Science.gov

Sample records for neural interface systems

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

    PubMed

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

    2015-08-01

    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. PMID:26737158

  2. Implantable microscale neural interfaces.

    PubMed

    Cheung, Karen C

    2007-12-01

    Implantable neural microsystems provide an interface to the nervous system, giving cellular resolution to physiological processes unattainable today with non-invasive methods. Such implantable microelectrode arrays are being developed to simultaneously sample signals at many points in the tissue, providing insight into processes such as movement control, memory formation, and perception. These electrode arrays have been microfabricated on a variety of substrates, including silicon, using both surface and bulk micromachining techniques, and more recently, polymers. Current approaches to achieving a stable long-term tissue interface focus on engineering the surface properties of the implant, including coatings that discourage protein adsorption or release bioactive molecules. The implementation of a wireless interface requires consideration of the necessary data flow, amplification, signal processing, and packaging. In future, the realization of a fully implantable neural microsystem will contribute to both diagnostic and therapeutic applications, such as a neuroprosthetic interface to restore motor functions in paralyzed patients. PMID:17252207

  3. Evolvable synthetic neural system

    NASA Technical Reports Server (NTRS)

    Curtis, Steven A. (Inventor)

    2009-01-01

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

  4. Optical Neural Interfaces

    PubMed Central

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

    2014-01-01

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

  5. A Neuromorphic Event-Based Neural Recording System for Smart Brain-Machine-Interfaces.

    PubMed

    Corradi, Federico; Indiveri, Giacomo

    2015-10-01

    Neural recording systems are a central component of Brain-Machince Interfaces (BMIs). In most of these systems the emphasis is on faithful reproduction and transmission of the recorded signal to remote systems for further processing or data analysis. Here we follow an alternative approach: we propose a neural recording system that can be directly interfaced locally to neuromorphic spiking neural processing circuits for compressing the large amounts of data recorded, carrying out signal processing and neural computation to extract relevant information, and transmitting only the low-bandwidth outcome of the processing to remote computing or actuating modules. The fabricated system includes a low-noise amplifier, a delta-modulator analog-to-digital converter, and a low-power band-pass filter. The bio-amplifier has a programmable gain of 45-54 dB, with a Root Mean Squared (RMS) input-referred noise level of 2.1 μV, and consumes 90 μW . The band-pass filter and delta-modulator circuits include asynchronous handshaking interface logic compatible with event-based communication protocols. We describe the properties of the neural recording circuits, validating them with experimental measurements, and present system-level application examples, by interfacing these circuits to a reconfigurable neuromorphic processor comprising an array of spiking neurons with plastic and dynamic synapses. The pool of neurons within the neuromorphic processor was configured to implement a recurrent neural network, and to process the events generated by the neural recording system in order to carry out pattern recognition. PMID:26513801

  6. Miniaturized neural interfaces and implants

    NASA Astrophysics Data System (ADS)

    Stieglitz, Thomas; Boretius, Tim; Ordonez, Juan; Hassler, Christina; Henle, Christian; Meier, Wolfgang; Plachta, Dennis T. T.; Schuettler, Martin

    2012-03-01

    Neural prostheses are technical systems that interface nerves to treat the symptoms of neurological diseases and to restore sensory of motor functions of the body. Success stories have been written with the cochlear implant to restore hearing, with spinal cord stimulators to treat chronic pain as well as urge incontinence, and with deep brain stimulators in patients suffering from Parkinson's disease. Highly complex neural implants for novel medical applications can be miniaturized either by means of precision mechanics technologies using known and established materials for electrodes, cables, and hermetic packages or by applying microsystems technologies. Examples for both approaches will be introduced and discussed. Electrode arrays for recording of electrocorticograms during presurgical epilepsy diagnosis have been manufactured using approved materials and a marking laser to achieve an integration density that is adequate in the context of brain machine interfaces, e.g. on the motor cortex. Microtechnologies have to be used for further miniaturization to develop polymer-based flexible and light weighted electrode arrays to interface the peripheral and central nervous system. Polyimide as substrate and insulation material will be discussed as well as several application examples for nerve interfaces like cuffs, filament like electrodes and large arrays for subdural implantation.

  7. Point-and-Click Cursor Control With an Intracortical Neural Interface System by Humans With Tetraplegia

    PubMed Central

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

    2012-01-01

    We present a point-and-click intracortical neural interface system (NIS) that enables humans with tetraplegia to volitionally move a 2-D computer cursor in any desired direction on a computer screen, hold it still, and click on the area of interest. This direct brain–computer interface extracts both discrete (click) and continuous (cursor velocity) signals from a single small population of neurons in human motor cortex. A key component of this system is a multi-state probabilistic decoding algorithm that simultaneously decodes neural spiking activity of a small population of neurons and outputs either a click signal or the velocity of the cursor. The algorithm combines a linear classifier, which determines whether the user is intending to click or move the cursor, with a Kalman filter that translates the neural population activity into cursor velocity. We present a paradigm for training the multi-state decoding algorithm using neural activity observed during imagined actions. Two human participants with tetraplegia (paralysis of the four limbs) performed a closed-loop radial target acquisition task using the point-and-click NIS over multiple sessions. We quantified point-and-click performance using various human-computer interaction measurements for pointing devices. We found that participants could control the cursor motion and click on specified targets with a small error rate (<3% in one participant). This study suggests that signals from a small ensemble of motor cortical neurons (~40) can be used for natural point-and-click 2-D cursor control of a personal computer. PMID:21278024

  8. A wireless transmission neural interface system for unconstrained non-human primates

    NASA Astrophysics Data System (ADS)

    Fernandez-Leon, Jose A.; Parajuli, Arun; Franklin, Robert; Sorenson, Michael; Felleman, Daniel J.; Hansen, Bryan J.; Hu, Ming; Dragoi, Valentin

    2015-10-01

    Objective. Studying the brain in large animal models in a restrained laboratory rig severely limits our capacity to examine brain circuits in experimental and clinical applications. Approach. To overcome these limitations, we developed a high-fidelity 96-channel wireless system to record extracellular spikes and local field potentials from the neocortex. A removable, external case of the wireless device is attached to a titanium pedestal placed in the animal skull. Broadband neural signals are amplified, multiplexed, and continuously transmitted as TCP/IP data at a sustained rate of 24 Mbps. A Xilinx Spartan 6 FPGA assembles the digital signals into serial data frames for transmission at 20 kHz though an 802.11n wireless data link on a frequency-shift key-modulated signal at 5.7-5.8 GHz to a receiver up to 10 m away. The system is powered by two CR123A, 3 V batteries for 2 h of operation. Main results. We implanted a multi-electrode array in visual area V4 of one anesthetized monkey (Macaca fascicularis) and in the dorsolateral prefrontal cortex (dlPFC) of a freely moving monkey (Macaca mulatta). The implanted recording arrays were electrically stable and delivered broadband neural data over a year of testing. For the first time, we compared dlPFC neuronal responses to the same set of stimuli (food reward) in restrained and freely moving conditions. Although we did not find differences in neuronal responses as a function of reward type in the restrained and unrestrained conditions, there were significant differences in correlated activity. This demonstrates that measuring neural responses in freely moving animals can capture phenomena that are absent in the traditional head-fixed paradigm. Significance. We implemented a wireless neural interface for multi-electrode recordings in freely moving non-human primates, which can potentially move systems neuroscience to a new direction by allowing one to record neural signals while animals interact with their environment.

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

    NASA Astrophysics Data System (ADS)

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

    2013-06-01

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

  10. EDITORIAL: Focus on the neural interface Focus on the neural interface

    NASA Astrophysics Data System (ADS)

    Durand, Dominique M.

    2009-10-01

    The possibility of an effective connection between neural tissue and computers has inspired scientists and engineers to develop new ways of controlling and obtaining information from the nervous system. These applications range from `brain hacking' to neural control of artificial limbs with brain signals. Notwithstanding the significant advances in neural prosthetics in the last few decades and the success of some stimulation devices such as cochlear prosthesis, neurotechnology remains below its potential for restoring neural function in patients with nervous system disorders. One of the reasons for this limited impact can be found at the neural interface and close attention to the integration between electrodes and tissue should improve the possibility of successful outcomes. The neural interfaces research community consists of investigators working in areas such as deep brain stimulation, functional neuromuscular/electrical stimulation, auditory prostheses, cortical prostheses, neuromodulation, microelectrode array technology, brain-computer/machine interfaces. Following the success of previous neuroprostheses and neural interfaces workshops, funding (from NIH) was obtained to establish a biennial conference in the area of neural interfaces. The first Neural Interfaces Conference took place in Cleveland, OH in 2008 and several topics from this conference have been selected for publication in this special section of the Journal of Neural Engineering. Three `perspectives' review the areas of neural regeneration (Corredor and Goldberg), cochlear implants (O'Leary et al) and neural prostheses (Anderson). Seven articles focus on various aspects of neural interfacing. One of the most popular of these areas is the field of brain-computer interfaces. Fraser et al, report on a method to generate robust control with simple signal processing algorithms of signals obtained with electrodes implanted in the brain. One problem with implanted electrode arrays, however, is that

  11. Analysis of neural activity in human motor cortex -- Towards brain machine interface system

    NASA Astrophysics Data System (ADS)

    Secundo, Lavi

    , the correlation of ECoG activity to kinematic parameters of arm movement is context-dependent, an important constraint to consider in future development of BMI systems. The third chapter delves into a fundamental organizational principle of the primate motor system---cortical control of contralateral limb movements. However, ipsilateral motor areas also appear to play a role in the control of ipsilateral limb movements. Several studies in monkeys have shown that individual neurons in ipsilateral primary motor cortex (M1) may represent, on average, the direction of movements of the ipsilateral arm. Given the increasing body of evidence demonstrating that neural ensembles can reliably represent information with a high temporal resolution, here we characterize the distributed neural representation of ipsilateral upper limb kinematics in both monkey and man. In two macaque monkeys trained to perform center-out reaching movements, we found that the ensemble spiking activity in M1 could continuously represent ipsilateral limb position. We also recorded cortical field potentials from three human subjects and also consistently found evidence of a neural representation for ipsilateral movement parameters. Together, our results demonstrate the presence of a high-fidelity neural representation for ipsilateral movement and illustrates that it can be successfully incorporated into a brain-machine interface.

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

    PubMed

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

    2016-04-01

    The prototype of an electronic bi-directional interface between the Peripheral Nervous System (PNS) and a neuro-controlled hand prosthesis is presented. The system is composed of 2 integrated circuits: a standard CMOS device for neural recording and a HVCMOS device for neural stimulation. The integrated circuits have been realized in 2 different 0.35μ m CMOS processes available from ams. The complete system incorporates 8 channels each including the analog front-end, the A/D conversion, based on a sigma delta architecture and a programmable stimulation module implemented as a 5-bit current DAC; two voltage boosters supply the output stimulation stage with a programmable voltage scalable up to 17V. Successful in-vivo experiments with rats having a TIME electrode implanted in the sciatic nerve were carried out, showing the capability of recording neural signals in the tens of microvolts, with a global noise of 7μ V r m s , and to selectively elicit the tibial and plantar muscles using different active sites of the electrode. PMID:27007860

  13. Regenerative Electrode Interfaces for Neural Prostheses.

    PubMed

    Thompson, Cort H; Zoratti, Marissa J; Langhals, Nicholas B; Purcell, Erin K

    2016-04-01

    Neural prostheses are electrode arrays implanted in the nervous system that record or stimulate electrical activity in neurons. Rapid growth in the use of neural prostheses in research and clinical applications has occurred in recent years, but instability and poor patency in the tissue-electrode interface undermines the longevity and performance of these devices. The application of tissue engineering strategies to the device interface is a promising approach to improve connectivity and communication between implanted electrodes and local neurons, and several research groups have developed new and innovative modifications to neural prostheses with the goal of seamless device-tissue integration. These approaches can be broadly categorized based on the strategy used to maintain and regenerate neurons at the device interface: (1) redesign of the prosthesis architecture to include finer-scale geometries and/or provide topographical cues to guide regenerating neural outgrowth, (2) incorporation of material coatings and bioactive molecules on the prosthesis to improve neuronal growth, viability, and adhesion, and (3) inclusion of cellular grafts to replenish the local neuron population or provide a target site for reinnervation (biohybrid devices). In addition to stabilizing the contact between neurons and electrodes, the potential to selectively interface specific subpopulations of neurons with individual electrode sites is a key advantage of regenerative interfaces. In this study, we review the development of regenerative interfaces for applications in both the peripheral and central nervous system. Current and future development of regenerative interfaces has the potential to improve the stability and selectivity of neural prostheses, improving the patency and resolution of information transfer between neurons and implanted electrodes. PMID:26421660

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

    NASA Astrophysics Data System (ADS)

    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.

  15. Evolvable Neural Software System

    NASA Technical Reports Server (NTRS)

    Curtis, Steven A.

