data Exchange (ETDEWEB)

    Artrith, Nongnuch; Morawietz, Tobias; Maschke, Marcus; Behler, Joerg [Lehrstuhl fuer Theoretische Chemie, Ruhr-Universitaet Bochum, D-44780 Bochum (Germany)

    2010-07-01

    Recently, artificial neural networks (NN) trained to first-principles data have shown to provide accurate potential energy surfaces for systems containing a single atomic species. In this work we present an extension of the NN approach to multicomponent systems by introducing physically motivated terms to deal with long-range interactions. This is a necessary condition for studying binary systems and general multicomponent systems with significant charge transfer. The capabilities of the method are demonstrated for crystal structures, amorphous structures, clusters, and surfaces of zinc oxide as a benchmark system. We show that the predicted energies and forces are in excellent agreement with reference density-functional theory calculations.

  16. Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence.

    Directory of Open Access Journals (Sweden)

    Yongcheng Li

    Full Text Available Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems.

  17. Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence.

    Science.gov (United States)

    Li, Yongcheng; Sun, Rong; Zhang, Bin; Wang, Yuechao; Li, Hongyi

    2015-01-01

    Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems.

  18. Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System.

    Science.gov (United States)

    Kim, Sungkon; Lee, Jungwhee; Park, Min-Seok; Jo, Byung-Wan

    2009-01-01

    This paper describes the procedures for development of signal analysis algorithms using artificial neural networks for Bridge Weigh-in-Motion (B-WIM) systems. Through the analysis procedure, the extraction of information concerning heavy traffic vehicles such as weight, speed, and number of axles from the time domain strain data of the B-WIM system was attempted. As one of the several possible pattern recognition techniques, an Artificial Neural Network (ANN) was employed since it could effectively include dynamic effects and bridge-vehicle interactions. A number of vehicle traveling experiments with sufficient load cases were executed on two different types of bridges, a simply supported pre-stressed concrete girder bridge and a cable-stayed bridge. Different types of WIM systems such as high-speed WIM or low-speed WIM were also utilized during the experiments for cross-checking and to validate the performance of the developed algorithms.

  19. Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System

    Directory of Open Access Journals (Sweden)

    Min-Seok Park

    2009-10-01

    Full Text Available This paper describes the procedures for development of signal analysis algorithms using artificial neural networks for Bridge Weigh-in-Motion (B-WIM systems. Through the analysis procedure, the extraction of information concerning heavy traffic vehicles such as weight, speed, and number of axles from the time domain strain data of the B-WIM system was attempted. As one of the several possible pattern recognition techniques, an Artificial Neural Network (ANN was employed since it could effectively include dynamic effects and bridge-vehicle interactions. A number of vehicle traveling experiments with sufficient load cases were executed on two different types of bridges, a simply supported pre-stressed concrete girder bridge and a cable-stayed bridge. Different types of WIM systems such as high-speed WIM or low-speed WIM were also utilized during the experiments for cross-checking and to validate the performance of the developed algorithms.

  20. Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems.

    Science.gov (United States)

    González-Gutiérrez, Carlos; Santos, Jesús Daniel; Martínez-Zarzuela, Mario; Basden, Alistair G; Osborn, James; Díaz-Pernas, Francisco Javier; De Cos Juez, Francisco Javier

    2017-06-02

    Many of the next generation of adaptive optics systems on large and extremely large telescopes require tomographic techniques in order to correct for atmospheric turbulence over a large field of view. Multi-object adaptive optics is one such technique. In this paper, different implementations of a tomographic reconstructor based on a machine learning architecture named "CARMEN" are presented. Basic concepts of adaptive optics are introduced first, with a short explanation of three different control systems used on real telescopes and the sensors utilised. The operation of the reconstructor, along with the three neural network frameworks used, and the developed CUDA code are detailed. Changes to the size of the reconstructor influence the training and execution time of the neural network. The native CUDA code turns out to be the best choice for all the systems, although some of the other frameworks offer good performance under certain circumstances.

  1. Simulation and stability analysis of neural network based control scheme for switched linear systems.

    Science.gov (United States)

    Singh, H P; Sukavanam, N

    2012-01-01

    This paper proposes a new adaptive neural network based control scheme for switched linear systems with parametric uncertainty and external disturbance. A key feature of this scheme is that the prior information of the possible upper bound of the uncertainty is not required. A feedforward neural network is employed to learn this upper bound. The adaptive learning algorithm is derived from Lyapunov stability analysis so that the system response under arbitrary switching laws is guaranteed uniformly ultimately bounded. A comparative simulation study with robust controller given in [Zhang L, Lu Y, Chen Y, Mastorakis NE. Robust uniformly ultimate boundedness control for uncertain switched linear systems. Computers and Mathematics with Applications 2008; 56: 1709-14] is presented. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

  2. Control Strategy Based on Wavelet Transform and Neural Network for Hybrid Power System

    Directory of Open Access Journals (Sweden)

    Y. D. Song

    2013-01-01

    Full Text Available This paper deals with an energy management of a hybrid power generation system. The proposed control strategy for the energy management is based on the combination of wavelet transform and neural network arithmetic. The hybrid system in this paper consists of an emulated wind turbine generator, PV panels, DC and AC loads, lithium ion battery, and super capacitor, which are all connected on a DC bus with unified DC voltage. The control strategy is responsible for compensating the difference between the generated power from the wind and solar generators and the demanded power by the loads. Wavelet transform decomposes the power difference into smoothed component and fast fluctuated component. In consideration of battery protection, the neural network is introduced to calculate the reference power of battery. Super capacitor (SC is controlled to regulate the DC bus voltage. The model of the hybrid system is developed in detail under Matlab/Simulink software environment.

