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Sample records for neural stimulation system

  1. Neural signal processing and closed-loop control algorithm design for an implanted neural recording and stimulation system.

    Science.gov (United States)

    Hamilton, Lei; McConley, Marc; Angermueller, Kai; Goldberg, David; Corba, Massimiliano; Kim, Louis; Moran, James; Parks, Philip D; Sang Chin; Widge, Alik S; Dougherty, Darin D; Eskandar, Emad N

    2015-08-01

    A fully autonomous intracranial device is built to continually record neural activities in different parts of the brain, process these sampled signals, decode features that correlate to behaviors and neuropsychiatric states, and use these features to deliver brain stimulation in a closed-loop fashion. In this paper, we describe the sampling and stimulation aspects of such a device. We first describe the signal processing algorithms of two unsupervised spike sorting methods. Next, we describe the LFP time-frequency analysis and feature derivation from the two spike sorting methods. Spike sorting includes a novel approach to constructing a dictionary learning algorithm in a Compressed Sensing (CS) framework. We present a joint prediction scheme to determine the class of neural spikes in the dictionary learning framework; and, the second approach is a modified OSort algorithm which is implemented in a distributed system optimized for power efficiency. Furthermore, sorted spikes and time-frequency analysis of LFP signals can be used to generate derived features (including cross-frequency coupling, spike-field coupling). We then show how these derived features can be used in the design and development of novel decode and closed-loop control algorithms that are optimized to apply deep brain stimulation based on a patient's neuropsychiatric state. For the control algorithm, we define the state vector as representative of a patient's impulsivity, avoidance, inhibition, etc. Controller parameters are optimized to apply stimulation based on the state vector's current state as well as its historical values. The overall algorithm and software design for our implantable neural recording and stimulation system uses an innovative, adaptable, and reprogrammable architecture that enables advancement of the state-of-the-art in closed-loop neural control while also meeting the challenges of system power constraints and concurrent development with ongoing scientific research designed

  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. An Implantable Wireless Neural Interface System for Simultaneous Recording and Stimulation of Peripheral Nerve with a Single Cuff Electrode.

    Science.gov (United States)

    Shon, Ahnsei; Chu, Jun-Uk; Jung, Jiuk; Kim, Hyungmin; Youn, Inchan

    2017-12-21

    Recently, implantable devices have become widely used in neural prostheses because they eliminate endemic drawbacks of conventional percutaneous neural interface systems. However, there are still several issues to be considered: low-efficiency wireless power transmission; wireless data communication over restricted operating distance with high power consumption; and limited functionality, working either as a neural signal recorder or as a stimulator. To overcome these issues, we suggest a novel implantable wireless neural interface system for simultaneous neural signal recording and stimulation using a single cuff electrode. By using widely available commercial off-the-shelf (COTS) components, an easily reconfigurable implantable wireless neural interface system was implemented into one compact module. The implantable device includes a wireless power consortium (WPC)-compliant power transmission circuit, a medical implant communication service (MICS)-band-based radio link and a cuff-electrode path controller for simultaneous neural signal recording and stimulation. During in vivo experiments with rabbit models, the implantable device successfully recorded and stimulated the tibial and peroneal nerves while communicating with the external device. The proposed system can be modified for various implantable medical devices, especially such as closed-loop control based implantable neural prostheses requiring neural signal recording and stimulation at the same time.

  4. An Implantable Wireless Neural Interface System for Simultaneous Recording and Stimulation of Peripheral Nerve with a Single Cuff Electrode

    Directory of Open Access Journals (Sweden)

    Ahnsei Shon

    2017-12-01

    Full Text Available Recently, implantable devices have become widely used in neural prostheses because they eliminate endemic drawbacks of conventional percutaneous neural interface systems. However, there are still several issues to be considered: low-efficiency wireless power transmission; wireless data communication over restricted operating distance with high power consumption; and limited functionality, working either as a neural signal recorder or as a stimulator. To overcome these issues, we suggest a novel implantable wireless neural interface system for simultaneous neural signal recording and stimulation using a single cuff electrode. By using widely available commercial off-the-shelf (COTS components, an easily reconfigurable implantable wireless neural interface system was implemented into one compact module. The implantable device includes a wireless power consortium (WPC-compliant power transmission circuit, a medical implant communication service (MICS-band-based radio link and a cuff-electrode path controller for simultaneous neural signal recording and stimulation. During in vivo experiments with rabbit models, the implantable device successfully recorded and stimulated the tibial and peroneal nerves while communicating with the external device. The proposed system can be modified for various implantable medical devices, especially such as closed-loop control based implantable neural prostheses requiring neural signal recording and stimulation at the same time.

  5. 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.

  6. 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.

  7. A fully implantable rodent neural stimulator

    Science.gov (United States)

    Perry, D. W. J.; Grayden, D. B.; Shepherd, R. K.; Fallon, J. B.

    2012-02-01

    The ability to electrically stimulate neural and other excitable tissues in behaving experimental animals is invaluable for both the development of neural prostheses and basic neurological research. We developed a fully implantable neural stimulator that is able to deliver two channels of intra-cochlear electrical stimulation in the rat. It is powered via a novel omni-directional inductive link and includes an on-board microcontroller with integrated radio link, programmable current sources and switching circuitry to generate charge-balanced biphasic stimulation. We tested the implant in vivo and were able to elicit both neural and behavioural responses. The implants continued to function for up to five months in vivo. While targeted to cochlear stimulation, with appropriate electrode arrays the stimulator is well suited to stimulating other neurons within the peripheral or central nervous systems. Moreover, it includes significant on-board data acquisition and processing capabilities, which could potentially make it a useful platform for telemetry applications, where there is a need to chronically monitor physiological variables in unrestrained animals.

  8. 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...

  9. 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...

  10. 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.

  11. 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.

  12. 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

  13. Large-scale multielectrode recording and stimulation of neural activity

    International Nuclear Information System (INIS)

    Sher, A.; Chichilnisky, E.J.; Dabrowski, W.; Grillo, A.A.; Grivich, M.; Gunning, D.; Hottowy, P.; Kachiguine, S.; Litke, A.M.; Mathieson, K.; Petrusca, D.

    2007-01-01

    Large circuits of neurons are employed by the brain to encode and process information. How this encoding and processing is carried out is one of the central questions in neuroscience. Since individual neurons communicate with each other through electrical signals (action potentials), the recording of neural activity with arrays of extracellular electrodes is uniquely suited for the investigation of this question. Such recordings provide the combination of the best spatial (individual neurons) and temporal (individual action-potentials) resolutions compared to other large-scale imaging methods. Electrical stimulation of neural activity in turn has two very important applications: it enhances our understanding of neural circuits by allowing active interactions with them, and it is a basis for a large variety of neural prosthetic devices. Until recently, the state-of-the-art in neural activity recording systems consisted of several dozen electrodes with inter-electrode spacing ranging from tens to hundreds of microns. Using silicon microstrip detector expertise acquired in the field of high-energy physics, we created a unique neural activity readout and stimulation framework that consists of high-density electrode arrays, multi-channel custom-designed integrated circuits, a data acquisition system, and data-processing software. Using this framework we developed a number of neural readout and stimulation systems: (1) a 512-electrode system for recording the simultaneous activity of as many as hundreds of neurons, (2) a 61-electrode system for electrical stimulation and readout of neural activity in retinas and brain-tissue slices, and (3) a system with telemetry capabilities for recording neural activity in the intact brain of awake, naturally behaving animals. We will report on these systems, their various applications to the field of neurobiology, and novel scientific results obtained with some of them. We will also outline future directions

  14. A distributed current stimulator ASIC for high density neural stimulation.

    Science.gov (United States)

    Jeong Hoan Park; Chaebin Kim; Seung-Hee Ahn; Tae Mok Gwon; Joonsoo Jeong; Sang Beom Jun; Sung June Kim

    2016-08-01

    This paper presents a novel distributed neural stimulator scheme. Instead of a single stimulator ASIC in the package, multiple ASICs are embedded at each electrode site for stimulation with a high density electrode array. This distributed architecture enables the simplification of wiring between electrodes and stimulator ASIC that otherwise could become too complex as the number of electrode increases. The individual ASIC chip is designed to have a shared data bus that independently controls multiple stimulating channels. Therefore, the number of metal lines is determined by the distributed ASICs, not by the channel number. The function of current steering is also implemented within each ASIC in order to increase the effective number of channels via pseudo channel stimulation. Therefore, the chip area can be used more efficiently. The designed chip was fabricated with area of 0.3 mm2 using 0.18 μm BCDMOS process, and the bench-top test was also conducted to validate chip performance.

  15. Infrared neural stimulation (INS) inhibits electrically evoked neural responses in the deaf white cat

    Science.gov (United States)

    Richter, Claus-Peter; Rajguru, Suhrud M.; Robinson, Alan; Young, Hunter K.

    2014-03-01

    Infrared neural stimulation (INS) has been used in the past to evoke neural activity from hearing and partially deaf animals. All the responses were excitatory. In Aplysia californica, Duke and coworkers demonstrated that INS also inhibits neural responses [1], which similar observations were made in the vestibular system [2, 3]. In deaf white cats that have cochleae with largely reduced spiral ganglion neuron counts and a significant degeneration of the organ of Corti, no cochlear compound action potentials could be observed during INS alone. However, the combined electrical and optical stimulation demonstrated inhibitory responses during irradiation with infrared light.

  16. 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.

  17. Modeling of light absorption in tissue during infrared neural stimulation

    Science.gov (United States)

    Thompson, Alexander C.; Wade, Scott A.; Brown, William G. A.; Stoddart, Paul R.

    2012-07-01

    A Monte Carlo model has been developed to simulate light transport and absorption in neural tissue during infrared neural stimulation (INS). A range of fiber core sizes and numerical apertures are compared illustrating the advantages of using simulations when designing a light delivery system. A range of wavelengths, commonly used for INS, are also compared for stimulation of nerves in the cochlea, in terms of both the energy absorbed and the change in temperature due to a laser pulse. Modeling suggests that a fiber with core diameter of 200 μm and NA=0.22 is optimal for optical stimulation in the geometry used and that temperature rises in the spiral ganglion neurons are as low as 0.1°C. The results show a need for more careful experimentation to allow different proposed mechanisms of INS to be distinguished.

  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. 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.

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

    Directory of Open Access Journals (Sweden)

    Thomas J Foutz

    Full Text Available 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.

  1. 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...... electrical stimulation is to restore various bodily functions (e.g. motor functions) in patients who have lost them due to injury or disease....

  2. Effects of reinforcement-blocking doses of pimozide on neural systems driven by rewarding stimulation of the MFB: a /sup 14/C-2-deoxyglucose analysis

    Energy Technology Data Exchange (ETDEWEB)

    Gomita, Y.; Gallistel, C.R.

    1982-10-01

    An analysis by means of /sup 14/C-2-deoxyglucose autoradiography of the neural systems unilaterally activated by the reinforcing stimulation used in the two accompanying papers revealed strong and reliable effects in the nucleus of the diagonal band of Broca, in the medial forebrain bundle (MFB) and/or the fornix throughout the diencephalon, and in the part of the anterior ventral tegmentum where the dopaminergic projection to the lateral habenula originates. The terminal fields of the dopaminergic forebrain projections were not affected, but there was bilateral suppression of lateral habenular activity. A second experiment found that the same systems are still activated by (automatically administered) reinforcing stimulation in rats treated with reinforcement blocking doses of pimozide. The only clear effect of pimozide was to reverse the bilateral suppressive effect of the stimulation on lateral habenular activity. Animals treated with pimozide show greatly elevated activity in the lateral habenula, whether or not they receive reinforcing stimulation. The results suggest that pimozide's effect on reinforcement is mediated by the circuitry interconnecting the lateral habenula with the nucleus of the diagonal band of Broca and/or the anterior ventral tegmentum.

  3. Correlation in stimulated respiratory neural noise

    Science.gov (United States)

    Hoop, Bernard; Burton, Melvin D.; Kazemi, Homayoun; Liebovitch, Larry S.

    1995-09-01

    Noise in spontaneous respiratory neural activity of the neonatal rat isolated brainstem-spinal cord preparation stimulated with acetylcholine (ACh) exhibits positive correlation. Neural activity from the C4 (phrenic) ventral spinal rootlet, integrated and corrected for slowly changing trend, is interpreted as a fractal record in time by rescaled range, relative dispersional, and power spectral analyses. The Hurst exponent H measured from time series of 64 consecutive signal levels recorded at 2 s intervals during perfusion of the preparation with artificial cerebrospinal fluid containing ACh at concentrations 62.5 to 1000 μM increases to a maximum of 0.875±0.087 (SD) at 250 μM ACh and decreases with higher ACh concentration. Corrections for bias in measurement of H were made using two different kinds of simulated fractional Gaussian noise. Within limits of experimental procedure and short data series, we conclude that in the presence of added ACh of concentration 250 to 500 μM, noise which occurs in spontaneous respiratory-related neural activity in the isolated brainstem-spinal cord preparation observed at uniform time intervals exhibits positive correlation.

  4. 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...

  5. 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

  6. 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.

  7. 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.

  8. Effects of oral stimulation with capsaicin on salivary secretion and neural activities in the autonomic system and the brain

    Directory of Open Access Journals (Sweden)

    Yoko Kono

    2018-06-01

    Conclusion: These results suggest that oral stimulation with capsaicin may be effective in improving oral conditions by increasing salivary flow and SIgA secretion, and in enhancing physical and mental conditions as indicated by sympathetic nerve and EEG changes.

  9. 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.

  10. Using Brain Stimulation to Disentangle Neural Correlates of Conscious Vision

    Directory of Open Access Journals (Sweden)

    Tom Alexander de Graaf

    2014-09-01

    Full Text Available Research into the neural correlates of consciousness (NCCs has blossomed, due to the advent of new and increasingly sophisticated brain research tools. Neuroimaging has uncovered a variety of brain processes that relate to conscious perception, obtained in a range of experimental paradigms. But methods such as fMRI or EEG do not always afford inference on the role these brain processes play in conscious vision. Such empirical neural correlates of consciousness could reflect neural prerequisites, neural consequences, or neural substrates of a conscious experience. Here, we take a closer look at the use of non-invasive brain stimulation (NIBS techniques in this context. We discuss and review how NIBS methodology can enlighten our understanding of brain mechanisms underlying conscious vision by disentangling the empirical neural correlates of consciousness.

  11. Stimulated Deep Neural Network for Speech Recognition

    Science.gov (United States)

    2016-09-08

    similarities. As illustrated in Figure 1(b), the network grid behaves as a smooth surface on each layer of a stimulated DNN. The nearby nodes in the...for HTK version 3.5),” 2015. [19] S. Tranter, M. Gales, R. Sinha, S. Umesh, and P. Woodland, “The development of the Cambridge University RT-04

  12. Locally optimal extracellular stimulation for chaotic desynchronization of neural populations.

    Science.gov (United States)

    Wilson, Dan; Moehlis, Jeff

    2014-10-01

    We use optimal control theory to design a methodology to find locally optimal stimuli for desynchronization of a model of neurons with extracellular stimulation. This methodology yields stimuli which lead to positive Lyapunov exponents, and hence desynchronizes a neural population. We analyze this methodology in the presence of interneuron coupling to make predictions about the strength of stimulation required to overcome synchronizing effects of coupling. This methodology suggests a powerful alternative to pulsatile stimuli for deep brain stimulation as it uses less energy than pulsatile stimuli, and could eliminate the time consuming tuning process.

  13. Using brain stimulation to disentangle neural correlates of conscious vision.

    Science.gov (United States)

    de Graaf, Tom A; Sack, Alexander T

    2014-01-01

    Research into the neural correlates of consciousness (NCCs) has blossomed, due to the advent of new and increasingly sophisticated brain research tools. Neuroimaging has uncovered a variety of brain processes that relate to conscious perception, obtained in a range of experimental paradigms. But methods such as functional magnetic resonance imaging or electroencephalography do not always afford inference on the functional role these brain processes play in conscious vision. Such empirical NCCs could reflect neural prerequisites, neural consequences, or neural substrates of a conscious experience. Here, we take a closer look at the use of non-invasive brain stimulation (NIBS) techniques in this context. We discuss and review how NIBS methodology can enlighten our understanding of brain mechanisms underlying conscious vision by disentangling the empirical NCCs.

  14. Radiant energy during infrared neural stimulation at the target structure

    Science.gov (United States)

    Richter, Claus-Peter; Rajguru, Suhrud; Stafford, Ryan; Stock, Stuart R.

    2013-03-01

    Infrared neural stimulation (INS) describes a method, by which an infrared laser is used to stimulate neurons. The major benefit of INS over stimulating neurons with electrical current is its spatial selectivity. To translate the technique into a clinical application it is important to know the energy required to stimulate the neural structure. With this study we provide measurements of the radiant exposure, at the target structure that is required to stimulate the auditory neurons. Flat polished fibers were inserted into scala tympani so that the spiral ganglion was in front of the optical fiber. Angle polished fibers were inserted along scala tympani, and rotating the beveled surface of the fiber allowed the radiation beam to be directed perpendicular to the spiral ganglion. The radiant exposure for stimulation at the modiolus for flat and angle polished fibers averaged 6.78+/-2.15 mJ/cm2. With the angle polished fibers, a 90º change in the orientation of the optical beam from an orientation that resulted in an INS-evoked maximum response, resulted in a 50% drop in the response amplitude. When the orientation of the beam was changed by 180º, such that it was directed opposite to the orientation with the maxima, minimum response amplitude was observed.

  15. Neural stimulators: A guide to imaging and postoperative appearances

    International Nuclear Information System (INIS)

    Adams, A.; Shand-Smith, J.; Watkins, L.; McEvoy, A.W.; Elneil, S.; Zrinzo, L.; Davagnanam, I.

    2014-01-01

    Implantable neural stimulators have been developed to aid patients with debilitating neurological conditions that are not amenable to other therapies. The aim of this article is to improve understanding of correct anatomical placement as well as the relevant imaging methods used to assess these devices. Potential complications following their insertion and an overview of the current indications and potential mechanism of action of these devices is provided

  16. A simple miniature device for wireless stimulation of neural circuits in small behaving animals.

    Science.gov (United States)

    Zhang, Yisi; Langford, Bruce; Kozhevnikov, Alexay

    2011-10-30

    The use of wireless neural stimulation devices offers significant advantages for neural stimulation experiments in behaving animals. We demonstrate a simple, low-cost and extremely lightweight wireless neural stimulation device which is made from off-the-shelf components. The device has low power consumption and does not require a high-power RF preamplifier. Neural stimulation can be carried out in either a voltage source mode or a current source mode. Using the device, we carry out wireless stimulation in the premotor brain area HVC of a songbird and demonstrate that such stimulation causes rapid perturbations of the acoustic structure of the song. Published by Elsevier B.V.

  17. Evidence of nonvagal neural stimulation of canine gastric acid secretion.

    Science.gov (United States)

    Tansy, M F; Probst, S J; Martin, J S

    1975-06-01

    In this study, we confirmed our original findings that central vagus stimulation is significantly associated with a subsequent increase in gastric mucus secretion. Central vagus stimulation following phenoxybenzamine hydrochloride administration was associated significantly with protracted elevations in secretory volume and titratable acid. We were unable to conclude that phenoxybenzamine itself in several pharmacologic dosages was associated with an increase in titratable acid. The acid secretory responses could be abolished by transection of the splanchnic nerves. Electrical stimulation of the peripheral part of the splanchnic nerve following administration of phenoxybenzamine was also associated with significant increases in secretory volume and titrable acidity. These secretory responses were not blocked by atropine but were diminished by burimamide. It is concluded that, in the dog, a largely heretofore unsuspected second neural pathway exists which is capable of influencing gastric acid secretion.

  18. 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.

  19. 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

  20. neural control system

    International Nuclear Information System (INIS)

    Elshazly, A.A.E.

    2002-01-01

    Automatic power stabilization control is the desired objective for any reactor operation , especially, nuclear power plants. A major problem in this area is inevitable gap between a real plant ant the theory of conventional analysis and the synthesis of linear time invariant systems. in particular, the trajectory tracking control of a nonlinear plant is a class of problems in which the classical linear transfer function methods break down because no transfer function can represent the system over the entire operating region . there is a considerable amount of research on the model-inverse approach using feedback linearization technique. however, this method requires a prices plant model to implement the exact linearizing feedback, for nuclear reactor systems, this approach is not an easy task because of the uncertainty in the plant parameters and un-measurable state variables . therefore, artificial neural network (ANN) is used either in self-tuning control or in improving the conventional rule-based exper system.the main objective of this thesis is to suggest an ANN, based self-learning controller structure . this method is capable of on-line reinforcement learning and control for a nuclear reactor with a totally unknown dynamics model. previously, researches are based on back- propagation algorithm . back -propagation (BP), fast back -propagation (FBP), and levenberg-marquardt (LM), algorithms are discussed and compared for reinforcement learning. it is found that, LM algorithm is quite superior

  1. 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.

  2. Ultra-nanocrystalline diamond electrodes: optimization towards neural stimulation applications.

    Science.gov (United States)

    Garrett, David J; Ganesan, Kumaravelu; Stacey, Alastair; Fox, Kate; Meffin, Hamish; Prawer, Steven

    2012-02-01

    Diamond is well known to possess many favourable qualities for implantation into living tissue including biocompatibility, biostability, and for some applications hardness. However, conducting diamond has not, to date, been exploited in neural stimulation electrodes due to very low electrochemical double layer capacitance values that have been previously reported. Here we present electrochemical characterization of ultra-nanocrystalline diamond electrodes grown in the presence of nitrogen (N-UNCD) that exhibit charge injection capacity values as high as 163 µC cm(-2) indicating that N-UNCD is a viable material for microelectrode fabrication. Furthermore, we show that the maximum charge injection of N-UNCD can be increased by tailoring growth conditions and by subsequent electrochemical activation. For applications requiring yet higher charge injection, we show that N-UNCD electrodes can be readily metalized with platinum or iridium, further increasing charge injection capacity. Using such materials an implantable neural stimulation device fabricated from a single piece of bio-permanent material becomes feasible. This has significant advantages in terms of the physical stability and hermeticity of a long-term bionic implant.

  3. Artificial Neural Network Analysis System

    Science.gov (United States)

    2001-02-27

    Contract No. DASG60-00-M-0201 Purchase request no.: Foot in the Door-01 Title Name: Artificial Neural Network Analysis System Company: Atlantic... Artificial Neural Network Analysis System 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Powell, Bruce C 5d. PROJECT NUMBER 5e. TASK NUMBER...34) 27-02-2001 Report Type N/A Dates Covered (from... to) ("DD MON YYYY") 28-10-2000 27-02-2001 Title and Subtitle Artificial Neural Network Analysis

  4. Fractal Interfaces for Stimulating and Recording Neural Implants

    Science.gov (United States)

    Watterson, William James

    From investigating movement in an insect to deciphering cognition in a human brain to treating Parkinson's disease, hearing loss, or even blindness, electronic implants are an essential tool for understanding the brain and treating neural diseases. Currently, the stimulating and recording resolution of these implants remains low. For instance, they can record all the neuron activity associated with movement in an insect, but are quite far from recording, at an individual neuron resolution, the large volumes of brain tissue associated with cognition. Likewise, there is remarkable success in the cochlear implant restoring hearing due to the relatively simple anatomy of the auditory nerves, but are failing to restore vision to the blind due to poor signal fidelity and transmission in stimulating the more complex anatomy of the visual nerves. The critically important research needed to improve the resolution of these implants is to optimize the neuron-electrode interface. This thesis explores geometrical and material modifications to both stimulating and recording electrodes which can improve the neuron-electrode interface. First, we introduce a fractal electrode geometry which radically improves the restored visual acuity achieved by retinal implants and leads to safe, long-term operation of the implant. Next, we demonstrate excellent neuron survival and neurite outgrowth on carbon nanotube electrodes, thus providing a safe biomaterial which forms a strong connection between the electrode and neurons. Additional preliminary evidence suggests carbon nanotubes patterned into a fractal geometry will provide further benefits in improving the electrode-neuron interface. Finally, we propose a novel implant based off field effect transistor technology which utilizes an interconnecting fractal network of semiconducting carbon nanotubes to record from thousands of neurons simutaneously at an individual neuron resolution. Taken together, these improvements have the potential to

  5. Analysis of Oscillatory Neural Activity in Series Network Models of Parkinson's Disease During Deep Brain Stimulation.

    Science.gov (United States)

    Davidson, Clare M; de Paor, Annraoi M; Cagnan, Hayriye; Lowery, Madeleine M

    2016-01-01

    Parkinson's disease is a progressive, neurodegenerative disorder, characterized by hallmark motor symptoms. It is associated with pathological, oscillatory neural activity in the basal ganglia. Deep brain stimulation (DBS) is often successfully used to treat medically refractive Parkinson's disease. However, the selection of stimulation parameters is based on qualitative assessment of the patient, which can result in a lengthy tuning period and a suboptimal choice of parameters. This study explores fourth-order, control theory-based models of oscillatory activity in the basal ganglia. Describing function analysis is applied to examine possible mechanisms for the generation of oscillations in interacting nuclei and to investigate the suppression of oscillations with high-frequency stimulation. The theoretical results for the suppression of the oscillatory activity obtained using both the fourth-order model, and a previously described second-order model, are optimized to fit clinically recorded local field potential data obtained from Parkinsonian patients with implanted DBS. Close agreement between the power of oscillations recorded for a range of stimulation amplitudes is observed ( R(2)=0.69-0.99 ). The results suggest that the behavior of the system and the suppression of pathological neural oscillations with DBS is well described by the macroscopic models presented. The results also demonstrate that in this instance, a second-order model is sufficient to model the clinical data, without the need for added complexity. Describing the system behavior with computationally efficient models could aid in the identification of optimal stimulation parameters for patients in a clinical environment.

  6. 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...

  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. Bidirectional neural interface: Closed-loop feedback control for hybrid neural systems.

    Science.gov (United States)

    Chou, Zane; Lim, Jeffrey; Brown, Sophie; Keller, Melissa; Bugbee, Joseph; Broccard, Frédéric D; Khraiche, Massoud L; Silva, Gabriel A; Cauwenberghs, Gert

    2015-01-01

    Closed-loop neural prostheses enable bidirectional communication between the biological and artificial components of a hybrid system. However, a major challenge in this field is the limited understanding of how these components, the two separate neural networks, interact with each other. In this paper, we propose an in vitro model of a closed-loop system that allows for easy experimental testing and modification of both biological and artificial network parameters. The interface closes the system loop in real time by stimulating each network based on recorded activity of the other network, within preset parameters. As a proof of concept we demonstrate that the bidirectional interface is able to establish and control network properties, such as synchrony, in a hybrid system of two neural networks more significantly more effectively than the same system without the interface or with unidirectional alternatives. This success holds promise for the application of closed-loop systems in neural prostheses, brain-machine interfaces, and drug testing.

  9. 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 J.; Cogan, Stuart F.

    2018-02-01

    Objective. 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). Approach. 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. Main results. 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 to 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 cm‑2. Finally, we demonstrated functionality by recording neural activity from basal ganglia nucleus of Zebra Finches and motor cortex of rat. Significance. 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

  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. Electrical Neural Stimulation and Simultaneous in Vivo Monitoring with Transparent Graphene Electrode Arrays Implanted in GCaMP6f Mice.

    Science.gov (United States)

    Park, Dong-Wook; Ness, Jared P; Brodnick, Sarah K; Esquibel, Corinne; Novello, Joseph; Atry, Farid; Baek, Dong-Hyun; Kim, Hyungsoo; Bong, Jihye; Swanson, Kyle I; Suminski, Aaron J; Otto, Kevin J; Pashaie, Ramin; Williams, Justin C; Ma, Zhenqiang

    2018-01-23

    Electrical stimulation using implantable electrodes is widely used to treat various neuronal disorders such as Parkinson's disease and epilepsy and is a widely used research tool in neuroscience studies. However, to date, devices that help better understand the mechanisms of electrical stimulation in neural tissues have been limited to opaque neural electrodes. Imaging spatiotemporal neural responses to electrical stimulation with minimal artifact could allow for various studies that are impossible with existing opaque electrodes. Here, we demonstrate electrical brain stimulation and simultaneous optical monitoring of the underlying neural tissues using carbon-based, fully transparent graphene electrodes implanted in GCaMP6f mice. Fluorescence imaging of neural activity for varying electrical stimulation parameters was conducted with minimal image artifact through transparent graphene electrodes. In addition, full-field imaging of electrical stimulation verified more efficient neural activation with cathode leading stimulation compared to anode leading stimulation. We have characterized the charge density limitation of capacitive four-layer graphene electrodes as 116.07-174.10 μC/cm 2 based on electrochemical impedance spectroscopy, cyclic voltammetry, failure bench testing, and in vivo testing. This study demonstrates the transparent ability of graphene neural electrodes and provides a method to further increase understanding and potentially improve therapeutic electrical stimulation in the central and peripheral nervous systems.

  12. All-optical bidirectional neural interfacing using hybrid multiphoton holographic optogenetic stimulation.

    Science.gov (United States)

    Paluch-Siegler, Shir; Mayblum, Tom; Dana, Hod; Brosh, Inbar; Gefen, Inna; Shoham, Shy

    2015-07-01

    Our understanding of neural information processing could potentially be advanced by combining flexible three-dimensional (3-D) neuroimaging and stimulation. Recent developments in optogenetics suggest that neurophotonic approaches are in principle highly suited for noncontact stimulation of network activity patterns. In particular, two-photon holographic optical neural stimulation (2P-HONS) has emerged as a leading approach for multisite 3-D excitation, and combining it with temporal focusing (TF) further enables axially confined yet spatially extended light patterns. Here, we study key steps toward bidirectional cell-targeted 3-D interfacing by introducing and testing a hybrid new 2P-TF-HONS stimulation path for accurate parallel optogenetic excitation into a recently developed hybrid multiphoton 3-D imaging system. The system is shown to allow targeted all-optical probing of in vitro cortical networks expressing channelrhodopsin-2 using a regeneratively amplified femtosecond laser source tuned to 905 nm. These developments further advance a prospective new tool for studying and achieving distributed control over 3-D neuronal circuits both in vitro and in vivo.

  13. Mathematical phenomenology of neural stimulation by periodic fields.

    Science.gov (United States)

    Balduzzo, M; Milone, F Ferro; Minelli, T A; Pittaro-Cadore, I; Turicchia, L

    2003-04-01

    Neuron synchronization has been hypothesized as the basic mechanism leading neurological phenomena like low electroencephalographic rhythm dimension or high coherence. Cognitive processes, such as associative memory, can also be explained in terms of neuron synchronization. Inspired by the analysis of an experiment on cortex periodic photostimulation, in resonance conditions, a simple network of integrate and fire (i and f) neurons, has been used to simulate cognitive perturbations by oscillatory and pulsate stimulation of the central nervous system (CNS). In view of realistic simulations of transcranial magnetic stimulation (TMS) phenomena, a discrete extension of the FitzHug-Nagumo nervous fiber model, endowed with regenerative nodes, has been developed too.

  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. Intelligent neural network diagnostic system

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2010-01-01

    Recently, artificial neural network (ANN) has made a significant mark in the domain of diagnostic applications. Neural networks are used to implement complex non-linear mappings (functions) using simple elementary units interrelated through connections with adaptive weights. The performance of the ANN is mainly depending on their topology structure and weights. Some systems have been developed using genetic algorithm (GA) to optimize the topology of the ANN. But, they suffer from some limitations. They are : (1) The computation time requires for training the ANN several time reaching for the average weight required, (2) Slowness of GA for optimization process and (3) Fitness noise appeared in the optimization of ANN. This research suggests new issues to overcome these limitations for finding optimal neural network architectures to learn particular problems. This proposed methodology is used to develop a diagnostic neural network system. It has been applied for a 600 MW turbo-generator as a case of real complex systems. The proposed system has proved its significant performance compared to two common methods used in the diagnostic applications.

  16. Design, implementation and testing of an implantable impedance-based feedback-controlled neural gastric stimulator

    International Nuclear Information System (INIS)

    Arriagada, A J; Jurkov, A S; Mintchev, M P; Neshev, E; Andrews, C N; Muench, G

    2011-01-01

    Functional neural gastrointestinal electrical stimulation (NGES) is a methodology of gastric electrical stimulation that can be applied as a possible treatment for disorders such as obesity and gastroparesis. NGES is capable of generating strong lumen-occluding local contractions that can produce retrograde or antegrade movement of gastric content. A feedback-controlled implantable NGES system has been designed, implemented and tested both in laboratory conditions and in an acute animal setting. The feedback system, based on gastric tissue impedance change, is aimed at reducing battery energy requirements and managing the phenomenon of gastric tissue accommodation. Acute animal testing was undertaken in four mongrel dogs (2 M, 2 F, weight 25.53 ± 7.3 kg) that underwent subserosal two-channel electrode implantation. Three force transducers sutured serosally along the gastric axis and a wireless signal acquisition system were utilized to record stimulation-generated contractions and tissue impedance variations respectively. Mechanically induced contractions in the stomach were utilized to indirectly generate a tissue impedance change that was detected by the feedback system. Results showed that increasing or decreasing impedance changes were detected by the implantable stimulator and that therapy can be triggered as a result. The implantable feedback system brings NGES one step closer to long term treatment of burdening gastric motility disorders in humans

  17. 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,

  18. Pilot acute study of feedback-controlled retrograde peristalsis invoked by neural gastric electrical stimulation

    International Nuclear Information System (INIS)

    Aelen, P; Jurkov, A; Aulanier, A; Mintchev, M P

    2009-01-01

    Neural gastric electrical stimulation (NGES) is a new method for invoking gastric contractions under microprocessor control. However, optimization of this technique using feedback mechanisms to minimize power consumption and maximize effectiveness has been lacking. The present pilot study proposes a prototype feedback-controlled neural gastric electric stimulator for the treatment of obesity. Both force-based and inter-electrode impedance-based feedback neurostimulators were implemented and tested. Four mongrel dogs (2 M, 2 F, weight 14.9 ± 2.3 kg) underwent subserosal implantation of two-channel, 1 cm, bipolar electrode leads and two force transducers in the distal antrum. Two of the dogs were stimulated with a force feedback system utilizing the force transducers, and the other two animals were stimulated utilizing an inter-electrode impedance-based feedback system utilizing the proximal electrode leads. Both feedback systems were able to recognize erythromycin-driven contractions of the stomach and were capable of overriding them with NGES-invoked retrograde contractions which exceeded the magnitudes of the erythromycin-driven contractions by an average of 100.6 ± 33.5% in all animals. The NGES-invoked contractions blocked the erythromycin-driven contractions past the proximal electrode pair and induced temporary gastroparesis in the vicinity of the distal force transducer despite the continuing erythromycin infusion. The amplitudes of the erythromycin-invoked contractions in the vicinity of the proximal force transducer decreased abruptly by an average of 47.9 ± 6.3% in all four dogs after triggering-invoked retrograde contractions, regardless of the specific feedback-controlled mechanism. The proposed technique could be helpful for retaining food longer in the stomach, thus inducing early satiety and diminishing food intake

  19. Neural System Prediction and Identification Challenge

    Directory of Open Access Journals (Sweden)

    Ioannis eVlachos

    2013-12-01

    Full Text Available Can we infer the function of a biological neural network (BNN if we know the connectivity and activity of all its constituent neurons? This question is at the core of neuroscience and, accordingly, various methods have been developed to record the activity and connectivity of as many neurons as possible. Surprisingly, there is no theoretical or computational demonstration that neuronal activity and connectivity are indeed sufficient to infer the function of a BNN. Therefore, we pose the Neural Systems Identification and Prediction Challenge (nuSPIC. We provide the connectivity and activity of all neurons and invite participants (i to infer the functions implemented (hard-wired in spiking neural networks (SNNs by stimulating and recording the activity of neurons and, (ii to implement predefined mathematical/biological functions using SNNs. The nuSPICs can be accessed via a web-interface to the NEST simulator and the user is not required to know any specific programming language. Furthermore, the nuSPICs can be used as a teaching tool. Finally, nuSPICs use the crowd-sourcing model to address scientific issues. With this computational approach we aim to identify which functions can be inferred by systematic recordings of neuronal activity and connectivity. In addition, nuSPICs will help the design and application of new experimental paradigms based on the structure of the SNN and the presumed function which is to be discovered.

  20. Neural system prediction and identification challenge.

    Science.gov (United States)

    Vlachos, Ioannis; Zaytsev, Yury V; Spreizer, Sebastian; Aertsen, Ad; Kumar, Arvind

    2013-01-01

    Can we infer the function of a biological neural network (BNN) if we know the connectivity and activity of all its constituent neurons?This question is at the core of neuroscience and, accordingly, various methods have been developed to record the activity and connectivity of as many neurons as possible. Surprisingly, there is no theoretical or computational demonstration that neuronal activity and connectivity are indeed sufficient to infer the function of a BNN. Therefore, we pose the Neural Systems Identification and Prediction Challenge (nuSPIC). We provide the connectivity and activity of all neurons and invite participants (1) to infer the functions implemented (hard-wired) in spiking neural networks (SNNs) by stimulating and recording the activity of neurons and, (2) to implement predefined mathematical/biological functions using SNNs. The nuSPICs can be accessed via a web-interface to the NEST simulator and the user is not required to know any specific programming language. Furthermore, the nuSPICs can be used as a teaching tool. Finally, nuSPICs use the crowd-sourcing model to address scientific issues. With this computational approach we aim to identify which functions can be inferred by systematic recordings of neuronal activity and connectivity. In addition, nuSPICs will help the design and application of new experimental paradigms based on the structure of the SNN and the presumed function which is to be discovered.

  1. 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.

  2. Functional electrical stimulation controlled by artificial neural networks: pilot experiments with simple movements are promising for rehabilitation applications.

    Science.gov (United States)

    Ferrante, Simona; Pedrocchi, Alessandra; Iannò, Marco; De Momi, Elena; Ferrarin, Maurizio; Ferrigno, Giancarlo

    2004-01-01

    This study falls within the ambit of research on functional electrical stimulation for the design of rehabilitation training for spinal cord injured patients. In this context, a crucial issue is the control of the stimulation parameters in order to optimize the patterns of muscle activation and to increase the duration of the exercises. An adaptive control system (NEURADAPT) based on artificial neural networks (ANNs) was developed to control the knee joint in accordance with desired trajectories by stimulating quadriceps muscles. This strategy includes an inverse neural model of the stimulated limb in the feedforward line and a neural network trained on-line in the feedback loop. NEURADAPT was compared with a linear closed-loop proportional integrative derivative (PID) controller and with a model-based neural controller (NEUROPID). Experiments on two subjects (one healthy and one paraplegic) show the good performance of NEURADAPT, which is able to reduce the time lag introduced by the PID controller. In addition, control systems based on ANN techniques do not require complicated calibration procedures at the beginning of each experimental session. After the initial learning phase, the ANN, thanks to its generalization capacity, is able to cope with a certain range of variability of skeletal muscle properties.