    2009-01-01

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

  16. Assistive technology and robotic control using motor cortex ensemble-based neural interface systems in humans with tetraplegia

    PubMed Central

    Donoghue, John P; Nurmikko, Arto; Black, Michael; Hochberg, Leigh R

    2007-01-01

    This review describes the rationale, early stage development, and initial human application of neural interface systems (NISs) for humans with paralysis. NISs are emerging medical devices designed to allow persons with paralysis to operate assistive technologies or to reanimate muscles based upon a command signal that is obtained directly from the brain. Such systems require the development of sensors to detect brain signals, decoders to transform neural activity signals into a useful command, and an interface for the user. We review initial pilot trial results of an NIS that is based on an intracortical microelectrode sensor that derives control signals from the motor cortex. We review recent findings showing, first, that neurons engaged by movement intentions persist in motor cortex years after injury or disease to the motor system, and second, that signals derived from motor cortex can be used by persons with paralysis to operate a range of devices. We suggest that, with further development, this form of NIS holds promise as a useful new neurotechnology for those with limited motor function or communication. We also discuss the additional potential for neural sensors to be used in the diagnosis and management of various neurological conditions and as a new way to learn about human brain function. PMID:17272345

  17. Micro- and Nanotechnologies for Optical Neural Interfaces

    PubMed Central

    Pisanello, Ferruccio; Sileo, Leonardo; De Vittorio, Massimo

    2016-01-01

    In last decade, the possibility to optically interface with the mammalian brain in vivo has allowed unprecedented investigation of functional connectivity of neural circuitry. Together with new genetic and molecular techniques to optically trigger and monitor neural activity, a new generation of optical neural interfaces is being developed, mainly thanks to the exploitation of both bottom-up and top-down nanofabrication approaches. This review highlights the role of nanotechnologies for optical neural interfaces, with particular emphasis on new devices and methodologies for optogenetic control of neural activity and unconventional methods for detection and triggering of action potentials using optically-active colloidal nanoparticles. PMID:27013939

  18. A Review of Organic and Inorganic Biomaterials for Neural Interfaces

    PubMed Central

    Fattahi, Pouria; Yang, Guang; Kim, Gloria

    2015-01-01

    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 first, 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. PMID:24677434

  19. NeuralWISP: A Wirelessly Powered Neural Interface With 1-m Range.

    PubMed

    Yeager, D J; Holleman, J; Prasad, R; Smith, J R; Otis, B P

    2009-12-01

    We present the NeuralWISP, a wireless neural interface operating from far-field radio-frequency RF energy. The NeuralWISP is compatible with commercial RF identification readers and operates at a range up to 1 m. It includes a custom low-noise, low-power amplifier integrated circuit for processing the neural signal and an analog spike detection circuit for reducing digital computational requirements and communications bandwidth. Our system monitors the neural signal and periodically transmits the spike density in a user-programmable time window. The entire system draws an average 20 muA from the harvested 1.8-V supply. PMID:23853285

  20. Intelligent Tutoring Systems: Formalization as Automata and Interface Design Using Neural Networks

    ERIC Educational Resources Information Center

    Curilem, S. Gloria; Barbosa, Andrea R.; de Azevedo, Fernando M.

    2007-01-01

    This article proposes a mathematical model of Intelligent Tutoring Systems (ITS), based on observations of the behaviour of these systems. One of the most important problems of pedagogical software is to establish a common language between the knowledge areas involved in their development, basically pedagogical, computing and domain areas. A…

  1. Flexible neural interfaces with integrated stiffening shank

    DOEpatents

    Tooker, Angela C.; Felix, Sarah H.; Pannu, Satinderpall S.; Shah, Kedar G.; Sheth, Heeral; Tolosa, Vanessa

    2016-07-26

    A neural interface includes a first dielectric material having at least one first opening for a first electrical conducting material, a first electrical conducting material in the first opening, and at least one first interconnection trace electrical conducting material connected to the first electrical conducting material. A stiffening shank material is located adjacent the first dielectric material, the first electrical conducting material, and the first interconnection trace electrical conducting material.

  2. Feasibility study for future implantable neural-silicon interface devices.

    PubMed

    Al-Armaghany, Allann; Yu, Bo; Mak, Terrence; Tong, Kin-Fai; Sun, Yihe

    2011-01-01

    The emerging neural-silicon interface devices bridge nerve systems with artificial systems and play a key role in neuro-prostheses and neuro-rehabilitation applications. Integrating neural signal collection, processing and transmission on a single device will make clinical applications more practical and feasible. This paper focuses on the wireless antenna part and real-time neural signal analysis part of implantable brain-machine interface (BMI) devices. We propose to use millimeter-wave for wireless connections between different areas of a brain. Various antenna, including microstrip patch, monopole antenna and substrate integrated waveguide antenna are considered for the intra-cortical proximity communication. A Hebbian eigenfilter based method is proposed for multi-channel neuronal spike sorting. Folding and parallel design techniques are employed to explore various structures and make a trade-off between area and power consumption. Field programmable logic arrays (FPGAs) are used to evaluate various structures. PMID:22254974

  3. Time to address the problems at the neural interface

    NASA Astrophysics Data System (ADS)

    Durand, Dominique M.; Ghovanloo, Maysam; Krames, Elliot

    2014-04-01

    Neural engineers have made significant, if not remarkable, progress in interfacing with the nervous system in the last ten years. In particular, neuromodulation of the brain has generated significant therapeutic benefits [1-5]. EEG electrodes can be used to communicate with patients with locked-in syndrome [6]. In the central nervous system (CNS), electrode arrays placed directly over or within the cortex can record neural signals related to the intent of the subject or patient [7, 8]. A similar technology has allowed paralyzed patients to control an otherwise normal skeletal system with brain signals [9, 10]. This technology has significant potential to restore function in these and other patients with neural disorders such as stroke [11]. Although there are several multichannel arrays described in the literature, the workhorse for these cortical interfaces has been the Utah array [12]. This 100-channel electrode array has been used in most studies on animals and humans since the 1990s and is commercially available. This array and other similar microelectrode arrays can record neural signals with high quality (high signal-to-noise ratio), but these signals fade and disappear after a few months and therefore the current technology is not reliable for extended periods of time. Therefore, despite these major advances in communicating with the brain, clinical translation cannot be implemented. The reasons for this failure are not known but clearly involve the interface between the electrode and the neural tissue. The Defense Advanced Research Project Agency (DARPA) as well as other federal funding agencies such as the National Science Foundation (NSF) and the National Institutes of Health have provided significant financial support to investigate this problem without much success. A recent funding program from DARPA was designed to establish the failure modes in order to generate a reliable neural interface technology and again was unsuccessful at producing a robust

  4. Shaping the Dynamics of a Bidirectional Neural Interface

    PubMed Central

    Vato, Alessandro; Semprini, Marianna; Maggiolini, Emma; Szymanski, Francois D.; Fadiga, Luciano; Panzeri, Stefano; Mussa-Ivaldi, Ferdinando A.

    2012-01-01

    Progress in decoding neural signals has enabled the development of interfaces that translate cortical brain activities into commands for operating robotic arms and other devices. The electrical stimulation of sensory areas provides a means to create artificial sensory information about the state of a device. Taken together, neural activity recording and microstimulation techniques allow us to embed a portion of the central nervous system within a closed-loop system, whose behavior emerges from the combined dynamical properties of its neural and artificial components. In this study we asked if it is possible to concurrently regulate this bidirectional brain-machine interaction so as to shape a desired dynamical behavior of the combined system. To this end, we followed a well-known biological pathway. In vertebrates, the communications between brain and limb mechanics are mediated by the spinal cord, which combines brain instructions with sensory information and organizes coordinated patterns of muscle forces driving the limbs along dynamically stable trajectories. We report the creation and testing of the first neural interface that emulates this sensory-motor interaction. The interface organizes a bidirectional communication between sensory and motor areas of the brain of anaesthetized rats and an external dynamical object with programmable properties. The system includes (a) a motor interface decoding signals from a motor cortical area, and (b) a sensory interface encoding the state of the external object into electrical stimuli to a somatosensory area. The interactions between brain activities and the state of the external object generate a family of trajectories converging upon a selected equilibrium point from arbitrary starting locations. Thus, the bidirectional interface establishes the possibility to specify not only a particular movement trajectory but an entire family of motions, which includes the prescribed reactions to unexpected perturbations. PMID

  5. A TinyOS-based wireless neural interface.

    PubMed

    Farshchi, Shahin; Mody, Istvan; Judy, Jack W

    2004-01-01

    The overlay of a neural interface upon a TinyOS-based sensing and communication platform is described. The system amplifies, digitally encodes, and transmits two EEG channels of neural signals from an un-tethered subject to a remote gateway, which routes the signals to a client PC. This work demonstrates the viability of the TinyOS-based sensor technology as a foundation for chronic remote biological monitoring applications, and thus provides an opportunity to create a system that can leverage from the frequent networking and communications advancements being made by the global TinyOS-development community. PMID:17271263

  6. Encapsulation of an integrated neural interface device with Parylene C.

    PubMed

    Hsu, Jui-Mei; Rieth, Loren; Normann, Richard A; Tathireddy, Prashant; Solzbacher, Florian

    2009-01-01

    Electronic neural interfaces have been developed to restore function to the nervous system for patients with neural disorders. A conformal and chronically stable dielectric encapsulation is required to protect the neural interface device from the harsh physiological environment and localize the active electrode tips. Chemical vapor deposited Parylene-C films were studied as a potential implantable dielectric encapsulation material using impedance spectroscopy and leakage current measurements. Both tests were performed in 37 degrees C saline solution, and showed that the films provided an electrically insulating encapsulation for more than one year. Isotropic and anisotropic oxygen plasma etching processes were compared for removing the Parylene-C insulation to expose the active electrode tips. Also, the relationship between tip exposure and electrode impedance was determined. The conformity and the uniformity of the Parylene-C coating were assessed using optical microscopy, and small thickness variations on the complex 3-D electrode arrays were observed. Parylene C was found to provide encapsulation and electrical insulation required for such neural interface devices for more than one year. Also, oxygen plasma etching was found to be an effective method to etch and pattern Parylene-C films. PMID:19224715

  7. Time to address the problems at the neural interface

    NASA Astrophysics Data System (ADS)

    Durand, Dominique M.; Ghovanloo, Maysam; Krames, Elliot

    2014-04-01

    Neural engineers have made significant, if not remarkable, progress in interfacing with the nervous system in the last ten years. In particular, neuromodulation of the brain has generated significant therapeutic benefits [1-5]. EEG electrodes can be used to communicate with patients with locked-in syndrome [6]. In the central nervous system (CNS), electrode arrays placed directly over or within the cortex can record neural signals related to the intent of the subject or patient [7, 8]. A similar technology has allowed paralyzed patients to control an otherwise normal skeletal system with brain signals [9, 10]. This technology has significant potential to restore function in these and other patients with neural disorders such as stroke [11]. Although there are several multichannel arrays described in the literature, the workhorse for these cortical interfaces has been the Utah array [12]. This 100-channel electrode array has been used in most studies on animals and humans since the 1990s and is commercially available. This array and other similar microelectrode arrays can record neural signals with high quality (high signal-to-noise ratio), but these signals fade and disappear after a few months and therefore the current technology is not reliable for extended periods of time. Therefore, despite these major advances in communicating with the brain, clinical translation cannot be implemented. The reasons for this failure are not known but clearly involve the interface between the electrode and the neural tissue. The Defense Advanced Research Project Agency (DARPA) as well as other federal funding agencies such as the National Science Foundation (NSF) and the National Institutes of Health have provided significant financial support to investigate this problem without much success. A recent funding program from DARPA was designed to establish the failure modes in order to generate a reliable neural interface technology and again was unsuccessful at producing a robust

  8. A 1microW 85nV/ radicalHz pseudo open-loop preamplifier with programmable band-pass filter for neural interface system.

    PubMed

    Chang, Sun-Il; Yoon, Euisik

    2009-01-01

    We report an energy efficient pseudo open-loop amplifier with programmable band-pass filter developed for neural interface systems. The proposed amplifier consumes 400nA at 2.5V power supply. The measured thermal noise level is 85nV/ radicalHz and input-referred noise is 1.69microV(rms) from 0.3Hz to 1 kHz. The amplifier has a noise efficiency factor of 2.43, the lowest in the differential topologies reported up to date to our knowledge. By programming the switched-capacitor frequency and bias current, we could control the bandwidth of the preamplifier from 138 mHz to 2.2 kHz to meet various application requirements. The entire preamplifier including band-pass filters has been realized in a small area of 0.043mm(2) using a 0.25microm CMOS technology. PMID:19964762

  9. Elastomeric and soft conducting microwires for implantable neural interfaces.

    PubMed

    Kolarcik, Christi L; Luebben, Silvia D; Sapp, Shawn A; Hanner, Jenna; Snyder, Noah; Kozai, Takashi D Y; Chang, Emily; Nabity, James A; Nabity, Shawn T; Lagenaur, Carl F; Cui, X Tracy

    2015-06-28

    Current designs for microelectrodes used for interfacing with the nervous system elicit a characteristic inflammatory response that leads to scar tissue encapsulation, electrical insulation of the electrode from the tissue and ultimately failure. Traditionally, relatively stiff materials like tungsten and silicon are employed which have mechanical properties several orders of magnitude different from neural tissue. This mechanical mismatch is thought to be a major cause of chronic inflammation and degeneration around the device. In an effort to minimize the disparity between neural interface devices and the brain, novel soft electrodes consisting of elastomers and intrinsically conducting polymers were fabricated. The physical, mechanical and electrochemical properties of these materials were extensively characterized to identify the formulations with the optimal combination of parameters including Young's modulus, elongation at break, ultimate tensile strength, conductivity, impedance and surface charge injection. Our final electrode has a Young's modulus of 974 kPa which is five orders of magnitude lower than tungsten and significantly lower than other polymer-based neural electrode materials. In vitro cell culture experiments demonstrated the favorable interaction between these soft materials and neurons, astrocytes and microglia, with higher neuronal attachment and a two-fold reduction in inflammatory microglia attachment on soft devices compared to stiff controls. Surface immobilization of neuronal adhesion proteins on these microwires further improved the cellular response. Finally, in vivo electrophysiology demonstrated the functionality of the elastomeric electrodes in recording single unit activity in the rodent visual cortex. The results presented provide initial evidence in support of the use of soft materials in neural interface applications. PMID:25993261

  10. Elastomeric and soft conducting microwires for implantable neural interfaces

    PubMed Central

    Kolarcik, Christi L.; Luebben, Silvia D.; Sapp, Shawn A.; Hanner, Jenna; Snyder, Noah; Kozai, Takashi D.Y.; Chang, Emily; Nabity, James A.; Nabity, Shawn T.; Lagenaur, Carl F.; Cui, X. Tracy