  3. Robust MPC for a non-linear system - a neural network approach

    Science.gov (United States)

    Luzar, Marcel; Witczak, Marcin

    2014-12-01

    The aim of the paper is to design a robust actuator fault-tolerant control for a non-linear discrete-time system. Considered system is described by the Linear Parameter-Varying (LPV) model obtained with recurrent neural network. The proposed solution starts with a discretetime quasi-LPV system identification using artificial neural network. Subsequently, the robust controller is proposed, which does not take into account actuator saturation level and deals with the previously estimated faults. To check if the compensation problem is feasible, the robust invariant set is employed, which takes into account actuator saturation level. When the current state does not belong to the set, then a predictive control is performed in order to make such set larger. This makes it possible to increase the domain of attraction, which makes the proposed methodology an efficient solution for the fault-tolerant control. The last part of the paper presents an experimental results regarding wind turbines.

  4. The study on a real-time remote monitoring system for Parkinson's disease patients with deep brain stimulators.

    Science.gov (United States)

    Chen, Yue; Hao, Hongwei; Chen, Hao; Tian, Ye; Li, Luming

    2014-01-01

    The Deep Brain Stimulation (DBS) has become a well-accepted treatment for Parkinson's disease patients around the world. However, postoperative care of the stimulators usually puts a heavy burden on the patients' families, especially in China. To solve the problem, this study developed a real-time remote monitoring system for deep brain stimulators. Based on Internet technologies, the system offers remote adjustment service so that in vivo stimulators could be programmed at patients' home by clinic caregivers. We tested the system on an experimental condition and the results have proved that this early exploration of remote monitoring deep brain stimulators was successful.

  5. Interval type-2 fuzzy neural network controller for a multivariable anesthesia system based on a hardware-in-the-loop simulation.

    Science.gov (United States)

    El-Nagar, Ahmad M; El-Bardini, Mohammad

    2014-05-01

    This manuscript describes the use of a hardware-in-the-loop simulation to simulate the control of a multivariable anesthesia system based on an interval type-2 fuzzy neural network (IT2FNN) controller. The IT2FNN controller consists of an interval type-2 fuzzy linguistic process as the antecedent part and an interval neural network as the consequent part. It has been proposed that the IT2FNN controller can be used for the control of a multivariable anesthesia system to minimize the effects of surgical stimulation and to overcome the uncertainty problem introduced by the large inter-individual variability of the patient parameters. The parameters of the IT2FNN controller were trained online using a back-propagation algorithm. Three experimental cases are presented. All of the experimental results show good performance for the proposed controller over a wide range of patient parameters. Additionally, the results show better performance than the type-1 fuzzy neural network (T1FNN) controller under the effect of surgical stimulation. The response of the proposed controller has a smaller settling time and a smaller overshoot compared with the T1FNN controller and the adaptive interval type-2 fuzzy logic controller (AIT2FLC). The values of the performance indices for the proposed controller are lower than those obtained for the T1FNN controller and the AIT2FLC. The IT2FNN controller is superior to the T1FNN controller for the handling of uncertain information due to the structure of type-2 fuzzy logic systems (FLSs), which are able to model and minimize the numerical and linguistic uncertainties associated with the inputs and outputs of the FLSs. Copyright © 2014 Elsevier B.V. All rights reserved.

  6. Investigation of neural-net based control strategies for improved power system dynamic performance

    Energy Technology Data Exchange (ETDEWEB)

    Sobajic, D.J. [Electric Power Research Institute, Palo Alto, CA (United States)

    1995-12-31

    The ability to accurately predict the behavior of a dynamic system is of essential importance in monitoring and control of complex processes. In this regard recent advances in neural-net base system identification represent a significant step toward development and design of a new generation of control tools for increased system performance and reliability. The enabling functionality is the one of accurate representation of a model of a nonlinear and nonstationary dynamic system. This functionality provides valuable new opportunities including: (1) The ability to predict future system behavior on the basis of actual system observations, (2) On-line evaluation and display of system performance and design of early warning systems, and (3) Controller optimization for improved system performance. In this presentation, we discuss the issues involved in definition and design of learning control systems and their impact on power system control. Several numerical examples are provided for illustrative purpose.

  7. Neural systems and hormones mediating attraction to infant and child faces

    Directory of Open Access Journals (Sweden)

    Lizhu eLuo

    2015-07-01

    Full Text Available We find infant faces highly attractive as a result of specific features which Konrad Lorenz termed Kindchenschema or baby schema, and this is considered to be an important adaptive trait for promoting protective and caregiving behaviors in adults, thereby increasing the chances of infant survival. This review first examines the behavioral support for this effect and physical and behavioral factors which can influence it. It next reviews the increasing number of neuroimaging and electrophysiological studies investigating the neural circuitry underlying this baby schema effect in both parents and non-parents of both sexes. Next it considers potential hormonal contributions to the baby schema effect in both sexes and then neural effects associated with reduced responses to infant cues in post-partum depression, anxiety and drug taking. Overall the findings reviewed reveal a very extensive neural circuitry involved in our perception of cutenessin infant faces with enhanced activation compared to adult faces being found in brain regions involved in face perception, attention, emotion, empathy, memory, reward and attachment, theory of mind and also control of motor responses.Both mothers and fathers also show evidence for enhanced responses in these same neural systems when viewing their own as opposed to another child. Furthermore, responses to infant cues in many of these neural systems are reduced in mothers with post-partum depression or anxiety or have taken addictive drugs throughout pregnancy. In general reproductively active women tend to rate infant faces as cuter than men, which may reflect both heightened attention to relev