  3. Electrical Stimulation Elicit Neural Stem Cells Activation:New Perspectives in CNS Repair

    Directory of Open Access Journals (Sweden)

    Ratrel eHuang

    2015-10-01

    Full Text Available Researchers are enthusiastically concerned about neural stem cell (NSC therapy in a wide array of diseases, including stroke, neurodegenerative disease, spinal cord injury (SCI and depression. Although enormous evidences have demonstrated that neurobehavioral improvement may benefit from NSC-supporting regeneration in animal models, approaches to endogenous and transplanted NSCs are blocked by hurdles of migration, proliferation, maturation and integration of NSCs. Electrical stimulation (ES may be a selective nondrug approach for mobilizing NSCs in the central nervous system (CNS. This technique is suitable for clinic application, because it is well established and its potential complications are manageable. Here, we provide a comprehensive review of the emerging positive role of different electrical cues in regulating NSC biology in vitro and in vivo, as well as biomaterial-based and chemical stimulation of NSCs. In the future, ES combined with stem cell therapy or other cues probably becomes an approach for promoting brain repair.

  4. 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…

  5. A wideband wireless neural stimulation platform for high-density microelectrode arrays.

    Science.gov (United States)

    Myers, Frank B; Simpson, Jim A; Ghovanloo, Maysam

    2006-01-01

    We describe a system that allows researchers to control an implantable neural microstimulator from a PC via a USB 2.0 interface and a novel dual-carrier wireless link, which provides separate data and power transmission. Our wireless stimulator, Interestim-2B (IS-2B), is a modular device capable of generating controlled-current stimulation pulse trains across 32 sites per module with support for a variety of stimulation schemes (biphasic/monophasic, bipolar/monopolar). We have developed software to generate multi-site stimulation commands for the IS-2B based on streaming data from artificial sensory devices such as cameras and microphones. For PC interfacing, we have developed a USB 2.0 microcontroller-based interface. Data is transmitted using frequency-shift keying (FSK) at 6/12 MHz to achieve a data rate of 3 Mb/s via a pair of rectangular coils. Power is generated using a class-E power amplifier operating at 1 MHz and transmitted via a separate pair of spiral planar coils which are oriented perpendicular to the data coils to minimize cross-coupling. We have successfully demonstrated the operation of the system by applying it as a visual prosthesis. Pulse-frequency modulated stimuli are generated in real-time based on a grayscale image from a webcam. These pulses are projected onto an 11x11 LED matrix that represents a 2D microelectrode array.

  6. Transcranial Magnetic Stimulation and Connectivity Mapping: Tools for Studying the Neural Bases of Brain Disorders

    OpenAIRE

    Hampson, M.; Hoffman, R. E.

    2010-01-01

    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...

  7. A wireless integrated circuit for 100-channel charge-balanced neural stimulation.

    Science.gov (United States)

    Thurgood, B K; Warren, D J; Ledbetter, N M; Clark, G A; Harrison, R R

    2009-12-01

    The authors present the design of an integrated circuit for wireless neural stimulation, along with benchtop and in - vivo experimental results. The chip has the ability to drive 100 individual stimulation electrodes with constant-current pulses of varying amplitude, duration, interphasic delay, and repetition rate. The stimulation is performed by using a biphasic (cathodic and anodic) current source, injecting and retracting charge from the nervous system. Wireless communication and power are delivered over a 2.765-MHz inductive link. Only three off-chip components are needed to operate the stimulator: a 10-nF capacitor to aid in power-supply regulation, a small capacitor (power and command reception. The chip was fabricated in a commercially available 0.6- mum 2P3M BiCMOS process. The chip was able to activate motor fibers to produce muscle twitches via a Utah Slanted Electrode Array implanted in cat sciatic nerve, and to activate sensory fibers to recruit evoked potentials in somatosensory cortex.

  8. DNA methyltransferase mediates dose-dependent stimulation of neural stem cell proliferation by folate.

    Science.gov (United States)

    Li, Wen; Yu, Min; Luo, Suhui; Liu, Huan; Gao, Yuxia; Wilson, John X; Huang, Guowei

    2013-07-01

    The proliferative response of neural stem cells (NSCs) to folate may play a critical role in the development, function and repair of the central nervous system. It is important to determine the dose-dependent effects of folate in NSC cultures that are potential sources of transplantable cells for therapies for neurodegenerative diseases. To determine the optimal concentration and mechanism of action of folate for stimulation of NSC proliferation in vitro, NSCs were exposed to folic acid or 5-methyltetrahydrofolate (5-MTHF) (0-200 μmol/L) for 24, 48 or 72 h. Immunocytochemistry and methyl thiazolyl tetrazolium assay showed that the optimal concentration of folic acid for NSC proliferation was 20-40 μmol/L. Stimulation of NSC proliferation by folic acid was associated with DNA methyltransferase (DNMT) activation and was attenuated by the DNMT inhibitor zebularine, which implies that folate dose-dependently stimulates NSC proliferation through a DNMT-dependent mechanism. Based on these new findings and previously published evidence, we have identified a mechanism by which folate stimulates NSC growth. Copyright © 2013 Elsevier Inc. All rights reserved.

  9. 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.

  10. 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.

  11. 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

  12. Study Under AC Stimulation on Excitement Properties of Weighted Small-World Biological Neural Networks with Side-Restrain Mechanism

    International Nuclear Information System (INIS)

    Yuan Wujie; Luo Xiaoshu; Jiang Pinqun

    2007-01-01

    In this paper, we propose a new model of weighted small-world biological neural networks based on biophysical Hodgkin-Huxley neurons with side-restrain mechanism. Then we study excitement properties of the model under alternating current (AC) stimulation. The study shows that the excitement properties in the networks are preferably consistent with the behavior properties of a brain nervous system under different AC stimuli, such as refractory period and the brain neural excitement response induced by different intensities of noise and coupling. The results of the study have reference worthiness for the brain nerve electrophysiology and epistemological science.

  13. 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

  14. 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

    Background 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. Methods 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. Results 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. Conclusions 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. PMID:25165974

  15. A Wireless Implantable Switched-Capacitor Based Optogenetic Stimulating System

    Science.gov (United States)

    Lee, Hyung-Min; Kwon, Ki-Yong; Li, Wen

    2015-01-01

    This paper presents a power-efficient implantable optogenetic interface using a wireless switched-capacitor based stimulating (SCS) system. The SCS efficiently charges storage capacitors directly from an inductive link and periodically discharges them into an array of micro-LEDs, providing high instantaneous power without affecting wireless link and system supply voltage. A custom-designed computer interface in LabVIEW environment wirelessly controls stimulation parameters through the inductive link, and an optrode array enables simultaneous neural recording along with optical stimulation. The 4-channel SCS system prototype has been implemented in a 0.35-μm CMOS process and combined with the optrode array. In vivo experiments involving light-induced local field potentials verified the efficacy of the SCS system. An implantable version of the SCS system with flexible hermetic sealing is under development for chronic experiments. PMID:25570099

  16. COMMUNICATION Designing a somatosensory neural prosthesis: percepts evoked by different patterns of thalamic stimulation

    Science.gov (United States)

    Heming, Ethan; Sanden, Andrew; Kiss, Zelma H. T.

    2010-12-01

    Although major advances have been made in the development of motor prostheses, fine motor control requires intuitive somatosensory feedback. Here we explored whether a thalamic site for a somatosensory neural prosthetic could provide natural somatic sensation to humans. Different patterns of electrical stimulation (obtained from thalamic spike trains) were applied in patients undergoing deep brain stimulation surgery. Changes in pattern produced different sensations, while preserving somatotopic representation. While most percepts were reported as 'unnatural', some stimulations produced more 'natural' sensations than others. However, the additional patterns did not elicit more 'natural' percepts than high-frequency (333 Hz) electrical stimulation. These features suggest that despite some limitations, the thalamus may be a feasible site for a somatosensory neural prosthesis and different stimulation patterns may be useful in its development.

  17. 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.

  18. Learning in Artificial Neural Systems

    Science.gov (United States)

    Matheus, Christopher J.; Hohensee, William E.

    1987-01-01

    This paper presents an overview and analysis of learning in Artificial Neural Systems (ANS's). It begins with a general introduction to neural networks and connectionist approaches to information processing. The basis for learning in ANS's is then described, and compared with classical Machine learning. While similar in some ways, ANS learning deviates from tradition in its dependence on the modification of individual weights to bring about changes in a knowledge representation distributed across connections in a network. This unique form of learning is analyzed from two aspects: the selection of an appropriate network architecture for representing the problem, and the choice of a suitable learning rule capable of reproducing the desired function within the given network. The various network architectures are classified, and then identified with explicit restrictions on the types of functions they are capable of representing. The learning rules, i.e., algorithms that specify how the network weights are modified, are similarly taxonomized, and where possible, the limitations inherent to specific classes of rules are outlined.

  19. Acoustic stimulation can induce a selective neural network response mediated by piezoelectric nanoparticles

    Science.gov (United States)

    Rojas, Camilo; Tedesco, Mariateresa; Massobrio, Paolo; Marino, Attilio; Ciofani, Gianni; Martinoia, Sergio; Raiteri, Roberto

    2018-06-01

    Objective. We aim to develop a novel non-invasive or minimally invasive method for neural stimulation to be applied in the study and treatment of brain (dys)functions and neurological disorders. Approach. We investigate the electrophysiological response of in vitro neuronal networks when subjected to low-intensity pulsed acoustic stimulation, mediated by piezoelectric nanoparticles adsorbed on the neuronal membrane. Main results. We show that the presence of piezoelectric barium titanate nanoparticles induces, in a reproducible way, an increase in network activity when excited by stationary ultrasound waves in the MHz regime. Such a response can be fully recovered when switching the ultrasound pulse off, depending on the generated pressure field amplitude, whilst it is insensitive to the duration of the ultrasound pulse in the range 0.5 s–1.5 s. We demonstrate that the presence of piezoelectric nanoparticles is necessary, and when applying the same acoustic stimulation to neuronal cultures without nanoparticles or with non-piezoelectric nanoparticles with the same size distribution, no network response is observed. Significance. We believe that our results open up an extremely interesting approach when coupled with suitable functionalization strategies of the nanoparticles in order to address specific neurons and/or brain areas and applied in vivo, thus enabling remote, non-invasive, and highly selective modulation of the activity of neuronal subpopulations of the central nervous system of mammalians.

  20. Analysis of deep brain stimulation electrode characteristics for neural recording

    Science.gov (United States)

    Kent, Alexander R.; Grill, Warren M.

    2014-08-01

    Objective. Closed-loop deep brain stimulation (DBS) systems have the potential to optimize treatment of movement disorders by enabling automatic adjustment of stimulation parameters based on a feedback signal. Evoked compound action potentials (ECAPs) and local field potentials (LFPs) recorded from the DBS electrode may serve as suitable closed-loop control signals. The objective of this study was to understand better the factors that influence ECAP and LFP recording, including the physical presence of the electrode, the geometrical dimensions of the electrode, and changes in the composition of the peri-electrode space across recording conditions. Approach. Coupled volume conductor-neuron models were used to calculate single-unit activity as well as ECAP responses and LFP activity from a population of model thalamic neurons. Main results. Comparing ECAPs and LFPs measured with and without the presence of the highly conductive recording contacts, we found that the presence of these contacts had a negligible effect on the magnitude of single-unit recordings, ECAPs (7% RMS difference between waveforms), and LFPs (5% change in signal magnitude). Spatial averaging across the contact surface decreased the ECAP magnitude in a phase-dependent manner (74% RMS difference), resulting from a differential effect of the contact on the contribution from nearby or distant elements, and decreased the LFP magnitude (25% change). Reductions in the electrode diameter or recording contact length increased signal energy and increased spatial sensitivity of single neuron recordings. Moreover, smaller diameter electrodes (500 µm) were more selective for recording from local cells over passing axons, with the opposite true for larger diameters (1500 µm). Changes in electrode dimensions had phase-dependent effects on ECAP characteristics, and generally had small effects on the LFP magnitude. ECAP signal energy and LFP magnitude decreased with tighter contact spacing (100 µm), compared to

  1. Using brain stimulation to disentangle neural correlates of conscious vision

    NARCIS (Netherlands)

    de Graaf, T.A.; Sack, A.T.

    2014-01-01

    Research into the neural correlates of consciousness (NCCs) has blossomed, due to the advent of new and increasingly sophisticated brain research tools. Neuroimaging has uncovered a variety of brain processes that relate to conscious perception, obtained in a range of experimental paradigms. But

  2. Compact, Energy-Efficient High-Frequency Switched Capacitor Neural Stimulator With Active Charge Balancing.

    Science.gov (United States)

    Hsu, Wen-Yang; Schmid, Alexandre

    2017-08-01

    Safety and energy efficiency are two major concerns for implantable neural stimulators. This paper presents a novel high-frequency, switched capacitor (HFSC) stimulation and active charge balancing scheme, which achieves high energy efficiency and well-controlled stimulation charge in the presence of large electrode impedance variations. Furthermore, the HFSC can be implemented in a compact size without any external component to simultaneously enable multichannel stimulation by deploying multiple stimulators. The theoretical analysis shows significant benefits over the constant-current and voltage-mode stimulation methods. The proposed solution was fabricated using a 0.18 μm high-voltage technology, and occupies only 0.035 mm 2 for a single stimulator. The measurement result shows 50% peak energy efficiency and confirms the effectiveness of active charge balancing to prevent the electrode dissolution.

  3. Neural Control of the Immune System

    Science.gov (United States)

    Sundman, Eva; Olofsson, Peder S.

    2014-01-01

    Neural reflexes support homeostasis by modulating the function of organ systems. Recent advances in neuroscience and immunology have revealed that neural reflexes also regulate the immune system. Activation of the vagus nerve modulates leukocyte cytokine production and alleviates experimental shock and autoimmune disease, and recent data have…

  4. 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.

  5. Stimulation Efficiency With Decaying Exponential Waveforms in a Wirelessly Powered Switched-Capacitor Discharge Stimulation System.

    Science.gov (United States)

    Lee, Hyung-Min; Howell, Bryan; Grill, Warren M; Ghovanloo, Maysam

    2018-05-01

    The purpose of this study was to test the feasibility of using a switched-capacitor discharge stimulation (SCDS) system for electrical stimulation, and, subsequently, determine the overall energy saved compared to a conventional stimulator. We have constructed a computational model by pairing an image-based volume conductor model of the cat head with cable models of corticospinal tract (CST) axons and quantified the theoretical stimulation efficiency of rectangular and decaying exponential waveforms, produced by conventional and SCDS systems, respectively. Subsequently, the model predictions were tested in vivo by activating axons in the posterior internal capsule and recording evoked electromyography (EMG) in the contralateral upper arm muscles. Compared to rectangular waveforms, decaying exponential waveforms with time constants >500 μs were predicted to require 2%-4% less stimulus energy to activate directly models of CST axons and 0.4%-2% less stimulus energy to evoke EMG activity in vivo. Using the calculated wireless input energy of the stimulation system and the measured stimulus energies required to evoke EMG activity, we predict that an SCDS implantable pulse generator (IPG) will require 40% less input energy than a conventional IPG to activate target neural elements. A wireless SCDS IPG that is more energy efficient than a conventional IPG will reduce the size of an implant, require that less wireless energy be transmitted through the skin, and extend the lifetime of the battery in the external power transmitter.

  6. 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

  7. Reactivating Neural Circuits with Clinically Accessible Stimulation to Restore Hand Function in Persons with Tetraplegia

    Science.gov (United States)

    2017-09-01

    AWARD NUMBER: W81XWH-16-1-0395 TITLE: Reactivating Neural Circuits with Clinically Accessible Stimulation to Restore Hand Function in...estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data...Clinically Accessible Stimulation to Restore Hand Function in Persons with Tetraplegia 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S

  8. 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.

  9. Simulation of activation and propagation delay during tripolar neural stimulation

    NARCIS (Netherlands)

    Goodall, E.V.; Goodall, Eleanor V.; Kosterman, L. Martin; Struijk, Johannes J.; Struijk, J.J.; Holsheimer, J.

    1993-01-01

    Computer simulations were perfonned to investigate the influence of stimulus amplitude on cathodal activation delay, propagation delay and blocking during stimulation with a bipolar cuff electrode. Activation and propagation delays were combined in a total delay term which was minimized between the

  10. 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.

  11. Stochastic Nonlinear Evolutional Model of the Large-Scaled Neuronal Population and Dynamic Neural Coding Subject to Stimulation

    International Nuclear Information System (INIS)

    Wang Rubin; Yu Wei

    2005-01-01

    In this paper, we investigate how the population of neuronal oscillators deals with information and the dynamic evolution of neural coding when the external stimulation acts on it. Numerically computing method is used to describe the evolution process of neural coding in three-dimensioned space. The numerical result proves that only the suitable stimulation can change the coupling structure and plasticity of neurons

  12. Neural neworks in a management information systems

    Directory of Open Access Journals (Sweden)

    Jana Weinlichová

    2009-01-01

    Full Text Available For having retrospection for all over the data which are used, analyzed, evaluated and for a future incident predictions are used Management Information Systems and Business Intelligence. In case of not to be able to apply standard methods of data processing there can be with benefit applied an Artificial Intelligence. In this article will be referred to proofed abilities of Neural Networks. The Neural Networks is supported by many software products related to provide effective solution of manager issues. Those products are given as primary support for manager issues solving. We were tried to find reciprocally between products using Neural Networks and between Management Information Systems for finding a real possibility of applying Neural Networks as a direct part of Management Information Systems (MIS. In the article are presented possibilities to apply Neural Networks on different types of tasks in MIS.

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

    International Nuclear Information System (INIS)

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

    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, 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. (paper)

  14. 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.

  15. The reliability of nonlinear least-squares algorithm for data analysis of neural response activity during sinusoidal rotational stimulation in semicircular canal neurons.

    Science.gov (United States)

    Ren, Pengyu; Li, Bowen; Dong, Shiyao; Chen, Lin; Zhang, Yuelin

    2018-01-01

    Although many mathematical methods were used to analyze the neural activity under sinusoidal stimulation within linear response range in vestibular system, the reliabilities of these methods are still not reported, especially in nonlinear response range. Here we chose nonlinear least-squares algorithm (NLSA) with sinusoidal model to analyze the neural response of semicircular canal neurons (SCNs) during sinusoidal rotational stimulation (SRS) over a nonlinear response range. Our aim was to acquire a reliable mathematical method for data analysis under SRS in vestibular system. Our data indicated that the reliability of this method in an entire SCNs population was quite satisfactory. However, the reliability was strongly negatively depended on the neural discharge regularity. In addition, stimulation parameters were the vital impact factors influencing the reliability. The frequency had a significant negative effect but the amplitude had a conspicuous positive effect on the reliability. Thus, NLSA with sinusoidal model resulted a reliable mathematical tool for data analysis of neural response activity under SRS in vestibular system and more suitable for those under the stimulation with low frequency but high amplitude, suggesting that this method can be used in nonlinear response range. This method broke out of the restriction of neural activity analysis under nonlinear response range and provided a solid foundation for future study in nonlinear response range in vestibular system.

  16. Echoes in correlated neural systems

    International Nuclear Information System (INIS)

    Helias, M; Tetzlaff, T; Diesmann, M

    2013-01-01

    Correlations are employed in modern physics to explain microscopic and macroscopic phenomena, like the fractional quantum Hall effect and the Mott insulator state in high temperature superconductors and ultracold atoms. Simultaneously probed neurons in the intact brain reveal correlations between their activity, an important measure to study information processing in the brain that also influences the macroscopic signals of neural activity, like the electroencephalogram (EEG). Networks of spiking neurons differ from most physical systems: the interaction between elements is directed, time delayed, mediated by short pulses and each neuron receives events from thousands of neurons. Even the stationary state of the network cannot be described by equilibrium statistical mechanics. Here we develop a quantitative theory of pairwise correlations in finite-sized random networks of spiking neurons. We derive explicit analytic expressions for the population-averaged cross correlation functions. Our theory explains why the intuitive mean field description fails, how the echo of single action potentials causes an apparent lag of inhibition with respect to excitation and how the size of the network can be scaled while maintaining its dynamical state. Finally, we derive a new criterion for the emergence of collective oscillations from the spectrum of the time-evolution propagator. (paper)

  17. 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.

  18. 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.

  19. Oscillatory neural representations in the sensory thalamus predict neuropathic pain relief by deep brain stimulation.

    Science.gov (United States)

    Huang, Yongzhi; Green, Alexander L; Hyam, Jonathan; Fitzgerald, James; Aziz, Tipu Z; Wang, Shouyan

    2018-01-01

    Understanding the function of sensory thalamic neural activity is essential for developing and improving interventions for neuropathic pain. However, there is a lack of investigation of the relationship between sensory thalamic oscillations and pain relief in patients with neuropathic pain. This study aims to identify the oscillatory neural characteristics correlated with pain relief induced by deep brain stimulation (DBS), and develop a quantitative model to predict pain relief by integrating characteristic measures of the neural oscillations. Measures of sensory thalamic local field potentials (LFPs) in thirteen patients with neuropathic pain were screened in three dimensional feature space according to the rhythm, balancing, and coupling neural behaviours, and correlated with pain relief. An integrated approach based on principal component analysis (PCA) and multiple regression analysis is proposed to integrate the multiple measures and provide a predictive model. This study reveals distinct thalamic rhythms of theta, alpha, high beta and high gamma oscillations correlating with pain relief. The balancing and coupling measures between these neural oscillations were also significantly correlated with pain relief. The study enriches the series research on the function of thalamic neural oscillations in neuropathic pain and relief, and provides a quantitative approach for predicting pain relief by DBS using thalamic neural oscillations. Copyright © 2017 Elsevier Inc. All rights reserved.

  20. Vagal stimulation targets select populations of intrinsic cardiac neurons to control neurally induced atrial fibrillation.

    Science.gov (United States)

    Salavatian, Siamak; Beaumont, Eric; Longpré, Jean-Philippe; Armour, J Andrew; Vinet, Alain; Jacquemet, Vincent; Shivkumar, Kalyanam; Ardell, Jeffrey L

    2016-11-01

    Mediastinal nerve stimulation (MNS) reproducibly evokes atrial fibrillation (AF) by excessive and heterogeneous activation of intrinsic cardiac (IC) neurons. This study evaluated whether preemptive vagus nerve stimulation (VNS) impacts MNS-induced evoked changes in IC neural network activity to thereby alter susceptibility to AF. IC neuronal activity in the right atrial ganglionated plexus was directly recorded in anesthetized canines (n = 8) using a linear microelectrode array concomitant with right atrial electrical activity in response to: 1) epicardial touch or great vessel occlusion vs. 2) stellate or vagal stimulation. From these stressors, post hoc analysis (based on the Skellam distribution) defined IC neurons so recorded as afferent, efferent, or convergent (afferent and efferent inputs) local circuit neurons (LCN). The capacity of right-sided MNS to modify IC activity in the induction of AF was determined before and after preemptive right (RCV)- vs. left (LCV)-sided VNS (15 Hz, 500 μs; 1.2× bradycardia threshold). Neuronal (n = 89) activity at baseline (0.11 ± 0.29 Hz) increased during MNS-induced AF (0.51 ± 1.30 Hz; P neuronal synchrony increased during neurally induced AF, a local neural network response mitigated by preemptive VNS. These antiarrhythmic effects persisted post-VNS for, on average, 26 min. In conclusion, VNS preferentially targets convergent LCNs and their interactive coherence to mitigate the potential for neurally induced AF. The antiarrhythmic properties imposed by VNS exhibit memory. Copyright © 2016 the American Physiological Society.

  1. The 128-channel fully differential digital integrated neural recording and stimulation interface.

    Science.gov (United States)

    Shahrokhi, Farzaneh; Abdelhalim, Karim; Serletis, Demitre; Carlen, Peter L; Genov, Roman

    2010-06-01

    We present a fully differential 128-channel integrated neural interface. It consists of an array of 8 X 16 low-power low-noise signal-recording and generation circuits for electrical neural activity monitoring and stimulation, respectively. The recording channel has two stages of signal amplification and conditioning with and a fully differential 8-b column-parallel successive approximation (SAR) analog-to-digital converter (ADC). The total measured power consumption of each recording channel, including the SAR ADC, is 15.5 ¿W. The measured input-referred noise is 6.08 ¿ Vrms over a 5-kHz bandwidth, resulting in a noise efficiency factor of 5.6. The stimulation channel performs monophasic or biphasic voltage-mode stimulation, with a maximum stimulation current of 5 mA and a quiescent power dissipation of 51.5 ¿W. The design is implemented in 0.35-¿m complementary metal-oxide semiconductor technology with the channel pitch of 200 ¿m for a total die size of 3.4 mm × 2.5 mm and a total power consumption of 9.33 mW. The neural interface was validated in in vitro recording of a low-Mg(2+)/high-K(+) epileptic seizure model in an intact hippocampus of a mouse.

  2. Surface-modified microelectrode array with flake nanostructure for neural recording and stimulation

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Ju-Hyun; Choi, Yang-Kyu [Nano-Oriented Bio-Electronics Lab, Department of Electrical Engineering, College of Information Science and Technology, KAIST, Daejeon 305-701 (Korea, Republic of); Kang, Gyumin; Nam, Yoonkey, E-mail: ynam@kaist.ac.kr, E-mail: ykchoi@ee.kaist.ac.kr [Department of Bio and Brain Engineering, KAIST, KAIST Institute for Nano-Century, Daejeon 305-701 (Korea, Republic of)

    2010-02-26

    A novel microelectrode modification method is reported for neural electrode engineering with a flake nanostructure (nanoflake). The nanoflake-modified electrodes are fabricated by combining conventional lithography and electrochemical deposition to implement a microelectrode array (MEA) on a glass substrate. The unique geometrical properties of nanoflake sharp tips and valleys are studied by optical, electrochemical and electrical methods in order to verify the advantages of using nanoflakes for neural recording devices. The in vitro recording and stimulation of cultured hippocampal neurons are demonstrated on the nanoflake-modified MEA and the clear action potentials are observed due to the nanoflake impedance reduction effect.

  3. 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.

  4. The development of neural stimulators: a review of preclinical safety and efficacy studies.

    Science.gov (United States)

    Shepherd, Robert K; Villalobos, Joel; Burns, Owen; Nayagam, David

    2018-05-14

    Given the rapid expansion of the field of neural stimulation and the rigorous regulatory approval requirements required before these devices can be applied clinically, it is important that there is clarity around conducting preclinical safety and efficacy studies required for the development of this technology. The present review examines basic design principles associated with the development of a safe neural stimulator and describes the suite of preclinical safety studies that need to be considered when taking a device to clinical trial. Neural stimulators are active implantable devices that provide therapeutic intervention, sensory feedback or improved motor control via electrical stimulation of neural or neuro-muscular tissue in response to trauma or disease. Because of their complexity, regulatory bodies classify these devices in the highest risk category (Class III), and they are therefore required to go through a rigorous regulatory approval process before progressing to market. The successful development of these devices is achieved through close collaboration across disciplines including engineers, scientists and a surgical/clinical team, and the adherence to clear design principles. Preclinical studies form one of several key components in the development pathway from concept to product release of neural stimulators. Importantly, these studies provide iterative feedback in order to optimise the final design of the device. Key components of any preclinical evaluation include: in vitro studies that are focussed on device reliability and include accelerated testing under highly controlled environments; in vivo studies using animal models of the disease or injury in order to assess safety and, given an appropriate animal model, the efficacy of the technology under both passive and electrically active conditions; and human cadaver and ex vivo studies designed to ensure the device's form factor conforms to human anatomy, to optimise the surgical approach and to

  5. 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.

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

    DEFF Research Database (Denmark)

    Zhang, Zhongyang; Xu, Ruodan; Wang, Zegao

    2017-01-01

    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......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...... 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...

  7. 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

  8. 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.

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

    Directory of Open Access Journals (Sweden)

    Huixia Zhao

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

  10. 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

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

    Science.gov (United States)

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

    2014-01-01

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

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

    Science.gov (United States)

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

    2014-01-01

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

  13. Neural neworks in a management information systems

    OpenAIRE

    Jana Weinlichová; Michael Štencl

    2009-01-01

    For having retrospection for all over the data which are used, analyzed, evaluated and for a future incident predictions are used Management Information Systems and Business Intelligence. In case of not to be able to apply standard methods of data processing there can be with benefit applied an Artificial Intelligence. In this article will be referred to proofed abilities of Neural Networks. The Neural Networks is supported by many software products related to provide effective solution of ma...

  14. Masking of infrared neural stimulation (INS) in hearing and deaf guinea pigs

    Science.gov (United States)

    Kadakia, Sama; Young, Hunter; Richter, Claus-Peter

    2013-03-01

    Spatial selective infrared neural stimulation has potential to improve neural prostheses, including cochlear implants. The heating of a confined target volume depolarizes the cell membrane and results in an action potential. Tissue heating may also results in thermal damage or the generation of a stress relaxation wave. Stress relaxation waves may result in a direct mechanical stimulation of remaining hair cells in the cochlea, so called optophony. Data are presented that quantify the effect of an acoustical stimulus (noise masker) on the response obtained with INS in normal hearing, acutely deafened, and chronic deaf animals. While in normal hearing animals an acoustic masker can reduce the response to INS, in acutely deafened animals the masking effect is reduced, and in chronic deaf animals this effect has not been detected. The responses to INS remain stable following the different degrees of cochlear damage.

  15. 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.

  16. Neural control of magnetic suspension systems

    Science.gov (United States)

    Gray, W. Steven

    1993-01-01

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

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

    International Nuclear Information System (INIS)

    Sun, Jinqiao; Sha, Bin; Zhou, Wenhao; Yang, Yi

    2010-01-01

    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.

  18. Application of neural networks in CRM systems

    Directory of Open Access Journals (Sweden)

    Bojanowska Agnieszka

    2017-01-01

    Full Text Available The central aim of this study is to investigate how to apply artificial neural networks in Customer Relationship Management (CRM. The paper presents several business applications of neural networks in software systems designed to aid CRM, e.g. in deciding on the profitability of building a relationship with a given customer. Furthermore, a framework for a neural-network based CRM software tool is developed. Building beneficial relationships with customers is generating considerable interest among various businesses, and is often mentioned as one of the crucial objectives of enterprises, next to their key aim: to bring satisfactory profit. There is a growing tendency among businesses to invest in CRM systems, which together with an organisational culture of a company aid managing customer relationships. It is the sheer amount of gathered data as well as the need for constant updating and analysis of this breadth of information that may imply the suitability of neural networks for the application in question. Neural networks exhibit considerably higher computational capabilities than sequential calculations because the solution to a problem is obtained without the need for developing a special algorithm. In the majority of presented CRM applications neural networks constitute and are presented as a managerial decision-taking optimisation tool.

  19. 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.

  20. Biphasic electrical currents stimulation promotes both proliferation and differentiation of fetal neural stem cells.

    Directory of Open Access Journals (Sweden)

    Keun-A Chang

    2011-04-01

    Full Text Available The use of non-chemical methods to differentiate stem cells has attracted researchers from multiple disciplines, including the engineering and the biomedical fields. No doubt, growth factor based methods are still the most dominant of achieving some level of proliferation and differentiation control--however, chemical based methods are still limited by the quality, source, and amount of the utilized reagents. Well-defined non-chemical methods to differentiate stem cells allow stem cell scientists to control stem cell biology by precisely administering the pre-defined parameters, whether they are structural cues, substrate stiffness, or in the form of current flow. We have developed a culture system that allows normal stem cell growth and the option of applying continuous and defined levels of electric current to alter the cell biology of growing cells. This biphasic current stimulator chip employing ITO electrodes generates both positive and negative currents in the same culture chamber without affecting surface chemistry. We found that biphasic electrical currents (BECs significantly increased the proliferation of fetal neural stem cells (NSCs. Furthermore, BECs also promoted the differentiation of fetal NSCs into neuronal cells, as assessed using immunocytochemistry. Our results clearly show that BECs promote both the proliferation and neuronal differentiation of fetal NSCs. It may apply to the development of strategies that employ NSCs in the treatment of various neurodegenerative diseases, such as Alzheimer's and Parkinson's diseases.

  1. Influence of attention focus on neural activity in the human spinal cord during thermal sensory stimulation.

    Science.gov (United States)

    Stroman, Patrick W; Coe, Brian C; Munoz, Doug P

    2011-01-01

    Perceptions of sensation and pain in healthy people are believed to be the net result of sensory input and descending modulation from brainstem and cortical regions depending on emotional and cognitive factors. Here, the influence of attention on neural activity in the spinal cord during thermal sensory stimulation of the hand was investigated with functional magnetic resonance imaging by systematically varying the participants' attention focus across and within repeated studies. Attention states included (1) attention to the stimulus by rating the sensation and (2) attention away from the stimulus by performing various mental tasks of watching a movie and identifying characters, detecting the direction of coherently moving dots within a randomly moving visual field and answering mentally-challenging questions. Functional MRI results spanning the cervical spinal cord and brainstem consistently demonstrated that the attention state had a significant influence on the activity detected in the cervical spinal cord, as well as in brainstem regions involved with the descending analgesia system. These findings have important implications for the detection and study of pain, and improved characterization of the effects of injury or disease. Copyright © 2011 Elsevier Inc. All rights reserved.

  2. Folic Acid supplementation stimulates notch signaling and cell proliferation in embryonic neural stem cells.

    Science.gov (United States)

    Liu, Huan; Huang, Guo-Wei; Zhang, Xu-Mei; Ren, Da-Lin; X Wilson, John

    2010-09-01

    The present study investigated the effect of folic acid supplementation on the Notch signaling pathway and cell proliferation in rat embryonic neural stem cells (NSCs). The NSCs were isolated from E14-16 rat brain and grown as neurospheres in serum-free suspension culture. Individual cultures were assigned to one of 3 treatment groups that differed according to the concentration of folic acid in the medium: Control (baseline folic acid concentration of 4 mg/l), low folic acid supplementation (4 mg/l above baseline, Folate-L) and high folic acid supplementation (40 mg/l above baseline, Folate-H). NSCs were identified by their expression of immunoreactive nestin and proliferating cells by incorporation of 5'bromo-2'deoxyuridine. Cell proliferation was also assessed by methyl thiazolyl tetrazolium assay. Notch signaling was analyzed by real-time PCR and western blot analyses of the expression of Notch1 and hairy and enhancer of split 5 (Hes5). Supplementation of NSCs with folic acid increased the mRNA and protein expression levels of Notch1 and Hes5. Folic acid supplementation also stimulated NSC proliferation dose-dependently. Embryonic NSCs respond to folic acid supplementation with increased Notch signaling and cell proliferation. This mechanism may mediate the effects of folic acid supplementation on neurogenesis in the embryonic nervous system.

  3. Canonical Wnt signaling transiently stimulates proliferation and enhances neurogenesis in neonatal neural progenitor cultures

    International Nuclear Information System (INIS)

    Hirsch, Cordula; Campano, Louise M.; Woehrle, Simon; Hecht, Andreas

    2007-01-01

    Canonical Wnt signaling triggers the formation of heterodimeric transcription factor complexes consisting of β-catenin and T cell factors, and thereby controls the execution of specific genetic programs. During the expansion and neurogenic phases of embryonic neural development canonical Wnt signaling initially controls proliferation of neural progenitor cells, and later neuronal differentiation. Whether Wnt growth factors affect neural progenitor cells postnatally is not known. Therefore, we have analyzed the impact of Wnt signaling on neural progenitors isolated from cerebral cortices of newborn mice. Expression profiling of pathway components revealed that these cells are fully equipped to respond to Wnt signals. However, Wnt pathway activation affected only a subset of neonatal progenitors and elicited a limited increase in proliferation and neuronal differentiation in distinct subsets of cells. Moreover, Wnt pathway activation only transiently stimulated S-phase entry but did not support long-term proliferation of progenitor cultures. The dampened nature of the Wnt response correlates with the predominant expression of inhibitory pathway components and the rapid actuation of negative feedback mechanisms. Interestingly, in differentiating cell cultures activation of canonical Wnt signaling reduced Hes1 and Hes5 expression suggesting that during postnatal neural development, Wnt/β-catenin signaling enhances neurogenesis from progenitor cells by interfering with Notch pathway activity

  4. Vagal stimulation targets select populations of intrinsic cardiac neurons to control neurally induced atrial fibrillation

    Science.gov (United States)

    Salavatian, Siamak; Beaumont, Eric; Longpré, Jean-Philippe; Armour, J. Andrew; Vinet, Alain; Jacquemet, Vincent; Shivkumar, Kalyanam

    2016-01-01

    Mediastinal nerve stimulation (MNS) reproducibly evokes atrial fibrillation (AF) by excessive and heterogeneous activation of intrinsic cardiac (IC) neurons. This study evaluated whether preemptive vagus nerve stimulation (VNS) impacts MNS-induced evoked changes in IC neural network activity to thereby alter susceptibility to AF. IC neuronal activity in the right atrial ganglionated plexus was directly recorded in anesthetized canines (n = 8) using a linear microelectrode array concomitant with right atrial electrical activity in response to: 1) epicardial touch or great vessel occlusion vs. 2) stellate or vagal stimulation. From these stressors, post hoc analysis (based on the Skellam distribution) defined IC neurons so recorded as afferent, efferent, or convergent (afferent and efferent inputs) local circuit neurons (LCN). The capacity of right-sided MNS to modify IC activity in the induction of AF was determined before and after preemptive right (RCV)- vs. left (LCV)-sided VNS (15 Hz, 500 μs; 1.2× bradycardia threshold). Neuronal (n = 89) activity at baseline (0.11 ± 0.29 Hz) increased during MNS-induced AF (0.51 ± 1.30 Hz; P < 0.001). Convergent LCNs were preferentially activated by MNS. Preemptive RCV reduced MNS-induced changes in LCN activity (by 70%) while mitigating MNS-induced AF (by 75%). Preemptive LCV reduced LCN activity by 60% while mitigating AF potential by 40%. IC neuronal synchrony increased during neurally induced AF, a local neural network response mitigated by preemptive VNS. These antiarrhythmic effects persisted post-VNS for, on average, 26 min. In conclusion, VNS preferentially targets convergent LCNs and their interactive coherence to mitigate the potential for neurally induced AF. The antiarrhythmic properties imposed by VNS exhibit memory. PMID:27591222

  5. 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.

  6. 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.

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

    Science.gov (United States)

    Hampson, M; Hoffman, R E

    2010-01-01

    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.

  8. 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.

  9. Integrated Neural Flight and Propulsion Control System

    Science.gov (United States)

    Kaneshige, John; Gundy-Burlet, Karen; Norvig, Peter (Technical Monitor)

    2001-01-01

    This paper describes an integrated neural flight and propulsion control system. which uses a neural network based approach for applying alternate sources of control power in the presence of damage or failures. Under normal operating conditions, the system utilizes conventional flight control surfaces. Neural networks are used to provide consistent handling qualities across flight conditions and for different aircraft configurations. Under damage or failure conditions, the system may utilize unconventional flight control surface allocations, along with integrated propulsion control, when additional control power is necessary for achieving desired flight control performance. In this case, neural networks are used to adapt to changes in aircraft dynamics and control allocation schemes. Of significant importance here is the fact that this system can operate without emergency or backup flight control mode operations. An additional advantage is that this system can utilize, but does not require, fault detection and isolation information or explicit parameter identification. Piloted simulation studies were performed on a commercial transport aircraft simulator. Subjects included both NASA test pilots and commercial airline crews. Results demonstrate the potential for improving handing qualities and significantly increasing survivability rates under various simulated failure conditions.