    2015-01-01

    Current designs for microelectrodes used for interfacing with the nervous system elicit a characteristic inflammatory response that leads to scar tissue encapsulation, electrical insulation of the electrode from the tissue and ultimately failure. Traditionally, relatively stiff materials like tungsten and silicon are employed which have mechanical properties several orders of magnitude different from neural tissue. This mechanical mismatch is thought to be a major cause of chronic inflammation and degeneration around the device. In an effort to minimize the disparity between neural interface devices and the brain, novel soft electrodes consisting of elastomers and intrinsically conducting polymers were fabricated. The physical, mechanical and electrochemical properties of these materials were extensively characterized to identify the formulations with the optimal combination of parameters including Young’s modulus, elongation at break, ultimate tensile strength, conductivity, impedance and surface charge injection. Our final electrode has a Young’s modulus of 974 kPa which is five orders of magnitude lower than tungsten and significantly lower than other polymer-based neural electrode materials. In vitro cell culture experiments demonstrated the favorable interaction between these soft materials and neurons, astrocytes and microglia, with higher neuronal attachment and a two-fold reduction in inflammatory microglia attachment on soft devices compared to stiff controls. Surface immobilization of neuronal adhesion proteins on these microwires further improved the cellular response. Finally, in vivo electrophysiology demonstrated the functionality of the elastomeric electrodes in recording single unit activity in the rodent visual cortex. The results presented provide initial evidence in support of the use of soft materials in neural interface applications. PMID:25993261

  11. Neural interfaces for upper-limb prosthesis control: opportunities to improve long-term reliability.

    PubMed

    Judy, Jack W

    2012-03-01

    Building on a long history of innovation in neural-recording interfaces, the Defense Advanced Research Projects Agency (DARPA) has launched a program to address the key challenges related to transitioning advanced neuroprosthesis technology to clinical use for amputated service members. The goal of the Reliable Neural Technology (RE-NET) Program is to develop new technology to extract information from the nervous system at a scale and rate needed to reliably control modern robotic prostheses over the lifetime of the amputee. The RE-NET program currently encompasses three separate efforts: histology for interface stability over time (HIST), reliable peripheral interfaces (RPIs), and reliable central nervous system (CNS) interfaces (RCIs). PMID:22481748

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

    NASA Astrophysics Data System (ADS)

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

    2015-02-01

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

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

    PubMed Central

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

    2015-01-01

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

  14. Braided Multi-Electrode Probes (BMEPs) for Neural Interfaces

    NASA Astrophysics Data System (ADS)

    Kim, Tae Gyo

    Although clinical use of invasive neural interfaces is very limited, due to safety and reliability concerns, the potential benefits of their use in brain machine interfaces (BMIs) seem promising and so they have been widely used in the research field. Microelectrodes as invasive neural interfaces are the core tool to record neural activities and their failure is a critical issue for BMI systems. Possible sources of this failure are neural tissue motions and their interactions with stiff electrode arrays or probes fixed to the skull. To overcome these tissue motion problems, we have developed novel braided multi-electrode probes (BMEPs). By interweaving ultra-fine wires into a tubular braid structure, we obtained a highly flexible multi-electrode probe. In this thesis we described BMEP designs and how to fabricate BMEPs, and explore experiments to show the advantages of BMEPs through a mechanical compliance comparison and a chronic immunohistological comparison with single 50microm nichrome wires used as a reference electrode type. Results from the mechanical compliance test showed that the bodies of BMEPs have 4 to 21 times higher compliance than the single 50microm wire and the tethers of BMEPs were 6 to 96 times higher compliance, depending on combinations of the wire size (9.6microm or 12.7microm), the wire numbers (12 or 24), and the length of tether (3, 5 or 10 mm). Results from the immunohistological comparison showed that both BMEPs and 50microm wires anchored to the skull caused stronger tissue reactions than unanchored BMEPs and 50microm wires, and 50microm wires caused stronger tissue reactions than BMEPs. In in-vivo tests with BMEPs, we succeeded in chronic recordings from the spinal cord of freely jumping frogs and in acute recordings from the spinal cord of decerebrate rats during air stepping which was evoked by mesencephalic locomotor region (MLR) stimulation. This technology may provide a stable and reliable neural interface to spinal cord

  15. Electro-optical Neural Platform Integrated with Nanoplasmonic Inhibition Interface.

    PubMed

    Yoo, Sangjin; Kim, Raeyoung; Park, Ji-Ho; Nam, Yoonkey

    2016-04-26

    Engineering of neural interfaces with nanomaterials for remote manipulation facilitates the development of platforms for the study and treatment of brain disorders, yet extending their capability to inhibiting the electrical activities of unmodified neurons has been difficult. Here we report the development of an electro-optical neural platform integrated with gold nanorods for simultaneous electrical excitation and readout, and photothermal inhibition of neural activities. A monolayer of gold nanorods was placed at the electrode-neuron interfaces of a microelectrode array for photothermal stimulation of neural activities. This nanoplasmonic interface interacted well with neurons and metal electrodes without affecting the biological and electrical properties. We demonstrated that spontaneous firing of neurons and their signal propagation along the neurites evoked by electrical stimulation were optically inhibited on this neural platform. We believe that our platform could be an alternative to the optogenetic approach and may ultimately be applied to prosthetic devices based on optical neuromodulation. PMID:26960013

  16. Conjugated Polymer Actuators for Articulating Neural Probes and Electrode Interfaces

    NASA Astrophysics Data System (ADS)

    Daneshvar, Eugene Dariush

    This thesis investigated the potential use of polypyrrole (PPy) doped with dodecylbenzenesulfonate (DBS) to controllably articulate (bend or guide) flexible neural probes and electrodes. PPy(DBS) actuation performance was characterized in the ionic mixture and temperature found in the brain. Nearly all the ions in aCSF were exchanged into the PPy---the cations Na +, K+, Mg2+, Ca2+, as well as the anion PO43-; Cl- was not present. Nevertheless, deflections in aCSF were comparable to those in NaDBS and they were monotonic with oxidation level: strain increased upon reduction, with no reversal of motion despite the mixture of ionic charges and valences being exchanged. Actuation depended on temperature. Upon warming, the cyclic voltammograms showed additional peaks and an increase of 70% in the consumed charge. Actuation strain was monotonic under these conditions, demonstrating that conducting polymer actuators can indeed be used for neural interface and neural probe applications. In addition, a novel microelectro-mechanical system (MEMS) was developed to measure previously disregarded residual stress in a bilayer actuator. Residual stresses are a major concern for MEMS devices as that they can dramatically influence their yield and functionality. This device introduced a new technique to measure micro-scaled actuation forces that may be useful for characterization of other MEMS actuators. Finally, a functional movable parylene-based neural electrode prototype was developed. Employing PPy(DBS) actuators, electrode projections were successfully controlled to either remain flat or actuate out-of-plane and into a brain phantom during insertion. An electrode projection 800 microm long and 50 microm wide was able to deflect almost 800 microm away from the probe substrate. Applications that do not require insertion into tissue may also benefit from the electrode projections described here. Implantable neural interface devices are a critical component to a broad class of

  17. Conductive porous scaffolds as potential neural interface materials.

    SciTech Connect

    Hedberg-Dirk, Elizabeth L.; Cicotte, Kirsten N.; Buerger, Stephen P.; Reece, Gregory; Dirk, Shawn M.; Lin, Patrick P.

    2011-11-01

    Our overall intent is to develop improved prosthetic devices with the use of nerve interfaces through which transected nerves may grow, such that small groups of nerve fibers come into close contact with electrode sites, each of which is connected to electronics external to the interface. These interfaces must be physically structured to allow nerve fibers to grow through them, either by being porous or by including specific channels for the axons. They must be mechanically compatible with nerves such that they promote growth and do not harm the nervous system, and biocompatible to promote nerve fiber growth and to allow close integration with biological tissue. They must exhibit selective and structured conductivity to allow the connection of electrode sites with external circuitry, and electrical properties must be tuned to enable the transmission of neural signals. Finally, the interfaces must be capable of being physically connected to external circuitry, e.g. through attached wires. We have utilized electrospinning as a tool to create conductive, porous networks of non-woven biocompatible fibers in order to meet the materials requirements for the neural interface. The biocompatible fibers were based on the known biocompatible material poly(dimethyl siloxane) (PDMS) as well as a newer biomaterial developed in our laboratories, poly(butylene fumarate) (PBF). Both of the polymers cannot be electrospun using conventional electrospinning techniques due to their low glass transition temperatures, so in situ crosslinking methodologies were developed to facilitate micro- and nano-fiber formation during electrospinning. The conductivity of the electrospun fiber mats was controlled by controlling the loading with multi-walled carbon nanotubes (MWNTs). Fabrication, electrical and materials characterization will be discussed along with initial in vivo experimental results.

  18. Neural machine interfaces for controlling multifunctional powered upper-limb prostheses.

    PubMed

    Ohnishi, Kengo; Weir, Richard F; Kuiken, Todd A

    2007-01-01

    This article investigates various neural machine interfaces for voluntary control of externally powered upper-limb prostheses. Epidemiology of upper limb amputation, as well as prescription and follow-up studies of externally powered upper-limb prostheses are discussed. The use of electromyographic interfaces and peripheral nerve interfaces for prosthetic control, as well as brain machine interfaces suitable for prosthetic control, are examined in detail along with available clinical results. In addition, studies on interfaces using muscle acoustic and mechanical properties and the problem of interfacing sensory information to the nervous system are discussed. PMID:17187470

  19. A CMOS Neural Interface for a Multichannel Vestibular Prosthesis.

    PubMed

    Hageman, Kristin N; Kalayjian, Zaven K; Tejada, Francisco; Chiang, Bryce; Rahman, Mehdi A; Fridman, Gene Y; Dai, Chenkai; Pouliquen, Philippe O; Georgiou, Julio; Della Santina, Charles C; Andreou, Andreas G

    2016-04-01

    We present a high-voltage CMOS neural-interface chip for a multichannel vestibular prosthesis (MVP) that measures head motion and modulates vestibular nerve activity to restore vision- and posture-stabilizing reflexes. This application specific integrated circuit neural interface (ASIC-NI) chip was designed to work with a commercially available microcontroller, which controls the ASIC-NI via a fast parallel interface to deliver biphasic stimulation pulses with 9-bit programmable current amplitude via 16 stimulation channels. The chip was fabricated in the ONSemi C5 0.5 micron, high-voltage CMOS process and can accommodate compliance voltages up to 12 V, stimulating vestibular nerve branches using biphasic current pulses up to 1.45±0.06 mA with durations as short as 10 μs/phase. The ASIC-NI includes a dedicated digital-to-analog converter for each channel, enabling it to perform complex multipolar stimulation. The ASIC-NI replaces discrete components that cover nearly half of the 2nd generation MVP (MVP2) printed circuit board, reducing the MVP system size by 48% and power consumption by 17%. Physiological tests of the ASIC-based MVP system (MVP2A) in a rhesus monkey produced reflexive eye movement responses to prosthetic stimulation similar to those observed when using the MVP2. Sinusoidal modulation of stimulus pulse rate from 68-130 pulses per second at frequencies from 0.1 to 5 Hz elicited appropriately-directed slow phase eye velocities ranging in amplitude from 1.9-16.7 °/s for the MVP2 and 2.0-14.2 °/s for the MVP2A. The eye velocities evoked by MVP2 and MVP2A showed no significant difference ( t-test, p=0.34), suggesting that the MVP2A achieves performance at least as good as the larger MVP2. PMID:25974945

  20. A CMOS Neural Interface for a Multichannel Vestibular Prosthesis

    PubMed Central

    Hageman, Kristin N.; Kalayjian, Zaven K.; Tejada, Francisco; Chiang, Bryce; Rahman, Mehdi A.; Fridman, Gene Y.; Dai, Chenkai; Pouliquen, Philippe O.; Georgiou, Julio; Della Santina, Charles C.; Andreou, Andreas G.

    2015-01-01

    We present a high-voltage CMOS neural-interface chip for a multichannel vestibular prosthesis (MVP) that measures head motion and modulates vestibular nerve activity to restore vision- and posture-stabilizing reflexes. This application specific integrated circuit neural interface (ASIC-NI) chip was designed to work with a commercially available microcontroller, which controls the ASIC-NI via a fast parallel interface to deliver biphasic stimulation pulses with 9-bit programmable current amplitude via 16 stimulation channels. The chip was fabricated in the ONSemi C5 0.5 micron, high-voltage CMOS process and can accommodate compliance voltages up to 12 V, stimulating vestibular nerve branches using biphasic current pulses up to 1.45 ± 0.06 mA with durations as short as 10 µs/phase. The ASIC-NI includes a dedicated digital-to-analog converter for each channel, enabling it to perform complex multipolar stimulation. The ASIC-NI replaces discrete components that cover nearly half of the 2nd generation MVP (MVP2) printed circuit board, reducing the MVP system size by 48% and power consumption by 17%. Physiological tests of the ASIC-based MVP system (MVP2A) in a rhesus monkey produced reflexive eye movement responses to prosthetic stimulation similar to those observed when using the MVP2. Sinusoidal modulation of stimulus pulse rate from 68–130 pulses per second at frequencies from 0.1 to 5 Hz elicited appropriately-directed slow phase eye velocities ranging in amplitude from 1.9–16.7°/s for the MVP2 and 2.0–14.2°/s for the MVP2A. The eye velocities evoked by MVP2 and MVP2A showed no significant difference (t-test, p = 0.034), suggesting that the MVP2A achieves performance at least as good as the larger MVP2. PMID:25974945

  1. Electronic dura mater for long-term multimodal neural interfaces

    NASA Astrophysics Data System (ADS)

    Minev, Ivan R.; Musienko, Pavel; Hirsch, Arthur; Barraud, Quentin; Wenger, Nikolaus; Moraud, Eduardo Martin; Gandar, Jérôme; Capogrosso, Marco; Milekovic, Tomislav; Asboth, Léonie; Torres, Rafael Fajardo; Vachicouras, Nicolas; Liu, Qihan; Pavlova, Natalia; Duis, Simone; Larmagnac, Alexandre; Vörös, Janos; Micera, Silvestro; Suo, Zhigang; Courtine, Grégoire; Lacour, Stéphanie P.

    2015-01-01

    The mechanical mismatch between soft neural tissues and stiff neural implants hinders the long-term performance of implantable neuroprostheses. Here, we designed and fabricated soft neural implants with the shape and elasticity of dura mater, the protective membrane of the brain and spinal cord. The electronic dura mater, which we call e-dura, embeds interconnects, electrodes, and chemotrodes that sustain millions of mechanical stretch cycles, electrical stimulation pulses, and chemical injections. These integrated modalities enable multiple neuroprosthetic applications. The soft implants extracted cortical states in freely behaving animals for brain-machine interface and delivered electrochemical spinal neuromodulation that restored locomotion after paralyzing spinal cord injury.