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

    Science.gov (United States)

    Abler, Birgit; Kumpfmüller, Daniela; Grön, Georg; Walter, Martin; Stingl, Julia; Seeringer, Angela

    2013-01-01

    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 expectation. Results

  11. 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

  12. Effects of vibratory stimulation-induced kinesthetic illusions on the neural activities of patients with stroke.

    Science.gov (United States)

    Kodama, Takayuki; Nakano, Hideki; Ohsugi, Hironori; Murata, Shin

    2016-01-01

    [Purpose] This study evaluated the influence of vibratory stimulation-induced kinesthetic illusion on brain function after stroke. [Subjects] Twelve healthy individuals and 13 stroke patients without motor or sensory loss participated. [Methods] Electroencephalograms were taken at rest and during vibratory stimulation. As a neurophysiological index of brain function, we measured the μ-rhythm, which is present mainly in the kinesthetic cortex and is attenuated by movement or motor imagery and compared the data using source localization analyses in the Standardized Low Resolution Brain Electromagnetic Tomography (sLORETA) program. [Results] At rest, μ-rhythms appeared in the sensorimotor and supplementary motor cortices in both healthy controls and stroke patients. Under vibratory stimulation, no μ-rhythm appeared in the sensorimotor cortex of either group. Moreover, in the supplementary motor area, which stores the motor imagery required for kinesthetic illusions, the μ-rhythms of patients were significantly stronger than those of the controls, although the μ-rhythms of both groups were reduced. Thus, differences in neural activity in the supplementary motor area were apparent between the subject groups. [Conclusion] Kinesthetic illusions do occur in patients with motor deficits due to stroke. The neural basis of the supplementary motor area in stroke patients may be functionally different from that found in healthy controls.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. Visual awareness suppression by pre-stimulus brain stimulation; a neural effect.

    Science.gov (United States)

    Jacobs, Christianne; Goebel, Rainer; Sack, Alexander T

    2012-01-02

    Transcranial magnetic stimulation (TMS) has established the functional relevance of early visual cortex (EVC) for visual awareness with great temporal specificity non-invasively in conscious human volunteers. Many studies have found a suppressive effect when TMS was applied over EVC 80-100 ms after the onset of the visual stimulus (post-stimulus TMS time window). Yet, few studies found task performance to also suffer when TMS was applied even before visual stimulus presentation (pre-stimulus TMS time window). This pre-stimulus TMS effect, however, remains controversially debated and its origin had mainly been ascribed to TMS-induced eye-blinking artifacts. Here, we applied chronometric TMS over EVC during the execution of a visual discrimination task, covering an exhaustive range of visual stimulus-locked TMS time windows ranging from -80 pre-stimulus to 300 ms post-stimulus onset. Electrooculographical (EoG) recordings, sham TMS stimulation, and vertex TMS stimulation controlled for different types of non-neural TMS effects. Our findings clearly reveal TMS-induced masking effects for both pre- and post-stimulus time windows, and for both objective visual discrimination performance and subjective visibility. Importantly, all effects proved to be still present after post hoc removal of eye blink trials, suggesting a neural origin for the pre-stimulus TMS suppression effect on visual awareness. We speculate based on our data that TMS exerts its pre-stimulus effect via generation of a neural state which interacts with subsequent visual input. Copyright © 2011 Elsevier Inc. All rights reserved.

  18. 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.

  19. 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...

  20. 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

  1. Analysis of complex systems using neural networks

    International Nuclear Information System (INIS)

    Uhrig, R.E.

    1992-01-01

    The application of neural networks, alone or in conjunction with other advanced technologies (expert systems, fuzzy logic, and/or genetic algorithms), to some of the problems of complex engineering systems has the potential to enhance the safety, reliability, and operability of these systems. Typically, the measured variables from the systems are analog variables that must be sampled and normalized to expected peak values before they are introduced into neural networks. Often data must be processed to put it into a form more acceptable to the neural network (e.g., a fast Fourier transformation of the time-series data to produce a spectral plot of the data). Specific applications described include: (1) Diagnostics: State of the Plant (2) Hybrid System for Transient Identification, (3) Sensor Validation, (4) Plant-Wide Monitoring, (5) Monitoring of Performance and Efficiency, and (6) Analysis of Vibrations. Although specific examples described deal with nuclear power plants or their subsystems, the techniques described can be applied to a wide variety of complex engineering systems

  2. 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.

  3. Collaborative Recurrent Neural Networks forDynamic Recommender Systems

    Science.gov (United States)

    2016-11-22

    JMLR: Workshop and Conference Proceedings 63:366–381, 2016 ACML 2016 Collaborative Recurrent Neural Networks for Dynamic Recommender Systems Young...an unprece- dented scale. Although such activity logs are abundantly available, most approaches to recommender systems are based on the rating...Recurrent Neural Network, Recommender System , Neural Language Model, Collaborative Filtering 1. Introduction As ever larger parts of the population

  4. 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.

  5. Modulating conscious movement intention by noninvasive brain stimulation and the underlying neural mechanisms.

    Science.gov (United States)

    Douglas, Zachary H; Maniscalco, Brian; Hallett, Mark; Wassermann, Eric M; He, Biyu J

    2015-05-06

    Conscious intention is a fundamental aspect of the human experience. Despite long-standing interest in the basis and implications of intention, its underlying neurobiological mechanisms remain poorly understood. Using high-definition transcranial DC stimulation (tDCS), we observed that enhancing spontaneous neuronal excitability in both the angular gyrus and the primary motor cortex caused the reported time of conscious movement intention to be ∼60-70 ms earlier. Slow brain waves recorded ∼2-3 s before movement onset, as well as hundreds of milliseconds after movement onset, independently correlated with the modulation of conscious intention by brain stimulation. These brain activities together accounted for 81% of interindividual variability in the modulation of movement intention by brain stimulation. A computational model using coupled leaky integrator units with biophysically plausible assumptions about the effect of tDCS captured the effects of stimulation on both neural activity and behavior. These results reveal a temporally extended brain process underlying conscious movement intention that spans seconds around movement commencement. Copyright © 2015 Douglas et al.

  6. 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.

  7. 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.

  8. 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.

  9. An Artificial Neural Network Controller for Intelligent Transportation Systems Applications

    Science.gov (United States)

    1996-01-01

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

  10. Dissociation between neural and vascular responses to sympathetic stimulation : contribution of local adrenergic receptor function

    Science.gov (United States)

    Jacob, G.; Costa, F.; Shannon, J.; Robertson, D.; Biaggioni, I.

    2000-01-01

    Sympathetic activation produced by various stimuli, eg, mental stress or handgrip, evokes regional vascular responses that are often nonhomogeneous. This phenomenon is believed to be the consequence of the recruitment of differential central neural pathways or of a sympathetically mediated vasodilation. The purpose of this study was to determine whether a similar heterogeneous response occurs with cold pressor stimulation and to test the hypothesis that local differences in adrenergic receptor function could be in part responsible for this diversity. In 8 healthy subjects, local norepinephrine spillover and blood flow were measured in arms and legs at baseline and during sympathetic stimulation induced by baroreflex mechanisms (nitroprusside infusion) or cold pressor stimulation. At baseline, legs had higher vascular resistance (27+/-5 versus 17+/-2 U, P=0.05) despite lower norepinephrine spillover (0.28+/-0.04 versus 0.4+/-0.05 mg. min(-1). dL(-1), P=0.03). Norepinephrine spillover increased similarly in both arms and legs during nitroprusside infusion and cold pressor stimulation. On the other hand, during cold stimulation, vascular resistance increased in arms but not in legs (20+/-9% versus -7+/-4%, P=0.03). Increasing doses of isoproterenol and phenylephrine were infused intra-arterially in arms and legs to estimate beta-mediated vasodilation and alpha-induced vasoconstriction, respectively. beta-Mediated vasodilation was significantly lower in legs compared with arms. Thus, we report a dissociation between norepinephrine spillover and vascular responses to cold stress in lower limbs characterized by a paradoxical decrease in local resistance despite increases in sympathetic activity. The differences observed in adrenergic receptor responses cannot explain this phenomenon.

  11. Wearable Neural Prostheses - Restoration of Sensory-Motor Function by Transcutaneous Electrical Stimulation

    OpenAIRE

    Micera, Silvestro; Keller, Thierry; Lawrence, Marc; Morari, Manfred; Popovic, Dejan B.

    2010-01-01

    In this article, we focus on the least invasive interface: transcutaneous ES (TES), i.e., the use of surface electrodes as an interface between the stimulator and sensory-motor systems. TES is delivered by a burst of short electrical charge pulses applied between pairs of electrodes positioned on the skin. Monophasic or charge-balanced biphasic (symmetric or asymmetric) stimulation pulses can be delivered. The latter ones have the advantage to provide contraction force while minimizing tissue...

  12. Wearable neural prostheses. Restoration of sensory-motor function by transcutaneous electrical stimulation.

    Science.gov (United States)

    Micera, Silvestro; Keller, Thierry; Lawrence, Marc; Morari, Manfred; Popović, Dejan B

    2010-01-01

    In this article, we focus on the least invasive interface: transcutaneous ES (TES), i.e., the use of surface electrodes as an interface between the stimulator and sensory-motor systems. TES is delivered by a burst of short electrical charge pulses applied between pairs of electrodes positioned on the skin. Monophasic or charge-balanced biphasic (symmetric or asymmetric) stimulation pulses can be delivered. The latter ones have the advantage to provide contraction force while minimizing tissue damage.

  13. When problem size matters: differential effects of brain stimulation on arithmetic problem solving and neural oscillations.

    Directory of Open Access Journals (Sweden)

    Bruno Rütsche

    Full Text Available The problem size effect is a well-established finding in arithmetic problem solving and is characterized by worse performance in problems with larger compared to smaller operand size. Solving small and large arithmetic problems has also been shown to involve different cognitive processes and distinct electroencephalography (EEG oscillations over the left posterior parietal cortex (LPPC. In this study, we aimed to provide further evidence for these dissociations by using transcranial direct current stimulation (tDCS. Participants underwent anodal (30min, 1.5 mA, LPPC and sham tDCS. After the stimulation, we recorded their neural activity using EEG while the participants solved small and large arithmetic problems. We found that the tDCS effects on performance and oscillatory activity critically depended on the problem size. While anodal tDCS improved response latencies in large arithmetic problems, it decreased solution rates in small arithmetic problems. Likewise, the lower-alpha desynchronization in large problems increased, whereas the theta synchronization in small problems decreased. These findings reveal that the LPPC is differentially involved in solving small and large arithmetic problems and demonstrate that the effects of brain stimulation strikingly differ depending on the involved neuro-cognitive processes.

  14. 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.

  15. Design and measurements of low power multichannel chip for recording and stimulation of neural activity.

    Science.gov (United States)

    Zoladz, M; Kmon, P; Grybos, P; Szczygiel, R; Kleczek, R; Otfinowski, P; Rauza, J

    2012-01-01

    A 64-channel Neuro-Stimulation-Recording chip named NRS64 for neural activity measurements has been designed and tested. The NRS64 occupies 5×5 mm² of silicon area and consumes only 25 µW/channel. A low cut-off frequency can be tuned in the 60 mHz-100 Hz range while a high cut-off frequency can be set to 4.7 kHz or 12 kHz. A voltage gain can be set to 139 V/V or 1100 V/V. A measured input referenced noise is 3.7 µV rms in 100 Hz-12 kHz band and 7.6 µV rms in 3 Hz-12 kHz band. A digital correction is used in each channel to tune the low cut-off frequency and offset voltage. Each channel is equipped additionally with a stimulation circuit with an artifact cancellation circuit. The stimulation circuit can be set with 8-bit resolution in six different ranges from 500 nA-512 µA range.

  16. Dynamic artificial neural networks with affective systems.

    Directory of Open Access Journals (Sweden)

    Catherine D Schuman

    Full Text Available Artificial neural networks (ANNs are processors that are trained to perform particular tasks. We couple a computational ANN with a simulated affective system in order to explore the interaction between the two. In particular, we design a simple affective system that adjusts the threshold values in the neurons of our ANN. The aim of this paper is to demonstrate that this simple affective system can control the firing rate of the ensemble of neurons in the ANN, as well as to explore the coupling between the affective system and the processes of long term potentiation (LTP and long term depression (LTD, and the effect of the parameters of the affective system on its performance. We apply our networks with affective systems to a simple pole balancing example and briefly discuss the effect of affective systems on network performance.

  17. Dynamical systems, attractors, and neural circuits.

    Science.gov (United States)

    Miller, Paul

    2016-01-01

    Biology is the study of dynamical systems. Yet most of us working in biology have limited pedagogical training in the theory of dynamical systems, an unfortunate historical fact that can be remedied for future generations of life scientists. In my particular field of systems neuroscience, neural circuits are rife with nonlinearities at all levels of description, rendering simple methodologies and our own intuition unreliable. Therefore, our ideas are likely to be wrong unless informed by good models. These models should be based on the mathematical theories of dynamical systems since functioning neurons are dynamic-they change their membrane potential and firing rates with time. Thus, selecting the appropriate type of dynamical system upon which to base a model is an important first step in the modeling process. This step all too easily goes awry, in part because there are many frameworks to choose from, in part because the sparsely sampled data can be consistent with a variety of dynamical processes, and in part because each modeler has a preferred modeling approach that is difficult to move away from. This brief review summarizes some of the main dynamical paradigms that can arise in neural circuits, with comments on what they can achieve computationally and what signatures might reveal their presence within empirical data. I provide examples of different dynamical systems using simple circuits of two or three cells, emphasizing that any one connectivity pattern is compatible with multiple, diverse functions.

  18. 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

  19. 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.

  20. 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.

  1. Models of the electrically stimulated binaural system: A review.

    Science.gov (United States)

    Dietz, Mathias

    2016-01-01

    In an increasing number of countries, the standard treatment for deaf individuals is moving toward the implantation of two cochlear implants. Today's device technology and fitting procedure, however, appears as if the two implants would serve two independent ears and brains. Many experimental studies have demonstrated that after careful matching and balancing of left and right stimulation in controlled laboratory studies most patients have almost normal sensitivity to interaural level differences and some sensitivity to interaural time differences (ITDs). Mechanisms underlying the limited ITD sensitivity are still poorly understood and many different aspects may contribute. Recent pioneering computational approaches identified some of the functional implications the electric input imposes on the neural brainstem circuits. Simultaneously these studies have raised new questions and certainly demonstrated that further refinement of the model stages is necessary. They join the experimental study's conclusions that binaural device technology, binaural fitting, specific speech coding strategies, and binaural signal processing algorithms are obviously missing components to maximize the benefit of bilateral implantation. Within this review, the existing models of the electrically stimulated binaural system are explained, compared, and discussed from a viewpoint of a "CI device with auditory system" and from that of neurophysiological research.

  2. Functional Magnetic Resonance Imaging Evaluation of Auricular Percutaneous Electrical Neural Field Stimulation for Fibromyalgia: Protocol for a Feasibility Study.

    Science.gov (United States)

    Gebre, Melat; Woodbury, Anna; Napadow, Vitaly; Krishnamurthy, Venkatagiri; Krishnamurthy, Lisa C; Sniecinski, Roman; Crosson, Bruce

    2018-02-06

    Fibromyalgia is a chronic pain state that includes widespread musculoskeletal pain, fatigue, psychiatric symptoms, cognitive and sleep disturbances, and multiple somatic symptoms. Current therapies are often insufficient or come with significant risks, and while there is an increasing demand for non-pharmacologic and especially non-opioid pain management such as that offered through complementary and alternative medicine therapies, there is currently insufficient evidence to recommend these therapies. Percutaneous electrical neural stimulation (PENS) is an evidence-based treatment option for pain conditions that involves electrical current stimulation through needles inserted into the skin. Percutaneous electrical neural field stimulation (PENFS) of the auricle is similar to PENS, but instead of targeting a single neurovascular bundle, PENFS stimulates the entire ear, covering all auricular branches of the cranial nerves, including the vagus nerve. The neural mechanisms of PENFS for fibromyalgia symptom relief are unknown. We hypothesize that PENFS treatment will decrease functional brain connectivity between the default mode network (DMN) and right posterior insula in fibromyalgia patients. We expect that the decrease in functional connectivity between the DMN and insula will correlate with patient-reported analgesic improvements as indicated by the Defense and Veterans Pain Rating Scale (DVPRS) and will be anti-correlated with patient-reported analgesic medication consumption. Exploratory analyses will be performed for further hypothesis generation. A total of 20 adults from the Atlanta Veterans Affairs Medical Center diagnosed with fibromyalgia will be randomized into 2 groups: 10 subjects to a control (standard therapy) group and 10 subjects to a PENFS treatment group. The pragmatic, standard therapy group will include pharmacologic treatments such as anticonvulsants, non-steroidal anti-inflammatory drugs, topical agents and physical therapy individualized to

  3. Differences between otolith- and semicircular canal-activated neural circuitry in the vestibular system.

    Science.gov (United States)

    Uchino, Yoshio; Kushiro, Keisuke

    2011-12-01

    In the last two decades, we have focused on establishing a reliable technique for focal stimulation of vestibular receptors to evaluate neural connectivity. Here, we summarize the vestibular-related neuronal circuits for the vestibulo-ocular reflex, vestibulocollic reflex, and vestibulospinal reflex arcs. The focal stimulating technique also uncovered some hidden neural mechanisms. In the otolith system, we identified two hidden neural mechanisms that enhance otolith receptor sensitivity. The first is commissural inhibition, which boosts sensitivity by incorporating inputs from bilateral otolith receptors, the existence of which was in contradiction to the classical understanding of the otolith system but was observed in the utricular system. The second mechanism, cross-striolar inhibition, intensifies the sensitivity of inputs from both sides of receptive cells across the striola in a single otolith sensor. This was an entirely novel finding and is typically observed in the saccular system. We discuss the possible functional meaning of commissural and cross-striolar inhibition. Finally, our focal stimulating technique was applied to elucidate the different constructions of axonal projections from each vestibular receptor to the spinal cord. We also discuss the possible function of the unique neural connectivity observed in each vestibular receptor system. Copyright © 2011 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

  4. Manipulation of food intake and weight dynamics using retrograde neural gastric electrical stimulation in a chronic canine model

    NARCIS (Netherlands)

    Aelen, P.; Neshev, E.; Cholette, M.; Crisanti, K.; Mitchell, P.; De bru, E.; Church, N.; Mintchev, M.P.

    2008-01-01

    Neural gastric electrical stimulation (NGES) could be a new technique for treating obesity. However, chronic animal experimentation exploring the efficacy of this therapy is lacking. In this study we investigated the utility of retrograde NGES in a chronic canine model. Nine mongrel dogs (26.8 ± 5.2

  5. Neural Activation during Anticipation of Near Pain-Threshold Stimulation Among the Pain-Fearful

    Directory of Open Access Journals (Sweden)

    Zhou Yang

    2016-07-01

    Full Text Available Fear of pain (FOP can increase risk for chronic pain and disability but little is known about corresponding neural responses in anticipation of potential pain. In this study, more (10 women, 6 men and less (7 women, 6 men pain-fearful groups underwent whole-brain functional magnetic resonance imaging (fMRI during anticipation of near pain-threshold stimulation. Groups did not differ in the proportion of stimuli judged to be painful but pain-fearful participants reported significantly more state fear prior to stimulus exposure. Within the entire sample, stronger activation was found in several pain regions (e.g., bilateral insula, midcingulate cortex (MCC, thalamus, superior frontal gyrus and visual areas linked to decoding stimulus valences (inferior orbital cortex during anticipation of painful stimuli. Between groups and correlation analyses indicated pain-fearful participants experienced comparatively more activity in regions implicated in evaluating potential threats and processing negative emotions during anticipation (i.e., MCC, mid occipital cortex, superior temporal pole, though group differences were not apparent in most so-called pain matrix regions. In sum, trait- and task-based FOP is associated with enhanced responsiveness in regions involved in threat processing and negative affect during anticipation of potentially painful stimulation.

  6. Neural Activation during Anticipation of Near Pain-Threshold Stimulation among the Pain-Fearful.

    Science.gov (United States)

    Yang, Zhou; Jackson, Todd; Huang, Chengzhi

    2016-01-01

    Fear of pain (FOP) can increase risk for chronic pain and disability but little is known about corresponding neural responses in anticipation of potential pain. In this study, more (10 women, 6 men) and less (7 women, 6 men) pain-fearful groups underwent whole-brain functional magnetic resonance imaging (fMRI) during anticipation of near pain-threshold stimulation. Groups did not differ in the proportion of stimuli judged to be painful but pain-fearful participants reported significantly more state fear prior to stimulus exposure. Within the entire sample, stronger activation was found in several pain perception regions (e.g., bilateral insula, midcingulate cortex (MCC), thalamus, superior frontal gyrus) and visual areas linked to decoding stimulus valences (inferior orbital cortex) during anticipation of "painful" stimuli. Between groups and correlation analyses indicated pain-fearful participants experienced comparatively more activity in regions implicated in evaluating potential threats and processing negative emotions during anticipation (i.e., MCC, mid occipital cortex, superior temporal pole), though group differences were not apparent in most so-called "pain matrix" regions. In sum, trait- and task-based FOP is associated with enhanced responsiveness in regions involved in threat processing and negative affect during anticipation of potentially painful stimulation.

  7. Neural systems for preparatory control of imitation.

    Science.gov (United States)

    Cross, Katy A; Iacoboni, Marco

    2014-01-01

    Humans have an automatic tendency to imitate others. Previous studies on how we control these tendencies have focused on reactive mechanisms, where inhibition of imitation is implemented after seeing an action. This work suggests that reactive control of imitation draws on at least partially specialized mechanisms. Here, we examine preparatory imitation control, where advance information allows control processes to be employed before an action is observed. Drawing on dual route models from the spatial compatibility literature, we compare control processes using biological and non-biological stimuli to determine whether preparatory imitation control recruits specialized neural systems that are similar to those observed in reactive imitation control. Results indicate that preparatory control involves anterior prefrontal, dorsolateral prefrontal, posterior parietal and early visual cortices regardless of whether automatic responses are evoked by biological (imitative) or non-biological stimuli. These results indicate both that preparatory control of imitation uses general mechanisms, and that preparatory control of imitation draws on different neural systems from reactive imitation control. Based on the regions involved, we hypothesize that preparatory control is implemented through top-down attentional biasing of visual processing.

  8. Theory of Neural Information Processing Systems

    International Nuclear Information System (INIS)

    Galla, Tobias

    2006-01-01

    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 10 11 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, Kuehn 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

  9. 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....

  10. 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

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

    Science.gov (United States)

    Cohen, Michael X; Gulbinaite, Rasa

    2017-02-15

    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 and time-frequency domains. The method, termed rhythmic entrainment source separation (RESS), is based on denoising source separation approaches that take advantage of the simultaneous but differential projection of neural activity to multiple electrodes or sensors. Our approach is a combination and extension of existing multivariate source separation methods. We demonstrate that RESS performs well on both simulated and empirical data, and outperforms conventional SSEP analysis methods based on selecting electrodes with the strongest SSEP response, as well as several other linear spatial filters. We also discuss the potential confound of overfitting, whereby the filter captures noise in absence of a signal. Matlab scripts are available to replicate and extend our simulations and methods. We conclude with some practical advice for optimizing SSEP data analyses and interpreting the results. Copyright © 2016 Elsevier Inc. All rights reserved.

  12. Granulocyte-colony stimulating factor controls neural and behavioral plasticity in response to cocaine.

    Science.gov (United States)

    Calipari, Erin S; Godino, Arthur; Peck, Emily G; Salery, Marine; Mervosh, Nicholas L; Landry, Joseph A; Russo, Scott J; Hurd, Yasmin L; Nestler, Eric J; Kiraly, Drew D

    2018-01-16

    Cocaine addiction is characterized by dysfunction in reward-related brain circuits, leading to maladaptive motivation to seek and take the drug. There are currently no clinically available pharmacotherapies to treat cocaine addiction. Through a broad screen of innate immune mediators, we identify granulocyte-colony stimulating factor (G-CSF) as a potent mediator of cocaine-induced adaptations. Here we report that G-CSF potentiates cocaine-induced increases in neural activity in the nucleus accumbens (NAc) and prefrontal cortex. In addition, G-CSF injections potentiate cocaine place preference and enhance motivation to self-administer cocaine, while not affecting responses to natural rewards. Infusion of G-CSF neutralizing antibody into NAc blocks the ability of G-CSF to modulate cocaine's behavioral effects, providing a direct link between central G-CSF action in NAc and cocaine reward. These results demonstrate that manipulating G-CSF is sufficient to alter the motivation for cocaine, but not natural rewards, providing a pharmacotherapeutic avenue to manipulate addictive behaviors without abuse potential.

  13. Neural correlates of mirth and laughter: a direct electrical cortical stimulation study.

    Science.gov (United States)

    Yamao, Yukihiro; Matsumoto, Riki; Kunieda, Takeharu; Shibata, Sumiya; Shimotake, Akihiro; Kikuchi, Takayuki; Satow, Takeshi; Mikuni, Nobuhiro; Fukuyama, Hidenao; Ikeda, Akio; Miyamoto, Susumu

    2015-05-01

    Laughter consists of both motor and emotional aspects. The emotional component, known as mirth, is usually associated with the motor component, namely, bilateral facial movements. Previous electrical cortical stimulation (ES) studies revealed that mirth was associated with the basal temporal cortex, inferior frontal cortex, and medial frontal cortex. Functional neuroimaging implicated a role for the left inferior frontal and bilateral temporal cortices in humor processing. However, the neural origins and pathways linking mirth with facial movements are still unclear. We hereby report two cases with temporal lobe epilepsy undergoing subdural electrode implantation in whom ES of the left basal temporal cortex elicited both mirth and laughter-related facial muscle movements. In one case with normal hippocampus, high-frequency ES consistently caused contralateral facial movement, followed by bilateral facial movements with mirth. In contrast, in another case with hippocampal sclerosis (HS), ES elicited only mirth at low intensity and short duration, and eventually laughter at higher intensity and longer duration. In both cases, the basal temporal language area (BTLA) was located within or adjacent to the cortex where ES produced mirth. In conclusion, the present direct ES study demonstrated that 1) mirth had a close relationship with language function, 2) intact mesial temporal structures were actively engaged in the beginning of facial movements associated with mirth, and 3) these emotion-related facial movements had contralateral dominance. Copyright © 2014 Elsevier Ltd. All rights reserved.

  14. A new paradigm of electrical stimulation to enhance sensory neural function.

    Science.gov (United States)

    Breen, Paul P; ÓLaighin, Gearóid; McIntosh, Caroline; Dinneen, Sean F; Quinlan, Leo R; Serrador, Jorge M

    2014-08-01

    The ability to improve peripheral neural transmission would have significant therapeutic potential in medicine. A technology of this kind could be used to restore and/or enhance sensory function in individuals with depressed sensory function, such as older adults or patients with peripheral neuropathies. The goal of this study was to investigate if a new paradigm of subsensory electrical noise stimulation enhances somatosensory function. Vibration (50Hz) was applied with a Neurothesiometer to the plantar aspect of the foot in the presence or absence of subsensory electrical noise (1/f type). The noise was applied at a proximal site, on a defined region of the tibial nerve path above the ankle. Vibration perception thresholds (VPT) of younger adults were measured in control and experimental conditions, in the absence or presence of noise respectively. An improvement of ∼16% in VPT was found in the presence of noise. These are the first data to demonstrate that modulation of axonal transmission with externally applied electrical noise improves perception of tactile stimuli in humans. Copyright © 2014 IPEM. All rights reserved.

  15. Sympathetic neural modulation of the immune system

    International Nuclear Information System (INIS)

    Madden, K.S.

    1989-01-01

    One route by which the central nervous system communicates with lymphoid organs in the periphery is through the sympathetic nervous system (SNS). To study SNS regulation of immune activity in vivo, selective removal of peripheral noradrenergic nerve fibers was achieved by administration of the neurotoxic drug, 6-hydroxydopamine (6-OHDA), to adult mice. To assess SNS influence on lymphocyte proliferation in vitro, uptake of 125 iododeoxyuridine ( 125 IUdR), a DNA precursor, was measured following 6-OHDA treatment. Sympathectomy prior to epicutaneous immunization with TNCB did not alter draining lymph nodes (LN) cell proliferation, whereas 6-OHDA treatment before footpad immunization with KLH reduced DNA synthesis in popliteal LN by 50%. In mice which were not deliberately immunized, sympathectomy stimulated 125 IUdR uptake inguinal and axillary LN, spleen, and bone marrow. In vitro, these LN and spleen cells exhibited decreased proliferation responses to the T cell mitogen, concanavalin A (Con A), whereas lipopolysaccharide (LPS)-stimulated IgG secretion was enhanced. Studies examining 51 Cr-labeled lymphocyte trafficking to LN suggested that altered cell migration may play a part in sympathectomy-induced changes in LN cell function

  16. Shared Neural Mechanisms for the Evaluation of Intense Sensory Stimulation and Economic Reward, Dependent on Stimulation-Seeking Behavior.

    Science.gov (United States)

    Norbury, Agnes; Valton, Vincent; Rees, Geraint; Roiser, Jonathan P; Husain, Masud

    2016-09-28

    Why are some people strongly motivated by intense sensory experiences? Here we investigated how people encode the value of an intense sensory experience compared with economic reward, and how this varies according to stimulation-seeking preference. Specifically, we used a novel behavioral task in combination with computational modeling to derive the value individuals assigned to the opportunity to experience an intense tactile stimulus (mild electric shock). We then examined functional imaging data recorded during task performance to see how the opportunity to experience the sensory stimulus was encoded in stimulation-seekers versus stimulation-avoiders. We found that for individuals who positively sought out this kind of sensory stimulation, there was common encoding of anticipated economic and sensory rewards in the ventromedial prefrontal cortex. Conversely, there was robust encoding of the modeled probability of receiving such stimulation in the insula only in stimulation-avoidant individuals. Finally, we found preliminary evidence that sensory prediction error signals may be positively signed for stimulation-seekers, but negatively signed for stimulation-avoiders, in the posterior cingulate cortex. These findings may help explain why high intensity sensory experiences are appetitive for some individuals, but not for others, and may have relevance for the increased vulnerability for some psychopathologies, but perhaps increased resilience for others, in high sensation-seeking individuals. People vary in their preference for intense sensory experiences. Here, we investigated how different individuals evaluate the prospect of an unusual sensory experience (electric shock), compared with the opportunity to gain a more traditional reward (money). We found that in a subset of individuals who sought out such unusual sensory stimulation, anticipation of the sensory outcome was encoded in the same way as that of monetary gain, in the ventromedial prefrontal cortex

  17. A distributed transducer system for functional electrical stimulation

    DEFF Research Database (Denmark)

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

    2001-01-01

    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......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 these problems by partitioning the RF transceiver and sensor/actuator functions onto separate integrated circuits. By amplifying measured neural signals directly at the measurements site and converting them into the digital domain before passing them to the transceiver the signal integrity is less likely...

  18. The first neural probe integrated with light source (blue laser diode) for optical stimulation and electrical recording.

    Science.gov (United States)

    Park, HyungDal; Shin, Hyun-Joon; Cho, Il-Joo; Yoon, Eui-sung; Suh, Jun-Kyo Francis; Im, Maesoon; Yoon, Euisik; Kim, Yong-Jun; Kim, Jinseok

    2011-01-01

    In this paper, we report a neural probe which can selectively stimulate target neurons optically through Si wet etched mirror surface and record extracellular neural signals in iridium oxide tetrodes. Consequently, the proposed approach provides to improve directional problem and achieve at least 150/m gap distance between stimulation and recording sites by wet etched mirror surface in V-groove. Also, we developed light source, blue laser diode (OSRAM Blue Laser Diode_PL 450), integration through simple jig for one-touch butt-coupling. Furthermore, optical power and impedance of iridium oxide tetrodes were measured as 200 μW on 5 mW from LD and 206.5 k Ω at 1 kHz and we demonstrated insertion test of probe in 0.5% agarose-gel successfully. We have successfully transmitted a light of 450 nm to optical fiber through the integrated LD using by butt-coupling method.

  19. 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

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

    Science.gov (United States)

    Censor, Nitzan; Cohen, Leonardo G

    2011-01-01

    In the last two decades, there has been a rapid development in the research of the physiological brain mechanisms underlying human motor learning and memory. While conventional memory research performed on animal models uses intracellular recordings, microfusion of protein inhibitors to specific brain areas and direct induction of focal brain lesions, human research has so far utilized predominantly behavioural approaches and indirect measurements of neural activity. 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 (excitation or inhibition) on memory generation and consolidation, contributing to the understanding of the neural substrates of motor learning. Depending on the parameters of stimulation, rTMS can also facilitate learning processes, presumably through purposeful modulation of excitability in specific brain regions. rTMS has also been used to gain valuable knowledge regarding the timeline of motor memory formation, from initial encoding to stabilization and long-term retention. In this review, we summarize insights gained using rTMS on the physiological and neural mechanisms of human motor learning and memory. We conclude by suggesting possible future research directions, some with direct clinical implications.

  1. 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.

  2. Bifurcation and chaos in neural excitable system

    International Nuclear Information System (INIS)

    Jing Zhujun; Yang Jianping; Feng Wei

    2006-01-01

    In this paper, we investigate the dynamical behaviors of neural excitable system without periodic external current (proposed by Chialvo [Generic excitable dynamics on a two-dimensional map. Chaos, Solitons and Fractals 1995;5(3-4):461-79] and with periodic external current as system's parameters vary. The existence and stability of three fixed points, bifurcation of fixed points, the conditions of existences of fold bifurcation, flip bifurcation and Hopf bifurcation are derived by using bifurcation theory and center manifold theorem. The chaotic existence in the sense of Marotto's definition of chaos is proved. We then give the numerical simulated results (using bifurcation diagrams, computations of Maximum Lyapunov exponent and phase portraits), which not only show the consistence with the analytic results but also display new and interesting dynamical behaviors, including the complete period-doubling and inverse period-doubling bifurcation, symmetry period-doubling bifurcations of period-3 orbit, simultaneous occurrence of two different routes (invariant cycle and period-doubling bifurcations) to chaos for a given bifurcation parameter, sudden disappearance of chaos at one critical point, a great abundance of period windows (period 2 to 10, 12, 19, 20 orbits, and so on) in transient chaotic regions with interior crises, strange chaotic attractors and strange non-chaotic attractor. In particular, the parameter k plays a important role in the system, which can leave the chaotic behavior or the quasi-periodic behavior to period-1 orbit as k varies, and it can be considered as an control strategy of chaos by adjusting the parameter k. Combining the existing results in [Generic excitable dynamics on a two-dimensional map. Chaos, Solitons and Fractals 1995;5(3-4):461-79] with the new results reported in this paper, a more complete description of the system is now obtained

  3. 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.

  4. 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 MnCl 2 solution through microinjection usually results in image blurring or toxicity due to the uncontrollable diffusion of Mn 2+ . 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 Mn 2+ ions, so that the system can release Mn 2+ ions rather than MnCl 2 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

  5. Systems for deep brain stimulation: review of technical features.

    Science.gov (United States)

    Amon, A; Alesch, F

    2017-09-01

    The use of deep brain stimulation (DBS) is an important treatment option for movement disorders and other medical conditions. Today, three major manufacturers provide implantable systems for DBS. Although the underlying principle is basically the same for all available systems, the differences in the technical features vary considerably. This article outlines aspects regarding the technical features of DBS systems. The differences between voltage and current sources are addressed and their effect on stimulation is shown. To maintain clinical benefit and minimize side effects the stimulation field has to be adapted to the requirements of the patient. Shaping of the stimulation field can be achieved by the electrode design and polarity configuration. Furthermore, the electric signal consisting of stimulation rate, stimulation amplitude and pulse width affect the stimulation field. Interleaving stimulation is an additional concept, which permits improved treatment outcomes. Therefore, the electrode design, the polarity, the electric signal, and the concept of interleaving stimulation are presented. The investigated systems can be also categorized as rechargeable and non-rechargeable, which is briefly discussed. Options for interconnecting different system components from various manufacturers are presented. The present paper summarizes the technical features and their combination possibilities, which can have a major impact on the therapeutic effect.

  6. PWR system simulation and parameter estimation with neural networks

    International Nuclear Information System (INIS)

    Akkurt, Hatice; Colak, Uener

    2002-01-01

    A detailed nonlinear model for a typical PWR system has been considered for the development of simulation software. Each component in the system has been represented by appropriate differential equations. The SCILAB software was used for solving nonlinear equations to simulate steady-state and transient operational conditions. Overall system has been constructed by connecting individual components to each other. The validity of models for individual components and overall system has been verified. The system response against given transients have been analyzed. A neural network has been utilized to estimate system parameters during transients. Different transients have been imposed in training and prediction stages with neural networks. Reactor power and system reactivity during the transient event have been predicted by the neural network. Results show that neural networks estimations are in good agreement with the calculated response of the reactor system. The maximum errors are within ±0.254% for power and between -0.146 and 0.353% for reactivity prediction cases. Steam generator parameters, pressure and water level, are also successfully predicted by the neural network employed in this study. The noise imposed on the input parameters of the neural network deteriorates the power estimation capability whereas the reactivity estimation capability is not significantly affected

  7. PWR system simulation and parameter estimation with neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Akkurt, Hatice; Colak, Uener E-mail: uc@nuke.hacettepe.edu.tr

    2002-11-01

    A detailed nonlinear model for a typical PWR system has been considered for the development of simulation software. Each component in the system has been represented by appropriate differential equations. The SCILAB software was used for solving nonlinear equations to simulate steady-state and transient operational conditions. Overall system has been constructed by connecting individual components to each other. The validity of models for individual components and overall system has been verified. The system response against given transients have been analyzed. A neural network has been utilized to estimate system parameters during transients. Different transients have been imposed in training and prediction stages with neural networks. Reactor power and system reactivity during the transient event have been predicted by the neural network. Results show that neural networks estimations are in good agreement with the calculated response of the reactor system. The maximum errors are within {+-}0.254% for power and between -0.146 and 0.353% for reactivity prediction cases. Steam generator parameters, pressure and water level, are also successfully predicted by the neural network employed in this study. The noise imposed on the input parameters of the neural network deteriorates the power estimation capability whereas the reactivity estimation capability is not significantly affected.

  8. 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.

  9. 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.

  10. A Portable Ultrasound System for Non-Invasive Ultrasonic Neuro-Stimulation.

    Science.gov (United States)

    Qiu, Weibao; Zhou, Juan; Chen, Yan; Su, Min; Li, Guofeng; Zhao, Huixia; Gu, Xianyi; Meng, De; Wang, Congzhi; Xiao, Yang; Lam, Kwok Ho; Dai, Jiyan; Zheng, Hairong

    2017-12-01

    Fundamental insights into the function of the neural circuits often follows from the advances in methodologies and tools for neuroscience. Electrode- and optical- based stimulation methods have been used widely for neuro-modulation with high resolution. However, they are suffering from inherent invasive surgical procedure. Ultrasound has been proved as a promising technology for neuro-stimulation in a non-invasive manner. However, no portable ultrasound system has been developed particularly for neuro-stimulation. The utilities used currently are assembled by traditional functional generator, power amplifier, and general transducer, therefore, resulting in lack of flexibility. This paper presents a portable system to achieve ultrasonic neuro-stimulation to satisfy various studies. The system incorporated a high voltage waveform generator and a matching circuit that were optimized for neuro-stimulation. A new switching mode power amplifier was designed and fabricated. The noise generated by the power amplifier was reduced (about 30 dB), and the size and weight were smaller in contrast with commercial equipment. In addition, a miniaturized ultrasound transducer was fabricated using Pb(Mg 1/3 Nb 2/3 )O 3 -PbTiO 3 (PMN-PT) 1-3 composite single crystal for the improved ultrasonic performance. The spatial peak temporal average pressure was higher than 250 kPa in the range of 0.5-5 MHz. In vitro and in vivo studies were conducted to show the performance of the system.