  2. The emergent neural modeling system.

    PubMed

    Aisa, Brad; Mingus, Brian; O'Reilly, Randy

    2008-10-01

    Emergent (http://grey.colorado.edu/emergent) is a powerful tool for the simulation of biologically plausible, complex neural systems that was released in August 2007. Inheriting decades of research and experience in network algorithms and modeling principles from its predecessors, PDP++ and PDP, Emergent has been redesigned as an efficient workspace for academic research and an engaging, easy-to-navigate environment for students. The system provides a modern and intuitive interface for programming and visualization centered around hierarchical, tree-based navigation and drag-and-drop reorganization. Emergent contains familiar, high-level simulation constructs such as Layers and Projections, a wide variety of algorithms, general-purpose data handling and analysis facilities and an integrated virtual environment for developing closed-loop cognitive agents. For students, the traditional role of a textbook has been enhanced by wikis embedded in every project that serve to explain, document, and help newcomers engage the interface and step through models using familiar hyperlinks. For advanced users, the software is easily extensible in all respects via runtime plugins, has a powerful shell with an integrated debugger, and a scripting language that is fully symmetric with the interface. Emergent strikes a balance between detailed, computationally expensive spiking neuron models and abstract, Bayesian or symbolic systems. This middle level of detail allows for the rapid development and successful execution of complex cognitive models while maintaining biological plausibility. PMID:18684591

  3. Living electrodes: tissue engineering the neural interface.

    PubMed

    Green, Rylie A; Lim, Khoon S; Henderson, William C; Hassarati, Rachelle T; Martens, Penny J; Lovell, Nigel H; Poole-Warren, Laura A

    2013-01-01

    Soft, cell integrated electrode coatings are proposed to address the problem of scar tissue encapsulation of stimulating neuroprosthetics. The aim of these studies was to prove the concept and feasibility of integrating a cell loaded hydrogel with existing electrode coating technologies. Layered conductive hydrogel constructs are embedded with neural cells and shown to both support cell growth and maintain electro activity. The safe charge injection limit of these electrodes was 8 times higher than conventional platinum (Pt) electrodes and the stiffness was four orders of magnitude lower than Pt. Future studies will determine the biological cues required to support stem cell differentiation from the electrode surface. PMID:24111345

  4. Early Interfaced Neural Activity from Chronic Amputated Nerves

    PubMed Central

    Garde, Kshitija; Keefer, Edward; Botterman, Barry; Galvan, Pedro; Romero, Mario I.

    2009-01-01

    Direct interfacing of transected peripheral nerves with advanced robotic prosthetic devices has been proposed as a strategy for achieving natural motor control and sensory perception of such bionic substitutes, thus fully functionally replacing missing limbs in amputees. Multi-electrode arrays placed in the brain and peripheral nerves have been used successfully to convey neural control of prosthetic devices to the user. However, reactive gliosis, micro hemorrhages, axonopathy and excessive inflammation currently limit their long-term use. Here we demonstrate that enticement of peripheral nerve regeneration through a non-obstructive multi-electrode array, after either acute or chronic nerve amputation, offers a viable alternative to obtain early neural recordings and to enhance long-term interfacing of nerve activity. Non-restrictive electrode arrays placed in the path of regenerating nerve fibers allowed the recording of action potentials as early as 8 days post-implantation with high signal-to-noise ratio, as long as 3 months in some animals, and with minimal inflammation at the nerve tissue-metal electrode interface. Our findings suggest that regenerative multi-electrode arrays of open design allow early and stable interfacing of neural activity from amputated peripheral nerves and might contribute towards conveying full neural control and sensory feedback to users of robotic prosthetic devices. PMID:19506704

  5. Three-Dimensional Flexible Electronics Enabled by Shape Memory Polymer Substrates for Responsive Neural Interfaces

    PubMed Central

    Ware, Taylor; Simon, Dustin; Hearon, Keith; Liu, Clive; Shah, Sagar; Reeder, Jonathan; Khodaparast, Navid; Kilgard, Michael P.; Maitland, Duncan J.; Rennaker, Robert L.; Voit, Walter E.

    2014-01-01

    Planar electronics processing methods have enabled neural interfaces to become more precise and deliver more information. However, this processing paradigm is inherently 2D and rigid. The resulting mechanical and geometrical mismatch at the biotic–abiotic interface can elicit an immune response that prevents effective stimulation. In this work, a thiol–ene/acrylate shape memory polymer is utilized to create 3D softening substrates for stimulation electrodes. This substrate system is shown to soften in vivo from more than 600 to 6 MPa. A nerve cuff electrode that coils around the vagus nerve in a rat and that drives neural activity is demonstrated. PMID:25530708

  6. Progress towards biocompatible intracortical microelectrodes for neural interfacing applications

    NASA Astrophysics Data System (ADS)

    Jorfi, Mehdi; Skousen, John L.; Weder, Christoph; Capadona, Jeffrey R.

    2015-02-01

    To ensure long-term consistent neural recordings, next-generation intracortical microelectrodes are being developed with an increased emphasis on reducing the neuro-inflammatory response. The increased emphasis stems from the improved understanding of the multifaceted role that inflammation may play in disrupting both biologic and abiologic components of the overall neural interface circuit. To combat neuro-inflammation and improve recording quality, the field is actively progressing from traditional inorganic materials towards approaches that either minimizes the microelectrode footprint or that incorporate compliant materials, bioactive molecules, conducting polymers or nanomaterials. However, the immune-privileged cortical tissue introduces an added complexity compared to other biomedical applications that remains to be fully understood. This review provides a comprehensive reflection on the current understanding of the key failure modes that may impact intracortical microelectrode performance. In addition, a detailed overview of the current status of various materials-based approaches that have gained interest for neural interfacing applications is presented, and key challenges that remain to be overcome are discussed. Finally, we present our vision on the future directions of materials-based treatments to improve intracortical microelectrodes for neural interfacing.

  7. Progress Towards Biocompatible Intracortical Microelectrodes for Neural Interfacing Applications

    PubMed Central

    Jorfi, Mehdi; Skousen, John L.; Weder, Christoph; Capadona, Jeffrey R.

    2015-01-01

    To ensure long-term consistent neural recordings, next-generation intracortical microelectrodes are being developed with an increased emphasis on reducing the neuro-inflammatory response. The increased emphasis stems from the improved understanding of the multifaceted role that inflammation may play in disrupting both biologic and abiologic components of the overall neural interface circuit. To combat neuro-inflammation and improve recording quality, the field is actively progressing from traditional inorganic materials towards approaches that either minimizes the microelectrode footprint or that incorporate compliant materials, bioactive molecules, conducting polymers or nanomaterials. However, the immune-privileged cortical tissue introduces an added complexity compared to other biomedical applications that remains to be fully understood. This review provides a comprehensive reflection on the current understanding of the key failure modes that may impact intracortical microelectrode performance. In addition, a detailed overview of the current status of various materials-based approaches that have gained interest for neural interfacing applications is presented, and key challenges that remain to be overcome are discussed. Finally, we present our vision on the future directions of materials-based treatments to improve intracortical microelectrodes for neural interfacing. PMID:25460808

  8. Neural Flight Control System

    NASA Technical Reports Server (NTRS)

    Gundy-Burlet, Karen

    2003-01-01

    The Neural Flight Control System (NFCS) was developed to address the need for control systems that can be produced and tested at lower cost, easily adapted to prototype vehicles and for flight systems that can accommodate damaged control surfaces or changes to aircraft stability and control characteristics resulting from failures or accidents. NFCS utilizes on a neural network-based flight control algorithm which automatically compensates for a broad spectrum of unanticipated damage or failures of an aircraft in flight. Pilot stick and rudder pedal inputs are fed into a reference model which produces pitch, roll and yaw rate commands. The reference model frequencies and gains can be set to provide handling quality characteristics suitable for the aircraft of interest. The rate commands are used in conjunction with estimates of the aircraft s stability and control (S&C) derivatives by a simplified Dynamic Inverse controller to produce virtual elevator, aileron and rudder commands. These virtual surface deflection commands are optimally distributed across the aircraft s available control surfaces using linear programming theory. Sensor data is compared with the reference model rate commands to produce an error signal. A Proportional/Integral (PI) error controller "winds up" on the error signal and adds an augmented command to the reference model output with the effect of zeroing the error signal. In order to provide more consistent handling qualities for the pilot, neural networks learn the behavior of the error controller and add in the augmented command before the integrator winds up. In the case of damage sufficient to affect the handling qualities of the aircraft, an Adaptive Critic is utilized to reduce the reference model frequencies and gains to stay within a flyable envelope of the aircraft.

  9. Modulation Depth Estimation and Variable Selection in State-Space Models for Neural Interfaces

    PubMed Central

    Hochberg, Leigh R.; Donoghue, John P.; Brown, Emery N.

    2015-01-01

    Rapid developments in neural interface technology are making it possible to record increasingly large signal sets of neural activity. Various factors such as asymmetrical information distribution and across-channel redundancy may, however, limit the benefit of high-dimensional signal sets, and the increased computational complexity may not yield corresponding improvement in system performance. High-dimensional system models may also lead to overfitting and lack of generalizability. To address these issues, we present a generalized modulation depth measure using the state-space framework that quantifies the tuning of a neural signal channel to relevant behavioral covariates. For a dynamical system, we develop computationally efficient procedures for estimating modulation depth from multivariate data. We show that this measure can be used to rank neural signals and select an optimal channel subset for inclusion in the neural decoding algorithm. We present a scheme for choosing the optimal subset based on model order selection criteria. We apply this method to neuronal ensemble spike-rate decoding in neural interfaces, using our framework to relate motor cortical activity with intended movement kinematics. With offline analysis of intracortical motor imagery data obtained from individuals with tetraplegia using the BrainGate neural interface, we demonstrate that our variable selection scheme is useful for identifying and ranking the most information-rich neural signals. We demonstrate that our approach offers several orders of magnitude lower complexity but virtually identical decoding performance compared to greedy search and other selection schemes. Our statistical analysis shows that the modulation depth of human motor cortical single-unit signals is well characterized by the generalized Pareto distribution. Our variable selection scheme has wide applicability in problems involving multisensor signal modeling and estimation in biomedical engineering systems. PMID

  10. Modulation depth estimation and variable selection in state-space models for neural interfaces.

    PubMed

    Malik, Wasim Q; Hochberg, Leigh R; Donoghue, John P; Brown, Emery N

    2015-02-01

    Rapid developments in neural interface technology are making it possible to record increasingly large signal sets of neural activity. Various factors such as asymmetrical information distribution and across-channel redundancy may, however, limit the benefit of high-dimensional signal sets, and the increased computational complexity may not yield corresponding improvement in system performance. High-dimensional system models may also lead to overfitting and lack of generalizability. To address these issues, we present a generalized modulation depth measure using the state-space framework that quantifies the tuning of a neural signal channel to relevant behavioral covariates. For a dynamical system, we develop computationally efficient procedures for estimating modulation depth from multivariate data. We show that this measure can be used to rank neural signals and select an optimal channel subset for inclusion in the neural decoding algorithm. We present a scheme for choosing the optimal subset based on model order selection criteria. We apply this method to neuronal ensemble spike-rate decoding in neural interfaces, using our framework to relate motor cortical activity with intended movement kinematics. With offline analysis of intracortical motor imagery data obtained from individuals with tetraplegia using the BrainGate neural interface, we demonstrate that our variable selection scheme is useful for identifying and ranking the most information-rich neural signals. We demonstrate that our approach offers several orders of magnitude lower complexity but virtually identical decoding performance compared to greedy search and other selection schemes. Our statistical analysis shows that the modulation depth of human motor cortical single-unit signals is well characterized by the generalized Pareto distribution. Our variable selection scheme has wide applicability in problems involving multisensor signal modeling and estimation in biomedical engineering systems. PMID

  11. EDITORIAL: Special issue containing contributions from the 39th Neural Interfaces Conference Special issue containing contributions from the 39th Neural Interfaces Conference

    NASA Astrophysics Data System (ADS)

    Weiland, James D.

    2011-07-01

    Implantable neural interfaces provide substantial benefits to individuals with neurological disorders. That was the unequivocal message delivered by speaker after speaker from the podium of the 39th Neural Interfaces Conference (NIC2010) held in Long Beach, California, in June 2010. Giving benefit to patients is the most important measure for any biomedical technology, and myriad presentations at NIC2010 made clear that implantable neurostimulation technology has achieved this goal. Cochlear implants allow deaf people to communicate through speech. Deep brain stimulators give back mobility and dexterity necessary for so many daily tasks that are often taken for granted. Chronic pain can be alleviated through spinal cord stimulation. Motor prosthesis systems have been demonstrated in humans, through both reanimation of paralyzed limbs and neural control of robotic arms. Earlier this year, a retinal prosthesis was approved for sale in Europe, providing some hope for the blind. In sum, current clinical implants have been tremendously beneficial for today's patients and experimental systems that will be translated to the clinic promise to expand the number of people helped through bioelectronic therapies. Yet there are significant opportunities for improvement. For sensory prostheses, patients report an artificial sensation, clearly different from the natural sensation they remember. Neuromodulation systems, such as deep brain stimulation and pain stimulators, often have side effects that are tolerated as long as the side effects are less impactful than the disease. The papers published in the special issue from NIC2010 reflect the maturing and expanding field of neural interfaces. Our field has moved past proof-of-principle demonstrations and is now focusing on proving the longevity required for clinical implementation of new devices, extending existing approaches to new diseases and improving current devices for better outcomes. Closed-loop neuromodulation is a

  12. Integration of active devices on smart polymers for neural interfaces

    NASA Astrophysics Data System (ADS)