  11. Diagnostic Neural Network Systems for the Electronic Circuits

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2014-01-01

    Neural Networks is one of the most important artificial intelligent approaches for solving the diagnostic processes. This research concerns with uses the neural networks for diagnosis of the electronic circuits. Modern electronic systems contain both the analog and digital circuits. But, diagnosis of the analog circuits suffers from great complexity due to their nonlinearity. To overcome this problem, the proposed system introduces a diagnostic system that uses the neural network to diagnose both the digital and analog circuits. So, it can face the new requirements for the modern electronic systems. A fault dictionary method was implemented in the system. Experimental results are presented on three electronic systems. They are: artificial kidney, wireless network and personal computer systems. The proposed system has improved the performance of the diagnostic systems when applied for these practical cases

  12. Vein matching using artificial neural network in vein authentication systems

    Science.gov (United States)

    Noori Hoshyar, Azadeh; Sulaiman, Riza

    2011-10-01

    Personal identification technology as security systems is developing rapidly. Traditional authentication modes like key; password; card are not safe enough because they could be stolen or easily forgotten. Biometric as developed technology has been applied to a wide range of systems. According to different researchers, vein biometric is a good candidate among other biometric traits such as fingerprint, hand geometry, voice, DNA and etc for authentication systems. Vein authentication systems can be designed by different methodologies. All the methodologies consist of matching stage which is too important for final verification of the system. Neural Network is an effective methodology for matching and recognizing individuals in authentication systems. Therefore, this paper explains and implements the Neural Network methodology for finger vein authentication system. Neural Network is trained in Matlab to match the vein features of authentication system. The Network simulation shows the quality of matching as 95% which is a good performance for authentication system matching.

  13. Precise positional measurement system in transcranial magnetic stimulation

    International Nuclear Information System (INIS)

    Inoue, Tomonori; Mishima, Yukuo; Hiwaki, Osamu

    2006-01-01

    Transcranial magnetic stimulation (TMS) is a method for noninvasive stimulation of cerebral cortex, and it has contributed to clinical and basic researches of brain function. In order to estimate the accurate stimulating points of the cortex in TMS, precise measurement of the subject's head and the stimulating coil is necessary. In this study, we have developed the positioning TMS system with a three-dimensional (3-D) digitizer and a multi-articular system. We proposed a method for the accurate measurement of a subject's head and cortex, in which the location data of the subject's face surface captured by a 3-D digitizer were superimposed on the magnetic resonance imaging (MRI) data of the subject's face surface. Using this system, the precise estimation of the stimulated sites of the cortex in TMS was achieved. The validity of the system was verified by the experiment on the TMS of the motor cortex. (author)

  14. Use of neural networks in the analysis of complex systems

    International Nuclear Information System (INIS)

    Uhrig, R.E.

    1992-01-01

    The application of neural networks, alone or in conjunction with other advanced technologies (expert systems, fuzzy logic, and/or genetic algorithms) to some of the problems of complex engineering systems has the potential to enhance the safety reliability and operability of these systems. The work described here deals with complex systems or parts of such systems that can be isolated from the total system. Typically, the measured variables from the systems are analog variables that must be sampled and normalized to expected peak values before they are introduced into neural networks. Often data must be processed to put it into a form more acceptable to the neural network. The neural networks are usually simulated on modern high-speed computers that carry out the calculations serially. However, it is possible to implement neural networks using specially designed microchips where the network calculations are truly carried out in parallel, thereby providing virtually instantaneous outputs for each set of inputs. Specific applications described include: Diagnostics: State of the Plant; Hybrid System for Transient Identification; Detection of Change of Mode in Complex Systems; Sensor Validation; Plant-Wide Monitoring; Monitoring of Performance and Efficiency; and Analysis of Vibrations. Although the specific examples described deal with nuclear power plants or their subsystems, the techniques described can be applied to a wide variety of complex engineering systems

  15. 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.

  16. Algorithmic design of a noise-resistant and efficient closed-loop deep brain stimulation system: A computational approach.

    Directory of Open Access Journals (Sweden)

    Sofia D Karamintziou

    Full Text Available Advances in the field of closed-loop neuromodulation call for analysis and modeling approaches capable of confronting challenges related to the complex neuronal response to stimulation and the presence of strong internal and measurement noise in neural recordings. Here we elaborate on the algorithmic aspects of a noise-resistant closed-loop subthalamic nucleus deep brain stimulation system for advanced Parkinson's disease and treatment-refractory obsessive-compulsive disorder, ensuring remarkable performance in terms of both efficiency and selectivity of stimulation, as well as in terms of computational speed. First, we propose an efficient method drawn from dynamical systems theory, for the reliable assessment of significant nonlinear coupling between beta and high-frequency subthalamic neuronal activity, as a biomarker for feedback control. Further, we present a model-based strategy through which optimal parameters of stimulation for minimum energy desynchronizing control of neuronal activity are being identified. The strategy integrates stochastic modeling and derivative-free optimization of neural dynamics based on quadratic modeling. On the basis of numerical simulations, we demonstrate the potential of the presented modeling approach to identify, at a relatively low computational cost, stimulation settings potentially associated with a significantly higher degree of efficiency and selectivity compared with stimulation settings determined post-operatively. Our data reinforce the hypothesis that model-based control strategies are crucial for the design of novel stimulation protocols at the backstage of clinical applications.

  17. Algorithmic design of a noise-resistant and efficient closed-loop deep brain stimulation system: A computational approach.

    Science.gov (United States)

    Karamintziou, Sofia D; Custódio, Ana Luísa; Piallat, Brigitte; Polosan, Mircea; Chabardès, Stéphan; Stathis, Pantelis G; Tagaris, George A; Sakas, Damianos E; Polychronaki, Georgia E; Tsirogiannis, George L; David, Olivier; Nikita, Konstantina S

    2017-01-01

    Advances in the field of closed-loop neuromodulation call for analysis and modeling approaches capable of confronting challenges related to the complex neuronal response to stimulation and the presence of strong internal and measurement noise in neural recordings. Here we elaborate on the algorithmic aspects of a noise-resistant closed-loop subthalamic nucleus deep brain stimulation system for advanced Parkinson's disease and treatment-refractory obsessive-compulsive disorder, ensuring remarkable performance in terms of both efficiency and selectivity of stimulation, as well as in terms of computational speed. First, we propose an efficient method drawn from dynamical systems theory, for the reliable assessment of significant nonlinear coupling between beta and high-frequency subthalamic neuronal activity, as a biomarker for feedback control. Further, we present a model-based strategy through which optimal parameters of stimulation for minimum energy desynchronizing control of neuronal activity are being identified. The strategy integrates stochastic modeling and derivative-free optimization of neural dynamics based on quadratic modeling. On the basis of numerical simulations, we demonstrate the potential of the presented modeling approach to identify, at a relatively low computational cost, stimulation settings potentially associated with a significantly higher degree of efficiency and selectivity compared with stimulation settings determined post-operatively. Our data reinforce the hypothesis that model-based control strategies are crucial for the design of novel stimulation protocols at the backstage of clinical applications.

  18. 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

  19. A Simple fMRI Compatible Robotic Stimulator to Study the Neural Mechanisms of Touch and Pain.

    Science.gov (United States)

    Riillo, F; Bagnato, C; Allievi, A G; Takagi, A; Fabrizi, L; Saggio, G; Arichi, T; Burdet, E

    2016-08-01

    This paper presents a simple device for the investigation of the human somatosensory system with functional magnetic imaging (fMRI). PC-controlled pneumatic actuation is employed to produce innocuous or noxious mechanical stimulation of the skin. Stimulation patterns are synchronized with fMRI and other relevant physiological measurements like electroencephalographic activity and vital physiological parameters. The system allows adjustable regulation of stimulation parameters and provides consistent patterns of stimulation. A validation experiment demonstrates that the system safely and reliably identifies clusters of functional activity in brain regions involved in the processing of pain. This new device is inexpensive, portable, easy-to-assemble and customizable to suit different experimental requirements. It provides robust and consistent somatosensory stimulation, which is of crucial importance to investigating the mechanisms of pain and its strong connection with the sense of touch.

  20. Effects of Electromagnetic Stimulation on Cell Density and Neural Markers in Murine Enteric Cell Cultures

    International Nuclear Information System (INIS)

    Carreon-Rodriguez, A.; Belkind-Gerson, J.; Serrano-Luna, G.; Canedo-Dorantes, L.

    2008-01-01

    Availability of adult stem cells from several organs like bone marrow, umbilical cord blood or peripheral blood has become a powerful therapeutic tool for many chronic diseases. Potential of adult stem cells for regeneration extents to other tissues among them the nervous system. However two obstacles should be resolved before such cells could be currently applied in clinical practice: a) slow growth rate and b) ability to form enough dense colonies in order to populate a specific injury or cellular deficiency. Many approaches have been explored as genetic differentiation programs, growth factors, and supplemented culture media, among others. Electromagnetic field stimulation of differentiation, proliferation, migration, and particularly on neurogenesis is little known. Since the biological effects of ELF-EMF are well documented, we hypothesize ELF-EMF could affect growth and maturation of stem cells derived of enteric tissue

  1. Bio-inspired spiking neural network for nonlinear systems control.

    Science.gov (United States)

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

    2018-08-01

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

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

    Directory of Open Access Journals (Sweden)

    Ursula S Hofstoetter

    Full Text Available 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

  3. 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.

  4. Stronger efferent suppression of cochlear neural potentials by contralateral acoustic stimulation in awake than in anesthetized chinchilla

    Directory of Open Access Journals (Sweden)

    Cristian eAedo

    2015-03-01

    Full Text Available There are two types of sensory cells in the mammalian cochlea, inner hair cells, which make synaptic contact with auditory-nerve afferent fibers, and outer hair cells that are innervated by crossed and uncrossed medial olivocochlear (MOC efferent fibers. Contralateral acoustic stimulation activates the uncrossed efferent MOC fibers reducing cochlear neural responses, thus modifying the input to the central auditory system. The chinchilla, among all studied mammals, displays the lowest percentage of uncrossed MOC fibers raising questions about the strength and frequency distribution of the contralateral-sound effect in this species. On the other hand, MOC effects on cochlear sensitivity have been mainly studied in anesthetized animals and since the MOC-neuron activity depends on the level of anesthesia, it is important to assess the influence of anesthesia in the strength of efferent effects. Seven adult chinchillas (Chinchilla laniger were chronically implanted with round-window electrodes in both cochleae. We compared the effect of contralateral sound in awake and anesthetized condition. Compound action potentials (CAP and cochlear microphonics (CM were measured in the ipsilateral cochlea in response to tones in absence and presence of contralateral sound. Control measurements performed after middle-ear muscles section in one animal discarded any possible middle-ear reflex activation. Contralateral sound produced CAP amplitude reductions in all chinchillas, with suppression effects greater by about 1-3 dB in awake than in anesthetized animals. In contrast, CM amplitude increases of up to 1.9 dB were found in only three awake chinchillas. In both conditions the strongest efferent effects were produced by contralateral tones at frequencies equal or close to those of ipsilateral tones. Contralateral CAP suppressions for 1-6 kHz ipsilateral tones corresponded to a span of uncrossed MOC fiber innervation reaching at least the central third of the

  5. Stronger efferent suppression of cochlear neural potentials by contralateral acoustic stimulation in awake than in anesthetized chinchilla.

    Science.gov (United States)

    Aedo, Cristian; Tapia, Eduardo; Pavez, Elizabeth; Elgueda, Diego; Delano, Paul H; Robles, Luis

    2015-01-01

    There are two types of sensory cells in the mammalian cochlea, inner hair cells, which make synaptic contact with auditory-nerve afferent fibers, and outer hair cells that are innervated by crossed and uncrossed medial olivocochlear (MOC) efferent fibers. Contralateral acoustic stimulation activates the uncrossed efferent MOC fibers reducing cochlear neural responses, thus modifying the input to the central auditory system. The chinchilla, among all studied mammals, displays the lowest percentage of uncrossed MOC fibers raising questions about the strength and frequency distribution of the contralateral-sound effect in this species. On the other hand, MOC effects on cochlear sensitivity have been mainly studied in anesthetized animals and since the MOC-neuron activity depends on the level of anesthesia, it is important to assess the influence of anesthesia in the strength of efferent effects. Seven adult chinchillas (Chinchilla laniger) were chronically implanted with round-window electrodes in both cochleae. We compared the effect of contralateral sound in awake and anesthetized condition. Compound action potentials (CAP) and cochlear microphonics (CM) were measured in the ipsilateral cochlea in response to tones in absence and presence of contralateral sound. Control measurements performed after middle-ear muscles section in one animal discarded any possible middle-ear reflex activation. Contralateral sound produced CAP amplitude reductions in all chinchillas, with suppression effects greater by about 1-3 dB in awake than in anesthetized animals. In contrast, CM amplitude increases of up to 1.9 dB were found in only three awake chinchillas. In both conditions the strongest efferent effects were produced by contralateral tones at frequencies equal or close to those of ipsilateral tones. Contralateral CAP suppressions for 1-6 kHz ipsilateral tones corresponded to a span of uncrossed MOC fiber innervation reaching at least the central third of the chinchilla cochlea.

  6. Design of a dynamic transcranial magnetic stimulation coil system.

    Science.gov (United States)

    Ge, Sheng; Jiang, Ruoli; Wang, Ruimin; Chen, Ji

    2014-08-01

    To study the brain activity at the whole-head range, transcranial magnetic stimulation (TMS) researchers need to investigate brain activity over the whole head at multiple locations. In the past, this has been accomplished with multiple single TMS coils that achieve quasi whole-head array stimulation. However, these designs have low resolution and are difficult to position and control over the skull. In this study, we propose a new dynamic whole-head TMS mesh coil system. This system was constructed using several sagittal and coronal directional wires. Using both simulation and real experimental data, we show that by varying the current direction and strength of each wire, this new coil system can form both circular coils or figure-eight coils that have the same features as traditional TMS coils. Further, our new system is superior to current coil systems because stimulation parameters such as size, type, location, and timing of stimulation can be dynamically controlled within a single experiment.

  7. Anomaly detection in an automated safeguards system using neural networks

    International Nuclear Information System (INIS)

    Whiteson, R.; Howell, J.A.

    1992-01-01

    An automated safeguards system must be able to detect an anomalous event, identify the nature of the event, and recommend a corrective action. Neural networks represent a new way of thinking about basic computational mechanisms for intelligent information processing. In this paper, we discuss the issues involved in applying a neural network model to the first step of this process: anomaly detection in materials accounting systems. We extend our previous model to a 3-tank problem and compare different neural network architectures and algorithms. We evaluate the computational difficulties in training neural networks and explore how certain design principles affect the problems. The issues involved in building a neural network architecture include how the information flows, how the network is trained, how the neurons in a network are connected, how the neurons process information, and how the connections between neurons are modified. Our approach is based on the demonstrated ability of neural networks to model complex, nonlinear, real-time processes. By modeling the normal behavior of the processes, we can predict how a system should be behaving and, therefore, detect when an abnormality occurs

  8. Enhanced control of electrochemical response in metallic materials in neural stimulation electrode applications

    Energy Technology Data Exchange (ETDEWEB)

    Watkins, K.G.; Steen, W.M.; Manna, I. [Univ. of Liverpool (United Kingdom)] [and others

    1996-12-31

    New means have been investigated for the production of electrode devices (stimulation electrodes) which could be implanted in the human body in order to control pain, activate paralysed limbs or provide electrode arrays for cochlear implants for the deaf or for the relief of tinitus. To achieve this ion implantation and laser materials processing techniques were employed. Ir was ion implanted in Ti-6Al-4V alloy and the surface subsequently enriched in the noble metal by dissolution in sulphuric acid. For laser materials processing techniques, investigation has been carried out on the laser cladding and laser alloying of Ir in Ti wire. A particular aim has been the determination of conditions required for the formation of a two phase Ir, Ir-rich, and Ti-rich microstructure which would enable subsequent removal of the non-noble phase to leave a highly porous noble metal with large real surface area and hence improved charge carrying capacity compared with conventional non porous electrodes. Evaluation of the materials produced has been carried out using repetitive cyclic voltammetry, amongst other techniques. For laser alloyed Ir on Ti wire, it has been found that differences in the melting point and density of the materials makes control of the cladding or alloying process difficult. Investigation of laser process parameters for the control of alloying and cladding in this system was carried out and a set of conditions for the successful production of two phase Ir-rich and Ti-rich components in a coating layer with strong metallurgical bonding to the Ti alloy substrate was derived. The laser processed material displays excellent potential for further development in providing stimulation electrodes with the current carrying capacity of Ir but in a form which is malleable and hence capable of formation into smaller electrodes with improved spatial resolution compared with presently employed electrodes.

  9. 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

  10. EGF–FGF2 stimulates the proliferation and improves the neuronal commitment of mouse epidermal neural crest stem cells (EPI-NCSCs)

    International Nuclear Information System (INIS)

    Bressan, Raul Bardini; Melo, Fernanda Rosene; Almeida, Patricia Alves; Bittencourt, Denise Avani; Visoni, Silvia; Jeremias, Talita Silva; Costa, Ana Paula; Leal, Rodrigo Bainy; Trentin, Andrea Gonçalves

    2014-01-01

    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 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 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 2 in neuronal differentiation protocols. - Highlights: • EPI-NCSCs express

  11. Representation of neural networks as Lotka-Volterra systems

    International Nuclear Information System (INIS)

    Moreau, Yves; Vandewalle, Joos; Louies, Stephane; Brenig, Leon

    1999-01-01

    We study changes of coordinates that allow the representation of the ordinary differential equations describing continuous-time recurrent neural networks into differential equations describing predator-prey models--also called Lotka-Volterra systems. We transform the equations for the neural network first into quasi-monomial form, where we express the vector field of the dynamical system as a linear combination of products of powers of the variables. In practice, this transformation is possible only if the activation function is the hyperbolic tangent or the logistic sigmoied. From this quasi-monomial form, we can directly transform the system further into Lotka-Volterra equations. The resulting Lotka-Volterra system is of higher dimension than the original system, but the behavior of its first variables is equivalent to the behavior of the original neural network

  12. 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...

  13. 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......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...... study of the networks themselves. With this end in view the following restrictions have been made: 1) Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. 2) Amongst numerous training algorithms, only the Recursive Prediction Error Method using...

  14. Neural network training by Kalman filtering in process system monitoring

    International Nuclear Information System (INIS)

    Ciftcioglu, Oe.

    1996-03-01

    Kalman filtering approach for neural network training is described. Its extended form is used as an adaptive filter in a nonlinear environment of the form a feedforward neural network. Kalman filtering approach generally provides fast training as well as avoiding excessive learning which results in enhanced generalization capability. The network is used in a process monitoring application where the inputs are measurement signals. Since the measurement errors are also modelled in Kalman filter the approach yields accurate training with the implication of accurate neural network model representing the input and output relationships in the application. As the process of concern is a dynamic system, the input source of information to neural network is time dependent so that the training algorithm presents an adaptive form for real-time operation for the monitoring task. (orig.)

  15. Thermal photovoltaic solar integrated system analysis using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Ashhab, S. [Hashemite Univ., Zarqa (Jordan). Dept. of Mechanical Engineering

    2007-07-01

    The energy demand in Jordan is primarily met by petroleum products. As such, the development of renewable energy systems is quite attractive. In particular, solar energy is a promising renewable energy source in Jordan and has been used for food canning, paper production, air-conditioning and sterilization. Artificial neural networks (ANNs) have received significant attention due to their capabilities in forecasting, modelling of complex nonlinear systems and control. ANNs have been used for forecasting solar energy. This paper presented a study that examined a thermal photovoltaic solar integrated system that was built in Jordan. Historical input-output system data that was collected experimentally was used to train an ANN that predicted the collector, PV module, pump and total efficiencies. The model predicted the efficiencies well and can therefore be utilized to find the operating conditions of the system that will produce the maximum system efficiencies. The paper provided a description of the photovoltaic solar system including equations for PV module efficiency; pump efficiency; and total efficiency. The paper also presented data relevant to the system performance and neural networks. The results of a neural net model were also presented based on the thermal PV solar integrated system data that was collected. It was concluded that the neural net model of the thermal photovoltaic solar integrated system set the background for achieving the best system performance. 10 refs., 6 figs.

  16. Neural network-based model reference adaptive control system.

    Science.gov (United States)

    Patino, H D; Liu, D

    2000-01-01

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

  17. High speed digital interfacing for a neural data acquisition system

    Directory of Open Access Journals (Sweden)

    Bahr Andreas

    2016-09-01

    Full Text Available Diseases like schizophrenia and genetic epilepsy are supposed to be caused by disorders in the early development of the brain. For the further investigation of these relationships a custom designed application specific integrated circuit (ASIC was developed that is optimized for the recording from neonatal mice [Bahr A, Abu-Saleh L, Schroeder D, Krautschneider W. 16 Channel Neural Recording Integrated Circuit with SPI Interface and Error Correction Coding. Proc. 9th BIOSTEC 2016. Biodevices: Rome, Italy, 2016; 1: 263; Bahr A, Abu-Saleh L, Schroeder D, Krautschneider W. Development of a neural recording mixed signal integrated circuit for biomedical signal acquisition. Biomed Eng Biomed Tech Abstracts 2015; 60(S1: 298–299; Bahr A, Abu-Saleh L, Schroeder D, Krautschneider WH. 16 Channel Neural Recording Mixed Signal ASIC. CDNLive EMEA 2015 Conference Proceedings, 2015.]. To enable the live display of the neural signals a multichannel neural data acquisition system with live display functionality is presented. It implements a high speed data transmission from the ASIC to a computer with a live display functionality. The system has been successfully implemented and was used in a neural recording of a head-fixed mouse.

  18. Nonlinear signal processing using neural networks: Prediction and system modelling

    Energy Technology Data Exchange (ETDEWEB)

    Lapedes, A.; Farber, R.

    1987-06-01

    The backpropagation learning algorithm for neural networks is developed into a formalism for nonlinear signal processing. We illustrate the method by selecting two common topics in signal processing, prediction and system modelling, and show that nonlinear applications can be handled extremely well by using neural networks. The formalism is a natural, nonlinear extension of the linear Least Mean Squares algorithm commonly used in adaptive signal processing. Simulations are presented that document the additional performance achieved by using nonlinear neural networks. First, we demonstrate that the formalism may be used to predict points in a highly chaotic time series with orders of magnitude increase in accuracy over conventional methods including the Linear Predictive Method and the Gabor-Volterra-Weiner Polynomial Method. Deterministic chaos is thought to be involved in many physical situations including the onset of turbulence in fluids, chemical reactions and plasma physics. Secondly, we demonstrate the use of the formalism in nonlinear system modelling by providing a graphic example in which it is clear that the neural network has accurately modelled the nonlinear transfer function. It is interesting to note that the formalism provides explicit, analytic, global, approximations to the nonlinear maps underlying the various time series. Furthermore, the neural net seems to be extremely parsimonious in its requirements for data points from the time series. We show that the neural net is able to perform well because it globally approximates the relevant maps by performing a kind of generalized mode decomposition of the maps. 24 refs., 13 figs.

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

    NARCIS (Netherlands)

    Hsu, W.Y.; Zanto, T.P.; van Schouwenburg, M.R.; Gazzaley, A.

    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

  20. Microfluidic systems for stem cell-based neural tissue engineering.

    Science.gov (United States)

    Karimi, Mahdi; Bahrami, Sajad; Mirshekari, Hamed; Basri, Seyed Masoud Moosavi; Nik, Amirala Bakhshian; Aref, Amir R; Akbari, Mohsen; Hamblin, Michael R

    2016-07-05

    Neural tissue engineering aims at developing novel approaches for the treatment of diseases of the nervous system, by providing a permissive environment for the growth and differentiation of neural cells. Three-dimensional (3D) cell culture systems provide a closer biomimetic environment, and promote better cell differentiation and improved cell function, than could be achieved by conventional two-dimensional (2D) culture systems. With the recent advances in the discovery and introduction of different types of stem cells for tissue engineering, microfluidic platforms have provided an improved microenvironment for the 3D-culture of stem cells. Microfluidic systems can provide more precise control over the spatiotemporal distribution of chemical and physical cues at the cellular level compared to traditional systems. Various microsystems have been designed and fabricated for the purpose of neural tissue engineering. Enhanced neural migration and differentiation, and monitoring of these processes, as well as understanding the behavior of stem cells and their microenvironment have been obtained through application of different microfluidic-based stem cell culture and tissue engineering techniques. As the technology advances it may be possible to construct a "brain-on-a-chip". In this review, we describe the basics of stem cells and tissue engineering as well as microfluidics-based tissue engineering approaches. We review recent testing of various microfluidic approaches for stem cell-based neural tissue engineering.

  1. Neural Signatures of Cognitive Flexibility and Reward Sensitivity Following Nicotinic Receptor Stimulation in Dependent Smokers: A Randomized Trial.

    Science.gov (United States)

    Lesage, Elise; Aronson, Sarah E; Sutherland, Matthew T; Ross, Thomas J; Salmeron, Betty Jo; Stein, Elliot A

    2017-06-01

    Withdrawal from nicotine is an important contributor to smoking relapse. Understanding how reward-based decision making is affected by abstinence and by pharmacotherapies such as nicotine replacement therapy and varenicline tartrate may aid cessation treatment. To independently assess the effects of nicotine dependence and stimulation of the nicotinic acetylcholine receptor on the ability to interpret valence information (reward sensitivity) and subsequently alter behavior as reward contingencies change (cognitive flexibility) in a probabilistic reversal learning task. Nicotine-dependent smokers and nonsmokers completed a probabilistic reversal learning task during acquisition of functional magnetic resonance imaging (fMRI) in a 2-drug, double-blind placebo-controlled crossover design conducted from January 21, 2009, to September 29, 2011. Smokers were abstinent from cigarette smoking for 12 hours for all sessions. In a fully Latin square fashion, participants in both groups underwent MRI twice while receiving varenicline and twice while receiving a placebo pill, wearing either a nicotine or a placebo patch. Imaging analysis was performed from June 15, 2015, to August 10, 2016. A well-established computational model captured effects of smoking status and administration of nicotine and varenicline on probabilistic reversal learning choice behavior. Neural effects of smoking status, nicotine, and varenicline were tested for on MRI contrasts that captured reward sensitivity and cognitive flexibility. The study included 24 nicotine-dependent smokers (12 women and 12 men; mean [SD] age, 35.8 [9.9] years) and 20 nonsmokers (10 women and 10 men; mean [SD] age, 30.4 [7.2] years). Computational modeling indicated that abstinent smokers were biased toward response shifting and that their decisions were less sensitive to the available evidence, suggesting increased impulsivity during withdrawal. These behavioral impairments were mitigated with nicotine and varenicline

  2. Chemical Stimulation of Engineered Geothermal Systems

    Energy Technology Data Exchange (ETDEWEB)

    Rose, Peter, E.

    2008-08-08

    The objective of this project is to design, develop and demonstrate methods for the chemical stimulation of candidate EGS reservoirs as well as the chemical treatment of mineral-scaled wellbores. First, a set of candidate chemical compounds capable of dissolving calcite was identified. A series of tests was then performed on each candidate in order to screen it for thermal stability and reactivity towards calcite. A detailed analysis was then performed on each compound that emerged from the screening tests in order to characterize its decay kinetics and reaction kinetics as functions of temperature and chemical composition. From among the compounds emerging from the laboratory studies, one compounds was chosen for a field experiment in order to verify the laboratory predictions.

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

    International Nuclear Information System (INIS)

    Miranda, P C; Correia, L; Salvador, R; Basser, P J

    2007-01-01

    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 -1 to 0.333 S m -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

  4. 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.

  5. Distinct Roles of Opioid and Dopamine Systems in Lateral Hypothalamic Intracranial Self-Stimulation.

    Science.gov (United States)

    Ide, Soichiro; Takahashi, Takehiro; Takamatsu, Yukio; Uhl, George R; Niki, Hiroaki; Sora, Ichiro; Ikeda, Kazutaka

    2017-05-01

    Opioid and dopamine systems play crucial roles in reward. Similarities and differences in the neural mechanisms of reward that are mediated by these 2 systems have remained largely unknown. Thus, in the present study, we investigated the differences in reward function in both µ-opioid receptor knockout mice and dopamine transporter knockout mice, important molecules in the opioid and dopamine systems. Mice were implanted with electrodes into the right lateral hypothalamus (l hour). Mice were then trained to put their muzzle into the hole in the head-dipping chamber for intracranial electrical stimulation, and the influences of gene knockout were assessed. Significant differences are observed between opioid and dopamine systems in reward function. µ-Opioid receptor knockout mice exhibited enhanced intracranial electrical stimulation, which induced dopamine release. They also exhibited greater motility under conditions of "despair" in both the tail suspension test and water wheel test. In contrast, dopamine transporter knockout mice maintained intracranial electrical stimulation responding even when more active efforts were required to obtain the reward. The absence of µ-opioid receptor or dopamine transporter did not lead to the absence of intracranial electrical stimulation responsiveness but rather differentially altered it. The present results in µ-opioid receptor knockout mice are consistent with the suppressive involvement of µ-opioid receptors in both positive incentive motivation associated with intracranial electrical stimulation and negative incentive motivation associated with depressive states. In contrast, the results in dopamine transporter knockout mice are consistent with the involvement of dopamine transporters in positive incentive motivation, especially its persistence. Differences in intracranial electrical stimulation in µ-opioid receptor and dopamine transporter knockout mice underscore the multidimensional nature of reward. © The Author

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

    OpenAIRE

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

    2016-01-01

    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 epidur...

  7. 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.

  8. 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

  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. Integrated evolutionary computation neural network quality controller for automated systems

    Energy Technology Data Exchange (ETDEWEB)

    Patro, S.; Kolarik, W.J. [Texas Tech Univ., Lubbock, TX (United States). Dept. of Industrial Engineering

    1999-06-01

    With increasing competition in the global market, more and more stringent quality standards and specifications are being demands at lower costs. Manufacturing applications of computing power are becoming more common. The application of neural networks to identification and control of dynamic processes has been discussed. The limitations of using neural networks for control purposes has been pointed out and a different technique, evolutionary computation, has been discussed. The results of identifying and controlling an unstable, dynamic process using evolutionary computation methods has been presented. A framework for an integrated system, using both neural networks and evolutionary computation, has been proposed to identify the process and then control the product quality, in a dynamic, multivariable system, in real-time.

  11. Temporal neural networks and transient analysis of complex engineering systems

    Science.gov (United States)

    Uluyol, Onder

    A theory is introduced for a multi-layered Local Output Gamma Feedback (LOGF) neural network within the paradigm of Locally-Recurrent Globally-Feedforward neural networks. It is developed for the identification, prediction, and control tasks of spatio-temporal systems and allows for the presentation of different time scales through incorporation of a gamma memory. It is initially applied to the tasks of sunspot and Mackey-Glass series prediction as benchmarks, then it is extended to the task of power level control of a nuclear reactor at different fuel cycle conditions. The developed LOGF neuron model can also be viewed as a Transformed Input and State (TIS) Gamma memory for neural network architectures for temporal processing. The novel LOGF neuron model extends the static neuron model by incorporating into it a short-term memory structure in the form of a digital gamma filter. A feedforward neural network made up of LOGF neurons can thus be used to model dynamic systems. A learning algorithm based upon the Backpropagation-Through-Time (BTT) approach is derived. It is applicable for training a general L-layer LOGF neural network. The spatial and temporal weights and parameters of the network are iteratively optimized for a given problem using the derived learning algorithm.

  12. 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.

  13. Stimulating transitions towards sustainable farming systems

    NARCIS (Netherlands)

    Elzen, B.; Barbier, M.; Cerf, M.; Grin, J.

    2012-01-01

    How can the dynamics of the agro-food sector in the long run be addressed? We argue that sustainable agro-food systems cannot be developed through a simple improvement of existing systems, but will require a transition. Therefore, we focus on how transitions to sustainability could be initiated and

  14. 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.

  15. 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.

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

    Science.gov (United States)

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

    2016-01-01

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

  17. Recording of the Neural Activity Induced by the Electrical Subthalamic Stimulation Using Ca2+ Imaging

    Science.gov (United States)

    Tamura, Atsushi; Yagi, Tetsuya; Osanai, Makoto

    The basal ganglia (BG) have important roles in some kind of motor control and learning. Parkinson's disease is one of the motor impairment disease. Recently, to recover a motor severity in patients of Parkinsonism, the stimulus electrode is implanted to the subthalamic nucleus, which is a part of the basal ganglia, and the deep brain stimulation (DBS) is often conducted. However, the effects of the DBS on the subthalamic neurons have not been elucidated. Thus, to analyze the effects of the electrical stimulation on the subthalamic neurons, we conducted the calcium imaging at the mouse subthalamic nucleus. When the single stimulus was applied to the subthalamic nucleus, the intracellular calcium ([Ca2+]i) transients were observed. In the case of application of the single electrical stimulation, the [Ca2+]i arose near the stimulus position. When 100 Hz 10-100 times tetanic stimulations were applied, the responded area and the amplitudes of [Ca2+]i transients were increased. The [Ca2+]i transients were disappeared almost completely on the action potential blockade, but blockade of the excitatory and the inhibitory synaptic transmission had little effects on the responded area and the amplitudes of the [Ca2+]i transients. These results suggested that the electrical stimulation to the subthalamic neurons led to activate the subthalamic neurons directly but not via synaptic transmissions. Thus, DBS may change the activity of the subthalamic neurons, hence, may alter the input-output relationship of the subthalamic neurons

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

    Indian Academy of Sciences (India)

    2016-08-26

    Aug 26, 2016 ... The paper describes a neural network-based script identification system which can be used in the machine reading of documents written in English, Hindi and Kannada language scripts. Script identification is a basic requirement in automation of document processing, in multi-script, multi-lingual ...

  20. Development of a hybrid system of artificial neural networks and ...

    African Journals Online (AJOL)

    Development of a hybrid system of artificial neural networks and artificial bee colony algorithm for prediction and modeling of customer choice in the market. ... attempted to present a new method for the modeling and prediction of customer choice in the market using the combination of artificial intelligence and data mining.

  1. The Method of System Test using I/O Stimulator

    International Nuclear Information System (INIS)

    Kim, Jang Yeol; Lee, Dong Young; Kim, Chang Hoi

    2016-01-01

    In general, the software development life cycle consists of the requirement phase, design phase, implementation phase, testing phase, integration phase (software-software integration, software-hardware integration), installation phase, and operation and maintenance phase. The fundamental parts to the testing phase are component test, software integration test, software hardware integration test, and system test. Among these tests, we focused in particular on system test using I/O stimulator. The system test operates the functionality, performance, and interface because system testing falls within the scope of black box testing, which should not require knowledge of the inner design of the code or logic. The purpose of developing a system test using a I/O stimulator is to save time and effort from the functionality and performance tests. We developed an I/O stimulator for the burning system test based on the operational scenario. The hardware, software, and manmachine- interface (MMI) are fundamental parts to the I/O stimulator. Signal sources from the I/O stimulator contain analog input, analog output, digital input, digital output, a Programmable Power Supply, an RS232C Serial interface and a dual port Ethernet interface. While automation cannot reproduce everything that a software engineer can do, it can be extremely useful for the system test. However, it does require the test criteria and a well-developed test suite of the testing scripts in order to be useful. In this study, we developed a multipurpose and cost-efficient I/O stimulator using LabVIEW program instead of the testing scripts

  2. The Method of System Test using I/O Stimulator

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Jang Yeol; Lee, Dong Young; Kim, Chang Hoi [KAERI, Daejeon (Korea, Republic of)

    2016-05-15

    In general, the software development life cycle consists of the requirement phase, design phase, implementation phase, testing phase, integration phase (software-software integration, software-hardware integration), installation phase, and operation and maintenance phase. The fundamental parts to the testing phase are component test, software integration test, software hardware integration test, and system test. Among these tests, we focused in particular on system test using I/O stimulator. The system test operates the functionality, performance, and interface because system testing falls within the scope of black box testing, which should not require knowledge of the inner design of the code or logic. The purpose of developing a system test using a I/O stimulator is to save time and effort from the functionality and performance tests. We developed an I/O stimulator for the burning system test based on the operational scenario. The hardware, software, and manmachine- interface (MMI) are fundamental parts to the I/O stimulator. Signal sources from the I/O stimulator contain analog input, analog output, digital input, digital output, a Programmable Power Supply, an RS232C Serial interface and a dual port Ethernet interface. While automation cannot reproduce everything that a software engineer can do, it can be extremely useful for the system test. However, it does require the test criteria and a well-developed test suite of the testing scripts in order to be useful. In this study, we developed a multipurpose and cost-efficient I/O stimulator using LabVIEW program instead of the testing scripts.

  3. Evaluation and statistical judgement of neural responses to sinusoidal stimulation in cases with superimposed drift and noise.

    Science.gov (United States)

    Jastreboff, P W

    1979-06-01

    Time histograms of neural responses evoked by sinuosidal stimulation often contain a slow drifting and an irregular noise which disturb Fourier analysis of these responses. Section 2 of this paper evaluates the extent to which a linear drift influences the Fourier analysis, and develops a combined Fourier and linear regression analysis for detecting and correcting for such a linear drift. Usefulness of this correcting method is demonstrated for the time histograms of actual eye movements and Purkinje cell discharges evoked by sinusoidal rotation of rabbits in the horizontal plane. In Sect. 3, the analysis of variance is adopted for estimating the probability of the random occurrence of the response curve extracted by Fourier analysis from noise. This method proved to be useful for avoiding false judgements as to whether the response curve was meaningful, particularly when the response was small relative to the contaminating noise.

  4. 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

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

    NARCIS (Netherlands)

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

    2015-01-01

    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

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

    NARCIS (Netherlands)

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

    2015-01-01

    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

  7. 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.

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

    Directory of Open Access Journals (Sweden)

    Wan-Yu Hsu

    Full Text Available 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.

  9. Neural mechanisms of selective attention in the somatosensory system.

    Science.gov (United States)

    Gomez-Ramirez, Manuel; Hysaj, Kristjana; Niebur, Ernst

    2016-09-01

    Selective attention allows organisms to extract behaviorally relevant information while ignoring distracting stimuli that compete for the limited resources of their central nervous systems. Attention is highly flexible, and it can be harnessed to select information based on sensory modality, within-modality feature(s), spatial location, object identity, and/or temporal properties. In this review, we discuss the body of work devoted to understanding mechanisms of selective attention in the somatosensory system. In particular, we describe the effects of attention on tactile behavior and corresponding neural activity in somatosensory cortex. Our focus is on neural mechanisms that select tactile stimuli based on their location on the body (somatotopic-based attention) or their sensory feature (feature-based attention). We highlight parallels between selection mechanisms in touch and other sensory systems and discuss several putative neural coding schemes employed by cortical populations to signal the behavioral relevance of sensory inputs. Specifically, we contrast the advantages and disadvantages of using a gain vs. spike-spike correlation code for representing attended sensory stimuli. We favor a neural network model of tactile attention that is composed of frontal, parietal, and subcortical areas that controls somatosensory cells encoding the relevant stimulus features to enable preferential processing throughout the somatosensory hierarchy. Our review is based on data from noninvasive electrophysiological and imaging data in humans as well as single-unit recordings in nonhuman primates. Copyright © 2016 the American Physiological Society.