    Avendano-Bolivar, Adrian Emmanuel

    The increasing ability to ever more precisely identify and measure neural interactions and other phenomena in the central and peripheral nervous systems is revolutionizing our understanding of the human body and brain. To facilitate further understanding, more sophisticated neural devices, perhaps using microelectronics processing, must be fabricated. Materials often used in these neural interfaces, while compatible with these fabrication processes, are not optimized for long-term use in the body and are often orders of magnitude stiffer than the tissue with which they interact. Using the smart polymer substrates described in this work, suitability for processing as well as chronic implantation is demonstrated. We explore how to integrate reliable circuitry onto these flexible, biocompatible substrates that can withstand the aggressive environment of the body. To increase the capabilities of these devices beyond individual channel sensing and stimulation, active electronics must also be included onto our systems. In order to add this functionality to these substrates and explore the limits of these devices, we developed a process to fabricate single organic thin film transistors with mobilities up to 0.4 cm2/Vs and threshold voltages close to 0V. A process for fabricating organic light emitting diodes on flexible substrates is also addressed. We have set a foundation and demonstrated initial feasibility for integrating multiple transistors onto thin-film flexible devices to create new applications, such as matrix addressable functionalized electrodes and organic light emitting diodes. A brief description on how to integrate waveguides for their use in optogenetics is addressed. We have built understanding about device constraints on mechanical, electrical and in vivo reliability and how various conditions affect the electronics' lifetime. We use a bi-layer gate dielectric using an inorganic material such as HfO 2 combined with organic Parylene-c. A study of

  13. A Fully Implantable 96-channel Neural Data Acquisition System

    PubMed Central

    Rizk, Michael; Bossetti, Chad A; Jochum, Thomas A; Callender, Stephen H; Nicolelis, Miguel A L; Turner, Dennis A; Wolf, Patrick D

    2009-01-01

    A fully implantable neural data acquisition system is a key component of a clinically viable brain-machine interface. This type of system must communicate with the outside world and obtain power without the use of wires that cross through the skin. We present a 96-channel fully implantable neural data acquisition system. This system performs spike detection and extraction within the body and wirelessly transmits data to an external unit. Power is supplied wirelessly through the use of inductively-coupled coils. The system was implanted acutely in sheep and successfully recorded, processed, and transmitted neural data. Bidirectional communication between the implanted system and an external unit was successful over a range of 2 m. The system is also shown to integrate well into a brain-machine interface. This demonstration of a high channel-count fully implanted neural data acquisition system is a critical step in the development of a clinically viable brain-machine interface. PMID:19255459

  14. Vertically aligned carbon nanofiber as nano-neuron interface for monitoring neural function

    SciTech Connect

    Ericson, Milton Nance; McKnight, Timothy E; Melechko, Anatoli Vasilievich; Simpson, Michael L; Morrison, Barclay; Yu, Zhe

    2012-01-01

    Neural chips, which are capable of simultaneous, multi-site neural recording and stimulation, have been used to detect and modulate neural activity for almost 30 years. As a neural interface, neural chips provide dynamic functional information for neural decoding and neural control. By improving sensitivity and spatial resolution, nano-scale electrodes may revolutionize neural detection and modulation at cellular and molecular levels as nano-neuron interfaces. We developed a carbon-nanofiber neural chip with lithographically defined arrays of vertically aligned carbon nanofiber electrodes and demonstrated its capability of both stimulating and monitoring electrophysiological signals from brain tissues in vitro and monitoring dynamic information of neuroplasticity. This novel nano-neuron interface can potentially serve as a precise, informative, biocompatible, and dual-mode neural interface for monitoring of both neuroelectrical and neurochemical activity at the single cell level and even inside the cell.

  15. Studies in RF Power Communication, SAR, and Temperature Elevation in Wireless Implantable Neural Interfaces

    PubMed Central

    Zhao, Yujuan; Tang, Lin; Rennaker, Robert; Hutchens, Chris; Ibrahim, Tamer S.

    2013-01-01

    Implantable neural interfaces are designed to provide a high spatial and temporal precision control signal implementing high degree of freedom real-time prosthetic systems. The development of a Radio Frequency (RF) wireless neural interface has the potential to expand the number of applications as well as extend the robustness and longevity compared to wired neural interfaces. However, it is well known that RF signal is absorbed by the body and can result in tissue heating. In this work, numerical studies with analytical validations are performed to provide an assessment of power, heating and specific absorption rate (SAR) associated with the wireless RF transmitting within the human head. The receiving antenna on the neural interface is designed with different geometries and modeled at a range of implanted depths within the brain in order to estimate the maximum receiving power without violating SAR and tissue temperature elevation safety regulations. Based on the size of the designed antenna, sets of frequencies between 1 GHz to 4 GHz have been investigated. As expected the simulations demonstrate that longer receiving antennas (dipole) and lower working frequencies result in greater power availability prior to violating SAR regulations. For a 15 mm dipole antenna operating at 1.24 GHz on the surface of the brain, 730 uW of power could be harvested at the Federal Communications Commission (FCC) SAR violation limit. At approximately 5 cm inside the head, this same antenna would receive 190 uW of power prior to violating SAR regulations. Finally, the 3-D bio-heat simulation results show that for all evaluated antennas and frequency combinations we reach FCC SAR limits well before 1 °C. It is clear that powering neural interfaces via RF is possible, but ultra-low power circuit designs combined with advanced simulation will be required to develop a functional antenna that meets all system requirements. PMID:24223123

  16. Integrating and Interfacing Library Systems.

    ERIC Educational Resources Information Center

    Boss, Richard W.

    1985-01-01

    This overview of local library online systems that integrate several functions covers functional integration, benefits of integrated systems, turnkey systems, minicomputer and microcomputer-based systems, interfacing automated systems, types of interfaces, linking homogenous and heterogeneous systems, role of vendors, library applications, linking…

  17. Neural bases of syntax-semantics interface processing.

    PubMed

    Malaia, Evguenia; Newman, Sharlene

    2015-06-01

    The binding problem-question of how information between the modules of the linguistic system is integrated during language processing-is as yet unresolved. The remarkable speed of language processing and comprehension (Pulvermüller et al. 2009) suggests that at least coarse semantic information (e.g. noun animacy) and syntactically-relevant information (e.g. verbal template) are integrated rapidly to allow for coarse comprehension. This EEG study investigated syntax-semantics interface processing during word-by-word sentence reading. As alpha-band neural activity serves as an inhibition mechanism for local networks, we used topographical distribution of alpha power to help identify the timecourse of the binding process. We manipulated the syntactic parameter of verbal event structure, and semantic parameter of noun animacy in reduced relative clauses (RRCs, e.g. "The witness/mansion seized/protected by the agent was in danger"), to investigate the neural bases of interaction between syntactic and semantic networks during sentence processing. The word-by-word stimulus presentation method in the present experiment required manipulation of both syntactic structure and semantic features in the working memory. The results demonstrated a gradient distribution of early components (biphasic posterior P1-N2 and anterior N1-P2) over function words "by" and "the", and the verb, corresponding to facilitation or conflict resulting from the syntactic (telicity) and semantic (animacy) cues in the preceding portion of the sentence. This was followed by assimilation of power distribution in the α band at the second noun. The flattened distribution of α power during the mental manipulation with high demand on working memory-thematic role re-assignment-demonstrates a state of α equilibrium with strong functional coupling between posterior and anterior regions. These results demonstrate that the processing of semantic and syntactic features during sentence comprehension proceeds

  18. On Design and Implementation of Neural-Machine Interface for Artificial Legs

    PubMed Central

    Zhang, Xiaorong; Liu, Yuhong; Zhang, Fan; Ren, Jin; Sun, Yan (Lindsay); Yang, Qing

    2011-01-01

    The quality of life of leg amputees can be improved dramatically by using a cyber physical system (CPS) that controls artificial legs based on neural signals representing amputees’ intended movements. The key to the CPS is the neural-machine interface (NMI) that senses electromyographic (EMG) signals to make control decisions. This paper presents a design and implementation of a novel NMI using an embedded computer system to collect neural signals from a physical system - a leg amputee, provide adequate computational capability to interpret such signals, and make decisions to identify user’s intent for prostheses control in real time. A new deciphering algorithm, composed of an EMG pattern classifier and a post-processing scheme, was developed to identify the user’s intended lower limb movements. To deal with environmental uncertainty, a trust management mechanism was designed to handle unexpected sensor failures and signal disturbances. Integrating the neural deciphering algorithm with the trust management mechanism resulted in a highly accurate and reliable software system for neural control of artificial legs. The software was then embedded in a newly designed hardware platform based on an embedded microcontroller and a graphic processing unit (GPU) to form a complete NMI for real time testing. Real time experiments on a leg amputee subject and an able-bodied subject have been carried out to test the control accuracy of the new NMI. Our extensive experiments have shown promising results on both subjects, paving the way for clinical feasibility of neural controlled artificial legs. PMID:22389637

  19. Encapsulating Elastically Stretchable Neural Interfaces: Yield, Resolution, and Recording/Stimulation of Neural Activity

    PubMed Central

    Morrison, Barclay; Goletiani, Cezar; Yu, Zhe; Wagner, Sigurd

    2013-01-01

    A high resolution elastically stretchable microelectrode array (SMEA) to interface with neural tissue is described. The SMEA consists of an elastomeric substrate, such as poly(dimethylsiloxane) (PDMS), elastically stretchable gold conductors, and an electrically insulating encapsulating layer in which contact holes are opened. We demonstrate the feasibility of producing contact holes with 40 µm × 40 µm openings, show why the adhesion of the encapsulation layer to the underlying silicone substrate is weakened during contact hole fabrication, and provide remedies. These improvements result in greatly increased fabrication yield and reproducibility. An SMEA with 28 microelectrodes was fabricated. The contact holes (100 µm × 100 µm) in the encapsulation layer are only ~10% the size of the previous generation, allowing a larger number of microelectrodes per unit area, thus affording the capability to interface with a smaller neural population per electrode. This new SMEA is used to record spontaneous and evoked activity in organotypic hippocampal tissue slices at 0% strain before stretching, at 5 % and 10 % equibiaxial strain, and again at 0% strain after relaxation. The noise of the recordings increases with increasing strain. The frequency of spontaneous neural activity also increases when the SMEA is stretched. Upon relaxation, the noise returns to pre-stretch levels, while the frequency of neural activity remains elevated. Stimulus-response curves at each strain level are measured. The SMEA shows excellent biocompatibility for at least two weeks. PMID:24093006

  20. Organic electrode coatings for next-generation neural interfaces.

    PubMed

    Aregueta-Robles, Ulises A; Woolley, Andrew J; Poole-Warren, Laura A; Lovell, Nigel H; Green, Rylie A

    2014-01-01

    Traditional neuronal interfaces utilize metallic electrodes which in recent years have reached a plateau in terms of the ability to provide safe stimulation at high resolution or rather with high densities of microelectrodes with improved spatial selectivity. To achieve higher resolution it has become clear that reducing the size of electrodes is required to enable higher electrode counts from the implant device. The limitations of interfacing electrodes including low charge injection limits, mechanical mismatch and foreign body response can be addressed through the use of organic electrode coatings which typically provide a softer, more roughened surface to enable both improved charge transfer and lower mechanical mismatch with neural tissue. Coating electrodes with conductive polymers or carbon nanotubes offers a substantial increase in charge transfer area compared to conventional platinum electrodes. These organic conductors provide safe electrical stimulation of tissue while avoiding undesirable chemical reactions and cell damage. However, the mechanical properties of conductive polymers are not ideal, as they are quite brittle. Hydrogel polymers present a versatile coating option for electrodes as they can be chemically modified to provide a soft and conductive scaffold. However, the in vivo chronic inflammatory response of these conductive hydrogels remains unknown. A more recent approach proposes tissue engineering the electrode interface through the use of encapsulated neurons within hydrogel coatings. This approach may provide a method for activating tissue at the cellular scale, however, several technological challenges must be addressed to demonstrate feasibility of this innovative idea. The review focuses on the various organic coatings which have been investigated to improve neural interface electrodes. PMID:24904405

  1. Organic electrode coatings for next-generation neural interfaces

    PubMed Central

    Aregueta-Robles, Ulises A.; Woolley, Andrew J.; Poole-Warren, Laura A.; Lovell, Nigel H.; Green, Rylie A.

    2014-01-01

    Traditional neuronal interfaces utilize metallic electrodes which in recent years have reached a plateau in terms of the ability to provide safe stimulation at high resolution or rather with high densities of microelectrodes with improved spatial selectivity. To achieve higher resolution it has become clear that reducing the size of electrodes is required to enable higher electrode counts from the implant device. The limitations of interfacing electrodes including low charge injection limits, mechanical mismatch and foreign body response can be addressed through the use of organic electrode coatings which typically provide a softer, more roughened surface to enable both improved charge transfer and lower mechanical mismatch with neural tissue. Coating electrodes with conductive polymers or carbon nanotubes offers a substantial increase in charge transfer area compared to conventional platinum electrodes. These organic conductors provide safe electrical stimulation of tissue while avoiding undesirable chemical reactions and cell damage. However, the mechanical properties of conductive polymers are not ideal, as they are quite brittle. Hydrogel polymers present a versatile coating option for electrodes as they can be chemically modified to provide a soft and conductive scaffold. However, the in vivo chronic inflammatory response of these conductive hydrogels remains unknown. A more recent approach proposes tissue engineering the electrode interface through the use of encapsulated neurons within hydrogel coatings. This approach may provide a method for activating tissue at the cellular scale, however, several technological challenges must be addressed to demonstrate feasibility of this innovative idea. The review focuses on the various organic coatings which have been investigated to improve neural interface electrodes. PMID:24904405

  2. Uniform and Non-uniform Perturbations in Brain-Machine Interface Task Elicit Similar Neural Strategies

    PubMed Central

    Armenta Salas, Michelle; Helms Tillery, Stephen I.

    2016-01-01

    The neural mechanisms that take place during learning and adaptation can be directly probed with brain-machine interfaces (BMIs). We developed a BMI controlled paradigm that enabled us to enforce learning by introducing perturbations which changed the relationship between neural activity and the BMI's output. We introduced a uniform perturbation to the system, through a visuomotor rotation (VMR), and a non-uniform perturbation, through a decorrelation task. The controller in the VMR was essentially unchanged, but produced an output rotated at 30° from the neurally specified output. The controller in the decorrelation trials decoupled the activity of neurons that were highly correlated in the BMI task by selectively forcing the preferred directions of these cell pairs to be orthogonal. We report that movement errors were larger in the decorrelation task, and subjects needed more trials to restore performance back to baseline. During learning, we measured decreasing trends in preferred direction changes and cross-correlation coefficients regardless of task type. Conversely, final adaptations in neural tunings were dependent on the type controller used (VMR or decorrelation). These results hint to the similar process the neural population might engage while adapting to new tasks, and how, through a global process, the neural system can arrive to individual solutions. PMID:27601981

  3. Uniform and Non-uniform Perturbations in Brain-Machine Interface Task Elicit Similar Neural Strategies.

    PubMed

    Armenta Salas, Michelle; Helms Tillery, Stephen I

    2016-01-01

    The neural mechanisms that take place during learning and adaptation can be directly probed with brain-machine interfaces (BMIs). We developed a BMI controlled paradigm that enabled us to enforce learning by introducing perturbations which changed the relationship between neural activity and the BMI's output. We introduced a uniform perturbation to the system, through a visuomotor rotation (VMR), and a non-uniform perturbation, through a decorrelation task. The controller in the VMR was essentially unchanged, but produced an output rotated at 30° from the neurally specified output. The controller in the decorrelation trials decoupled the activity of neurons that were highly correlated in the BMI task by selectively forcing the preferred directions of these cell pairs to be orthogonal. We report that movement errors were larger in the decorrelation task, and subjects needed more trials to restore performance back to baseline. During learning, we measured decreasing trends in preferred direction changes and cross-correlation coefficients regardless of task type. Conversely, final adaptations in neural tunings were dependent on the type controller used (VMR or decorrelation). These results hint to the similar process the neural population might engage while adapting to new tasks, and how, through a global process, the neural system can arrive to individual solutions. PMID:27601981

  4. Titania nanotube arrays as interfaces for neural prostheses

    PubMed Central

    Sorkin, Jonathan A.; Hughes, Stephen; Soares, Paulo; Popat, Ketul C.

    2015-01-01

    Neural prostheses have become ever more acceptable treatments for many different types of neurological damage and disease. Here we investigate the use of two different morphologies of titania nanotube arrays as interfaces to advance the longevity and effectiveness of these prostheses. The nanotube arrays were characterized for their nanotopography, crystallinity, conductivity, wettability, surface mechanical properties and adsorption of key proteins: fibrinogen, albumin and laminin. The loosely packed nanotube arrays fabricated using a diethylene glycol based electrolyte, contained a higher presence of the anatase crystal phase and were subsequently more conductive. These arrays yielded surfaces with higher wettability and lower modulus than the densely packed nanotube arrays fabricated using water based electrolyte. Further the adhesion, proliferation and differentiation of the C17.2 neural stem cell line was investigated on the nanotube arrays. The proliferation ratio of the cells as well as the level of neuronal differentiation was seen to increase on the loosely packed arrays. The results indicate that loosely packed nanotube arrays similar to the ones produced here with a DEG based electrolyte, may provide a favorable template for growth and maintenance of C17.2 neural stem cell line. PMID:25687003

  5. Drug release from porous silicon for stable neural interface

    NASA Astrophysics Data System (ADS)

    Sun, Tao; Tsang, Wei Mong; Park, Woo-Tae

    2014-02-01

    70 μm-thick porous Si (PSi) layer with the pore size of 11.1 ± 7.6 nm was formed on an 8-in. Si wafer via an anodization process for the microfabrication of a microelectrode to record neural signals. To reduce host tissue responses to the microelectrode and achieve a stable neural interface, water-soluble dexamethesone (Dex) was loaded into the PSi via incubation with the drug solution overnight. After the drug loading process, the pore size of PSi reduced to 4.7 ± 2.6 nm on the basis of scanning electron microscopic (SEM) images, while its wettability was remarkably enhanced. Fluorescence images demonstrated that Dex was loaded into the porous structure of the PSi. Degradation rate of the PSi was investigated by incubation in distilled water for 21 days. Moreover, the drug release profile of the Dex-loaded PSi was a combination of an initial burst release and subsequent sustained release. To evaluate cellular responses to the drug release from the PSi, primary astrocytes were seeded on the surface of samples. After 2 days of culture, the Dex-loaded PSi could not only moderately prevent astrocyte adhesion in comparison with Si, but also more effectively suppress the activation of primary astrocytes than unloaded PSi due to the drug release. Therefore, it might be an effective method to reduce host tissue responses and stabilize the quality of the recorded neural signal by means of loading drugs into the PSi component of the microelectrode.

  6. An Implantable Wireless Neural Interface for Recording Cortical Circuit Dynamics in Moving Primates

    PubMed Central

    Borton, David A.; Yin, Ming; Aceros, Juan; Nurmikko, Arto

    2013-01-01

    Objective Neural interface technology suitable for clinical translation has the potential to significantly impact the lives of amputees, spinal cord injury victims, and those living with severe neuromotor disease. Such systems must be chronically safe, durable, and effective. Approach We have designed and implemented a neural interface microsystem, housed in a compact, subcutaneous, and hermetically sealed titanium enclosure. The implanted device interfaces the brain with a 510k-approved, 100-element silicon-based MEA via a custom hermetic feedthrough design. Full spectrum neural signals were amplified (0.1Hz to 7.8kHz, ×200 gain) and multiplexed by a custom application specific integrated circuit, digitized, and then packaged for transmission. The neural data (24 Mbps) was transmitted by a wireless data link carried on an frequency shift key modulated signal at 3.2GHz and 3.8GHz to a receiver 1 meter away by design as a point-to-point communication link for human clinical use. The system was powered by an embedded medical grade rechargeable Li-ion battery for 7-hour continuous operation between recharge via an inductive transcutaneous wireless power link at 2MHz. Main results Device verification and early validation was performed in both swine and non-human primate freely-moving animal models and showed that the wireless implant was electrically stable, effective in capturing and delivering broadband neural data, and safe for over one year of testing. In addition, we have used the multichannel data from these mobile animal models to demonstrate the ability to decode neural population dynamics associated with motor activity. Significance We have developed an implanted wireless broadband neural recording device evaluated in non-human primate and swine. The use of this new implantable neural interface technology can provide insight on how to advance human neuroprostheses beyond the present early clinical trials. Further, such tools enable mobile patient use, have

  7. An implantable wireless neural interface for recording cortical circuit dynamics in moving primates

    NASA Astrophysics Data System (ADS)

    Borton, David A.; Yin, Ming; Aceros, Juan; Nurmikko, Arto

    2013-04-01

    Objective. Neural interface technology suitable for clinical translation has the potential to significantly impact the lives of amputees, spinal cord injury victims and those living with severe neuromotor disease. Such systems must be chronically safe, durable and effective. Approach. We have designed and implemented a neural interface microsystem, housed in a compact, subcutaneous and hermetically sealed titanium enclosure. The implanted device interfaces the brain with a 510k-approved, 100-element silicon-based microelectrode array via a custom hermetic feedthrough design. Full spectrum neural signals were amplified (0.1 Hz to 7.8 kHz, 200× gain) and multiplexed by a custom application specific integrated circuit, digitized and then packaged for transmission. The neural data (24 Mbps) were transmitted by a wireless data link carried on a frequency-shift-key-modulated signal at 3.2 and 3.8 GHz to a receiver 1 m away by design as a point-to-point communication link for human clinical use. The system was powered by an embedded medical grade rechargeable Li-ion battery for 7 h continuous operation between recharge via an inductive transcutaneous wireless power link at 2 MHz. Main results. Device verification and early validation were performed in both swine and non-human primate freely-moving animal models and showed that the wireless implant was electrically stable, effective in capturing and delivering broadband neural data, and safe for over one year of testing. In addition, we have used the multichannel data from these mobile animal models to demonstrate the ability to decode neural population dynamics associated with motor activity. Significance. We have developed an implanted wireless broadband neural recording device evaluated in non-human primate and swine. The use of this new implantable neural interface technology can provide insight into how to advance human neuroprostheses beyond the present early clinical trials. Further, such tools enable mobile

  8. A TinyOS-enabled MICA2-based wireless neural interface.

    PubMed

    Farshchi, Shahin; Nuyujukian, Paul H; Pesterev, Aleksey; Mody, Istvan; Judy, Jack W

    2006-07-01

    Existing approaches used to develop compact low-power multichannel wireless neural recording systems range from creating custom-integrated circuits to assembling commercial-off-the-shelf (COTS) PC-based components. Custom-integrated-circuit designs yield extremely compact and low-power devices at the expense of high development and upgrade costs and turn-around times, while assembling COTS-PC-technology yields high performance at the expense of large system size and increased power consumption. To achieve a balance between implementing an ultra-compact custom-fabricated neural transceiver and assembling COTS-PC-technology, an overlay of a neural interface upon the TinyOS-based MICA2 platform is described. The system amplifies, digitally encodes, and transmits neural signals real-time at a rate of 9.6 kbps, while consuming less than 66 mW of power. The neural signals are received and forwarded to a client PC over a serial connection. This data rate can be divided for recording on up to 6 channels, with a resolution of 8 bits/sample. This work demonstrates the strengths and limitations of the TinyOS-based sensor technology as a foundation for chronic remote biological monitoring applications and, thus, provides an opportunity to create a system that can leverage from the frequent networking and communications advancements being made by the global TinyOS-development community. PMID:16830946

  9. Integrated wireless neural interface based on the Utah electrode array.

    PubMed

    Kim, S; Bhandari, R; Klein, M; Negi, S; Rieth, L; Tathireddy, P; Toepper, M; Oppermann, H; Solzbacher, F

    2009-04-01

    This report presents results from research towards a fully integrated, wireless neural interface consisting of a 100-channel microelectrode array, a custom-designed signal processing and telemetry IC, an inductive power receiving coil, and SMD capacitors. An integration concept for such a device was developed, and the materials and methods used to implement this concept were investigated. We developed a multi-level hybrid assembly process that used the Utah Electrode Array (UEA) as a circuit board. The signal processing IC was flip-chip bonded to the UEA using Au/Sn reflow soldering, and included amplifiers for up to 100 channels, signal processing units, an RF transmitter, and a power receiving and clock recovery module. An under bump metallization (UBM) using potentially biocompatible materials was developed and optimized, which consisted of a sputter deposited Ti/Pt/Au thin film stack with layer thicknesses of 50/150/150 nm, respectively. After flip-chip bonding, an underfiller was applied between the IC and the UEA to improve mechanical stability and prevent fluid ingress in in vivo conditions. A planar power receiving coil fabricated by patterning electroplated gold films on polyimide substrates was connected to the IC by using a custom metallized ceramic spacer and SnCu reflow soldering. The SnCu soldering was also used to assemble SMD capacitors on the UEA. The mechanical properties and stability of the optimized interconnections between the UEA and the IC and SMD components were measured. Measurements included the tape tests to evaluate UBM adhesion, shear testing between the Au/Sn solder bumps and the substrate, and accelerated lifetime testing of the long-term stability for the underfiller material coated with a a-SiC(x):H by PECVD, which was intended as a device encapsulation layer. The materials and processes used to generate the integrated neural interface device were found to yield a robust and reliable integrated package. PMID:19067174

  10. Topographic guidance based on microgrooved electroactive composite films for neural interface.

    PubMed

    Shi, Xiaoyao; Xiao, Yinghong; Xiao, Hengyang; Harris, Gary; Wang, Tongxin; Che, Jianfei

    2016-09-01

    Topographical features are essential to neural interface for better neuron attachment and growth. This paper presents a facile and feasible route to fabricate an electroactive and biocompatible micro-patterned Single-walled carbon nanotube/poly(3,4-ethylenedioxythiophene) composite films (SWNT/PEDOT) for interface of neural electrodes. The uniform SWNT/PEDOT composite films with nanoscale pores and microscale grooves significantly enlarged the electrode-electrolyte interface, facilitated ion transfer within the bulk film, and more importantly, provided topology cues for the proliferation and differentiation of neural cells. Electrochemical analyses indicated that the introduction of PEDOT greatly improved the stability of the SWNT/PEDOT composite film and decreased the electrode/electrolyte interfacial impedance. Further, in vitro culture of rat pheochromocytoma (PC12) cells and MTT testing showed that the grooved SWNT/PEDOT composite film was non-toxic and favorable to guide the growth and extension of neurite. Our results demonstrated that the fabricated microscale groove patterns were not only beneficial in the development of models for nervous system biology, but also in creating therapeutic approaches for nerve injuries. PMID:27295493

  11. Visualization of the intact interface between neural tissue and implanted microelectrode arrays.

    PubMed

    Holecko, Matthew M; Williams, Justin C; Massia, Stephen P

    2005-12-01

    This research presents immunohistochemical strategies for assessing the interactions at the immediate interface between micro-scale implanted devices and the surrounding brain tissue during inflammatory astrogliotic reactions. This includes preparation, microscopy and analysis techniques for obtaining images of the intimate contact between neural cells and the surface of implantable micro-electromechanical systems (MEMS) devices. The ability to visualize the intact interface between an implant and the surrounding tissue allows researchers to examine tissue that is unchanged from its native implanted state. Conversely, current popular techniques involve removing the implant. This tends to cause damage to the tissue immediately surrounding the implant and can hinder one's ability to differentiate inflammatory responses to the implant versus physical damage occurring from removal of the implant from the tissue. Due to advances in microscopy and staining techniques, it is now possible to visualize the intact tissue-implant interface. This paper presents the development of imaging techniques for visualizing the intact interface between neural tissue and implanted devices. This is particularly important for understanding both the acute and chronic neuroinflammatory responses to devices intended for long-term use in a prosthetic system. Non-functional, unbonded devices were imaged in vitro and in vivo at different times post-implantation via a range of techniques. Using these techniques, detailed interactions could be seen between delicate cellular processes and the electrode surface, which would have been destroyed using conventional histology processes. PMID:16317233

  12. Using reinforcement learning to provide stable brain-machine interface control despite neural input reorganization.

    PubMed

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

    2014-01-01

    Brain-machine interface (BMI) systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder's neural input space (e.g. neurons appearing or being lost amongst electrode recordings). These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI) to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled. PMID:24498055

  13. Using Reinforcement Learning to Provide Stable Brain-Machine Interface Control Despite Neural Input Reorganization

    PubMed Central

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

    2014-01-01

    Brain-machine interface (BMI) systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder’s neural input space (e.g. neurons appearing or being lost amongst electrode recordings). These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI) to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled. PMID:24498055

  14. Automated Fluid Interface System (AFIS)

    NASA Technical Reports Server (NTRS)

    1990-01-01

    Automated remote fluid servicing will be necessary for future space missions, as future satellites will be designed for on-orbit consumable replenishment. In order to develop an on-orbit remote servicing capability, a standard interface between a tanker and the receiving satellite is needed. The objective of the Automated Fluid Interface System (AFIS) program is to design, fabricate, and functionally demonstrate compliance with all design requirements for an automated fluid interface system. A description and documentation of the Fairchild AFIS design is provided.