  10. 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.

  11. Fault diagnosis system of electromagnetic valve using neural network filter

    International Nuclear Information System (INIS)

    Hayashi, Shoji; Odaka, Tomohiro; Kuroiwa, Jousuke; Ogura, Hisakazu

    2008-01-01

    This paper is concerned with the gas leakage fault detection of electromagnetic valve using a neural network filter. In modern plants, the ability to detect and identify gas leakage faults is becoming increasingly important. The main difficulty in detecting gas leakage faults by sound signals lies in the fact that the practical plants are usually very noisy. To solve this difficulty, a neural network filter is used to eliminate background noise and raise the signal noise ratio of the sound signal. The background noise is assumed as a dynamic system, and an accurate mathematical model of the dynamic system can be established using a neural network filter. The predicted error between predicted values and practical ones constitutes the output of the filter. If the predicted error is zero, then there is no leakage. If the predicted error is greater than a certain value, then there is a leakage fault. Through application to practical pneumatic systems, it is verified that the neural network filter was effective in gas leakage detection. (author)

  12. 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.

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

    Science.gov (United States)

    Pashut, Tamar; Wolfus, Shuki; Friedman, Alex; Lavidor, Michal; Bar-Gad, Izhar; Yeshurun, Yosef; Korngreen, Alon

    2011-03-01

    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.

  14. 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.

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

    Science.gov (United States)

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

    2016-11-14

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

  16. Three neural network based sensor systems for environmental monitoring

    International Nuclear Information System (INIS)

    Keller, P.E.; Kouzes, R.T.; Kangas, L.J.

    1994-05-01

    Compact, portable systems capable of quickly identifying contaminants in the field are of great importance when monitoring the environment. One of the missions of the Pacific Northwest Laboratory is to examine and develop new technologies for environmental restoration and waste management at the Hanford Site. In this paper, three prototype sensing systems are discussed. These prototypes are composed of sensing elements, data acquisition system, computer, and neural network implemented in software, and are capable of automatically identifying contaminants. The first system employs an array of tin-oxide gas sensors and is used to identify chemical vapors. The second system employs an array of optical sensors and is used to identify the composition of chemical dyes in liquids. The third system contains a portable gamma-ray spectrometer and is used to identify radioactive isotopes. In these systems, the neural network is used to identify the composition of the sensed contaminant. With a neural network, the intense computation takes place during the training process. Once the network is trained, operation consists of propagating the data through the network. Since the computation involved during operation consists of vector-matrix multiplication and application of look-up tables unknown samples can be rapidly identified in the field

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

    OpenAIRE

    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 and time-frequency domains. The method, termed rhythmic entrainment source separation (RESS), is based on denoising source separation approaches that take advantage of the simultaneous but differen...

  18. Modulating Conscious Movement Intention by Noninvasive Brain Stimulation and the Underlying Neural Mechanisms

    OpenAIRE

    Douglas, Zachary H.; Maniscalco, Brian; Hallett, Mark; Wassermann, Eric M.; He, Biyu J.

    2015-01-01

    Conscious intention is a fundamental aspect of the human experience. Despite long-standing interest in the basis and implications of intention, its underlying neurobiological mechanisms remain poorly understood. Using high-definition transcranial DC stimulation (tDCS), we observed that enhancing spontaneous neuronal excitability in both the angular gyrus and the primary motor cortex caused the reported time of conscious movement intention to be ∼60–70 ms earlier. Slow brain waves recorded ∼2–...

  19. Neural multigrid for gauge theories and other disordered systems

    International Nuclear Information System (INIS)

    Baeker, M.; Kalkreuter, T.; Mack, G.; Speh, M.

    1992-09-01

    We present evidence that multigrid works for wave equations in disordered systems, e.g. in the presence of gauge fields, no matter how strong the disorder, but one needs to introduce a 'neural computations' point of view into large scale simulations: First, the system must learn how to do the simulations efficiently, then do the simulation (fast). The method can also be used to provide smooth interpolation kernels which are needed in multigrid Monte Carlo updates. (orig.)

  20. Neural computing thermal comfort index for HVAC systems

    International Nuclear Information System (INIS)

    Atthajariyakul, S.; Leephakpreeda, T.

    2005-01-01

    The primary purpose of a heating, ventilating and air conditioning (HVAC) system within a building is to make occupants comfortable. Without real time determination of human thermal comfort, it is not feasible for the HVAC system to yield controlled conditions of the air for human comfort all the time. This paper presents a practical approach to determine human thermal comfort quantitatively via neural computing. The neural network model allows real time determination of the thermal comfort index, where it is not practical to compute the conventional predicted mean vote (PMV) index itself in real time. The feed forward neural network model is proposed as an explicit function of the relation of the PMV index to accessible variables, i.e. the air temperature, wet bulb temperature, globe temperature, air velocity, clothing insulation and human activity. An experiment in an air conditioned office room was done to demonstrate the effectiveness of the proposed methodology. The results show good agreement between the thermal comfort index calculated from the neural network model in real time and those calculated from the conventional PMV model

  1. ARTIFICIAL NEURAL NETWORKS BASED GEARS MATERIAL SELECTION HYBRID INTELLIGENT SYSTEM

    Institute of Scientific and Technical Information of China (English)

    X.C. Li; W.X. Zhu; G. Chen; D.S. Mei; J. Zhang; K.M. Chen

    2003-01-01

    An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples,the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.

  2. 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.

  3. Network connectivity and individual responses to brain stimulation in the human motor system.

    Science.gov (United States)

    Cárdenas-Morales, Lizbeth; Volz, Lukas J; Michely, Jochen; Rehme, Anne K; Pool, Eva-Maria; Nettekoven, Charlotte; Eickhoff, Simon B; Fink, Gereon R; Grefkes, Christian

    2014-07-01

    The mechanisms driving cortical plasticity in response to brain stimulation are still incompletely understood. We here explored whether neural activity and connectivity in the motor system relate to the magnitude of cortical plasticity induced by repetitive transcranial magnetic stimulation (rTMS). Twelve right-handed volunteers underwent functional magnetic resonance imaging during rest and while performing a simple hand motor task. Resting-state functional connectivity, task-induced activation, and task-related effective connectivity were assessed for a network of key motor areas. We then investigated the effects of intermittent theta-burst stimulation (iTBS) on motor-evoked potentials (MEP) for up to 25 min after stimulation over left primary motor cortex (M1) or parieto-occipital vertex (for control). ITBS-induced increases in MEP amplitudes correlated negatively with movement-related fMRI activity in left M1. Control iTBS had no effect on M1 excitability. Subjects with better response to M1-iTBS featured stronger preinterventional effective connectivity between left premotor areas and left M1. In contrast, resting-state connectivity did not predict iTBS aftereffects. Plasticity-related changes in M1 following brain stimulation seem to depend not only on local factors but also on interconnected brain regions. Predominantly activity-dependent properties of the cortical motor system are indicative of excitability changes following induction of cortical plasticity with rTMS. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  4. 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.

  5. Parameter estimation in space systems using recurrent neural networks

    Science.gov (United States)

    Parlos, Alexander G.; Atiya, Amir F.; Sunkel, John W.

    1991-01-01

    The identification of time-varying parameters encountered in space systems is addressed, using artificial neural systems. A hybrid feedforward/feedback neural network, namely a recurrent multilayer perception, is used as the model structure in the nonlinear system identification. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard back-propagation-learning algorithm is modified and it is used for both the off-line and on-line supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying parameters of nonlinear dynamic systems is investigated by estimating the mass properties of a representative large spacecraft. The changes in the spacecraft inertia are predicted using a trained neural network, during two configurations corresponding to the early and late stages of the spacecraft on-orbit assembly sequence. The proposed on-line mass properties estimation capability offers encouraging results, though, further research is warranted for training and testing the predictive capabilities of these networks beyond nominal spacecraft operations.

  6. Analysis of the DWPF glass pouring system using neural networks

    International Nuclear Information System (INIS)

    Calloway, T.B. Jr.; Jantzen, C.M.

    1997-01-01

    Neural networks were used to determine the sensitivity of 39 selected Melter/Melter Off Gas and Melter Feed System process parameters as related to the Defense Waste Processing Facility (DWPF) Melter Pour Spout Pressure during the overall analysis and resolution of the DWPF glass production and pouring issues. Two different commercial neural network software packages were used for this analysis. Models were developed and used to determine the critical parameters which accurately describe the DWPF Pour Spout Pressure. The model created using a low-end software package has a root mean square error of ± 0.35 inwc ( 2 = 0.77) with respect to the plant data used to validate and test the model. The model created using a high-end software package has a R 2 = 0.97 with respect to the plant data used to validate and test the model. The models developed for this application identified the key process parameters which contribute to the control of the DWPF Melter Pour Spout pressure during glass pouring operations. The relative contribution and ranking of the selected parameters was determined using the modeling software. Neural network computing software was determined to be a cost-effective software tool for process engineers performing troubleshooting and system performance monitoring activities. In remote high-level waste processing environments, neural network software is especially useful as a replacement for sensors which have failed and are costly to replace. The software can be used to accurately model critical remotely installed plant instrumentation. When the instrumentation fails, the software can be used to provide a soft sensor to replace the actual sensor, thereby decreasing the overall operating cost. Additionally, neural network software tools require very little training and are especially useful in mining or selecting critical variables from the vast amounts of data collected from process computers

  7. 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.

  8. Statistical Physics of Neural Systems with Nonadditive Dendritic Coupling

    Directory of Open Access Journals (Sweden)

    David Breuer

    2014-03-01

    Full Text Available How neurons process their inputs crucially determines the dynamics of biological and artificial neural networks. In such neural and neural-like systems, synaptic input is typically considered to be merely transmitted linearly or sublinearly by the dendritic compartments. Yet, single-neuron experiments report pronounced supralinear dendritic summation of sufficiently synchronous and spatially close-by inputs. Here, we provide a statistical physics approach to study the impact of such nonadditive dendritic processing on single-neuron responses and the performance of associative-memory tasks in artificial neural networks. First, we compute the effect of random input to a neuron incorporating nonlinear dendrites. This approach is independent of the details of the neuronal dynamics. Second, we use those results to study the impact of dendritic nonlinearities on the network dynamics in a paradigmatic model for associative memory, both numerically and analytically. We find that dendritic nonlinearities maintain network convergence and increase the robustness of memory performance against noise. Interestingly, an intermediate number of dendritic branches is optimal for memory functionality.

  9. Neural network-based expert system for severe accident management

    International Nuclear Information System (INIS)

    Klopp, G.T.; Silverman, E.B.

    1992-01-01

    This paper presents the results of the second phase of a three-phase Severe Accident Management expert system program underway at Commonwealth Edison Company (CECo). Phase I successfully demonstrated the feasibility of Artificial Neural Networks to support several of the objectives of severe accident management. Simulated accident scenarios were generated by the Modular Accident Analysis Program (MAAP) code currently in use by CECo as part of their Individual Plant Evaluations (IPE)/Accident Management Program. The primary objectives of the second phase were to develop and demonstrate four capabilities of neural networks with respect to nuclear power plant severe accident monitoring and prediction. The results of this work would form the foundation of a demonstration system which included expert system performance features. These capabilities included the ability to: (1) Predict the time available prior to support plate (and reactor vessel) failure; (2) Calculate the time remaining until recovery actions were too late to prevent core damage; (3) Predict future parameter values of each of the MAAP parameter variables; and (4) Detect simulated sensor failure and provide best-value estimates for further processing in the presence of a sensor failure. A variety of accident scenarios for the Zion and Dresden plants were used to train and test the neural network expert system. These included large and small break LOCAs as well as a range of transient events. 3 refs., 1 fig., 1 tab

  10. 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.

  11. 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.

  12. Nonlinear dynamical system approaches towards neural prosthesis

    International Nuclear Information System (INIS)

    Torikai, Hiroyuki; Hashimoto, Sho

    2011-01-01

    An asynchronous discrete-state spiking neurons is a wired system of shift registers that can mimic nonlinear dynamics of an ODE-based neuron model. The control parameter of the neuron is the wiring pattern among the registers and thus they are suitable for on-chip learning. In this paper an asynchronous discrete-state spiking neuron is introduced and its typical nonlinear phenomena are demonstrated. Also, a learning algorithm for a set of neurons is presented and it is demonstrated that the algorithm enables the set of neurons to reconstruct nonlinear dynamics of another set of neurons with unknown parameter values. The learning function is validated by FPGA experiments.

  13. Integrating Artificial Immune, Neural and Endrocine Systems in Autonomous Sailing Robots

    Science.gov (United States)

    2010-09-24

    system - Development of an adaptive hormone system capable of changing operation and control of the neural network depending on changing enviromental ...and control of the neural network depending on changing enviromental conditions • First basic design of the MOOP and a simple neural-endocrine based

  14. 1 mm3-sized optical neural stimulator based on CMOS integrated photovoltaic power receiver

    Science.gov (United States)

    Tokuda, Takashi; Ishizu, Takaaki; Nattakarn, Wuthibenjaphonchai; Haruta, Makito; Noda, Toshihiko; Sasagawa, Kiyotaka; Sawan, Mohamad; Ohta, Jun

    2018-04-01

    In this work, we present a simple complementary metal-oxide semiconductor (CMOS)-controlled photovoltaic power-transfer platform that is suitable for very small (less than or equal to 1-2 mm) electronic devices such as implantable health-care devices or distributed nodes for the Internet of Things. We designed a 1.25 mm × 1.25 mm CMOS power receiver chip that contains integrated photovoltaic cells. We characterized the CMOS-integrated power receiver and successfully demonstrated blue light-emitting diode (LED) operation powered by infrared light. Then, we integrated the CMOS chip and a few off-chip components into a 1-mm3 implantable optogenetic stimulator, and demonstrated the operation of the device.

  15. 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.

  16. Fuzzy-Neural Automatic Daylight Control System

    Directory of Open Access Journals (Sweden)

    Grif H. Şt.

    2011-12-01

    Full Text Available The paper presents the design and the tuning of a CMAC controller (Cerebellar Model Articulation Controller implemented in an automatic daylight control application. After the tuning process of the controller, the authors studied the behavior of the automatic lighting control system (ALCS in the presence of luminance disturbances. The luminance disturbances were produced by the authors in night conditions and day conditions as well. During the night conditions, the luminance disturbances were produced by turning on and off a halogen desk lamp. During the day conditions the luminance disturbances were produced in two ways: by daylight contributions changes achieved by covering and uncovering a part of the office window and by turning on and off a halogen desk lamp. During the day conditions the luminance disturbances, produced by turning on and off the halogen lamp, have a smaller amplitude than those produced during the night conditions. The luminance disturbance during the night conditions was a helpful tool to select the proper values of the learning rate for CMAC controller. The luminance disturbances during the day conditions were a helpful tool to demonstrate the right setting of the CMAC controller.

  17. Reliability analysis of a consecutive r-out-of-n: F system based on neural networks

    International Nuclear Information System (INIS)

    Habib, Aziz; Alsieidi, Ragab; Youssef, Ghada

    2009-01-01

    In this paper, we present a generalized Markov reliability and fault-tolerant model, which includes the effects of permanent fault and intermittent fault for reliability evaluations based on neural network techniques. The reliability of a consecutive r-out-of-n: F system was obtained with a three-layer connected neural network represents a discrete time state reliability Markov model of the system. Such that we fed the neural network with the desired reliability of the system under design. Then we extracted the parameters of the system from the neural weights at the convergence of the neural network to the desired reliability. Finally, we obtain simulation results.

  18. Probing neural mechanisms underlying auditory stream segregation in humans by transcranial direct current stimulation (tDCS).

    Science.gov (United States)

    Deike, Susann; Deliano, Matthias; Brechmann, André

    2016-10-01

    One hypothesis concerning the neural underpinnings of auditory streaming states that frequency tuning of tonotopically organized neurons in primary auditory fields in combination with physiological forward suppression is necessary for the separation of representations of high-frequency A and low-frequency B tones. The extent of spatial overlap between the tonotopic activations of A and B tones is thought to underlie the perceptual organization of streaming sequences into one coherent or two separate streams. The present study attempts to interfere with these mechanisms by transcranial direct current stimulation (tDCS) and to probe behavioral outcomes reflecting the perception of ABAB streaming sequences. We hypothesized that tDCS by modulating cortical excitability causes a change in the separateness of the representations of A and B tones, which leads to a change in the proportions of one-stream and two-stream percepts. To test this, 22 subjects were presented with ambiguous ABAB sequences of three different frequency separations (∆F) and had to decide on their current percept after receiving sham, anodal, or cathodal tDCS over the left auditory cortex. We could confirm our hypothesis at the most ambiguous ∆F condition of 6 semitones. For anodal compared with sham and cathodal stimulation, we found a significant decrease in the proportion of two-stream perception and an increase in the proportion of one-stream perception. The results demonstrate the feasibility of using tDCS to probe mechanisms underlying auditory streaming through the use of various behavioral measures. Moreover, this approach allows one to probe the functions of auditory regions and their interactions with other processing stages. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  19. Neural networks for feedback feedforward nonlinear control systems.

    Science.gov (United States)

    Parisini, T; Zoppoli, R

    1994-01-01

    This paper deals with the problem of designing feedback feedforward control strategies to drive the state of a dynamic system (in general, nonlinear) so as to track any desired trajectory joining the points of given compact sets, while minimizing a certain cost function (in general, nonquadratic). Due to the generality of the problem, conventional methods are difficult to apply. Thus, an approximate solution is sought by constraining control strategies to take on the structure of multilayer feedforward neural networks. After discussing the approximation properties of neural control strategies, a particular neural architecture is presented, which is based on what has been called the "linear-structure preserving principle". The original functional problem is then reduced to a nonlinear programming one, and backpropagation is applied to derive the optimal values of the synaptic weights. Recursive equations to compute the gradient components are presented, which generalize the classical adjoint system equations of N-stage optimal control theory. Simulation results related to nonlinear nonquadratic problems show the effectiveness of the proposed method.

  20. Dynamics of a neural system with a multiscale architecture

    Science.gov (United States)

    Breakspear, Michael; Stam, Cornelis J

    2005-01-01

    The architecture of the brain is characterized by a modular organization repeated across a hierarchy of spatial scales—neurons, minicolumns, cortical columns, functional brain regions, and so on. It is important to consider that the processes governing neural dynamics at any given scale are not only determined by the behaviour of other neural structures at that scale, but also by the emergent behaviour of smaller scales, and the constraining influence of activity at larger scales. In this paper, we introduce a theoretical framework for neural systems in which the dynamics are nested within a multiscale architecture. In essence, the dynamics at each scale are determined by a coupled ensemble of nonlinear oscillators, which embody the principle scale-specific neurobiological processes. The dynamics at larger scales are ‘slaved’ to the emergent behaviour of smaller scales through a coupling function that depends on a multiscale wavelet decomposition. The approach is first explicated mathematically. Numerical examples are then given to illustrate phenomena such as between-scale bifurcations, and how synchronization in small-scale structures influences the dynamics in larger structures in an intuitive manner that cannot be captured by existing modelling approaches. A framework for relating the dynamical behaviour of the system to measured observables is presented and further extensions to capture wave phenomena and mode coupling are suggested. PMID:16087448

  1. Artificial Neural Network for Location Estimation in Wireless Communication Systems

    Directory of Open Access Journals (Sweden)

    Chien-Sheng Chen

    2012-03-01

    Full Text Available In a wireless communication system, wireless location is the technique used to estimate the location of a mobile station (MS. To enhance the accuracy of MS location prediction, we propose a novel algorithm that utilizes time of arrival (TOA measurements and the angle of arrival (AOA information to locate MS when three base stations (BSs are available. Artificial neural networks (ANN are widely used techniques in various areas to overcome the problem of exclusive and nonlinear relationships. When the MS is heard by only three BSs, the proposed algorithm utilizes the intersections of three TOA circles (and the AOA line, based on various neural networks, to estimate the MS location in non-line-of-sight (NLOS environments. Simulations were conducted to evaluate the performance of the algorithm for different NLOS error distributions. The numerical analysis and simulation results show that the proposed algorithms can obtain more precise location estimation under different NLOS environments.

  2. Artificial neural network for location estimation in wireless communication systems.

    Science.gov (United States)

    Chen, Chien-Sheng

    2012-01-01

    In a wireless communication system, wireless location is the technique used to estimate the location of a mobile station (MS). To enhance the accuracy of MS location prediction, we propose a novel algorithm that utilizes time of arrival (TOA) measurements and the angle of arrival (AOA) information to locate MS when three base stations (BSs) are available. Artificial neural networks (ANN) are widely used techniques in various areas to overcome the problem of exclusive and nonlinear relationships. When the MS is heard by only three BSs, the proposed algorithm utilizes the intersections of three TOA circles (and the AOA line), based on various neural networks, to estimate the MS location in non-line-of-sight (NLOS) environments. Simulations were conducted to evaluate the performance of the algorithm for different NLOS error distributions. The numerical analysis and simulation results show that the proposed algorithms can obtain more precise location estimation under different NLOS environments.

  3. Semi-empirical neural network models of controlled dynamical systems

    Directory of Open Access Journals (Sweden)

    Mihail V. Egorchev

    2017-12-01

    Full Text Available A simulation approach is discussed for maneuverable aircraft motion as nonlinear controlled dynamical system under multiple and diverse uncertainties including knowledge imperfection concerning simulated plant and its environment exposure. The suggested approach is based on a merging of theoretical knowledge for the plant with training tools of artificial neural network field. The efficiency of this approach is demonstrated using the example of motion modeling and the identification of the aerodynamic characteristics of a maneuverable aircraft. A semi-empirical recurrent neural network based model learning algorithm is proposed for multi-step ahead prediction problem. This algorithm sequentially states and solves numerical optimization subproblems of increasing complexity, using each solution as initial guess for subsequent subproblem. We also consider a procedure for representative training set acquisition that utilizes multisine control signals.

  4. Neural regulation of duodenal alkali secretion: Effects of electrical field stimulation

    International Nuclear Information System (INIS)

    Crampton, J.R.; Gibbons, L.G.; Rees, W.D.W.

    1988-01-01

    The role of transmitters released from enteric neurons in regulating bicarbonate secretion by the proximal duodenum has been studied using electrical field stimulation (EFS). Stripped duodenal mucosa from Rana catesbeiana was mounted as an intact tube over circular platinum electrode,s and luminal alkalinization was measured by pH stat titration before, during, and after EFS. Transmucosal potential difference (PD) was simultaneously measured before and after EFS by paired electrodes. Square-wave pulses 50 V, 2 ms in duration, at 10 Hz were delivered in trains of 0.5 s at 1 Hz for periods of 15 min after stable basal secretion. This resulted in a 63 ± 27% increase in alkalinization that returned to basal values after cessation of the stimulus, without change in transmucosal PD. Serosal-to-lumen [ 3 H]mannitol flux was not affected. Repetition of the stimulus resulted in a similar responses for as long as the tissue remained viable. The response to EFS was abolished by tetrodotoxin and veratrine indicating that intrinsic neurons were responsible for mediating the effect. In addition, the effect was blocked by serosal dinitrophenol, indicating that the secretory response occurred by a metabolically dependent process. These results indicate that alkalinization by proximal duodenum may be controlled by neurotransmitter release from local enteric neurons

  5. Development of an accident diagnosis system using a dynamic neural network for nuclear power plants

    International Nuclear Information System (INIS)

    Lee, Seung Jun; Kim, Jong Hyun; Seong, Poong Hyun

    2004-01-01

    In this work, an accident diagnosis system using the dynamic neural network is developed. In order to help the plant operators to quickly identify the problem, perform diagnosis and initiate recovery actions ensuring the safety of the plant, many operator support system and accident diagnosis systems have been developed. Neural networks have been recognized as a good method to implement an accident diagnosis system. However, conventional accident diagnosis systems that used neural networks did not consider a time factor sufficiently. If the neural network could be trained according to time, it is possible to perform more efficient and detailed accidents analysis. Therefore, this work suggests a dynamic neural network which has different features from existing dynamic neural networks. And a simple accident diagnosis system is implemented in order to validate the dynamic neural network. After training of the prototype, several accident diagnoses were performed. The results show that the prototype can detect the accidents correctly with good performances

  6. Placebo neural systems: nitric oxide, morphine and the dopamine brain reward and motivation circuitries.

    Science.gov (United States)

    Fricchione, Gregory; Stefano, George B

    2005-05-01

    Evidence suggests that the placebo response is related to the tonic effects of constitutive nitric oxide in neural, vascular and immune tissues. Constitutive nitric oxide levels play a role in the modulation of dopamine outflow in the nigrostriatal movement and the mesolimbic and mesocortical reward and motivation circuitries. Endogenous morphine, which stimulates constitutive nitric oxide, may be an important signal molecule working at mu receptors on gamma aminobutyric acid B interneurons to disinhibit nigral and tegmental dopamine output. We surmise that placebo induced belief will activate the prefrontal cortex with downstream stimulatory effects on these dopamine systems as well as on periaqueductal grey opioid output neurons. Placebo responses in Parkinson's disease, depression and pain disorder may result. In addition, mesolimbic/mesocortical control of the stress response systems may provide a way for the placebo response to benefit other medical conditions.

  7. 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

  8. Fluvoxamine stimulates oligodendrogenesis of cultured neural stem cells and attenuates inflammation and demyelination in an animal model of multiple sclerosis.

    Science.gov (United States)

    Ghareghani, Majid; Zibara, Kazem; Sadeghi, Heibatollah; Dokoohaki, Shima; Sadeghi, Hossein; Aryanpour, Roya; Ghanbari, Amir

    2017-07-07

    Multiple Sclerosis (MS) require medications controlling severity of the pathology and depression, affecting more than half of the patients. In this study, the effect of antidepressant drug fluvoxamine, a selective serotonin reuptake inhibitor, was investigated in vitro and in vivo. Nanomolar concentrations of fluvoxamine significantly increased cell viability and proliferation of neural stem cells (NSCs) through increasing mRNA expression of Notch1, Hes1 and Ki-67, and protein levels of NICD. Also, physiological concentrations of fluvoxamine were optimal for NSC differentiation toward oligodendrocytes, astrocytes and neurons. In addition, fluvoxamine attenuated experimental autoimmune encephalomyelitis (EAE) severity, a rat MS model, by significantly decreasing its clinical scores. Moreover, fluvoxamine treated EAE rats showed a decrease in IFN-γ serum levels and an increase in IL-4, pro- and anti-inflammatory cytokines respectively, compared to untreated EAE rats. Furthermore, immune cell infiltration and demyelination plaque significantly decreased in spinal cords of fluvoxamine-treated rats, which was accompanied by an increase in protein expression of MBP and GFAP positive cells and a decrease in lactate serum levels, a new biomarker of MS progression. In summary, besides its antidepressant activity, fluvoxamine stimulates proliferation and differentiation of NSCs particularly toward oligodendrocytes, a producer of CNS myelin.

  9. Emotion recognition impairment and apathy after subthalamic nucleus stimulation in Parkinson's disease have separate neural substrates.

    Science.gov (United States)

    Drapier, D; Péron, J; Leray, E; Sauleau, P; Biseul, I; Drapier, S; Le Jeune, F; Travers, D; Bourguignon, A; Haegelen, C; Millet, B; Vérin, M

    2008-09-01

    To test the hypothesis that emotion recognition and apathy share the same functional circuit involving the subthalamic nucleus (STN). A consecutive series of 17 patients with advanced Parkinson's disease (PD) was assessed 3 months before (M-3) and 3 months (M+3) after STN deep brain stimulation (DBS). Mean (+/-S.D.) age at surgery was 56.9 (8.7) years. Mean disease duration at surgery was 11.8 (2.6) years. Apathy was measured using the Apathy Evaluation Scale (AES) at both M-3 and M3. Patients were also assessed using a computerised paradigm of facial emotion recognition [Ekman, P., & Friesen, W. V. (1976). Pictures of facial affect. Palo Alto: Consulting Psychologist Press] before and after STN DBS. Prior to this, the Benton Facial Recognition Test was used to check that the ability to perceive faces was intact. Apathy had significantly worsened at M3 (42.5+/-8.9, p=0.006) after STN-DBS, in relation to the preoperative assessment (37.2+/-5.5). There was also a significant reduction in recognition percentages for facial expressions of fear (43.1%+/-22.9 vs. 61.6%+/-21.4, p=0.022) and sadness (52.7%+/-19.1 vs. 67.6%+/-22.8, p=0.031) after STN DBS. However, the postoperative worsening of apathy and emotion recognition impairment were not correlated. Our results confirm that the STN is involved in both the apathy and emotion recognition networks. However, the absence of any correlation between apathy and emotion recognition impairment suggests that the worsening of apathy following surgery could not be explained by a lack of facial emotion recognition and that its behavioural and cognitive components should therefore also be taken into consideration.

  10. 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.

  11. 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...

  12. 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

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

    Directory of Open Access Journals (Sweden)

    Yuanyuan Mi

    2016-09-01

    Full Text Available Neural systems display rich short-term dynamics at various levels, e.g., spike-frequencyadaptation (SFA at single neurons, and short-term facilitation (STF and depression (STDat neuronal synapses. These dynamical features typically covers a broad range of time scalesand exhibit large diversity in different brain regions. It remains unclear what the computationalbenefit for the brain to have such variability in short-term dynamics is. In this study, we proposethat the brain can exploit such dynamical features to implement multiple seemingly contradictorycomputations in a single neural circuit. To demonstrate this idea, we use continuous attractorneural network (CANN as a working model and include STF, SFA and STD with increasing timeconstants in their dynamics. Three computational tasks are considered, which are persistent activity,adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, andhence cannot be implemented by a single dynamical feature or any combination with similar timeconstants. However, with properly coordinated STF, SFA and STD, we show that the network isable to implement the three computational tasks concurrently. We hope this study will shed lighton the understanding of how the brain orchestrates its rich dynamics at various levels to realizediverse cognitive functions.

  14. Neural network application to aircraft control system design

    Science.gov (United States)

    Troudet, Terry; Garg, Sanjay; Merrill, Walter C.

    1991-01-01

    The feasibility of using artificial neural network as control systems for modern, complex aerospace vehicles is investigated via an example aircraft control design study. The problem considered is that of designing a controller for an integrated airframe/propulsion longitudinal dynamics model of a modern fighter aircraft to provide independent control of pitch rate and airspeed responses to pilot command inputs. An explicit model following controller using H infinity control design techniques is first designed to gain insight into the control problem as well as to provide a baseline for evaluation of the neurocontroller. Using the model of the desired dynamics as a command generator, a multilayer feedforward neural network is trained to control the vehicle model within the physical limitations of the actuator dynamics. This is achieved by minimizing an objective function which is a weighted sum of tracking errors and control input commands and rates. To gain insight in the neurocontrol, linearized representations of the nonlinear neurocontroller are analyzed along a commanded trajectory. Linear robustness analysis tools are then applied to the linearized neurocontroller models and to the baseline H infinity based controller. Future areas of research identified to enhance the practical applicability of neural networks to flight control design.

  15. Neural network application to aircraft control system design

    Science.gov (United States)

    Troudet, Terry; Garg, Sanjay; Merrill, Walter C.

    1991-01-01

    The feasibility of using artificial neural networks as control systems for modern, complex aerospace vehicles is investigated via an example aircraft control design study. The problem considered is that of designing a controller for an integrated airframe/propulsion longitudinal dynamics model of a modern fighter aircraft to provide independent control of pitch rate and airspeed responses to pilot command inputs. An explicit model following controller using H infinity control design techniques is first designed to gain insight into the control problem as well as to provide a baseline for evaluation of the neurocontroller. Using the model of the desired dynamics as a command generator, a multilayer feedforward neural network is trained to control the vehicle model within the physical limitations of the actuator dynamics. This is achieved by minimizing an objective function which is a weighted sum of tracking errors and control input commands and rates. To gain insight in the neurocontrol, linearized representations of the nonlinear neurocontroller are analyzed along a commanded trajectory. Linear robustness analysis tools are then applied to the linearized neurocontroller models and to the baseline H infinity based controller. Future areas of research are identified to enhance the practical applicability of neural networks to flight control design.

  16. 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.

  17. 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.

  18. 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

  19. Olfactory systems and neural circuits that modulate predator odor fear

    Directory of Open Access Journals (Sweden)

    Lorey K. Takahashi

    2014-03-01

    Full Text Available When prey animals detect the odor of a predator a constellation of fear-related autonomic, endocrine, and behavioral responses rapidly occur to facilitate survival. How olfactory sensory systems process predator odor and channel that information to specific brain circuits is a fundamental issue that is not clearly understood. However, research in the last 15 years has begun to identify some of the essential features of the sensory detection systems and brain structures that underlie predator odor fear. For instance, the main (MOS and accessory olfactory systems (AOS detect predator odors and different types of predator odors are sensed by specific receptors located in either the MOS or AOS. However, complex predator chemosignals may be processed by both the MOS and AOS, which complicate our understanding of the specific neural circuits connected directly and indirectly from the MOS and AOS to activate the physiological and behavioral components of unconditioned and conditioned fear. Studies indicate that brain structures including the dorsal periaqueductal gray, paraventricular nucleus of the hypothalamus, and the medial amygdala appear to be broadly involved in predator odor induced autonomic activity and hypothalamic-pituitary-adrenal stress hormone secretion. The medial amygdala also plays a key role in predator odor unconditioned fear behavior and retrieval of contextual fear memory associated with prior predator odor experiences. Other neural structures including the bed nucleus of the stria terminalis and the ventral hippocampus appear prominently involve in predator odor fear behavior. The basolateral amygdala, medial hypothalamic nuclei, and medial prefrontal cortex are also activated by some but not all predator odors. Future research that characterizes how distinct predator odors are uniquely processed in olfactory systems and neural circuits will provide significant insights into the differences of how diverse predator odors activate

  20. Olfactory systems and neural circuits that modulate predator odor fear

    Science.gov (United States)

    Takahashi, Lorey K.

    2014-01-01

    When prey animals detect the odor of a predator a constellation of fear-related autonomic, endocrine, and behavioral responses rapidly occur to facilitate survival. How olfactory sensory systems process predator odor and channel that information to specific brain circuits is a fundamental issue that is not clearly understood. However, research in the last 15 years has begun to identify some of the essential features of the sensory detection systems and brain structures that underlie predator odor fear. For instance, the main (MOS) and accessory olfactory systems (AOS) detect predator odors and different types of predator odors are sensed by specific receptors located in either the MOS or AOS. However, complex predator chemosignals may be processed by both the MOS and AOS, which complicate our understanding of the specific neural circuits connected directly and indirectly from the MOS and AOS to activate the physiological and behavioral components of unconditioned and conditioned fear. Studies indicate that brain structures including the dorsal periaqueductal gray (DPAG), paraventricular nucleus (PVN) of the hypothalamus, and the medial amygdala (MeA) appear to be broadly involved in predator odor induced autonomic activity and hypothalamic-pituitary-adrenal (HPA) stress hormone secretion. The MeA also plays a key role in predator odor unconditioned fear behavior and retrieval of contextual fear memory associated with prior predator odor experiences. Other neural structures including the bed nucleus of the stria terminalis and the ventral hippocampus (VHC) appear prominently involved in predator odor fear behavior. The basolateral amygdala (BLA), medial hypothalamic nuclei, and medial prefrontal cortex (mPFC) are also activated by some but not all predator odors. Future research that characterizes how distinct predator odors are uniquely processed in olfactory systems and neural circuits will provide significant insights into the differences of how diverse predator

  1. 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.

  2. An intra-neural microstimulation system for ultra-high field magnetic resonance imaging and magnetoencephalography.

    Science.gov (United States)

    Glover, Paul M; Watkins, Roger H; O'Neill, George C; Ackerley, Rochelle; Sanchez-Panchuelo, Rosa; McGlone, Francis; Brookes, Matthew J; Wessberg, Johan; Francis, Susan T

    2017-10-01

    Intra-neural microstimulation (INMS) is a technique that allows the precise delivery of low-current electrical pulses into human peripheral nerves. Single unit INMS can be used to stimulate individual afferent nerve fibres during microneurography. Combining this with neuroimaging allows the unique monitoring of central nervous system activation in response to unitary, controlled tactile input, with functional magnetic resonance imaging (fMRI) providing exquisite spatial localisation of brain activity and magnetoencephalography (MEG) high temporal resolution. INMS systems suitable for use within electrophysiology laboratories have been available for many years. We describe an INMS system specifically designed to provide compatibility with both ultra-high field (7T) fMRI and MEG. Numerous technical and safety issues are addressed. The system is fully analogue, allowing for arbitrary frequency and amplitude INMS stimulation. Unitary recordings obtained within both the MRI and MEG screened-room environments are comparable with those obtained in 'clean' electrophysiology recording environments. Single unit INMS (current met. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

  3. 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.

  4. Spinal Cord Stimulation (SCS) with Anatomically Guided (3D) Neural Targeting Shows Superior Chronic Axial Low Back Pain Relief Compared to Traditional SCS-LUMINA Study.

    Science.gov (United States)

    Veizi, Elias; Hayek, Salim M; North, James; Brent Chafin, T; Yearwood, Thomas L; Raso, Louis; Frey, Robert; Cairns, Kevin; Berg, Anthony; Brendel, John; Haider, Nameer; McCarty, Matthew; Vucetic, Henry; Sherman, Alden; Chen, Lilly; Mekel-Bobrov, Nitzan

    2017-08-01

    The aim of this study was to determine whether spinal cord stimulation (SCS) using 3D neural targeting provided sustained overall and low back pain relief in a broad routine clinical practice population. This was a multicenter, open-label observational study with an observational arm and retrospective analysis of a matched cohort. After IPG implantation, programming was done using a patient-specific, model-based algorithm to adjust for lead position (3D neural targeting) or previous generation software (traditional). Demographics, medical histories, SCS parameters, pain locations, pain intensities, disabilities, and safety data were collected for all patients. A total of 213 patients using 3D neural targeting were included, with a trial-to-implant ratio of 86%. Patients used seven different lead configurations, with 62% receiving 24 to 32 contacts, and a broad range of stimulation parameters utilizing a mean of 14.3 (±6.1) contacts. At 24 months postimplant, pain intensity decreased significantly from baseline (ΔNRS = 4.2, N = 169, P  pain subgroup (ΔNRS = 5.3, N = 91, P  low back pain also decreased significantly from baseline to 24 months (ΔNRS = 4.1, N = 70, P  pain responder rates of 51% (traditional SCS) and 74% (neural targeting SCS) and axial low back pain responder rates of 41% and 71% in the traditional SCS and neural targeting SCS cohorts, respectively. Lastly, complications occurred in a total of 33 of the 213 patients, with a 1.6% lead replacement rate and a 1.6% explant rate. Our results suggest that 3D neural targeting SCS and its associated hardware flexibility provide effective treatment for both chronic leg and chronic axial low back pain that is significantly superior to traditional SCS. © 2017 American Academy of Pain Medicine. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

  5. Neural correlates of consciousness

    African Journals Online (AJOL)

    neural cells.1 Under this approach, consciousness is believed to be a product of the ... possible only when the 40 Hz electrical hum is sustained among the brain circuits, ... expect the brain stem ascending reticular activating system. (ARAS) and the ... related synchrony of cortical neurons.11 Indeed, stimulation of brainstem ...

  6. Monitoring nuclear reactor systems using neural networks and fuzzy logic

    International Nuclear Information System (INIS)

    Ikonomopoulos, A.; Tsoukalas, L.H.; Uhrig, R.E.; Mullens, J.A.

    1991-01-01

    A new approach is presented that demonstrates the potential of trained artificial neural networks (ANNs) as generators of membership functions for the purpose of monitoring nuclear reactor systems. ANN's provide a complex-to-simple mapping of reactor parameters in a process analogous to that of measurement. Through such ''virtual measurements'' the value of parameters with operational significance, e.g., control-valve-disk-position, valve-line-up or performance can be determined. In the methodology presented the output of a virtual measuring device is a set of membership functions which independently represent different states of the system. Utilizing a fuzzy logic representation offers the advantage of describing the state of the system in a condensed form, developed through linguistic descriptions and convenient for application in monitoring, diagnostics and generally control algorithms. The developed methodology is applied to the problem of measuring the disk position of the secondary flow control valve of an experimental reactor using data obtained during a start-up. The enhanced noise tolerance of the methodology is clearly demonstrated as well as a method for selecting the actual output. The results suggest that it is possible to construct virtual measuring devices through artificial neural networks mapping dynamic time series to a set of membership functions and thus enhance the capability of monitoring systems. 8 refs., 11 figs., 1 tab

  7. Monitoring nuclear reactor systems using neural networks and fuzzy logic

    International Nuclear Information System (INIS)

    Ikonomopoulos, A.; Tsoukalas, L.H.; Uhrig, R.E.; Mullens, J.A.

    1992-01-01

    A new approach is presented that demonstrates the potential of trained artificial neural networks (ANNs) as generators of membership functions for the purpose of monitoring nuclear reactor systems. ANN's provide a complex-to-simple mapping of reactor parameters in a process analogous to that of measurement. Through such virtual measurements the value of parameters with operational significance, e.g., control-valve-disk-position, valve-line-up-or performance can be determined. In the methodology presented the output of virtual measuring device is a set of membership functions which independently represent different states of the system. Utilizing a fuzzy logic representation offers the advantage of describing the state of the system in a condensed form, developed through linguistic descriptions and convenient for application in monitoring, diagnostics and generally control algorithms. The developed methodology is applied to the problem of measuring the disk position of the secondary flow control is clearly demonstrated as well as a method for selecting the actual output. The results suggest that it is possible to construct virtual measuring devices through artificial neural networks mapping dynamic time series to a set of membership functions and thus enhance the capability of monitoring systems

  8. A wirelessly controlled implantable LED system for deep brain optogenetic stimulation

    Science.gov (United States)

    Rossi, Mark A.; Go, Vinson; Murphy, Tracy; Fu, Quanhai; Morizio, James; Yin, Henry H.

    2015-01-01

    In recent years optogenetics has rapidly become an essential technique in neuroscience. Its temporal and spatial specificity, combined with efficacy in manipulating neuronal activity, are especially useful in studying the behavior of awake behaving animals. Conventional optogenetics, however, requires the use of lasers and optic fibers, which can place considerable restrictions on behavior. Here we combined a wirelessly controlled interface and small implantable light-emitting diode (LED) that allows flexible and precise placement of light source to illuminate any brain area. We tested this wireless LED system in vivo, in transgenic mice expressing channelrhodopsin-2 in striatonigral neurons expressing D1-like dopamine receptors. In all mice tested, we were able to elicit movements reliably. The frequency of twitches induced by high power stimulation is proportional to the frequency of stimulation. At lower power, contraversive turning was observed. Moreover, the implanted LED remains effective over 50 days after surgery, demonstrating the long-term stability of the light source. Our results show that the wireless LED system can be used to manipulate neural activity chronically in behaving mice without impeding natural movements. PMID:25713516

  9. Stimulating the diffusion of photovoltaic systems: A behavioural perspective

    International Nuclear Information System (INIS)

    Jager, Wander

    2006-01-01

    This paper first discusses consumer motives for adopting photovoltaic systems (PV systems) from a behavioural-theoretical perspective. Different motives are discussed within a framework of underlying needs and the time sensitivity of various outcomes. Next, empirical data are presented concerning the motives of buyers of PV systems after a promotional and support campaign in the city of Groningen (the Netherlands). Financial support and general problem awareness are found to be critical motives, but the (strong) positive effects of information meetings, technical support meetings and social networks are also identified. Conclusions focus on the critical motives for adopting a PV system, and suggestions are presented concerning policy measures to stimulate the further diffusion of PV systems

  10. Development of optically stimulated luminescence reader systems in BARC

    International Nuclear Information System (INIS)

    Kulkarni, M.S.

    2008-01-01

    BARC has very vast experience in the development of thermoluminescence (TL) reader systems both for routine personnel monitoring and research application. However, optically stimulated luminescence (OSL) related instrumentation is a recent development in BARC. The increasing popularity of OSL technique in the radiation dosimetry applications in the recent past has driven investigation and developmental programme in the OSL measurement facilities at BARC. As the consequence of the efforts directed towards the indigenous development of OSL reader system, OSL readers with various readout modes like continuous wave (CW) OSL mode, linear intensity modulated OSL (LM-OSL), pulsed OSL (POSL) have been developed. In addition to these conventional modes of operation a novel non-linear OSL mode (NL-OSL) has also been developed for the OSL measurements. This paper reviews the details of the development of OSL reader system including experience with high intensity blue/green LED stimulation light source and detection system. Also discussed are recently developed versatile integrated TL/OSL reader systems for TL and OSL measurements. (author)

  11. Effects of transcranial direct current stimulation on hemichannel pannexin-1 and neural plasticity in rat model of cerebral infarction.

    Science.gov (United States)

    Jiang, T; Xu, R X; Zhang, A W; Di, W; Xiao, Z J; Miao, J Y; Luo, N; Fang, Y N

    2012-12-13

    The aim of this study was to investigate the effects of transcranial direct current stimulation (TDCS) on hemichannel pannexin-1 (PX1) in cortical neurons and neural plasticity, and explore the optimal time window of TDCS therapy after stroke. Adult male Sprague-Dawley rats (n=90) were randomly assigned to sham operation, middle cerebral artery occlusion (MCAO), and TDCS groups, and underwent sham operation, unilateral middle cerebral artery (MCA) electrocoagulation, and unilateral MCA electrocoagulation plus TDCS (daily anodal and cathodal 10 Hz, 0.1 mA TDCS for 30 min beginning day 1 after stroke), respectively. Motor function was assessed using the beam walking test (BWT), and density of dendritic spines (DS) and PX1 mRNA expression were compared among groups on days 3, 7, and 14 after stroke. Effects of PX1 blockage on DS in hippocampal neurons after hypoxia-ischemia were observed. TDCS significantly improved motor function on days 7 and 14 after stroke as indicated by reduced BWT scores compared with the MCAO group. The density of DS was decreased after stroke; the TDCS group had increased DS density compared with the MCAO group on days 3, 7, and 14 (all P<0.0001). Cerebral infarction induced increased PX1 mRNA expression on days 3, 7, and 14 (P<0.0001), and the peak PX1 mRNA expression was observed on day 7. TDCS did not decrease the up-regulated PX1 mRNA expression after stroke on day 3, but did reduce the increased post-stroke PX1 mRNA expression on days 7 and 14 (P<0.0001). TDCS increased the DS density after stroke, indicating that it may promote neural plasticity after stroke. TDCS intervention from day 7 to day 14 after stroke demonstrated motor function improvement and can down-regulate the elevated PX1 mRNA expression after stroke. Copyright © 2012 IBRO. Published by Elsevier Ltd. All rights reserved.

  12. Phase transitions in glassy systems via convolutional neural networks

    Science.gov (United States)

    Fang, Chao

    Machine learning is a powerful approach commonplace in industry to tackle large data sets. Most recently, it has found its way into condensed matter physics, allowing for the first time the study of, e.g., topological phase transitions and strongly-correlated electron systems. The study of spin glasses is plagued by finite-size effects due to the long thermalization times needed. Here we use convolutional neural networks in an attempt to detect a phase transition in three-dimensional Ising spin glasses. Our results are compared to traditional approaches.

  13. NEURAL NETWORK SYSTEM FOR DIAGNOSTICS OF AVIATION DESIGNATION PRODUCTS

    Directory of Open Access Journals (Sweden)

    В. Єременко

    2011-02-01

    Full Text Available In the article for solving the classification problem of the technical state of the  object, proposed to use a hybrid neural network with a Kohonen layer and multilayer perceptron. The information-measuring system can be used for standardless diagnostics, cluster analysis and to classify the products which made from composite materials. The advantage of this architecture is flexibility, high performance, ability to use different methods for collecting diagnostic information about unit under test, high reliability of information processing

  14. 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.

  15. Optimizing Markovian modeling of chaotic systems with recurrent neural networks

    International Nuclear Information System (INIS)

    Cechin, Adelmo L.; Pechmann, Denise R.; Oliveira, Luiz P.L. de

    2008-01-01

    In this paper, we propose a methodology for optimizing the modeling of an one-dimensional chaotic time series with a Markov Chain. The model is extracted from a recurrent neural network trained for the attractor reconstructed from the data set. Each state of the obtained Markov Chain is a region of the reconstructed state space where the dynamics is approximated by a specific piecewise linear map, obtained from the network. The Markov Chain represents the dynamics of the time series in its statistical essence. An application to a time series resulted from Lorenz system is included

  16. Stimulation of the peripheral nervous system for pain control.

    Science.gov (United States)

    Long, D M

    1983-01-01

    hopeful trials. The technique is inexpensive, places the patient in control of his own pain, and has no known serious side effects. Its widespread application awaits the development of reasonable systems to provide this service to physicians and patients. Stimulation-induced analgesia deserves a place in the armamentarium of every physician dealing with the complaint of pain.

  17. 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.

  18. A novel neural prosthesis providing long-term electrocorticography recording and cortical stimulation for epilepsy and brain-computer interface.

    Science.gov (United States)

    Romanelli, Pantaleo; Piangerelli, Marco; Ratel, David; Gaude, Christophe; Costecalde, Thomas; Puttilli, Cosimo; Picciafuoco, Mauro; Benabid, Alim; Torres, Napoleon

    2018-05-11

    OBJECTIVE Wireless technology is a novel tool for the transmission of cortical signals. Wireless electrocorticography (ECoG) aims to improve the safety and diagnostic gain of procedures requiring invasive localization of seizure foci and also to provide long-term recording of brain activity for brain-computer interfaces (BCIs). However, no wireless devices aimed at these clinical applications are currently available. The authors present the application of a fully implantable and externally rechargeable neural prosthesis providing wireless ECoG recording and direct cortical stimulation (DCS). Prolonged wireless ECoG monitoring was tested in nonhuman primates by using a custom-made device (the ECoG implantable wireless 16-electrode [ECOGIW-16E] device) containing a 16-contact subdural grid. This is a preliminary step toward large-scale, long-term wireless ECoG recording in humans. METHODS The authors implanted the ECOGIW-16E device over the left sensorimotor cortex of a nonhuman primate ( Macaca fascicularis), recording ECoG signals over a time span of 6 months. Daily electrode impedances were measured, aiming to maintain the impedance values below a threshold of 100 KΩ. Brain mapping was obtained through wireless cortical stimulation at fixed intervals (1, 3, and 6 months). After 6 months, the device was removed. The authors analyzed cortical tissues by using conventional histological and immunohistological investigation to assess whether there was evidence of damage after the long-term implantation of the grid. RESULTS The implant was well tolerated; no neurological or behavioral consequences were reported in the monkey, which resumed his normal activities within a few hours of the procedure. The signal quality of wireless ECoG remained excellent over the 6-month observation period. Impedance values remained well below the threshold value; the average impedance per contact remains approximately 40 KΩ. Wireless cortical stimulation induced movements of the upper

  19. 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

  20. Application of talairach coordinates for transcranial magnetic stimulation navigation system

    International Nuclear Information System (INIS)

    Ahn, Se-Jong; Kim, Jong-Woo; Sin, Sung-Wook; Yoo, Jin-Young; An, Hyojin; Chung, Sung-Taek; Yoon, Sejin

    2011-01-01

    Since the development of transcranial magnetic stimulation (TMS) in 1985, its clinical and experimental studies and therapeutic applications have been widely being investigated. MRI-based neuronavigational systems have been developed and used for positioning of the magnetic coil, which is the main problem of most TMS studies. The functional brain map provided by these systems, however, may be unsuitable for a population-based study since it does not describe the location of brain structures independent from individual differences in brain, and also, it would be difficult to localize particular point of brain since there is no reference point excepting anatomical structure. In this paper, neuronavigational approach of TMS and application of Talairach coordinate system are introduced. We expect that this concept of the system will allow not only to perform the population-based study taking individual anatomy into account, but also to help physician to localize specific point in the Talairach coordinates. (author)

  1. 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.

  2. Using Pulse Width Modulation for Wireless Transmission of Neural Signals in Multichannel Neural Recording Systems

    Science.gov (United States)

    Yin, Ming; Ghovanloo, Maysam

    2013-01-01

    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-μm 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 ~ 2.26 Mb/s. PMID:19497823

  3. A Gamma Memory Neural Network for System Identification

    Science.gov (United States)

    Motter, Mark A.; Principe, Jose C.

    1992-01-01

    A gamma neural network topology is investigated for a system identification application. A discrete gamma memory structure is used in the input layer, providing delayed values of both the control inputs and the network output to the input layer. The discrete gamma memory structure implements a tapped dispersive delay line, with the amount of dispersion regulated by a single, adaptable parameter. The network is trained using static back propagation, but captures significant features of the system dynamics. The system dynamics identified with the network are the Mach number dynamics of the 16 Foot Transonic Tunnel at NASA Langley Research Center, Hampton, Virginia. The training data spans an operating range of Mach numbers from 0.4 to 1.3.

  4. Artificial neural network analysis of triple effect absorption refrigeration systems

    Energy Technology Data Exchange (ETDEWEB)

    Hajizadeh Aghdam, A. [Department of Mechanical Engineering, Islamic Azad University (Iran, Islamic Republic of)], email: a.hajizadeh@iaukashan.ac.ir; Nazmara, H.; Farzaneh, B. [Department of Mechanical Engineering, University of Tabriz (Iran, Islamic Republic of)], email: h.nazmara@nioec.org, email: b_farzaneh_ms@yahoo.com

    2011-07-01

    In this study, artificial neural networks are utilized to predict the performance of triple effect series and parallel flow absorption refrigeration systems, with lithium bromide/water as the working fluid. Important parameters such as high generator and evaporator temperatures were varied and their effects on the performance characteristics of the refrigeration unit were observed. Absorption refrigeration systems make energy savings possible because they can use heat energy to produce cooling, in place of the electricity used for conventional vapour compression chillers. In addition, non-conventional sources of energy (such as solar, waste heat, and geothermal) can be utilized as their primary energy input. Moreover, absorption units use environmentally friendly working fluid pairs instead of CFCs and HCFCs, which affect the ozone layer. Triple effect absorption cycles were analysed. Results apply for both series and parallel flow systems. A relative preference for parallel-flow over series-flow is also shown.

  5. Implantable Neural Interfaces for Sharks

    Science.gov (United States)

    2007-05-01

    technology for recording and stimulating from the auditory and olfactory sensory nervous systems of the awake, swimming nurse shark , G. cirratum (Figures...overlay of the central nervous system of the nurse shark on a horizontal MR image. Implantable Neural Interfaces for Sharks ...Neural Interfaces for Characterizing Population Responses to Odorants and Electrical Stimuli in the Nurse Shark , Ginglymostoma cirratum.” AChemS Abs

  6. Speaker diarization system using HXLPS and deep neural network

    Directory of Open Access Journals (Sweden)

    V. Subba Ramaiah

    2018-03-01

    Full Text Available In general, speaker diarization is defined as the process of segmenting the input speech signal and grouped the homogenous regions with regard to the speaker identity. The main idea behind this system is that it is able to discriminate the speaker signal by assigning the label of the each speaker signal. Due to rapid growth of broadcasting and meeting, the speaker diarization is burdensome to enhance the readability of the speech transcription. In order to solve this issue, Holoentropy with the eXtended Linear Prediction using autocorrelation Snapshot (HXLPS and deep neural network (DNN is proposed for the speaker diarization system. The HXLPS extraction method is newly developed by incorporating the Holoentropy with the XLPS. Once we attain the features, the speech and non-speech signals are detected by the Voice Activity Detection (VAD method. Then, i-vector representation of every segmented signal is obtained using Universal Background Model (UBM model. Consequently, DNN is utilized to assign the label for the speaker signal which is then clustered according to the speaker label. The performance is analysed using the evaluation metrics, such as tracking distance, false alarm rate and diarization error rate. The outcome of the proposed method ensures the better diarization performance by achieving the lower DER of 1.36% based on lambda value and DER of 2.23% depends on the frame length. Keywords: Speaker diarization, HXLPS feature extraction, Voice activity detection, Deep neural network, Speaker clustering, Diarization Error Rate (DER

  7. The Principle of the Micro-Electronic Neural Bridge and a Prototype System Design.

    Science.gov (United States)

    Huang, Zong-Hao; Wang, Zhi-Gong; Lu, Xiao-Ying; Li, Wen-Yuan; Zhou, Yu-Xuan; Shen, Xiao-Yan; Zhao, Xin-Tai

    2016-01-01

    The micro-electronic neural bridge (MENB) aims to rebuild lost motor function of paralyzed humans by routing movement-related signals from the brain, around the damage part in the spinal cord, to the external effectors. This study focused on the prototype system design of the MENB, including the principle of the MENB, the neural signal detecting circuit and the functional electrical stimulation (FES) circuit design, and the spike detecting and sorting algorithm. In this study, we developed a novel improved amplitude threshold spike detecting method based on variable forward difference threshold for both training and bridging phase. The discrete wavelet transform (DWT), a new level feature coefficient selection method based on Lilliefors test, and the k-means clustering method based on Mahalanobis distance were used for spike sorting. A real-time online spike detecting and sorting algorithm based on DWT and Euclidean distance was also implemented for the bridging phase. Tested by the data sets available at Caltech, in the training phase, the average sensitivity, specificity, and clustering accuracies are 99.43%, 97.83%, and 95.45%, respectively. Validated by the three-fold cross-validation method, the average sensitivity, specificity, and classification accuracy are 99.43%, 97.70%, and 96.46%, respectively.

  8. Neurofeedback Control of the Human GABAergic System Using Non-invasive Brain Stimulation.

    Science.gov (United States)

    Koganemaru, Satoko; Mikami, Yusuke; Maezawa, Hitoshi; Ikeda, Satoshi; Ikoma, Katsunori; Mima, Tatsuya

    2018-06-01

    Neurofeedback has been a powerful method for self-regulating brain activities to elicit potential ability of human mind. GABA is a major inhibitory neurotransmitter in the central nervous system. Transcranial magnetic stimulation (TMS) is a tool that can evaluate the GABAergic system within the primary motor cortex (M1) using paired-pulse stimuli, short intracortical inhibition (SICI). Herein we investigated whether neurofeedback learning using SICI enabled us to control the GABAergic system within the M1 area. Forty-five healthy subjects were randomly divided into two groups: those receiving SICI neurofeedback learning or those receiving no neurofeedback (control) learning. During both learning periods, subjects made attempts to change the size of a circle, which was altered according to the degree of SICI in the SICI neurofeedback learning group, and which was altered independent of the degree of SICI in the control learning group. Results demonstrated that the SICI neurofeedback learning group showed a significant enhancement in SICI. Moreover, this group showed a significant reduction in choice reaction time compared to the control group. Our findings indicate that humans can intrinsically control the intracortical GABAergic system within M1 and can thus improve motor behaviors by SICI neurofeedback learning. SICI neurofeedback learning is a novel and promising approach to control our neural system and potentially represents a new therapy for patients with abnormal motor symptoms caused by CNS disorders. Copyright © 2018 IBRO. Published by Elsevier Ltd. All rights reserved.

  9. Hybrid case-neural network (CNN) diagnostic system

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2010-01-01

    recently, the mobile health care has a great attention for the researcher and people all over the world. Case based reasoning (CBR) systems have proved their performance as world wide web (WWW) medical diagnostic systems. They were preferred rather than different reasoning approaches due to their high performance and results' explanation. But, their operations require a complex knowledge acquisition and management processes. On the other hand, it is found that, artificial neural network (ANN) has a great acceptance as a classifier methodology using a little amount of knowledge. But, ANN lacks of an explanation capability .The present research introduces a new web-based hybrid diagnostic system that can use the ANN inside the CBR , cycle.It can provide higher performance for the web diagnostic systems. Besides, the proposed system can be used as a web diagnostic system. It can be applied for diagnosis different types of systems in several domains. It has been applied in diagnosis of the cancer diseases that has a great spreading in recent years as a case of study . However, the suggested system has proved its acceptance in the manner.

  10. 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.

  11. The exploitation of neural networks in automotive engine management systems

    Energy Technology Data Exchange (ETDEWEB)

    Shayler, P.J.; Goodman, M. [University of Nottingham (United Kingdom); Ma, T. [Ford Motor Company, Dagenham (United Kingdom). Research and Engineering Centre

    2000-07-01

    The use of electronic engine control systems on spark ignition engines has enabled a high degree of performance optimisation to be achieved. The range of functions performed by these systems, and the level of performance demanded, is rising and thus so are development times and costs. Neural networks have attracted attention as having the potential to simplify software development and improve the performance of this software. The scope and nature of possible applications is described. In particular, the pattern recognition and classification abilities of networks are applied to crankshaft speed fluctuation data for engine-fault diagnosis, and multidimensional mapping capabilities are investigated as an alternative to large 'lookup' tables and calibration functions. (author)

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

    DEFF Research Database (Denmark)

    Jouffroy, Guillaume; Jouffroy, Jerome

    Central Pattern Generators (CPG) are oscillatory systems that are responsible for generating rhythmic patterns at the origin of many biological activities such as for example locomotion or digestion. These systems are generally modelled as recurrent neural networks whose parameters are tuned so...... that the network oscillates in a suitable way, this tuning being a non trivial task. It also appears that the link with the physical body that these oscillatory entities control has a fundamental importance, and it seems that most bodies used for experimental validation in the literature (walking robots, lamprey...... 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....

  13. Direct process estimation from tomographic data using artificial neural systems

    Science.gov (United States)

    Mohamad-Saleh, Junita; Hoyle, Brian S.; Podd, Frank J.; Spink, D. M.

    2001-07-01

    The paper deals with the goal of component fraction estimation in multicomponent flows, a critical measurement in many processes. Electrical capacitance tomography (ECT) is a well-researched sensing technique for this task, due to its low-cost, non-intrusion, and fast response. However, typical systems, which include practicable real-time reconstruction algorithms, give inaccurate results, and existing approaches to direct component fraction measurement are flow-regime dependent. In the investigation described, an artificial neural network approach is used to directly estimate the component fractions in gas-oil, gas-water, and gas-oil-water flows from ECT measurements. A 2D finite- element electric field model of a 12-electrode ECT sensor is used to simulate ECT measurements of various flow conditions. The raw measurements are reduced to a mutually independent set using principal components analysis and used with their corresponding component fractions to train multilayer feed-forward neural networks (MLFFNNs). The trained MLFFNNs are tested with patterns consisting of unlearned ECT simulated and plant measurements. Results included in the paper have a mean absolute error of less than 1% for the estimation of various multicomponent fractions of the permittivity distribution. They are also shown to give improved component fraction estimation compared to a well known direct ECT method.

  14. 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.

  15. Application of neural networks to connectional expert system for identification of transients in nuclear power plants

    International Nuclear Information System (INIS)

    Cheon, Se Woo; Kim, Wan Joo; Chang, Soon Heung; Roh, Myung Sub

    1991-01-01

    The Back-propagation Neural Network (BPN) algorithm is applied to connectionist expert system for the identification of BWR transients. Several powerful features of neural network-based expert systems over traditional rule-based expert systems are described. The general mapping capability of the neural networks enables to identify transients easily. A number of case studies were performed with emphasis on the applicability of the neural networks to the diagnostic domain. It is revealed that the BPN algorithm can identify transients properly, even when incomplete or untrained symptoms are given. It is also shown that multiple transients are easily identified

  16. Neural-network hybrid control for antilock braking systems.

    Science.gov (United States)

    Lin, Chih-Min; Hsu, C F

    2003-01-01

    The antilock braking systems are designed to maximize wheel traction by preventing the wheels from locking during braking, while also maintaining adequate vehicle steerability; however, the performance is often degraded under harsh road conditions. In this paper, a hybrid control system with a recurrent neural network (RNN) observer is developed for antilock braking systems. This hybrid control system is comprised of an ideal controller and a compensation controller. The ideal controller, containing an RNN uncertainty observer, is the principal controller; and the compensation controller is a compensator for the difference between the system uncertainty and the estimated uncertainty. Since for dynamic response the RNN has capabilities superior to the feedforward NN, it is utilized for the uncertainty observer. The Taylor linearization technique is employed to increase the learning ability of the RNN. In addition, the on-line parameter adaptation laws are derived based on a Lyapunov function, so the stability of the system can be guaranteed. Simulations are performed to demonstrate the effectiveness of the proposed NN hybrid control system for antilock braking control under various road conditions.

  17. 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

  18. 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.

  19. 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.

  20. 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.

  1. Decoupling control of vehicle chassis system based on neural network inverse system

    Science.gov (United States)

    Wang, Chunyan; Zhao, Wanzhong; Luan, Zhongkai; Gao, Qi; Deng, Ke

    2018-06-01

    Steering and suspension are two important subsystems affecting the handling stability and riding comfort of the chassis system. In order to avoid the interference and coupling of the control channels between active front steering (AFS) and active suspension subsystems (ASS), this paper presents a composite decoupling control method, which consists of a neural network inverse system and a robust controller. The neural network inverse system is composed of a static neural network with several integrators and state feedback of the original chassis system to approach the inverse system of the nonlinear systems. The existence of the inverse system for the chassis system is proved by the reversibility derivation of Interactor algorithm. The robust controller is based on the internal model control (IMC), which is designed to improve the robustness and anti-interference of the decoupled system by adding a pre-compensation controller to the pseudo linear system. The results of the simulation and vehicle test show that the proposed decoupling controller has excellent decoupling performance, which can transform the multivariable system into a number of single input and single output systems, and eliminate the mutual influence and interference. Furthermore, it has satisfactory tracking capability and robust performance, which can improve the comprehensive performance of the chassis system.

  2. 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.

  3. Orphan nuclear receptor TLX activates Wnt/β-catenin signalling to stimulate neural stem cell proliferation and self-renewal

    Science.gov (United States)

    Qu, Qiuhao; Sun, Guoqiang; Li, Wenwu; Yang, Su; Ye, Peng; Zhao, Chunnian; Yu, Ruth T.; Gage, Fred H.; Evans, Ronald M.; Shi, Yanhong

    2010-01-01

    The nuclear receptor TLX (also known as NR2E1) is essential for adult neural stem cell self-renewal; however, the molecular mechanisms involved remain elusive. Here we show that TLX activates the canonical Wnt/β-catenin pathway in adult mouse neural stem cells. Furthermore, we demonstrate that Wnt/β-catenin signalling is important in the proliferation and self-renewal of adult neural stem cells in the presence of epidermal growth factor and fibroblast growth factor. Wnt7a and active β-catenin promote neural stem cell self-renewal, whereas the deletion of Wnt7a or the lentiviral transduction of axin, a β-catenin inhibitor, led to decreased cell proliferation in adult neurogenic areas. Lentiviral transduction of active β-catenin led to increased numbers of type B neural stem cells in the subventricular zone of adult brains, whereas deletion of Wnt7a or TLX resulted in decreased numbers of neural stem cells retaining bromodeoxyuridine label in the adult brain. Both Wnt7a and active β-catenin significantly rescued a TLX (also known as Nr2e1) short interfering RNA-induced deficiency in neural stem cell proliferation. Lentiviral transduction of an active β-catenin increased cell proliferation in neurogenic areas of TLX-null adult brains markedly. These results strongly support the hypothesis that TLX acts through the Wnt/β-catenin pathway to regulate neural stem cell proliferation and self-renewal. Moreover, this study suggests that neural stem cells can promote their own self-renewal by secreting signalling molecules that act in an autocrine/paracrine mode. PMID:20010817

  4. Orphan nuclear receptor TLX activates Wnt/beta-catenin signalling to stimulate neural stem cell proliferation and self-renewal.

    Science.gov (United States)

    Qu, Qiuhao; Sun, Guoqiang; Li, Wenwu; Yang, Su; Ye, Peng; Zhao, Chunnian; Yu, Ruth T; Gage, Fred H; Evans, Ronald M; Shi, Yanhong

    2010-01-01

    The nuclear receptor TLX (also known as NR2E1) is essential for adult neural stem cell self-renewal; however, the molecular mechanisms involved remain elusive. Here we show that TLX activates the canonical Wnt/beta-catenin pathway in adult mouse neural stem cells. Furthermore, we demonstrate that Wnt/beta-catenin signalling is important in the proliferation and self-renewal of adult neural stem cells in the presence of epidermal growth factor and fibroblast growth factor. Wnt7a and active beta-catenin promote neural stem cell self-renewal, whereas the deletion of Wnt7a or the lentiviral transduction of axin, a beta-catenin inhibitor, led to decreased cell proliferation in adult neurogenic areas. Lentiviral transduction of active beta-catenin led to increased numbers of type B neural stem cells in the subventricular zone of adult brains, whereas deletion of Wnt7a or TLX resulted in decreased numbers of neural stem cells retaining bromodeoxyuridine label in the adult brain. Both Wnt7a and active beta-catenin significantly rescued a TLX (also known as Nr2e1) short interfering RNA-induced deficiency in neural stem cell proliferation. Lentiviral transduction of an active beta-catenin increased cell proliferation in neurogenic areas of TLX-null adult brains markedly. These results strongly support the hypothesis that TLX acts through the Wnt/beta-catenin pathway to regulate neural stem cell proliferation and self-renewal. Moreover, this study suggests that neural stem cells can promote their own self-renewal by secreting signalling molecules that act in an autocrine/paracrine mode.

  5. Inhibition of systemic inflammation by central action of the neuropeptide alpha-melanocyte- stimulating hormone.

    Science.gov (United States)

    Delgado Hernàndez, R; Demitri, M T; Carlin, A; Meazza, C; Villa, P; Ghezzi, P; Lipton, J M; Catania, A

    1999-01-01

    The neuropeptide alpha-melanocyte stimulating hormone (alpha-MSH) reduces fever and acute inflammation in the skin when administered centrally. The aim of the present research was to determine whether central alpha-MSH can also reduce signs of systemic inflammation in mice with endotoxemia. Increases in serum tumor necrosis factor-alpha and nitric oxide, induced by intraperitoneal administration of endotoxin, were modulated by central injection of a small concentration of alpha-MSH. Inducible nitric oxide synthase (iNOS) activity and iNOS mRNA in lungs and liver were likewise modulated by central alpha-MSH. Lung myeloperoxidase activity, a marker of neutrophil infiltration, was increased in endotoxemic mice; the increase was significantly less in lungs of mice treated with central alpha-MSH. Intraperitoneal administration of the small dose of alpha-MSH that was effective centrally did not alter any of the markers of inflammation. In experiments using immunoneutralization of central alpha-MSH, we tested the idea that endogenous peptide induced within the brain during systemic inflammation modulates host responses to endotoxic challenge in peripheral tissues. The data showed that proinflammatory agents induced by endotoxin in the circulation, lungs, and liver were significantly greater after blockade of central alpha-MSH. The results suggest that anti-inflammatory influences of neural origin that are triggered by alpha-MSH could be used to treat systemic inflammation.

  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. 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

  8. Symptom based diagnostic system using artificial neural networks

    International Nuclear Information System (INIS)

    Santosh; Vinod, Gopika; Saraf, R.K.

    2003-01-01

    Nuclear power plant experiences a number of transients during its operations. In case of such an undesired plant condition generally known as an initiating event, the operator has to carry out diagnostic and corrective actions. The operator's response may be too late to mitigate or minimize the negative consequences in such scenarios. The objective of this work is to develop an operator support system based on artificial neural networks that will assist the operator to identify the initiating events at the earliest stages of their developments. A symptom based diagnostic system has been developed to investigate the initiating events. Neutral networks are utilized for carrying out the event identification by continuously monitoring process parameters. Whenever an event is detected, the system will display the necessary operator actions along with the initiating event. The system will also show the graphical trend of process parameters that are relevant to the event. This paper describes the features of the software that is used to monitor the reactor. (author)

  9. 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.

  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....

  11. Algebraic and adaptive learning in neural control systems

    Science.gov (United States)

    Ferrari, Silvia

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

  12. 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...

  13. 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...... 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...

  14. On the Universality and Non-Universality of Spiking Neural P Systems With Rules on Synapses.

    Science.gov (United States)

    Song, Tao; Xu, Jinbang; Pan, Linqiang

    2015-12-01

    Spiking neural P systems with rules on synapses are a new variant of spiking neural P systems. In the systems, the neuron contains only spikes, while the spiking/forgetting rules are moved on the synapses. It was obtained that such system with 30 neurons (using extended spiking rules) or with 39 neurons (using standard spiking rules) is Turing universal. In this work, this number is improved to 6. Specifically, we construct a Turing universal spiking neural P system with rules on synapses having 6 neurons, which can generate any set of Turing computable natural numbers. As well, it is obtained that spiking neural P system with rules on synapses having less than two neurons are not Turing universal: i) such systems having one neuron can characterize the family of finite sets of natural numbers; ii) the family of sets of numbers generated by the systems having two neurons is included in the family of semi-linear sets of natural numbers.

  15. 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.

  16. Controlled PVTS oil and gas production stimulation system

    Energy Technology Data Exchange (ETDEWEB)

    Ospina-Racines, E

    1970-02-01

    By completing oil- or gas-producing wells according to the PVTS method and energizing the flow of the oil-gas fluids in the reservoir with a small horse-power gas compressor at the wellhead, the following oil and gas production features are attained: (1) Original reservoir story energy conditions are restored, improved, used, and conserved while producing oil and/or gas. (2) The flow of oil or gas in the pay formation to the well bore is stimulated by gas compressor energy, outside of the reservoir system. The pressure drawdown is developed by gas-compressor energy in the well casing and not in the pay formation. (3) The stored energy of the reservoir is conserved while producing oil or gas. The potential energy (pressure) of the reservoir can be used to advantage up to bubble point of the virgin crude. (4) Producible reserves are increased from 4-to 5-fold by the conservation of reservoir energy. Present-day primary oil production practice yields a maximum of 20% of the oil in place by depleting the original reservoir energy. The PVTS system will yield over 80% + of oil in place. (5) Producible gas reserves can be increased greatly by establishing a low abandonment pressure at will. The principal features of the PVTS well mechanism and energy injection method are illustrated by a schematic diagram.

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

    Science.gov (United States)

    Wai, Rong-Jong; Lee, Jeng-Dao

    2008-01-01

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

  18. 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.

  19. Flood forecasting within urban drainage systems using NARX neural network.

    Science.gov (United States)

    Abou Rjeily, Yves; Abbas, Oras; Sadek, Marwan; Shahrour, Isam; Hage Chehade, Fadi

    2017-11-01

    Urbanization activity and climate change increase the runoff volumes, and consequently the surcharge of the urban drainage systems (UDS). In addition, age and structural failures of these utilities limit their capacities, and thus generate hydraulic operation shortages, leading to flooding events. The large increase in floods within urban areas requires rapid actions from the UDS operators. The proactivity in taking the appropriate actions is a key element in applying efficient management and flood mitigation. Therefore, this work focuses on developing a flooding forecast system (FFS), able to alert in advance the UDS managers for possible flooding. For a forecasted storm event, a quick estimation of the water depth variation within critical manholes allows a reliable evaluation of the flood risk. The Nonlinear Auto Regressive with eXogenous inputs (NARX) neural network was chosen to develop the FFS as due to its calculation nature it is capable of relating water depth variation in manholes to rainfall intensities. The campus of the University of Lille is used as an experimental site to test and evaluate the FFS proposed in this paper.

  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. A neural network method for solving a system of linear variational inequalities

    International Nuclear Information System (INIS)

    Lan Hengyou; Cui Yishun

    2009-01-01

    In this paper, we transmute the solution for a new system of linear variational inequalities to an equilibrium point of neural networks, and by using analytic technique, some sufficient conditions are presented. Further, the estimation of the exponential convergence rates of the neural networks is investigated. The new and useful results obtained in this paper generalize and improve the corresponding results of recent works.

  3. 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.

  4. Neural network models for biological waste-gas treatment systems.

    Science.gov (United States)

    Rene, Eldon R; Estefanía López, M; Veiga, María C; Kennes, Christian

    2011-12-15

    This paper outlines the procedure for developing artificial neural network (ANN) based models for three bioreactor configurations used for waste-gas treatment. The three bioreactor configurations chosen for this modelling work were: biofilter (BF), continuous stirred tank bioreactor (CSTB) and monolith bioreactor (MB). Using styrene as the model pollutant, this paper also serves as a general database of information pertaining to the bioreactor operation and important factors affecting gas-phase styrene removal in these biological systems. Biological waste-gas treatment systems are considered to be both advantageous and economically effective in treating a stream of polluted air containing low to moderate concentrations of the target contaminant, over a rather wide range of gas-flow rates. The bioreactors were inoculated with the fungus Sporothrix variecibatus, and their performances were evaluated at different empty bed residence times (EBRT), and at different inlet styrene concentrations (C(i)). The experimental data from these bioreactors were modelled to predict the bioreactors performance in terms of their removal efficiency (RE, %), by adequate training and testing of a three-layered back propagation neural network (input layer-hidden layer-output layer). Two models (BIOF1 and BIOF2) were developed for the BF with different combinations of easily measurable BF parameters as the inputs, that is concentration (gm(-3)), unit flow (h(-1)) and pressure drop (cm of H(2)O). The model developed for the CSTB used two inputs (concentration and unit flow), while the model for the MB had three inputs (concentration, G/L (gas/liquid) ratio, and pressure drop). Sensitivity analysis in the form of absolute average sensitivity (AAS) was performed for all the developed ANN models to ascertain the importance of the different input parameters, and to assess their direct effect on the bioreactors performance. The performance of the models was estimated by the regression

  5. 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

  6. The System for Assessment of the Effectiveness of Personnel Stimulation on the Basis of Integral Index

    Directory of Open Access Journals (Sweden)

    O. K.

    2017-12-01

    Full Text Available The article’s objective is to construct the system for assessment ofpersonnel manage­ ment performance and its testing on a sample o f Ukrainian companies. Problems o f person­ nel stimulation at company level are highlighted. It is proposed to build the mechanism for company personnel stimulation by functional approach. Indicators to be used for assess­ ing the means o f personnel stimulation (material stimulation, organizational stimulation, social and psychological stimulation and personnel management modeling are given. The algorithm for estimating the integral index o f the personnel stimulation effectiveness is recommended. Estimation o f the index o f social and psychological stimulation of company personnel by the proposed method is illustrated. By use o f the proposed algorithm it is demonstrated that the material stimulation (salary rate is the most important determinant o f the highly effective personnel stimulation. The integral index o f the personnel stimulation effectiveness is estimated by correlation and regression analysis for a sample of Ukrainian companies over 2011-2015. On its basis, the companies under study are grouped by level of personnel stimulation. Corrective measures to increase the integral index o f the personnel stimulation effectiveness in each company under study are proposed. The matrix o f correc­ tive measures on personnel stimulation at these companies is constructed and used to find the range o f estimates o f the integral index o f the personnel stimulation effectiveness. The quality o f personnel stimulation mechanism at company level is assessed on the basis o f the estimated integral index o f the personnel stimulation effectiveness.