  15. Memory Storage and Neural Systems.

    ERIC Educational Resources Information Center

    Alkon, Daniel L.

    1989-01-01

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

  16. User interfaces to expert systems

    SciTech Connect

    Agarwal, A.; Emrich, M.L.

    1988-10-01

    Expert Systems are becoming increasingly popular in environments where the user is not well versed in computers or the subject domain. They offer expert advice and can also explain their lines of reasoning. As these systems are applied to highly technical areas, they become complex and large. Therefore, User Systems Interfaces (USIs) become critical. This paper discusses recent technologies that can be applied to improved user communication. In particular, bar menus/graphics, mouse interfaces, touch screens, and voice links will be highlighted. Their applications in the context of SOFTMAN (The Software Manager Apprentice) a knowledge-based system are discussed. 18 refs., 2 figs.

  17. Control aspects of motor neural prosthesis: sensory interface.

    PubMed

    Popović, Dejan B; Dosen, Strahinja; Popović, Mirjana B; Stefanović, Filip; Kojović, Jovana

    2007-01-01

    A neural prosthesis (NP) has two applications: permanent assistance of function, and temporary assistance that contributes to long-term recovery of function. Here, we address control issues for a therapeutic NP which uses surface electrodes. We suggest that the effective NP for therapy needs to implement rule-based control. Rule-based control relies on the triggering of preprogrammed sequences of electrical stimulation by the sensory signals. The sensory system in the therapeutic NP needs to be simple for installation, allow self-calibration, it must be robust, and sufficiently redundant in order to guarantee safe operation. The sensory signals need to generate control signals; hence, sensory fusion is needed. MEMS technology today provides sensors that fulfill the technical requirements (accelerometers, gyroscopes, force sensing resistors). Therefore, the task was to design a sensory signal processing method from the mentioned solid state sensors that would recognize phases during the gait cycle. This is necessary for the control of multi channel electrical stimulation. The sensory fusion consists of the following two phases: 1) estimation of vertical and horizontal components of the ground reaction force, center of pressure, and joint angles from the solid-state sensors, and 2) fusion of the estimated signals into a sequence of command signals. The first phase was realized by the use of artificial neural networks and adaptive neuro-fuzzy inference systems, while the second by the use of inductive learning described in our earlier work [1]. PMID:18002969

  18. Neural system prediction and identification challenge

    PubMed Central

    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. PMID:24399966

  19. Human facial neural activities and gesture recognition for machine-interfacing applications

    PubMed Central

    Hamedi, M; Salleh, Sh-Hussain; Tan, TS; Ismail, K; Ali, J; Dee-Uam, C; Pavaganun, C; Yupapin, PP

    2011-01-01

    The authors present a new method of recognizing different human facial gestures through their neural activities and muscle movements, which can be used in machine-interfacing applications. Human–machine interface (HMI) technology utilizes human neural activities as input controllers for the machine. Recently, much work has been done on the specific application of facial electromyography (EMG)-based HMI, which have used limited and fixed numbers of facial gestures. In this work, a multipurpose interface is suggested that can support 2–11 control commands that can be applied to various HMI systems. The significance of this work is finding the most accurate facial gestures for any application with a maximum of eleven control commands. Eleven facial gesture EMGs are recorded from ten volunteers. Detected EMGs are passed through a band-pass filter and root mean square features are extracted. Various combinations of gestures with a different number of gestures in each group are made from the existing facial gestures. Finally, all combinations are trained and classified by a Fuzzy c-means classifier. In conclusion, combinations with the highest recognition accuracy in each group are chosen. An average accuracy >90% of chosen combinations proved their ability to be used as command controllers. PMID:22267930

  20. Computational Assessment of Neural Probe and Brain Tissue Interface under Transient Motion.

    PubMed

    Polanco, Michael; Bawab, Sebastian; Yoon, Hargsoon

    2016-01-01

    The functional longevity of a neural probe is dependent upon its ability to minimize injury risk during the insertion and recording period in vivo, which could be related to motion-related strain between the probe and surrounding tissue. A series of finite element analyses was conducted to study the extent of the strain induced within the brain in an area around a neural probe. This study focuses on the transient behavior of neural probe and brain tissue interface with a viscoelastic model. Different stages of the interface from initial insertion of neural probe to full bonding of the probe by astro-glial sheath formation are simulated utilizing analytical tools to investigate the effects of relative motion between the neural probe and the brain while friction coefficients and kinematic frequencies are varied. The analyses can provide an in-depth look at the quantitative benefits behind using soft materials for neural probes. PMID:27322338

  1. Computational Assessment of Neural Probe and Brain Tissue Interface under Transient Motion

    PubMed Central

    Polanco, Michael; Bawab, Sebastian; Yoon, Hargsoon

    2016-01-01

    The functional longevity of a neural probe is dependent upon its ability to minimize injury risk during the insertion and recording period in vivo, which could be related to motion-related strain between the probe and surrounding tissue. A series of finite element analyses was conducted to study the extent of the strain induced within the brain in an area around a neural probe. This study focuses on the transient behavior of neural probe and brain tissue interface with a viscoelastic model. Different stages of the interface from initial insertion of neural probe to full bonding of the probe by astro-glial sheath formation are simulated utilizing analytical tools to investigate the effects of relative motion between the neural probe and the brain while friction coefficients and kinematic frequencies are varied. The analyses can provide an in-depth look at the quantitative benefits behind using soft materials for neural probes. PMID:27322338

  2. Intelligent interfaces for expert systems

    NASA Technical Reports Server (NTRS)

    Villarreal, James A.; Wang, Lui

    1988-01-01

    Vital to the success of an expert system is an interface to the user which performs intelligently. A generic intelligent interface is being developed for expert systems. This intelligent interface was developed around the in-house developed Expert System for the Flight Analysis System (ESFAS). The Flight Analysis System (FAS) is comprised of 84 configuration controlled FORTRAN subroutines that are used in the preflight analysis of the space shuttle. In order to use FAS proficiently, a person must be knowledgeable in the areas of flight mechanics, the procedures involved in deploying a certain payload, and an overall understanding of the FAS. ESFAS, still in its developmental stage, is taking into account much of this knowledge. The generic intelligent interface involves the integration of a speech recognizer and synthesizer, a preparser, and a natural language parser to ESFAS. The speech recognizer being used is capable of recognizing 1000 words of connected speech. The natural language parser is a commercial software package which uses caseframe instantiation in processing the streams of words from the speech recognizer or the keyboard. The systems configuration is described along with capabilities and drawbacks.

  3. A microsystem integration platform dedicated to build multi-chip-neural interfaces.

    PubMed

    Ayoub, Amer E; Gosselin, Benoit; Sawan, Mohamad

    2007-01-01

    In this paper, we present an electrical discharge machining (EDM) technique associated with electrochemical steps to construct an appropriate biological interface to neural tissues. The presented microprobe design permits to short the time of production compared to available techniques, while improving the integrity of the electrodes. In addition, we are using a 3D approach to create compact and independent microsystem integration platefrom incorporating array of electrodes and signal processing chips. System-in-package and die-stacking are used to connect the integrated circuits and the array of electrodes on the platform. This approach enables to build a device that will fit in a volume smaller than 1.7 x 1.7 x 3.0 mm(3). This demonstrates the possibility of creating small devices that are suitable to fit in restricted areas for interfacing the brain. PMID:18003539

  4. Brain-computer interfaces: an overview of the hardware to record neural signals from the cortex.

    PubMed

    Stieglitz, Thomas; Rubehn, Birthe; Henle, Christian; Kisban, Sebastian; Herwik, Stanislav; Ruther, Patrick; Schuettler, Martin

    2009-01-01

    Brain-computer interfaces (BCIs) record neural signals from cortical origin with the objective to control a user interface for communication purposes, a robotic artifact or artificial limb as actuator. One of the key components of such a neuroprosthetic system is the neuro-technical interface itself, the electrode array. In this chapter, different designs and manufacturing techniques will be compared and assessed with respect to scaling and assembling limitations. The overview includes electroencephalogram (EEG) electrodes and epicortical brain-machine interfaces to record local field potentials (LFPs) from the surface of the cortex as well as intracortical needle electrodes that are intended to record single-unit activity. Two exemplary complementary technologies for micromachining of polyimide-based arrays and laser manufacturing of silicone rubber are presented and discussed with respect to spatial resolution, scaling limitations, and system properties. Advanced silicon micromachining technologies have led to highly sophisticated intracortical electrode arrays for fundamental neuroscientific applications. In this chapter, major approaches from the USA and Europe will be introduced and compared concerning complexity, modularity, and reliability. An assessment of the different technological solutions comparable to a strength weaknesses opportunities, and threats (SWOT) analysis might serve as guidance to select the adequate electrode array configuration for each control paradigm and strategy to realize robust, fast, and reliable BCIs. PMID:19660664

  5. Volitional control of neural activity: implications for brain–computer interfaces

    PubMed Central

    Fetz, Eberhard E

    2007-01-01

    Successful operation of brain–computer interfaces (BCI) and brain–machine interfaces (BMI) depends significantly on the degree to which neural activity can be volitionally controlled. This paper reviews evidence for such volitional control in a variety of neural signals, with particular emphasis on the activity of cortical neurons. Some evidence comes from conventional experiments that reveal volitional modulation in neural activity related to behaviours, including real and imagined movements, cognitive imagery and shifts of attention. More direct evidence comes from studies on operant conditioning of neural activity using biofeedback, and from BCI/BMI studies in which neural activity controls cursors or peripheral devices. Limits in the degree of accuracy of control in the latter studies can be attributed to several possible factors. Some of these factors, particularly limited practice time, can be addressed with long-term implanted BCIs. Preliminary observations with implanted circuits implementing recurrent BCIs are summarized. PMID:17234689

  6. The LILARTI neural network system

    SciTech Connect

    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.

  7. Tracking Neural Modulation Depth by Dual Sequential Monte Carlo Estimation on Point Processes for Brain-Machine Interfaces.

    PubMed

    Wang, Yiwen; She, Xiwei; Liao, Yuxi; Li, Hongbao; Zhang, Qiaosheng; Zhang, Shaomin; Zheng, Xiaoxiang; Principe, Jose

    2016-08-01

    Classic brain-machine interface (BMI) approaches decode neural signals from the brain responsible for achieving specific motor movements, which subsequently command prosthetic devices. Brain activities adaptively change during the control of the neuroprosthesis in BMIs, where the alteration of the preferred direction and the modulation of the gain depth are observed. The static neural tuning models have been limited by fixed codes, resulting in a decay of decoding performance over the course of the movement and subsequent instability in motor performance. To achieve stable performance, we propose a dual sequential Monte Carlo adaptive point process method, which models and decodes the gradually changing modulation depth of individual neuron over the course of a movement. We use multichannel neural spike trains from the primary motor cortex of a monkey trained to perform a target pursuit task using a joystick. Our results show that our computational approach successfully tracks the neural modulation depth over time with better goodness-of-fit than classic static neural tuning models, resulting in smaller errors between the true kinematics and the estimations in both simulated and real data. Our novel decoding approach suggests that the brain may employ such strategies to achieve stable motor output, i.e., plastic neural tuning is a feature of neural systems. BMI users may benefit from this adaptive algorithm to achieve more complex and controlled movement outcomes. PMID:26584486

  8. Photochemically modified diamond-like carbon surfaces for neural interfaces.

    PubMed

    Hopper, A P; Dugan, J M; Gill, A A; Regan, E M; Haycock, J W; Kelly, S; May, P W; Claeyssens, F

    2016-01-01

    Diamond-like carbon (DLC) was modified using a UV functionalization method to introduce surface-bound amine and aldehyde groups. The functionalization process rendered the DLC more hydrophilic and significantly increased the viability of neurons seeded to the surface. The amine functionalized DLC promoted adhesion of neurons and fostered neurite outgrowth to a degree indistinguishable from positive control substrates (glass coated with poly-L-lysine). The aldehyde-functionalized surfaces performed comparably to the amine functionalized surfaces and both additionally supported the adhesion and growth of primary rat Schwann cells. DLC has many properties that are desirable in biomaterials. With the UV functionalization method demonstrated here it may be possible to harness these properties for the development of implantable devices to interface with the nervous system. PMID:26478422

  9. Co-Design Method and Wafer-Level Packaging Technique of Thin-Film Flexible Antenna and Silicon CMOS Rectifier Chips for Wireless-Powered Neural Interface Systems.

    PubMed

    Okabe, Kenji; Jeewan, Horagodage Prabhath; Yamagiwa, Shota; Kawano, Takeshi; Ishida, Makoto; Akita, Ippei

    2015-01-01

    In this paper, a co-design method and a wafer-level packaging technique of a flexible antenna and a CMOS rectifier chip for use in a small-sized implantable system on the brain surface are proposed. The proposed co-design method optimizes the system architecture, and can help avoid the use of external matching components, resulting in the realization of a small-size system. In addition, the technique employed to assemble a silicon large-scale integration (LSI) chip on the very thin parylene film (5 μm) enables the integration of the rectifier circuits and the flexible antenna (rectenna). In the demonstration of wireless power transmission (WPT), the fabricated flexible rectenna achieved a maximum efficiency of 0.497% with a distance of 3 cm between antennas. In addition, WPT with radio waves allows a misalignment of 185% against antenna size, implying that the misalignment has a less effect on the WPT characteristics compared with electromagnetic induction. PMID:26694407

  10. Co-Design Method and Wafer-Level Packaging Technique of Thin-Film Flexible Antenna and Silicon CMOS Rectifier Chips for Wireless-Powered Neural Interface Systems

    PubMed Central

    Okabe, Kenji; Jeewan, Horagodage Prabhath; Yamagiwa, Shota; Kawano, Takeshi; Ishida, Makoto; Akita, Ippei

    2015-01-01

    In this paper, a co-design method and a wafer-level packaging technique of a flexible antenna and a CMOS rectifier chip for use in a small-sized implantable system on the brain surface are proposed. The proposed co-design method optimizes the system architecture, and can help avoid the use of external matching components, resulting in the realization of a small-size system. In addition, the technique employed to assemble a silicon large-scale integration (LSI) chip on the very thin parylene film (5 μm) enables the integration of the rectifier circuits and the flexible antenna (rectenna). In the demonstration of wireless power transmission (WPT), the fabricated flexible rectenna achieved a maximum efficiency of 0.497% with a distance of 3 cm between antennas. In addition, WPT with radio waves allows a misalignment of 185% against antenna size, implying that the misalignment has a less effect on the WPT characteristics compared with electromagnetic induction. PMID:26694407

  11. Learning in Artificial Neural Systems

    NASA Technical Reports Server (NTRS)

    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.