  7. A novel neural-wavelet approach for process diagnostics and complex system modeling

    Science.gov (United States)

    Gao, Rong

    Neural networks have been effective in several engineering applications because of their learning abilities and robustness. However certain shortcomings, such as slow convergence and local minima, are always associated with neural networks, especially neural networks applied to highly nonlinear and non-stationary problems. These problems can be effectively alleviated by integrating a new powerful tool, wavelets, into conventional neural networks. The multi-resolution analysis and feature localization capabilities of the wavelet transform offer neural networks new possibilities for learning. A neural wavelet network approach developed in this thesis enjoys fast convergence rate with little possibility to be caught at a local minimum. It combines the localization properties of wavelets with the learning abilities of neural networks. Two different testbeds are used for testing the efficiency of the new approach. The first is magnetic flowmeter-based process diagnostics: here we extend previous work, which has demonstrated that wavelet groups contain process information, to more general process diagnostics. A loop at Applied Intelligent Systems Lab (AISL) is used for collecting and analyzing data through the neural-wavelet approach. The research is important for thermal-hydraulic processes in nuclear and other engineering fields. The neural-wavelet approach developed is also tested with data from the electric power grid. More specifically, the neural-wavelet approach is used for performing short-term and mid-term prediction of power load demand. In addition, the feasibility of determining the type of load using the proposed neural wavelet approach is also examined. The notion of cross scale product has been developed as an expedient yet reliable discriminator of loads. Theoretical issues involved in the integration of wavelets and neural networks are discussed and future work outlined.

  8. A compact electroencephalogram recording device with integrated audio stimulation system

    Science.gov (United States)

    Paukkunen, Antti K. O.; Kurttio, Anttu A.; Leminen, Miika M.; Sepponen, Raimo E.

    2010-06-01

    A compact (96×128×32 mm3, 374 g), battery-powered, eight-channel electroencephalogram recording device with an integrated audio stimulation system and a wireless interface is presented. The recording device is capable of producing high-quality data, while the operating time is also reasonable for evoked potential studies. The effective measurement resolution is about 4 nV at 200 Hz sample rate, typical noise level is below 0.7 μVrms at 0.16-70 Hz, and the estimated operating time is 1.5 h. An embedded audio decoder circuit reads and plays wave sound files stored on a memory card. The activities are controlled by an 8 bit main control unit which allows accurate timing of the stimuli. The interstimulus interval jitter measured is less than 1 ms. Wireless communication is made through bluetooth and the data recorded are transmitted to an external personal computer (PC) interface in real time. The PC interface is implemented with LABVIEW® and in addition to data acquisition it also allows online signal processing, data storage, and control of measurement activities such as contact impedance measurement, for example. The practical application of the device is demonstrated in mismatch negativity experiment with three test subjects.

  9. 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.

  10. A new evolutionary system for evolving artificial neural networks.

    Science.gov (United States)

    Yao, X; Liu, Y

    1997-01-01

    This paper presents a new evolutionary system, i.e., EPNet, for evolving artificial neural networks (ANNs). The evolutionary algorithm used in EPNet is based on Fogel's evolutionary programming (EP). Unlike most previous studies on evolving ANN's, this paper puts its emphasis on evolving ANN's behaviors. Five mutation operators proposed in EPNet reflect such an emphasis on evolving behaviors. Close behavioral links between parents and their offspring are maintained by various mutations, such as partial training and node splitting. EPNet evolves ANN's architectures and connection weights (including biases) simultaneously in order to reduce the noise in fitness evaluation. The parsimony of evolved ANN's is encouraged by preferring node/connection deletion to addition. EPNet has been tested on a number of benchmark problems in machine learning and ANNs, such as the parity problem, the medical diagnosis problems, the Australian credit card assessment problem, and the Mackey-Glass time series prediction problem. The experimental results show that EPNet can produce very compact ANNs with good generalization ability in comparison with other algorithms.

  11. A neural network approach to the study of dynamics and structure of molecular systems

    International Nuclear Information System (INIS)

    Getino, C.; Sumpter, B.G.; Noid, D.W.

    1994-01-01

    Neural networks are used to study intramolecular energy flow in molecular systems (tetratomics to macromolecules), developing new techniques for efficient analysis of data obtained from molecular-dynamics and quantum mechanics calculations. Neural networks can map phase space points to intramolecular vibrational energies along a classical trajectory (example of complicated coordinate transformation), producing reasonably accurate values for any region of the multidimensional phase space of a tetratomic molecule. Neural network energy flow predictions are found to significantly enhance the molecular-dynamics method to longer time-scales and extensive averaging of trajectories for macromolecular systems. Pattern recognition abilities of neural networks can be used to discern phase space features. Neural networks can also expand model calculations by interpolation of costly quantum mechanical ab initio data, used to develop semiempirical potential energy functions

  12. Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network

    Energy Technology Data Exchange (ETDEWEB)

    Du, Zhimin; Jin, Xinqiao; Yang, Yunyu [School of Mechanical Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, Shanghai (China)

    2009-09-15

    Wavelet neural network, the integration of wavelet analysis and neural network, is presented to diagnose the faults of sensors including temperature, flow rate and pressure in variable air volume (VAV) systems to ensure well capacity of energy conservation. Wavelet analysis is used to process the original data collected from the building automation first. With three-level wavelet decomposition, the series of characteristic information representing various operation conditions of the system are obtained. In addition, neural network is developed to diagnose the source of the fault. To improve the diagnosis efficiency, three data groups based on several physical models or balances are classified and constructed. Using the data decomposed by three-level wavelet, the neural network can be well trained and series of convergent networks are obtained. Finally, the new measurements to diagnose are similarly processed by wavelet. And the well-trained convergent neural networks are used to identify the operation condition and isolate the source of the fault. (author)

  13. 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

  14. Radial basis function neural network for power system load-flow

    International Nuclear Information System (INIS)

    Karami, A.; Mohammadi, M.S.

    2008-01-01

    This paper presents a method for solving the load-flow problem of the electric power systems using radial basis function (RBF) neural network with a fast hybrid training method. The main idea is that some operating conditions (values) are needed to solve the set of non-linear algebraic equations of load-flow by employing an iterative numerical technique. Therefore, we may view the outputs of a load-flow program as functions of the operating conditions. Indeed, we are faced with a function approximation problem and this can be done by an RBF neural network. The proposed approach has been successfully applied to the 10-machine and 39-bus New England test system. In addition, this method has been compared with that of a multi-layer perceptron (MLP) neural network model. The simulation results show that the RBF neural network is a simpler method to implement and requires less training time to converge than the MLP neural network. (author)

  15. A Fault Diagnosis Approach for the Hydraulic System by Artificial Neural Networks

    OpenAIRE

    Xiangyu He; Shanghong He

    2014-01-01

    Based on artificial neural networks, a fault diagnosis approach for the hydraulic system was proposed in this paper. Normal state samples were used as the training data to develop a dynamic general regression neural network (DGRNN) model. The trained DGRNN model then served as the fault determinant to diagnose test faults and the work condition of the hydraulic system was identified. Several typical faults of the hydraulic system were used to verify the fault diagnosis approach. Experiment re...

  16. An artificial neural network for modeling reliability, availability and maintainability of a repairable system

    International Nuclear Information System (INIS)

    Rajpal, P.S.; Shishodia, K.S.; Sekhon, G.S.

    2006-01-01

    The paper explores the application of artificial neural networks to model the behaviour of a complex, repairable system. A composite measure of reliability, availability and maintainability parameters has been proposed for measuring the system performance. The artificial neural network has been trained using past data of a helicopter transportation facility. It is used to simulate behaviour of the facility under various constraints. The insights obtained from results of simulation are useful in formulating strategies for optimal operation of the system

  17. Development of the disable software reporting system on the basis of the neural network

    Science.gov (United States)

    Gavrylenko, S.; Babenko, O.; Ignatova, E.

    2018-04-01

    The PE structure of malicious and secure software is analyzed, features are highlighted, binary sign vectors are obtained and used as inputs for training the neural network. A software model for detecting malware based on the ART-1 neural network was developed, optimal similarity coefficients were found, and testing was performed. The obtained research results showed the possibility of using the developed system of identifying malicious software in computer systems protection systems

  18. Statistical mechanics of complex neural systems and high dimensional data

    International Nuclear Information System (INIS)

    Advani, Madhu; Lahiri, Subhaneil; Ganguli, Surya

    2013-01-01

    Recent experimental advances in neuroscience have opened new vistas into the immense complexity of neuronal networks. This proliferation of data challenges us on two parallel fronts. First, how can we form adequate theoretical frameworks for understanding how dynamical network processes cooperate across widely disparate spatiotemporal scales to solve important computational problems? Second, how can we extract meaningful models of neuronal systems from high dimensional datasets? To aid in these challenges, we give a pedagogical review of a collection of ideas and theoretical methods arising at the intersection of statistical physics, computer science and neurobiology. We introduce the interrelated replica and cavity methods, which originated in statistical physics as powerful ways to quantitatively analyze large highly heterogeneous systems of many interacting degrees of freedom. We also introduce the closely related notion of message passing in graphical models, which originated in computer science as a distributed algorithm capable of solving large inference and optimization problems involving many coupled variables. We then show how both the statistical physics and computer science perspectives can be applied in a wide diversity of contexts to problems arising in theoretical neuroscience and data analysis. Along the way we discuss spin glasses, learning theory, illusions of structure in noise, random matrices, dimensionality reduction and compressed sensing, all within the unified formalism of the replica method. Moreover, we review recent conceptual connections between message passing in graphical models, and neural computation and learning. Overall, these ideas illustrate how statistical physics and computer science might provide a lens through which we can uncover emergent computational functions buried deep within the dynamical complexities of neuronal networks. (paper)

  19. CMOS Image Sensor and System for Imaging Hemodynamic Changes in Response to Deep Brain Stimulation.

    Science.gov (United States)

    Zhang, Xiao; Noor, Muhammad S; McCracken, Clinton B; Kiss, Zelma H T; Yadid-Pecht, Orly; Murari, Kartikeya

    2016-06-01

    Deep brain stimulation (DBS) is a therapeutic intervention used for a variety of neurological and psychiatric disorders, but its mechanism of action is not well understood. It is known that DBS modulates neural activity which changes metabolic demands and thus the cerebral circulation state. However, it is unclear whether there are correlations between electrophysiological, hemodynamic and behavioral changes and whether they have any implications for clinical benefits. In order to investigate these questions, we present a miniaturized system for spectroscopic imaging of brain hemodynamics. The system consists of a 144 ×144, [Formula: see text] pixel pitch, high-sensitivity, analog-output CMOS imager fabricated in a standard 0.35 μm CMOS process, along with a miniaturized imaging system comprising illumination, focusing, analog-to-digital conversion and μSD card based data storage. This enables stand alone operation without a computer, nor electrical or fiberoptic tethers. To achieve high sensitivity, the pixel uses a capacitive transimpedance amplifier (CTIA). The nMOS transistors are in the pixel while pMOS transistors are column-parallel, resulting in a fill factor (FF) of 26%. Running at 60 fps and exposed to 470 nm light, the CMOS imager has a minimum detectable intensity of 2.3 nW/cm(2) , a maximum signal-to-noise ratio (SNR) of 49 dB at 2.45 μW/cm(2) leading to a dynamic range (DR) of 61 dB while consuming 167 μA from a 3.3 V supply. In anesthetized rats, the system was able to detect temporal, spatial and spectral hemodynamic changes in response to DBS.

  20. 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)

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

    International Nuclear Information System (INIS)

    Martinez B, M.R.; Ortiz R, J.M.; Vega C, H.R.

    2006-01-01

    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)

  2. Opposing Effects of α2- and β-Adrenergic Receptor Stimulation on Quiescent Neural Precursor Cell Activity and Adult Hippocampal Neurogenesis

    Science.gov (United States)

    Prosper, Boris W.; Marathe, Swanand; Husain, Basma F. A.; Kernie, Steven G.; Bartlett, Perry F.; Vaidya, Vidita A.

    2014-01-01

    Norepinephrine regulates latent neural stem cell activity and adult hippocampal neurogenesis, and has an important role in modulating hippocampal functions such as learning, memory and mood. Adult hippocampal neurogenesis is a multi-stage process, spanning from the activation and proliferation of hippocampal stem cells, to their differentiation into neurons. However, the stage-specific effects of noradrenergic receptors in regulating adult hippocampal neurogenesis remain poorly understood. In this study, we used transgenic Nestin-GFP mice and neurosphere assays to show that modulation of α2- and β-adrenergic receptor activity directly affects Nestin-GFP/GFAP-positive precursor cell population albeit in an opposing fashion. While selective stimulation of α2-adrenergic receptors decreases precursor cell activation, proliferation and immature neuron number, stimulation of β-adrenergic receptors activates the quiescent precursor pool and enhances their proliferation in the adult hippocampus. Furthermore, our data indicate no major role for α1-adrenergic receptors, as we did not observe any change in either the activation and proliferation of hippocampal precursors following selective stimulation or blockade of α1-adrenergic receptors. Taken together, our data suggest that under physiological as well as under conditions that lead to enhanced norepinephrine release, the balance between α2- and β-adrenergic receptor activity regulates precursor cell activity and hippocampal neurogenesis. PMID:24922313

  3. A New Controller to Enhance PV System Performance Based on Neural Network

    Directory of Open Access Journals (Sweden)

    Roshdy A AbdelRassoul

    2017-06-01

    Full Text Available In recent years, a radical increase of photovoltaic (PV power generators installation took place because of increased efficiency of solar cells, as well as the growth of manufacturing technology of solar panels. This paper shows the operation and modeling of photovoltaic systems, particularly designing neural controller to control the system. Neural controller is optimized using particle swarm optimization (PSO   leads to getting the best performance of the designed PV system. Using neural network the maximum overshoot and rise time obtained become 0.00001% and 0.1798 seconds, respectively also this paper introduce a comparison between some kind of controller for PV system.In recent years, a radical increase of photovoltaic (PV power generators installation took place because of increased efficiency of solar cells, as well as the growth of manufacturing technology of solar panels. This paper shows the operation and modeling of photovoltaic systems, particularly designing neural controller to control the system. Neural controller is optimized using particle swarm optimization (PSO   leads to getting the best performance of the designed PV system. Using neural network the maximum overshoot and rise time obtained become 0.00001% and 0.1798 seconds, respectively also this paper introduce a comparison between some kind of controller for PV system.

  4. 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...

  5. 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...

  6. Computational neural network regression model for Host based Intrusion Detection System

    Directory of Open Access Journals (Sweden)

    Sunil Kumar Gautam

    2016-09-01

    Full Text Available The current scenario of information gathering and storing in secure system is a challenging task due to increasing cyber-attacks. There exists computational neural network techniques designed for intrusion detection system, which provide security to single machine and entire network's machine. In this paper, we have used two types of computational neural network models, namely, Generalized Regression Neural Network (GRNN model and Multilayer Perceptron Neural Network (MPNN model for Host based Intrusion Detection System using log files that are generated by a single personal computer. The simulation results show correctly classified percentage of normal and abnormal (intrusion class using confusion matrix. On the basis of results and discussion, we found that the Host based Intrusion Systems Model (HISM significantly improved the detection accuracy while retaining minimum false alarm rate.

  7. Effective deep brain stimulation suppresses low frequency network oscillations in the basal ganglia by regularizing neural firing patterns

    Science.gov (United States)

    McConnell, George C.; So, Rosa Q.; Hilliard, Justin D; Lopomo, Paola; Grill, Warren M.

    2012-01-01

    Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for the motor symptoms of Parkinson’s disease (PD). The effects of DBS depend strongly on stimulation frequency: high frequencies (>90Hz) improve motor symptoms, while low frequencies (basal ganglia were studied in the unilateral 6-hydroxydopamine lesioned rat model of PD. Only high frequency DBS reversed motor symptoms and the effectiveness of DBS depended strongly on stimulation frequency in a manner reminiscent of its clinical effects in persons with PD. Quantification of single-unit activity in the globus pallidus externa (GPe) and substantia nigra reticulata (SNr) revealed that high frequency DBS, but not low frequency DBS, reduced pathological low frequency oscillations (~9Hz) and entrained neurons to fire at the stimulation frequency. Similarly, the coherence between simultaneously recorded pairs of neurons within and across GPe and SNr shifted from the pathological low frequency band to the stimulation frequency during high frequency DBS, but not during low frequency DBS. The changes in firing patterns in basal ganglia neurons were not correlated with changes in firing rate. These results indicate that high frequency DBS is more effective than low frequency DBS, not as a result of changes in firing rate, but rather due to its ability to replace pathological low frequency network oscillations with a regularized pattern of neuronal firing. PMID:23136407

  8. Effective deep brain stimulation suppresses low-frequency network oscillations in the basal ganglia by regularizing neural firing patterns.

    Science.gov (United States)

    McConnell, George C; So, Rosa Q; Hilliard, Justin D; Lopomo, Paola; Grill, Warren M

    2012-11-07

    Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for the motor symptoms of Parkinson's disease (PD). The effects of DBS depend strongly on stimulation frequency: high frequencies (>90 Hz) improve motor symptoms, while low frequencies (basal ganglia were studied in the unilateral 6-hydroxydopamine lesioned rat model of PD. Only high-frequency DBS reversed motor symptoms, and the effectiveness of DBS depended strongly on stimulation frequency in a manner reminiscent of its clinical effects in persons with PD. Quantification of single-unit activity in the globus pallidus externa (GPe) and substantia nigra reticulata (SNr) revealed that high-frequency DBS, but not low-frequency DBS, reduced pathological low-frequency oscillations (∼9 Hz) and entrained neurons to fire at the stimulation frequency. Similarly, the coherence between simultaneously recorded pairs of neurons within and across GPe and SNr shifted from the pathological low-frequency band to the stimulation frequency during high-frequency DBS, but not during low-frequency DBS. The changes in firing patterns in basal ganglia neurons were not correlated with changes in firing rate. These results indicate that high-frequency DBS is more effective than low-frequency DBS, not as a result of changes in firing rate, but rather due to its ability to replace pathological low-frequency network oscillations with a regularized pattern of neuronal firing.

  9. SWANN: The Snow Water Artificial Neural Network Modelling System

    Science.gov (United States)

    Broxton, P. D.; van Leeuwen, W.; Biederman, J. A.

    2017-12-01

    Snowmelt from mountain forests is important for water supply and ecosystem health. Along Arizona's Mogollon Rim, snowmelt contributes to rivers and streams that provide a significant water supply for hydro-electric power generation, agriculture, and human consumption in central Arizona. In this project, we are building a snow monitoring system for the Salt River Project (SRP), which supplies water and power to millions of customers in the Phoenix metropolitan area. We are using process-based hydrological models and artificial neural networks (ANNs) to generate information about both snow water equivalent (SWE) and snow cover. The snow-cover data is generated with ANNs that are applied to Landsat and MODIS satellite reflectance data. The SWE data is generated using a combination of gridded SWE estimates generated by process-based snow models and ANNs that account for variations in topography, forest cover, and solar radiation. The models are trained and evaluated with snow data from SNOTEL stations as well as from aerial LiDAR and field data that we collected this past winter in northern Arizona, as well as with similar data from other sites in the Southwest US. These snow data are produced in near-real time, and we have built a prototype decision support tool to deliver them to SRP. This tool is designed to provide daily-to annual operational monitoring of spatial and temporal changes in SWE and snow cover conditions over the entire Salt River Watershed (covering 17,000 km2), and features advanced web mapping capabilities and watershed analytics displayed as graphical data.

  10. 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.

  11. A Sliding Mode Control-based on a RBF Neural Network for Deburring Industry Robotic Systems

    OpenAIRE

    Tao, Yong; Zheng, Jiaqi; Lin, Yuanchang

    2016-01-01

    A sliding mode control method based on radial basis function (RBF) neural network is proposed for the deburring of industry robotic systems. First, a dynamic model for deburring the robot system is established. Then, a conventional SMC scheme is introduced for the joint position tracking of robot manipulators. The RBF neural network based sliding mode control (RBFNN-SMC) has the ability to learn uncertain control actions. In the RBFNN-SMC scheme, the adaptive tuning algorithms for network par...

  12. 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.

  13. 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.

  14. Neural networks for combined control of capacitor banks and voltage regulators in distribution systems

    Energy Technology Data Exchange (ETDEWEB)

    Gu, Z.; Rizy, D.T.

    1996-02-01

    A neural network for controlling shunt capacitor banks and feeder voltage regulators in electric distribution systems is presented. The objective of the neural controller is to minimize total I{sup 2}R losses and maintain all bus voltages within standard limits. The performance of the neural network for different input selections and training data is discussed and compared. Two different input selections are tried, one using the previous control states of the capacitors and regulator along with measured line flows and voltage which is equivalent to having feedback and the other with measured line flows and voltage without previous control settings. The results indicate that the neural net controller with feedback can outperform the one without. Also, proper selection of a training data set that adequately covers the operating space of the distribution system is important for achieving satisfactory performance with the neural controller. The neural controller is tested on a radially configured distribution system with 30 buses, 5 switchable capacitor banks an d one nine tap line regulator to demonstrate the performance characteristics associated with these principles. Monte Carlo simulations show that a carefully designed and relatively compact neural network with a small but carefully developed training set can perform quite well under slight and extreme variation of loading conditions.

  15. 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.

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

    Science.gov (United States)

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

    1991-01-01

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

  17. 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.

  18. Effect of electrical stimulation on neural regeneration via the p38-RhoA and ERK1/2-Bcl-2 pathways in spinal cord-injured rats.

    Science.gov (United States)

    Joo, Min Cheol; Jang, Chul Hwan; Park, Jong Tae; Choi, Seung Won; Ro, Seungil; Kim, Min Seob; Lee, Moon Young

    2018-02-01

    Although electrical stimulation is therapeutically applied for neural regeneration in patients, it remains unclear how electrical stimulation exerts its effects at the molecular level on spinal cord injury (SCI). To identify the signaling pathway involved in electrical stimulation improving the function of injured spinal cord, 21 female Sprague-Dawley rats were randomly assigned to three groups: control (no surgical intervention, n = 6), SCI (SCI only, n = 5), and electrical simulation (ES; SCI induction followed by ES treatment, n = 10). A complete spinal cord transection was performed at the 10 th thoracic level. Electrical stimulation of the injured spinal cord region was applied for 4 hours per day for 7 days. On days 2 and 7 post SCI, the Touch-Test Sensory Evaluators and the Basso-Beattie-Bresnahan locomotor scale were used to evaluate rat sensory and motor function. Somatosensory-evoked potentials of the tibial nerve of a hind paw of the rat were measured to evaluate the electrophysiological function of injured spinal cord. Western blot analysis was performed to measure p38-RhoA and ERK1/2-Bcl-2 pathways related protein levels in the injured spinal cord. Rat sensory and motor functions were similar between SCI and ES groups. Compared with the SCI group, in the ES group, the latencies of the somatosensory-evoked potential of the tibial nerve of rats were significantly shortened, the amplitudes were significantly increased, RhoA protein level was significantly decreased, protein gene product 9.5 expression, ERK1/2, p38, and Bcl-2 protein levels in the spinal cord were significantly increased. These data suggest that ES can promote the recovery of electrophysiological function of the injured spinal cord through regulating p38-RhoA and ERK1/2-Bcl-2 pathway-related protein levels in the injured spinal cord.

  19. Effect of electrical stimulation on neural regeneration via the p38-RhoA and ERK1/2-Bcl-2 pathways in spinal cord-injured rats

    Science.gov (United States)

    Joo, Min Cheol; Jang, Chul Hwan; Park, Jong Tae; Choi, Seung Won; Ro, Seungil; Kim, Min Seob; Lee, Moon Young

    2018-01-01

    Although electrical stimulation is therapeutically applied for neural regeneration in patients, it remains unclear how electrical stimulation exerts its effects at the molecular level on spinal cord injury (SCI). To identify the signaling pathway involved in electrical stimulation improving the function of injured spinal cord, 21 female Sprague-Dawley rats were randomly assigned to three groups: control (no surgical intervention, n = 6), SCI (SCI only, n = 5), and electrical simulation (ES; SCI induction followed by ES treatment, n = 10). A complete spinal cord transection was performed at the 10th thoracic level. Electrical stimulation of the injured spinal cord region was applied for 4 hours per day for 7 days. On days 2 and 7 post SCI, the Touch-Test Sensory Evaluators and the Basso-Beattie-Bresnahan locomotor scale were used to evaluate rat sensory and motor function. Somatosensory-evoked potentials of the tibial nerve of a hind paw of the rat were measured to evaluate the electrophysiological function of injured spinal cord. Western blot analysis was performed to measure p38-RhoA and ERK1/2-Bcl-2 pathways related protein levels in the injured spinal cord. Rat sensory and motor functions were similar between SCI and ES groups. Compared with the SCI group, in the ES group, the latencies of the somatosensory-evoked potential of the tibial nerve of rats were significantly shortened, the amplitudes were significantly increased, RhoA protein level was significantly decreased, protein gene product 9.5 expression, ERK1/2, p38, and Bcl-2 protein levels in the spinal cord were significantly increased. These data suggest that ES can promote the recovery of electrophysiological function of the injured spinal cord through regulating p38-RhoA and ERK1/2-Bcl-2 pathway-related protein levels in the injured spinal cord. PMID:29557386

  20. 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...

  1. Diagnosis of mechanical pumping system using neural networks and system parameters analysis

    International Nuclear Information System (INIS)

    Tsai, Tai Ming; Wang, Wei Hui

    2009-01-01

    Normally, a mechanical pumping system is equipped to monitor some of the important input and output signals which are set to the prescribed values. This paper addressed dealing with these signals to establish the database of input- output relation by using a number of neural network models through learning algorithms. These signals encompass normal and abnormal running conditions. The abnormal running conditions were artificially generated. Meanwhile, for the purpose of setting up an on-line diagnosis network, the learning speed and accuracy of three kinds of networks, viz., the backpropagation (BPN), radial basis function (RBF) and adaptive linear (ADALINE) neural networks have been compared and assessed. The assessment criteria of the networks are compared with the correlation result matrix in terms of the neuron vectors. Both BPN and RBF are judged by the maximum vector based on the post-regression analysis, and the ADALINE is judged by the minimum vector based on the least mean square error analysis. By ignoring the neural network training time, it has been shown that if the mechanical diagnosis system is tackled off-line, the RBF method is suggested. However, for on-line diagnosis, the BPN method is recommended

  2. Diagnosis of mechanical pumping system using neural networks and system parameters analysis

    Energy Technology Data Exchange (ETDEWEB)

    Tsai, Tai Ming; Wang, Wei Hui [National Taiwan Ocean University, Keelung (China)

    2009-01-15

    Normally, a mechanical pumping system is equipped to monitor some of the important input and output signals which are set to the prescribed values. This paper addressed dealing with these signals to establish the database of input- output relation by using a number of neural network models through learning algorithms. These signals encompass normal and abnormal running conditions. The abnormal running conditions were artificially generated. Meanwhile, for the purpose of setting up an on-line diagnosis network, the learning speed and accuracy of three kinds of networks, viz., the backpropagation (BPN), radial basis function (RBF) and adaptive linear (ADALINE) neural networks have been compared and assessed. The assessment criteria of the networks are compared with the correlation result matrix in terms of the neuron vectors. Both BPN and RBF are judged by the maximum vector based on the post-regression analysis, and the ADALINE is judged by the minimum vector based on the least mean square error analysis. By ignoring the neural network training time, it has been shown that if the mechanical diagnosis system is tackled off-line, the RBF method is suggested. However, for on-line diagnosis, the BPN method is recommended

  3. 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, ...

  4. Computer simulation system of neural PID control on nuclear reactor

    International Nuclear Information System (INIS)

    Chen Yuzhong; Yang Kaijun; Shen Yongping

    2001-01-01

    Neural network proportional integral differential (PID) controller on nuclear reactor is designed, and the control process is simulated by computer. The simulation result show that neutral network PID controller can automatically adjust its parameter to ideal state, and good control result can be gotten in reactor control process

  5. Distinct Neural-Functional Effects of Treatments With Selective Serotonin Reuptake Inhibitors, Electroconvulsive Therapy, and Transcranial Magnetic Stimulation and Their Relations to Regional Brain Function in Major Depression: A Meta-analysis.

    Science.gov (United States)

    Chau, David T; Fogelman, Phoebe; Nordanskog, Pia; Drevets, Wayne C; Hamilton, J Paul

    2017-05-01

    Functional neuroimaging studies have examined the neural substrates of treatments for major depressive disorder (MDD). Low sample size and methodological heterogeneity, however, undermine the generalizability of findings from individual studies. We conducted a meta-analysis to identify reliable neural changes resulting from different modes of treatment for MDD and compared them with each other and with reliable neural functional abnormalities observed in depressed versus control samples. We conducted a meta-analysis of studies reporting changes in brain activity (e.g., as indexed by positron emission tomography) following treatments with selective serotonin reuptake inhibitors (SSRIs), electroconvulsive therapy (ECT), or transcranial magnetic stimulation. Additionally, we examined the statistical reliability of overlap among thresholded meta-analytic SSRI, ECT, and transcranial magnetic stimulation maps as well as a map of abnormal neural function in MDD. Our meta-analysis revealed that 1) SSRIs decrease activity in the anterior insula, 2) ECT decreases activity in central nodes of the default mode network, 3) transcranial magnetic stimulation does not result in reliable neural changes, and 4) regional effects of these modes of treatment do not significantly overlap with each other or with regions showing reliable functional abnormality in MDD. SSRIs and ECT produce neurally distinct effects relative to each other and to the functional abnormalities implicated in depression. These treatments therefore may exert antidepressant effects by diminishing neural functions not implicated in depression but that nonetheless impact mood. We discuss how the distinct neural changes resulting from SSRIs and ECT can account for both treatment effects and side effects from these therapies as well as how to individualize these treatments. Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

  6. An application of artificial neural intelligence for personal dose assessment using a multi-area OSL dosimetry system

    International Nuclear Information System (INIS)

    Lee, S.-Y.Sang-Yoon.; Kim, B.-H.Bong-Hwan; Lee, K.J.Kun Jai

    2001-01-01

    Significant advances have been made in recent years to improve measurement technology and performance of phosphor materials in the fields of optically stimulated luminescence (OSL) dosimetry. Pulsed and continuous wave OSL studies recently carried out on α-Al 2 O 3 : C have shown that the material seems to be the most promising for routine application of OSL for dosimetric purposes. The main objective of the study is to propose a new personal dosimetry system using α-Al 2 O 3 : C by taking advantage of its optical properties and energy dependencies. In the process of the study, a new dose assessment algorithm was developed using artificial neural networks in hopes of achieving a higher degree of accuracy and precision in personal OSL dosimetry system. The original hypothesis of this work is that the spectral information of an X- and γ-ray fields may be obtained by the analysis of the response of a multi-element system. In this study, a feedforward neural network using the error back-propagation method with Bayesian optimization was applied for the response unfolding procedure. The validation of the proposed algorithm was investigated by unfolding the 10 measured responses of α-Al 2 O 3 : C for arbitrarily mixed photon fields which range from 20 to 662 keV

  7. Intelligent neural network and fuzzy logic control of industrial and power systems

    Science.gov (United States)

    Kuljaca, Ognjen

    The main role played by neural network and fuzzy logic intelligent control algorithms today is to identify and compensate unknown nonlinear system dynamics. There are a number of methods developed, but often the stability analysis of neural network and fuzzy control systems was not provided. This work will meet those problems for the several algorithms. Some more complicated control algorithms included backstepping and adaptive critics will be designed. Nonlinear fuzzy control with nonadaptive fuzzy controllers is also analyzed. An experimental method for determining describing function of SISO fuzzy controller is given. The adaptive neural network tracking controller for an autonomous underwater vehicle is analyzed. A novel stability proof is provided. The implementation of the backstepping neural network controller for the coupled motor drives is described. Analysis and synthesis of adaptive critic neural network control is also provided in the work. Novel tuning laws for the system with action generating neural network and adaptive fuzzy critic are given. Stability proofs are derived for all those control methods. It is shown how these control algorithms and approaches can be used in practical engineering control. Stability proofs are given. Adaptive fuzzy logic control is analyzed. Simulation study is conducted to analyze the behavior of the adaptive fuzzy system on the different environment changes. A novel stability proof for adaptive fuzzy logic systems is given. Also, adaptive elastic fuzzy logic control architecture is described and analyzed. A novel membership function is used for elastic fuzzy logic system. The stability proof is proffered. Adaptive elastic fuzzy logic control is compared with the adaptive nonelastic fuzzy logic control. The work described in this dissertation serves as foundation on which analysis of particular representative industrial systems will be conducted. Also, it gives a good starting point for analysis of learning abilities of

  8. Compact holographic optical neural network system for real-time pattern recognition

    Science.gov (United States)

    Lu, Taiwei; Mintzer, David T.; Kostrzewski, Andrew A.; Lin, Freddie S.

    1996-08-01

    One of the important characteristics of artificial neural networks is their capability for massive interconnection and parallel processing. Recently, specialized electronic neural network processors and VLSI neural chips have been introduced in the commercial market. The number of parallel channels they can handle is limited because of the limited parallel interconnections that can be implemented with 1D electronic wires. High-resolution pattern recognition problems can require a large number of neurons for parallel processing of an image. This paper describes a holographic optical neural network (HONN) that is based on high- resolution volume holographic materials and is capable of performing massive 3D parallel interconnection of tens of thousands of neurons. A HONN with more than 16,000 neurons packaged in an attache case has been developed. Rotation- shift-scale-invariant pattern recognition operations have been demonstrated with this system. System parameters such as the signal-to-noise ratio, dynamic range, and processing speed are discussed.

  9. System-Level Design of a 64-Channel Low Power Neural Spike Recording Sensor.

    Science.gov (United States)

    Delgado-Restituto, Manuel; Rodriguez-Perez, Alberto; Darie, Angela; Soto-Sanchez, Cristina; Fernandez-Jover, Eduardo; Rodriguez-Vazquez, Angel

    2017-04-01

    This paper reports an integrated 64-channel neural spike recording sensor, together with all the circuitry to process and configure the channels, process the neural data, transmit via a wireless link the information and receive the required instructions. Neural signals are acquired, filtered, digitized and compressed in the channels. Additionally, each channel implements an auto-calibration algorithm which individually configures the transfer characteristics of the recording site. The system has two transmission modes; in one case the information captured by the channels is sent as uncompressed raw data; in the other, feature vectors extracted from the detected neural spikes are released. Data streams coming from the channels are serialized by the embedded digital processor. Experimental results, including in vivo measurements, show that the power consumption of the complete system is lower than 330 μW.

  10. Inductive differentiation of two neural lineages reconstituted in a microculture system from Xenopus early gastrula cells.

    Science.gov (United States)

    Mitani, S; Okamoto, H

    1991-05-01

    Neural induction of ectoderm cells has been reconstituted and examined in a microculture system derived from dissociated early gastrula cells of Xenopus laevis. We have used monoclonal antibodies as specific markers to monitor cellular differentiation from three distinct ectoderm lineages in culture (N1 for CNS neurons from neural tube, Me1 for melanophores from neural crest and E3 for skin epidermal cells from epidermal lineages). CNS neurons and melanophores differentiate when deep layer cells of the ventral ectoderm (VE, prospective epidermis region; 150 cells/culture) and an appropriate region of the marginal zone (MZ, prospective mesoderm region; 5-150 cells/culture) are co-cultured, but not in cultures of either cell type on their own; VE cells cultured alone yield epidermal cells as we have previously reported. The extent of inductive neural differentiation in the co-culture system strongly depends on the origin and number of MZ cells initially added to culture wells. The potency to induce CNS neurons is highest for dorsal MZ cells and sharply decreases as more ventrally located cells are used. The same dorsoventral distribution of potency is seen in the ability of MZ cells to inhibit epidermal differentiation. In contrast, the ability of MZ cells to induce melanophores shows the reverse polarity, ventral to dorsal. These data indicate that separate developmental mechanisms are used for the induction of neural tube and neural crest lineages. Co-differentiation of CNS neurons or melanophores with epidermal cells can be obtained in a single well of co-cultures of VE cells (150) and a wide range of numbers of MZ cells (5 to 100). Further, reproducible differentiation of both neural lineages requires intimate association between cells from the two gastrula regions; virtually no differentiation is obtained when cells from the VE and MZ are separated in a culture well. These results indicate that the inducing signals from MZ cells for both neural tube and neural

  11. Adaptive neural network/expert system that learns fault diagnosis for different structures

    Science.gov (United States)

    Simon, Solomon H.

    1992-08-01

    Corporations need better real-time monitoring and control systems to improve productivity by watching quality and increasing production flexibility. The innovative technology to achieve this goal is evolving in the form artificial intelligence and neural networks applied to sensor processing, fusion, and interpretation. By using these advanced Al techniques, we can leverage existing systems and add value to conventional techniques. Neural networks and knowledge-based expert systems can be combined into intelligent sensor systems which provide real-time monitoring, control, evaluation, and fault diagnosis for production systems. Neural network-based intelligent sensor systems are more reliable because they can provide continuous, non-destructive monitoring and inspection. Use of neural networks can result in sensor fusion and the ability to model highly, non-linear systems. Improved models can provide a foundation for more accurate performance parameters and predictions. We discuss a research software/hardware prototype which integrates neural networks, expert systems, and sensor technologies and which can adapt across a variety of structures to perform fault diagnosis. The flexibility and adaptability of the prototype in learning two structures is presented. Potential applications are discussed.

  12. 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.

  13. 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.

  14. Playing the electric light orchestra--how electrical stimulation of visual cortex elucidates the neural basis of perception.

    Science.gov (United States)

    Cicmil, Nela; Krug, Kristine

    2015-09-19

    Vision research has the potential to reveal fundamental mechanisms underlying sensory experience. Causal experimental approaches, such as electrical microstimulation, provide a unique opportunity to test the direct contributions of visual cortical neurons to perception and behaviour. But in spite of their importance, causal methods constitute a minority of the experiments used to investigate the visual cortex to date. We reconsider the function and organization of visual cortex according to results obtained from stimulation techniques, with a special emphasis on electrical stimulation of small groups of cells in awake subjects who can report their visual experience. We compare findings from humans and monkeys, striate and extrastriate cortex, and superficial versus deep cortical layers, and identify a number of revealing gaps in the 'causal map' of visual cortex. Integrating results from different methods and species, we provide a critical overview of the ways in which causal approaches have been used to further our understanding of circuitry, plasticity and information integration in visual cortex. Electrical stimulation not only elucidates the contributions of different visual areas to perception, but also contributes to our understanding of neuronal mechanisms underlying memory, attention and decision-making.

  15. Playing the electric light orchestra—how electrical stimulation of visual cortex elucidates the neural basis of perception

    Science.gov (United States)

    Cicmil, Nela; Krug, Kristine

    2015-01-01

    Vision research has the potential to reveal fundamental mechanisms underlying sensory experience. Causal experimental approaches, such as electrical microstimulation, provide a unique opportunity to test the direct contributions of visual cortical neurons to perception and behaviour. But in spite of their importance, causal methods constitute a minority of the experiments used to investigate the visual cortex to date. We reconsider the function and organization of visual cortex according to results obtained from stimulation techniques, with a special emphasis on electrical stimulation of small groups of cells in awake subjects who can report their visual experience. We compare findings from humans and monkeys, striate and extrastriate cortex, and superficial versus deep cortical layers, and identify a number of revealing gaps in the ‘causal map′ of visual cortex. Integrating results from different methods and species, we provide a critical overview of the ways in which causal approaches have been used to further our understanding of circuitry, plasticity and information integration in visual cortex. Electrical stimulation not only elucidates the contributions of different visual areas to perception, but also contributes to our understanding of neuronal mechanisms underlying memory, attention and decision-making. PMID:26240421

  16. 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

  17. 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.

  18. Development of network-based multichannel neuromuscular electrical stimulation system for stroke rehabilitation.

    Science.gov (United States)

    Qu, Hongen; Xie, Yongji; Liu, Xiaoxuan; He, Xin; Hao, Manzhao; Bao, Yong; Xie, Qing; Lan, Ning

    2016-01-01

    Neuromuscular electrical stimulation (NMES) is a promising assistive technology for stroke rehabilitation. Here we present the design and development of a multimuscle stimulation system as an emerging therapy for people with paretic stroke. A network-based multichannel NMES system was integrated based on dual bus architecture of communication and an H-bridge current regulator with a power booster. The structure of the system was a body area network embedded with multiple stimulators and a communication protocol of controlled area network to transmit muscle stimulation parameter information to individual stimulators. A graphical user interface was designed to allow clinicians to specify temporal patterns and muscle stimulation parameters. We completed and tested a prototype of the hardware and communication software modules of the multichannel NMES system. The prototype system was first verified in nondisabled subjects for safety, and then tested in subjects with stroke for feasibility with assisting multijoint movements. Results showed that synergistic stimulation of multiple muscles in subjects with stroke improved performance of multijoint movements with more natural velocity profiles at elbow and shoulder and reduced acromion excursion due to compensatory trunk rotation. The network-based NMES system may provide an innovative solution that allows more physiological activation of multiple muscles in multijoint task training for patients with stroke.

  19. Psychological Processing in Chronic Pain: A Neural Systems Approach

    OpenAIRE

    Simons, Laura; Elman, Igor; Borsook, David

    2013-01-01

    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 implementati...

  20. 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

  1. Towards a closed-loop cochlear implant system: application of embedded monitoring of peripheral and central neural activity.

    Science.gov (United States)

    Mc Laughlin, Myles; Lu, Thomas; Dimitrijevic, Andrew; Zeng, Fan-Gang

    2012-07-01

    Although the cochlear implant (CI) is widely considered the most successful neural prosthesis, it is essentially an open-loop system that requires extensive initial fitting and frequent tuning to maintain a high, but not necessarily optimal, level of performance. Two developments in neuroscience and neuroengineering now make it feasible to design a closed-loop CI. One development is the recording and interpretation of evoked potentials (EPs) from the peripheral to the central nervous system. The other is the embedded hardware and software of a modern CI that allows recording of EPs. We review EPs that are pertinent to behavioral functions from simple signal detection and loudness growth to speech discrimination and recognition. We also describe signal processing algorithms used for electric artifact reduction and cancellation, critical to the recording of electric EPs. We then present a conceptual design for a closed-loop CI that utilizes in an innovative way the embedded implant receiver and stimulators to record short latency compound action potentials ( ~1 ms), auditory brainstem responses (1-10 ms) and mid-to-late cortical potentials (20-300 ms). We compare EPs recorded using the CI to EPs obtained using standard scalp electrodes recording techniques. Future applications and capabilities are discussed in terms of the development of a new generation of closed-loop CIs and other neural prostheses.

  2. 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 ...

  3. Integrated Markov-neural reliability computation method: A case for multiple automated guided vehicle system

    International Nuclear Information System (INIS)

    Fazlollahtabar, Hamed; Saidi-Mehrabad, Mohammad; Balakrishnan, Jaydeep

    2015-01-01

    This paper proposes an integrated Markovian and back propagation neural network approaches to compute reliability of a system. While states of failure occurrences are significant elements for accurate reliability computation, Markovian based reliability assessment method is designed. Due to drawbacks shown by Markovian model for steady state reliability computations and neural network for initial training pattern, integration being called Markov-neural is developed and evaluated. To show efficiency of the proposed approach comparative analyses are performed. Also, for managerial implication purpose an application case for multiple automated guided vehicles (AGVs) in manufacturing networks is conducted. - Highlights: • Integrated Markovian and back propagation neural network approach to compute reliability. • Markovian based reliability assessment method. • Managerial implication is shown in an application case for multiple automated guided vehicles (AGVs) in manufacturing networks

  4. 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.

  5. A review of organic and inorganic biomaterials for neural interfaces.

    Science.gov (United States)

    Fattahi, Pouria; Yang, Guang; Kim, Gloria; Abidian, Mohammad Reza

    2014-03-26

    Recent advances in nanotechnology have generated wide interest in applying nanomaterials for neural prostheses. An ideal neural interface should create seamless integration into the nervous system and performs reliably for long periods of time. As a result, many nanoscale materials not originally developed for neural interfaces become attractive candidates to detect neural signals and stimulate neurons. In this comprehensive review, an overview of state-of-the-art microelectrode technologies provided fi rst, with focus on the material properties of these microdevices. The advancements in electro active nanomaterials are then reviewed, including conducting polymers, carbon nanotubes, graphene, silicon nanowires, and hybrid organic-inorganic nanomaterials, for neural recording, stimulation, and growth. Finally, technical and scientific challenges are discussed regarding biocompatibility, mechanical mismatch, and electrical properties faced by these nanomaterials for the development of long-lasting functional neural interfaces.

  6. 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.

  7. 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.

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

    Science.gov (United States)

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

    2015-03-01

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

  9. A modular neural network scheme applied to fault diagnosis in electric power systems.

    Science.gov (United States)

    Flores, Agustín; Quiles, Eduardo; García, Emilio; Morant, Francisco; Correcher, Antonio

    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.

  10. 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.

  11. Coupling Strength and System Size Induce Firing Activity of Globally Coupled Neural Network

    International Nuclear Information System (INIS)

    Wei Duqu; Luo Xiaoshu; Zou Yanli

    2008-01-01

    We investigate how firing activity of globally coupled neural network depends on the coupling strength C and system size N. Network elements are described by space-clamped FitzHugh-Nagumo (SCFHN) neurons with the values of parameters at which no firing activity occurs. It is found that for a given appropriate coupling strength, there is an intermediate range of system size where the firing activity of globally coupled SCFHN neural network is induced and enhanced. On the other hand, for a given intermediate system size level, there exists an optimal value of coupling strength such that the intensity of firing activity reaches its maximum. These phenomena imply that the coupling strength and system size play a vital role in firing activity of neural network

  12. 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.

  13. Brain changes following four weeks of unimanual motor training: Evidence from behavior, neural stimulation, cortical thickness, and functional MRI.

    Science.gov (United States)

    Sale, Martin V; Reid, Lee B; Cocchi, Luca; Pagnozzi, Alex M; Rose, Stephen E; Mattingley, Jason B

    2017-09-01

    Although different aspects of neuroplasticity can be quantified with behavioral probes, brain stimulation, and brain imaging assessments, no study to date has combined all these approaches into one comprehensive assessment of brain plasticity. Here, 24 healthy right-handed participants practiced a sequence of finger-thumb opposition movements for 10 min each day with their left hand. After 4 weeks, performance for the practiced sequence improved significantly (P left (mean increase: 53.0% practiced, 6.5% control) and right (21.0%; 15.8%) hands. Training also induced significant (cluster p-FWE right hemisphere, 301 voxel cluster; left hemisphere 700 voxel cluster), and sensorimotor cortices and superior parietal lobules (right hemisphere 864 voxel cluster; left hemisphere, 1947 voxel cluster). Transcranial magnetic stimulation over the right ("trained") primary motor cortex yielded a 58.6% mean increase in a measure of motor evoked potential amplitude, as recorded at the left abductor pollicis brevis muscle. Cortical thickness analyses based on structural MRI suggested changes in the right precentral gyrus, right post central gyrus, right dorsolateral prefrontal cortex, and potentially the right supplementary motor area. Such findings are consistent with LTP-like neuroplastic changes in areas that were already responsible for finger sequence execution, rather than improved recruitment of previously nonutilized tissue. Hum Brain Mapp 38:4773-4787, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  14. Coding of level of ambiguity within neural systems mediating choice.

    Science.gov (United States)

    Lopez-Paniagua, Dan; Seger, Carol A

    2013-01-01

    Data from previous neuroimaging studies exploring neural activity associated with uncertainty suggest varying levels of activation associated with changing degrees of uncertainty in neural regions that mediate choice behavior. The present study used a novel task that parametrically controlled the amount of information hidden from the subject; levels of uncertainty ranged from full ambiguity (no information about probability of winning) through multiple levels of partial ambiguity, to a condition of risk only (zero ambiguity with full knowledge of the probability of winning). A parametric analysis compared a linear model in which weighting increased as a function of level of ambiguity, and an inverted-U quadratic models in which partial ambiguity conditions were weighted most heavily. Overall we found that risk and all levels of ambiguity recruited a common "fronto-parietal-striatal" network including regions within the dorsolateral prefrontal cortex, intraparietal sulcus, and dorsal striatum. Activation was greatest across these regions and additional anterior and superior prefrontal regions for the quadratic function which most heavily weighs trials with partial ambiguity. These results suggest that the neural regions involved in decision processes do not merely track the absolute degree ambiguity or type of uncertainty (risk vs. ambiguity). Instead, recruitment of prefrontal regions may result from greater degree of difficulty in conditions of partial ambiguity: when information regarding reward probabilities important for decision making is hidden or not easily obtained the subject must engage in a search for tractable information. Additionally, this study identified regions of activity related to the valuation of potential gains associated with stimuli or options (including the orbitofrontal and medial prefrontal cortices and dorsal striatum) and related to winning (including orbitofrontal cortex and ventral striatum).

  15. 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

  16. Sign Language Recognition System using Neural Network for Digital Hardware Implementation

    International Nuclear Information System (INIS)

    Vargas, Lorena P; Barba, Leiner; Torres, C O; Mattos, L

    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.

  17. 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 neural network uses a modified unscented kalman filter (UKF) with forgetting factor scheme as the required on-line learning algorithm. The effectiveness of the resulting identification approach is tested and evaluated on a simulated benchmark hybrid system....

  18. Inverse approach for determination of the coils location during magnetic stimulation

    International Nuclear Information System (INIS)

    Marinova, Iliana; Kovachev, Ludmil

    2002-01-01

    An inverse approach using neural networks is extended and applied for determination of coils location during magnetic stimulation. The major constructions of magnetic stimulation coils have been investigated. The electric and magnetic fields are modelled using finite element method and integral equation method. The effects of changing the construction of coils and the frequency to the effect of magnetic stimulation are analysed. The results show that the coils for magnetic stimulation characterize with different focality and magnetic field concentration. The proposed inverse approach using neural networks is very useful for determination the spatial position of the stimulation coils especially when the location of the coil system is required to be changed dynamically. (Author)

  19. An intelligent sales forecasting system through integration of artificial neural networks and fuzzy neural networks with fuzzy weight elimination.

    Science.gov (United States)

    Kuo, R J; Wu, P; Wang, C P

    2002-09-01

    Sales forecasting plays a very prominent role in business strategy. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average (ARMA). However, sales forecasting is very complicated owing to influence by internal and external environments. Recently, artificial neural networks (ANNs) have also been applied in sales forecasting since their promising performances in the areas of control and pattern recognition. However, further improvement is still necessary since unique circumstances, e.g. promotion, cause a sudden change in the sales pattern. Thus, this study utilizes a proposed fuzzy neural network (FNN), which is able to eliminate the unimportant weights, for the sake of learning fuzzy IF-THEN rules obtained from the marketing experts with respect to promotion. The result from FNN is further integrated with the time series data through an ANN. Both the simulated and real-world problem results show that FNN with weight elimination can have lower training error compared with the regular FNN. Besides, real-world problem results also indicate that the proposed estimation system outperforms the conventional statistical method and single ANN in accuracy.

  20. Neural correlates of hemispheric dominance and ipsilaterality within the vestibular system.

    Science.gov (United States)

    Janzen, J; Schlindwein, P; Bense, S; Bauermann, T; Vucurevic, G; Stoeter, P; Dieterich, M

    2008-10-01

    Earlier functional imaging studies on the processing of vestibular information mainly focused on cortical activations due to stimulation of the horizontal semicircular canals in right-handers. Two factors were found to determine its processing in the temporo-parietal cortex: a dominance of the non-dominant hemisphere and an ipsilaterality of the neural pathways. In an investigation of the role of these factors in the vestibular otoliths, we used vestibular evoked myogenic potentials (VEMPs) in a fMRI study of monaural saccular-otolith stimulation. Our aim was to (1) analyze the hemispheric dominance for saccular-otolith information in healthy left-handers, (2) determine if there is a predominance of the ipsilateral saccular-otolith projection, and (3) evaluate the impact of both factors on the temporo-parieto-insular activation pattern. A block design with three stimulation and rest conditions was applied: (1) 102 dB-VEMP stimulation; (2) 65 dB-control-acoustic stimulation, (3) 102 dB-white-noise-control stimulation. After subtraction of acoustic side effects, bilateral activations were found in the posterior insula, the superior/middle/transverse temporal gyri, and the inferior parietal lobule. The distribution of the saccular-otolith activations was influenced by the two factors but with topographic disparity: whereas the inferior parts of the temporo-parietal cortex were mainly influenced by the ipsilaterality of the pathways, the upper parts reflected the dominance of the non-dominant hemisphere. This is in contrast to the processing of acoustic stimulation, which showed a predominance of the contralateral pathways. Our study proves the importance of the hemispheric preponderance also in left-handers, which is of relevance in the superior parts of the insula gyrus V, the inferior parietal lobule, and the superior temporal gyri.

  1. Expression of c-fos gene in central nervous system of adult medaka (Oryzias latipes) after hypergravity stimulation

    Science.gov (United States)

    Shimomura, S.; Ijiri, K.

    The immediate-early genes serve as useful neurobiological tools for mapping brain activity induced by a sensory stimulation. In this study, we have examined brain activity related to gravity perception of medaka (Oryzias latipes) by use of c-fos. The gene, which is homologous to the c-fos genes of other vertebrates, was identified in medaka. Functionally important domains are highly conserved among all the vertebrate species analyzed. Intraperitoneal administration of kainic acid transiently induced the c-fos mRNAs in medaka brain. The results indicate that the expression of c-fos can be utilized as a suitable anatomical marker for the increased neural activities in the central nervous system of medaka. Fish were continuously exposed to 3G hypergravity by centrifugation. Investigation of c-fos mRNA expression showed that c-fos mRNA significantly increased 30 minutes after a start of 3G exposure. The distribution of its transcripts within brains was analyzed by an in situ hybridization method. The 3G-treated medakas displayed c-fos positive cells in their brainstem regions, which are related to vestibular function, such as torus semicircularis, posterior octavu nucleus, nucleus tangentialis and inferior olive. Our results established the method to trace the activated area in the fish brain following gravity stimulation. The method will be a useful tool for understanding gravity perception in the brain.

  2. Statistical analysis and decoding of neural activity in the rodent geniculate ganglion using a metric-based inference system.

    Directory of Open Access Journals (Sweden)

    Wei Wu

    Full Text Available We analyzed the spike discharge patterns of two types of neurons in the rodent peripheral gustatory system, Na specialists (NS and acid generalists (AG to lingual stimulation with NaCl, acetic acid, and mixtures of the two stimuli. Previous computational investigations found that both spike rate and spike timing contribute to taste quality coding. These studies used commonly accepted computational methods, but they do not provide a consistent statistical evaluation of spike trains. In this paper, we adopted a new computational framework that treated each spike train as an individual data point for computing summary statistics such as mean and variance in the spike train space. We found that these statistical summaries properly characterized the firing patterns (e. g. template and variability and quantified the differences between NS and AG neurons. The same framework was also used to assess the discrimination performance of NS and AG neurons and to remove spontaneous background activity or "noise" from the spike train responses. The results indicated that the new metric system provided the desired decoding performance and noise-removal improved stimulus classification accuracy, especially of neurons with high spontaneous rates. In summary, this new method naturally conducts statistical analysis and neural decoding under one consistent framework, and the results demonstrated that individual peripheral-gustatory neurons generate a unique and reliable firing pattern during sensory stimulation and that this pattern can be reliably decoded.

  3. 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

  4. 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.

  5. 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

  6. 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.

  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

    2014-01-01

    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) motor evoked-potentials (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 MEPs 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 insights into the cortical effects of cTBS and could be useful for exploring cTBS-induced plasticity outside of the motor cortex. PMID:23190020

  8. Color Image Encryption Algorithm Based on TD-ERCS System and Wavelet Neural Network

    Directory of Open Access Journals (Sweden)

    Kun Zhang

    2015-01-01

    Full Text Available In order to solve the security problem of transmission image across public networks, a new image encryption algorithm based on TD-ERCS system and wavelet neural network is proposed in this paper. According to the permutation process and the binary XOR operation from the chaotic series by producing TD-ERCS system and wavelet neural network, it can achieve image encryption. This encryption algorithm is a reversible algorithm, and it can achieve original image in the rule inverse process of encryption algorithm. Finally, through computer simulation, the experiment results show that the new chaotic encryption algorithm based on TD-ERCS system and wavelet neural network is valid and has higher security.

  9. 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...

  10. Neural network modeling of nonlinear systems based on Volterra series extension of a linear model

    Science.gov (United States)

    Soloway, Donald I.; Bialasiewicz, Jan T.

    1992-01-01

    A Volterra series approach was applied to the identification of nonlinear systems which are described by a neural network model. A procedure is outlined by which a mathematical model can be developed from experimental data obtained from the network structure. Applications of the results to the control of robotic systems are discussed.

  11. 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.

  12. An Improved Recurrent Neural Network for Complex-Valued Systems of Linear Equation and Its Application to Robotic Motion Tracking.

    Science.gov (United States)

    Ding, Lei; Xiao, Lin; Liao, Bolin; Lu, Rongbo; Peng, Hua

    2017-01-01

    To obtain the online solution of complex-valued systems of linear equation in complex domain with higher precision and higher convergence rate, a new neural network based on Zhang neural network (ZNN) is investigated in this paper. First, this new neural network for complex-valued systems of linear equation in complex domain is proposed and theoretically proved to be convergent within finite time. Then, the illustrative results show that the new neural network model has the higher precision and the higher convergence rate, as compared with the gradient neural network (GNN) model and the ZNN model. Finally, the application for controlling the robot using the proposed method for the complex-valued systems of linear equation is realized, and the simulation results verify the effectiveness and superiorness of the new neural network for the complex-valued systems of linear equation.

  13. Efficient decoding with steady-state Kalman filter in neural interface systems.

    Science.gov (United States)

    Malik, Wasim Q; Truccolo, Wilson; Brown, Emery N; Hochberg, Leigh R

    2011-02-01

    The Kalman filter is commonly used in neural interface systems to decode neural activity and estimate the desired movement kinematics. We analyze a low-complexity Kalman filter implementation in which the filter gain is approximated by its steady-state form, computed offline before real-time decoding commences. We evaluate its performance using human motor cortical spike train data obtained from an intracortical recording array as part of an ongoing pilot clinical trial. We demonstrate that the standard Kalman filter gain converges to within 95% of the steady-state filter gain in 1.5±0.5 s (mean ±s.d.). The difference in the intended movement velocity decoded by the two filters vanishes within 5 s, with a correlation coefficient of 0.99 between the two decoded velocities over the session length. We also find that the steady-state Kalman filter reduces the computational load (algorithm execution time) for decoding the firing rates of 25±3 single units by a factor of 7.0±0.9. We expect that the gain in computational efficiency will be much higher in systems with larger neural ensembles. The steady-state filter can thus provide substantial runtime efficiency at little cost in terms of estimation accuracy. This far more efficient neural decoding approach will facilitate the practical implementation of future large-dimensional, multisignal neural interface systems.

  14. Electrical and optical co-stimulation in the deaf white cat

    Science.gov (United States)

    Cao, Zhiping; Xu, Yingyue; Tan, Xiaodong; Suematsu, Naofumi; Robinson, Alan; Richter, Claus-Peter

    2018-02-01

    Spatial selectivity of neural stimulation with photons, such as infrared neural stimulation (INS) is higher than the selectivity obtained with electrical stimulation. To obtain more independent channels for stimulation in neural prostheses, INS may be implemented to better restore the fidelity of the damaged neural system. However, irradiation with infrared light also bares the risk of heat accumulation in the target tissue with subsequent neural damage. Lowering the threshold for stimulation could reduce the amount of heat delivered to the tissue and the risk for subsequent tissue damage. It has been shown in the rat sciatic nerve that simultaneous irradiation with infrared light and the delivery of biphasic sub-threshold electrical pulses can reduce the threshold for INS [1]. In this study, deaf white cats have been used to test whether opto-electrical co-stimulation can reduce the stimulation threshold for INS in the auditory system too. The cochleae of the deaf white cats have largely reduced spiral ganglion neuron counts and significant degeneration of the organ of Corti and do not respond to acoustic stimuli. Combined electrical and optical stimulation was used to demonstrate that simultaneous stimulation with infrared light and biphasic electrical pulses can reduce the threshold for stimulation.

  15. Unusual social behavior in HPC-1/syntaxin1A knockout mice is caused by disruption of the oxytocinergic neural system.

    Science.gov (United States)

    Fujiwara, Tomonori; Sanada, Masumi; Kofuji, Takefumi; Akagawa, Kimio

    2016-07-01

    HPC-1/syntaxin1A (STX1A), a neuronal soluble N-ethylmaleimide-sensitive fusion attachment protein receptor, contributes to neural function in the CNS by regulating transmitter release. Recent studies reported that STX1A is associated with human neuropsychological disorders, such as autism spectrum disorder and attention deficit hyperactivity disorder. Previously, we showed that STX1A null mutant mice (STX1A KO) exhibit neuropsychological abnormalities, such as fear memory deficits, attenuation of latent inhibition, and unusual social behavior. These observations suggested that STX1A may be involved in the neuropsychological basis of these abnormalities. Here, to study the neural basis of social behavior, we analyzed the profile of unusual social behavior in STX1A KO with a social novelty preference test, which is a useful method for quantification of social behavior. Interestingly, the unusual social behavior in STX1A KO was partially rescued by intracerebroventricular administration of oxytocin (OXT). In vivo microdialysis studies revealed that the extracellular OXT concentration in the CNS of STX1A KO was significantly lower compared with wild-type mice. Furthermore, dopamine-induced OXT release was reduced in STX1A KO. These results suggested that STX1A plays an important role in social behavior through regulation of the OXTergic neural system. Dopamine (DA) release is reduced in CNS of syntaxin1A null mutant mice (STX1A KO). Unusual social behavior was observed in STX1A KO. We found that oxytocin (OXT) release, which was stimulated by DA, was reduced and was rescued the unusual social behavior in STX1A KO was rescued by OXT. These results indicated that STX1A plays an important role in promoting social behavior through regulation of DA-induced OXT release in amygdala. © 2016 International Society for Neurochemistry.

  16. Neural networks

    International Nuclear Information System (INIS)

    Denby, Bruce; Lindsey, Clark; Lyons, Louis

    1992-01-01

    The 1980s saw a tremendous renewal of interest in 'neural' information processing systems, or 'artificial neural networks', among computer scientists and computational biologists studying cognition. Since then, the growth of interest in neural networks in high energy physics, fueled by the need for new information processing technologies for the next generation of high energy proton colliders, can only be described as explosive

  17. Artificial neural systems for interpretation and inversion of seismic data

    Science.gov (United States)

    Calderon-Macias, Carlos

    The goal of this work is to investigate the feasibility of using neural network (NN) models for solving geophysical exploration problems. First, a feedforward neural network (FNN) is used to solve inverse problems. The operational characteristics of a FNN are primarily controlled by a set of weights and a nonlinear function that performs a mapping between two sets of data. In a process known as training, the FNN weights are iteratively adjusted to perform the mapping. After training, the computed weights encode important features of the data that enable one pattern to be distinguished from another. Synthetic data computed from an ensemble of earth models and the corresponding models provide the training data. Two training methods are studied: the backpropagation method which is a gradient scheme, and a global optimization method called very fast simulated annealing (VFSA). A trained network is then used to predict models from new data (e.g., data from a new location) in a one-step procedure. The application of this method to the problems of obtaining formation resistivities and layer thicknesses from resistivity sounding data and 1D velocity models from seismic data shows that trained FNNs produce reasonably accurate earth models when observed data are input to the FNNs. In a second application, a FNN is used for automating the NMO correction process of seismic reflection data. The task of the FNN is to map CMP data at control locations along a seismic line into subsurface velocities. The network is trained while the velocity analyses are performed at the control locations. Once trained, the computed weights are used as an operator that acts on the remaining CMP data as a velocity interpolator, resulting in a fast method for NMO correction. The second part of this dissertation describes the application of a Hopfield neural network (HNN) to the problems of deconvolution and multiple attenuation. In these applications, the unknown parameters (reflection coefficients

  18. A stem cell medium containing neural stimulating factor induces a pancreatic cancer stem-like cell-enriched population

    Science.gov (United States)

    WATANABE, YUSAKU; YOSHIMURA, KIYOSHI; YOSHIKAWA, KOICHI; TSUNEDOMI, RYOICHI; SHINDO, YOSHITARO; MATSUKUMA, SOU; MAEDA, NORIKO; KANEKIYO, SHINSUKE; SUZUKI, NOBUAKI; KURAMASU, ATSUO; SONODA, KOUHEI; TAMADA, KOJI; KOBAYASHI, SEI; SAYA, HIDEYUKI; HAZAMA, SHOICHI; OKA, MASAAKI

    2014-01-01

    Cancer stem cells (CSCs) have been studied for their self-renewal capacity and pluripotency, as well as their resistance to anticancer therapy and their ability to metastasize to distant organs. CSCs are difficult to study because their population is quite low in tumor specimens. To overcome this problem, we established a culture method to induce a pancreatic cancer stem-like cell (P-CSLC)-enriched population from human pancreatic cancer cell lines. Human pancreatic cancer cell lines established at our department were cultured in CSC-inducing media containing epidermal growth factor (EGF), basic fibroblast growth factor (bFGF), leukemia inhibitory factor (LIF), neural cell survivor factor-1 (NSF-1), and N-acetylcysteine. Sphere cells were obtained and then transferred to a laminin-coated dish and cultured for approximately two months. The surface markers, gene expression, aldehyde dehydrogenase (ALDH) activity, cell cycle, and tumorigenicity of these induced cells were examined for their stem cell-like characteristics. The population of these induced cells expanded within a few months. The ratio of CD24high, CD44high, epithelial specific antigen (ESA) high, and CD44variant (CD44v) high cells in the induced cells was greatly enriched. The induced cells stayed in the G0/G1 phase and demonstrated mesenchymal and stemness properties. The induced cells had high tumorigenic potential. Thus, we established a culture method to induce a P-CSLCenriched population from human pancreatic cancer cell lines. The CSLC population was enriched approximately 100-fold with this method. Our culture method may contribute to the precise analysis of CSCs and thus support the establishment of CSC-targeting therapy. PMID:25118635

  19. 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.

  20. 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.

  1. Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

    International Nuclear Information System (INIS)

    Wang, L; Zhang, Y Y; Ding, L

    2006-01-01

    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

  2. ISC feedforward control of gasoline engine. Adaptive system using neural network; Jidoshayo gasoline engine no ISC feedforward seigyo. Neural network wo mochiita tekioka

    Energy Technology Data Exchange (ETDEWEB)

    Kinugawa, N; Morita, S; Takiyama, T [Osaka City University, Osaka (Japan)

    1997-10-01

    For fuel economy and a good driver`s feeling, it is necessary for idle-speed to keep at a constant low speed. But keeping low speed has danger of engine stall when the engine torque is disturbed by the alternator, and so on. In this paper, adaptive feedforward idle-speed control system against electrical loads was investigated. This system was based on the reversed tansfer functions of the object system, and a neural network was used to adapt this system for aging. Then, this neural network was also used for creating feedforward table map. Good experimental results were obtained. 2 refs., 11 figs.

  3. Antinociception induced by stimulating amygdaloid nuclei in rats: changes produced by systemically administered antagonists

    Directory of Open Access Journals (Sweden)

    M.A. Oliveira

    1998-05-01

    Full Text Available The antinociceptive effects of stimulating the medial (ME and central (CE nuclei of the amygdala in rats were evaluated by the changes in the latency for the tail withdrawal reflex to noxious heating of the skin. A 30-s period of sine-wave stimulation of the ME or CE produced a significant and short increase in the duration of tail flick latency. A 15-s period of stimulation was ineffective. Repeated stimulation of these nuclei at 48-h intervals produced progressively smaller effects. The antinociception evoked from the ME was significantly reduced by the previous systemic administration of naloxone, methysergide, atropine, phenoxybenzamine, and propranolol, but not by mecamylamine, all given at the dose of 1.0 mg/kg. Previous systemic administration of naloxone, atropine, and propranolol, but not methysergide, phenoxybenzamine, or mecamylamine, was effective against the effects of stimulating the CE. We conclude that the antinociceptive effects of stimulating the ME involve at least opioid, serotonergic, adrenergic, and muscarinic cholinergic descending mechanisms. The effects of stimulating the CE involve at least opioid, ß-adrenergic, and muscarinic cholinergic descending mechanisms.

  4. Evolving networks and the development of neural systems

    International Nuclear Information System (INIS)

    Johnson, Samuel; Marro, J; Torres, Joaquín J

    2010-01-01

    It is now generally assumed that the heterogeneity of most networks in nature probably arises via preferential attachment of some sort. However, the origin of various other topological features, such as degree–degree correlations and related characteristics, is often not clear, and they may arise from specific functional conditions. We show how it is possible to analyse a very general scenario in which nodes can gain or lose edges according to any (e.g., nonlinear) function of local and/or global degree information. Applying our method to two rather different examples of brain development—synaptic pruning in humans and the neural network of the worm C. Elegans—we find that simple biologically motivated assumptions lead to very good agreement with experimental data. In particular, many nontrivial topological features of the worm's brain arise naturally at a critical point

  5. Neural Networks for Self-tuning Control Systems

    Directory of Open Access Journals (Sweden)

    A. Noriega Ponce

    2004-01-01

    Full Text Available In this paper, we presented a self-tuning control algorithm based on a three layers perceptron type neural network. The proposed algorithm is advantageous in the sense that practically a previous training of the net is not required and some changes in the set-point are generally enough to adjust the learning coefficient. Optionally, it is possible to introduce a self-tuning mechanism of the learning coefficient although by the moment it is not possible to give final conclusions about this possibility. The proposed algorithm has the special feature that the regulation error instead of the net output error is retropropagated for the weighting coefficients modifications. 

  6. 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.

  7. Stimulating neural plasticity with real-time fMRI neurofeedback in Huntington's disease: A proof of concept study.

    Science.gov (United States)

    Papoutsi, Marina; Weiskopf, Nikolaus; Langbehn, Douglas; Reilmann, Ralf; Rees, Geraint; Tabrizi, Sarah J

    2018-03-01

    Novel methods that stimulate neuroplasticity are increasingly being studied to treat neurological and psychiatric conditions. We sought to determine whether real-time fMRI neurofeedback training is feasible in Huntington's disease (HD), and assess any factors that contribute to its effectiveness. In this proof-of-concept study, we used this technique to train 10 patients with HD to volitionally regulate the activity of their supplementary motor area (SMA). We collected detailed behavioral and neuroimaging data before and after training to examine changes of brain function and structure, and cognitive and motor performance. We found that patients overall learned to increase activity of the target region during training with variable effects on cognitive and motor behavior. Improved cognitive and motor performance after training predicted increases in pre-SMA grey matter volume, fMRI activity in the left putamen, and increased SMA-left putamen functional connectivity. Although we did not directly target the putamen and corticostriatal connectivity during neurofeedback training, our results suggest that training the SMA can lead to regulation of associated networks with beneficial effects in behavior. We conclude that neurofeedback training can induce plasticity in patients with Huntington's disease despite the presence of neurodegeneration, and the effects of training a single region may engage other regions and circuits implicated in disease pathology. © 2017 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.

  8. Diffusion Tensor Imaging Evaluation of Neural Network Development in Patients Undergoing Therapeutic Repetitive Transcranial Magnetic Stimulation following Stroke

    Directory of Open Access Journals (Sweden)

    Naoki Yamada

    2018-01-01

    Full Text Available We aimed to investigate plastic changes in cerebral white matter structures using diffusion tensor imaging following a 15-day stroke rehabilitation program. We compared the detection of cerebral plasticity between generalized fractional anisotropy (GFA, a novel tool for investigating white matter structures, and fractional anisotropy (FA. Low-frequency repetitive transcranial magnetic stimulation (LF-rTMS of 2400 pulses applied to the nonlesional hemisphere and 240 min intensive occupation therapy (OT daily over 15 days. Motor function was evaluated using the Fugl-Meyer assessment (FMA and Wolf Motor Function Test (WMFT. Patients underwent diffusion tensor magnetic resonance imaging (MRI on admission and discharge, from which bilateral FA and GFA values in Brodmann area (BA 4 and BA6 were calculated. Motor function improved following treatment (p<0.001. Treatment increased GFA values for both the lesioned and nonlesioned BA4 (p<0.05, p<0.001, resp.. Changes in GFA value for BA4 of the lesioned hemisphere were significantly inversely correlated with changes in WMFT scores (R2=0.363, p<0.05. Our findings indicate that the GFA may have a potentially more useful ability than FA to detect changes in white matter structures in areas of fiber intersection for any such future investigations.

  9. Study on algorithm of process neural network for soft sensing in sewage disposal system

    Science.gov (United States)

    Liu, Zaiwen; Xue, Hong; Wang, Xiaoyi; Yang, Bin; Lu, Siying

    2006-11-01

    A new method of soft sensing based on process neural network (PNN) for sewage disposal system is represented in the paper. PNN is an extension of traditional neural network, in which the inputs and outputs are time-variation. An aggregation operator is introduced to process neuron, and it makes the neuron network has the ability to deal with the information of space-time two dimensions at the same time, so the data processing enginery of biological neuron is imitated better than traditional neuron. Process neural network with the structure of three layers in which hidden layer is process neuron and input and output are common neurons for soft sensing is discussed. The intelligent soft sensing based on PNN may be used to fulfill measurement of the effluent BOD (Biochemical Oxygen Demand) from sewage disposal system, and a good training result of soft sensing was obtained by the method.

  10. A Wavelet Neural Network Optimal Control Model for Traffic-Flow Prediction in Intelligent Transport Systems

    Science.gov (United States)

    Huang, Darong; Bai, Xing-Rong

    Based on wavelet transform and neural network theory, a traffic-flow prediction model, which was used in optimal control of Intelligent Traffic system, is constructed. First of all, we have extracted the scale coefficient and wavelet coefficient from the online measured raw data of traffic flow via wavelet transform; Secondly, an Artificial Neural Network model of Traffic-flow Prediction was constructed and trained using the coefficient sequences as inputs and raw data as outputs; Simultaneous, we have designed the running principium of the optimal control system of traffic-flow Forecasting model, the network topological structure and the data transmitted model; Finally, a simulated example has shown that the technique is effectively and exactly. The theoretical results indicated that the wavelet neural network prediction model and algorithms have a broad prospect for practical application.

  11. Neural Stem Cells: Implications for the Conventional Radiotherapy of Central Nervous System Malignancies

    International Nuclear Information System (INIS)

    Barani, Igor J.; Benedict, Stanley H.; Lin, Peck-Sun

    2007-01-01

    Advances in basic neuroscience related to neural stem cells and their malignant counterparts are challenging traditional models of central nervous system tumorigenesis and intrinsic brain repair. Neurogenesis persists into adulthood predominantly in two neurogenic centers: subventricular zone and subgranular zone. Subventricular zone is situated adjacent to lateral ventricles and subgranular zone is confined to the dentate gyrus of the hippocampus. Neural stem cells not only self-renew and differentiate along multiple lineages in these regions, but also contribute to intrinsic brain plasticity and repair. Ionizing radiation can depopulate these exquisitely sensitive regions directly or impair in situ neurogenesis by indirect, dose-dependent and inflammation-mediated mechanisms, even at doses <2 Gy. This review discusses the fundamental neural stem cell concepts within the framework of cumulative clinical experience with the treatment of central nervous system malignancies using conventional radiotherapy

  12. Towards an Irritable Bowel Syndrome Control System Based on Artificial Neural Networks

    Science.gov (United States)

    Podolski, Ina; Rettberg, Achim

    To solve health problems with medical applications that use complex algorithms is a trend nowadays. It could also be a chance to help patients with critical problems caused from nerve irritations to overcome them and provide a better living situation. In this paper a system for monitoring and controlling the nerves from the intestine is described on a theoretical basis. The presented system could be applied to the irritable bowel syndrome. For control a neural network is used. The advantages for using a neural network for the control of irritable bowel syndrome are the adaptation and learning. These two aspects are important because the syndrome behavior varies from patient to patient and have also concerning the time a lot of variations with respect to each patient. The developed neural network is implemented and can be simulated. Therefore, it can be shown how the network monitor and control the nerves for individual input parameters.

  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. 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.

  15. Application of neural networks to software quality modeling of a very large telecommunications system.

    Science.gov (United States)

    Khoshgoftaar, T M; Allen, E B; Hudepohl, J P; Aud, S J

    1997-01-01

    Society relies on telecommunications to such an extent that telecommunications software must have high reliability. Enhanced measurement for early risk assessment of latent defects (EMERALD) is a joint project of Nortel and Bell Canada for improving the reliability of telecommunications software products. This paper reports a case study of neural-network modeling techniques developed for the EMERALD system. The resulting neural network is currently in the prototype testing phase at Nortel. Neural-network models can be used to identify fault-prone modules for extra attention early in development, and thus reduce the risk of operational problems with those modules. We modeled a subset of modules representing over seven million lines of code from a very large telecommunications software system. The set consisted of those modules reused with changes from the previous release. The dependent variable was membership in the class of fault-prone modules. The independent variables were principal components of nine measures of software design attributes. We compared the neural-network model with a nonparametric discriminant model and found the neural-network model had better predictive accuracy.

  16. 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 sh