  12. Neural substrates for semantic memory of familiar songs: is there an interface between lyrics and melodies?

    PubMed

    Saito, Yoko; Ishii, Kenji; Sakuma, Naoko; Kawasaki, Keiichi; Oda, Keiichi; Mizusawa, Hidehiro

    2012-01-01

    Findings on song perception and song production have increasingly suggested that common but partially distinct neural networks exist for processing lyrics and melody. However, the neural substrates of song recognition remain to be investigated. The purpose of this study was to examine the neural substrates involved in the accessing "song lexicon" as corresponding to a representational system that might provide links between the musical and phonological lexicons using positron emission tomography (PET). We exposed participants to auditory stimuli consisting of familiar and unfamiliar songs presented in three ways: sung lyrics (song), sung lyrics on a single pitch (lyrics), and the sung syllable 'la' on original pitches (melody). The auditory stimuli were designed to have equivalent familiarity to participants, and they were recorded at exactly the same tempo. Eleven right-handed nonmusicians participated in four conditions: three familiarity decision tasks using song, lyrics, and melody and a sound type decision task (control) that was designed to engage perceptual and prelexical processing but not lexical processing. The contrasts (familiarity decision tasks versus control) showed no common areas of activation between lyrics and melody. This result indicates that essentially separate neural networks exist in semantic memory for the verbal and melodic processing of familiar songs. Verbal lexical processing recruited the left fusiform gyrus and the left inferior occipital gyrus, whereas melodic lexical processing engaged the right middle temporal sulcus and the bilateral temporo-occipital cortices. Moreover, we found that song specifically activated the left posterior inferior temporal cortex, which may serve as an interface between verbal and musical representations in order to facilitate song recognition. PMID:23029492

  13. Long term in vitro functional stability and recording longevity of fully integrated wireless neural interfaces based on the Utah Slant Electrode Array.

    PubMed

    Sharma, Asha; Rieth, Loren; Tathireddy, Prashant; Harrison, Reid; Oppermann, Hermann; Klein, Matthias; Töpper, Michael; Jung, Erik; Normann, Richard; Clark, Gregory; Solzbacher, Florian

    2011-08-01

    We evaluate the encapsulation and packaging reliability of a fully integrated wireless neural interface based on a Utah Slant Electrode Array/integrated neural interface-recording version 5 (USEA/INI-R5) system by monitoring the long term in vitro functional stability and recording longevity. The INI encapsulated with 6 µm Parylene-C was immersed in phosphate buffered saline (PBS) for a period of over 276 days (with the monitoring of the functional device still ongoing). The full functionality (wireless radio-frequency power, command and signal transmission) and the ability of the electrodes to record artificial neural signals even after 276 days of PBS soaking with little change (within 14%) in signal/noise amplitude constitute a major milestone in long term stability and allow us to study and evaluate the encapsulation reliability, functional stability and its potential usefulness for a wireless neural interface for future chronic implants. PMID:21775785

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

    PubMed

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

    2011-02-01

    Recording and extracting characteristic brain signals in freely moving animals is the basic and significant requirement in the study of brain-computer interface (BCI). To record animal's behaving and extract characteristic brain signals simultaneously could help understand the complex behavior of neural ensembles. Here, a system was established to record and analyse extracellular discharge in freely moving rats for the study of BCI. It comprised microelectrode and micro-driver assembly, analog front end (AFE), programmer system on chip (PSoC), wireless communication and the LabVIEW used as the platform for the graphic user interface. PMID:21485182

  15. EPICS system: system structure and user interface

    SciTech Connect

    West, R.E.; Bartlett, J.F.; Bobbitt, J.S.; Lahey, T.E.; Kramper, B.J.; MacKinnon, B.A.

    1984-02-01

    This paper present the user's view of and the general organization of the EPICS control system at Fermilab. Various subsystems of the EPICS control system are discussed. These include the user command language, software protection, the device database, remote computer interfaces, and several application utilities. This paper is related to two other papers on EPICS: an overview paper and a detailed implementation paper.

  16. Neural network based system for equipment surveillance

    DOEpatents

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

    1998-04-28

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

  17. Neural network based system for equipment surveillance

    DOEpatents

    Vilim, Richard B.; Gross, Kenneth C.; Wegerich, Stephan W.

    1998-01-01

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

  18. The neural crest: a versatile organ system.

    PubMed

    Zhang, Dongcheng; Ighaniyan, Samiramis; Stathopoulos, Lefteris; Rollo, Benjamin; Landman, Kerry; Hutson, John; Newgreen, Donald

    2014-09-01

    The neural crest is the name given to the strip of cells at the junction between neural and epidermal ectoderm in neurula-stage vertebrate embryos, which is later brought to the dorsal neural tube as the neural folds elevate. The neural crest is a heterogeneous and multipotent progenitor cell population whose cells undergo EMT then extensively and accurately migrate throughout the embryo. Neural crest cells contribute to nearly every organ system in the body, with derivatives of neuronal, glial, neuroendocrine, pigment, and also mesodermal lineages. This breadth of developmental capacity has led to the neural crest being termed the fourth germ layer. The neural crest has occupied a prominent place in developmental biology, due to its exaggerated migratory morphogenesis and its remarkably wide developmental potential. As such, neural crest cells have become an attractive model for developmental biologists for studying these processes. Problems in neural crest development cause a number of human syndromes and birth defects known collectively as neurocristopathies; these include Treacher Collins syndrome, Hirschsprung disease, and 22q11.2 deletion syndromes. Tumors in the neural crest lineage are also of clinical importance, including the aggressive melanoma and neuroblastoma types. These clinical aspects have drawn attention to the selection or creation of neural crest progenitor cells, particularly of human origin, for studying pathologies of the neural crest at the cellular level, and also for possible cell therapeutics. The versatility of the neural crest lends itself to interlinked research, spanning basic developmental biology, birth defect research, oncology, and stem/progenitor cell biology and therapy. PMID:25227568

  19. A regenerative microchannel neural interface for recording from and stimulating peripheral axons in vivo

    NASA Astrophysics Data System (ADS)

    FitzGerald, James J.; Lago, Natalia; Benmerah, Samia; Serra, Jordi; Watling, Christopher P.; Cameron, Ruth E.; Tarte, Edward; Lacour, Stéphanie P.; McMahon, Stephen B.; Fawcett, James W.

    2012-02-01

    Neural interfaces are implanted devices that couple the nervous system to electronic circuitry. They are intended for long term use to control assistive technologies such as muscle stimulators or prosthetics that compensate for loss of function due to injury. Here we present a novel design of interface for peripheral nerves. Recording from axons is complicated by the small size of extracellular potentials and the concentration of current flow at nodes of Ranvier. Confining axons to microchannels of ˜100 µm diameter produces amplified potentials that are independent of node position. After implantation of microchannel arrays into rat sciatic nerve, axons regenerated through the channels forming ‘mini-fascicles’, each typically containing ˜100 myelinated fibres and one or more blood vessels. Regenerated motor axons reconnected to distal muscles, as demonstrated by the recovery of an electromyogram and partial prevention of muscle atrophy. Efferent motor potentials and afferent signals evoked by muscle stretch or cutaneous stimulation were easily recorded from the mini-fascicles and were in the range of 35-170 µV. Individual motor units in distal musculature were activated from channels using stimulus currents in the microampere range. Microchannel interfaces are a potential solution for applications such as prosthetic limb control or enhancing recovery after nerve injury.

  20. A regenerative microchannel neural interface for recording from and stimulating peripheral axons in vivo.

    PubMed

    FitzGerald, James J; Lago, Natalia; Benmerah, Samia; Serra, Jordi; Watling, Christopher P; Cameron, Ruth E; Tarte, Edward; Lacour, Stéphanie P; McMahon, Stephen B; Fawcett, James W

    2012-02-01

    Neural interfaces are implanted devices that couple the nervous system to electronic circuitry. They are intended for long term use to control assistive technologies such as muscle stimulators or prosthetics that compensate for loss of function due to injury. Here we present a novel design of interface for peripheral nerves. Recording from axons is complicated by the small size of extracellular potentials and the concentration of current flow at nodes of Ranvier. Confining axons to microchannels of ~100 µm diameter produces amplified potentials that are independent of node position. After implantation of microchannel arrays into rat sciatic nerve, axons regenerated through the channels forming 'mini-fascicles', each typically containing ~100 myelinated fibres and one or more blood vessels. Regenerated motor axons reconnected to distal muscles, as demonstrated by the recovery of an electromyogram and partial prevention of muscle atrophy. Efferent motor potentials and afferent signals evoked by muscle stretch or cutaneous stimulation were easily recorded from the mini-fascicles and were in the range of 35-170 µV. Individual motor units in distal musculature were activated from channels using stimulus currents in the microampere range. Microchannel interfaces are a potential solution for applications such as prosthetic limb control or enhancing recovery after nerve injury. PMID:22258138

  1. Neural Control of the Immune System

    ERIC Educational Resources Information Center

    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…

  2. Neural network system for traffic flow management

    NASA Astrophysics Data System (ADS)

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

    1992-09-01

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

  3. Using brain-computer interfaces to induce neural plasticity and restore function

    NASA Astrophysics Data System (ADS)

    Grosse-Wentrup, Moritz; Mattia, Donatella; Oweiss, Karim

    2011-04-01

    Analyzing neural signals and providing feedback in realtime is one of the core characteristics of a brain-computer interface (BCI). As this feature may be employed to induce neural plasticity, utilizing BCI technology for therapeutic purposes is increasingly gaining popularity in the BCI community. In this paper, we discuss the state-of-the-art of research on this topic, address the principles of and challenges in inducing neural plasticity by means of a BCI, and delineate the problems of study design and outcome evaluation arising in this context. We conclude with a list of open questions and recommendations for future research in this field.

  4. Biomaterials. Electronic dura mater for long-term multimodal neural interfaces.

    PubMed

    Minev, Ivan R; Musienko, Pavel; Hirsch, Arthur; Barraud, Quentin; Wenger, Nikolaus; Moraud, Eduardo Martin; Gandar, Jérôme; Capogrosso, Marco; Milekovic, Tomislav; Asboth, Léonie; Torres, Rafael Fajardo; Vachicouras, Nicolas; Liu, Qihan; Pavlova, Natalia; Duis, Simone; Larmagnac, Alexandre; Vörös, Janos; Micera, Silvestro; Suo, Zhigang; Courtine, Grégoire; Lacour, Stéphanie P

    2015-01-01

    The mechanical mismatch between soft neural tissues and stiff neural implants hinders the long-term performance of implantable neuroprostheses. Here, we designed and fabricated soft neural implants with the shape and elasticity of dura mater, the protective membrane of the brain and spinal cord. The electronic dura mater, which we call e-dura, embeds interconnects, electrodes, and chemotrodes that sustain millions of mechanical stretch cycles, electrical stimulation pulses, and chemical injections. These integrated modalities enable multiple neuroprosthetic applications. The soft implants extracted cortical states in freely behaving animals for brain-machine interface and delivered electrochemical spinal neuromodulation that restored locomotion after paralyzing spinal cord injury. PMID:25574019

  5. Approaches for the efficient extraction and processing of biopotentials in implantable neural interfacing microsystems.

    PubMed

    Gosselin, Benoit

    2011-01-01

    The accelerating pace of research in neurosciences and rehabilitation engineering has created a considerable demand for implantable microsystems capable of interfacing with large groups of neurons. Such microsystems must provide multiple recording channels incorporating low-noise amplifiers, filters, data converters, neural signal processing circuitry, power management units and low-power transmitters to extract and wirelessly transfer the relevant neural data outside the body for computing and storage. This paper is reviewing several electronic recording strategies to address the challenge of operating large numbers of channels to gather the neural information from several neurons within very low-power constraints. PMID:22255671

  6. iSpike: a spiking neural interface for the iCub robot.

    PubMed

    Gamez, D; Fidjeland, A K; Lazdins, E

    2012-06-01

    This paper presents iSpike: a C++ library that interfaces between spiking neural network simulators and the iCub humanoid robot. It uses a biologically inspired approach to convert the robot's sensory information into spikes that are passed to the neural network simulator, and it decodes output spikes from the network into motor signals that are sent to control the robot. Applications of iSpike range from embodied models of the brain to the development of intelligent robots using biologically inspired spiking neural networks. iSpike is an open source library that is available for free download under the terms of the GPL. PMID:22617339

  7. High-density stretchable microelectrode arrays: An integrated technology platform for neural and muscular surface interfacing

    NASA Astrophysics Data System (ADS)

    Guo, Liang

    2011-12-01

    Numerous applications in neuroscience research and neural prosthetics, such as retinal prostheses, spinal-cord surface stimulation for prosthetics, electrocorticogram (ECoG) recording for epilepsy detection, etc., involve electrical interaction with soft excitable tissues using a surface stimulation and/or recording approach. These applications require an interface that is able to set up electrical communications with a high throughput between electronics and the excitable tissue and that can dynamically conform to the shape of the soft tissue. Being a compliant and biocompatible material with mechanical impedance close to that of soft tissues, polydimethylsiloxane (PDMS) offers excellent potential as the substrate material for such neural interfaces. However, fabrication of electrical functionalities on PDMS has long been very challenging. This thesis work has successfully overcome many challenges associated with PDMS-based microfabrication and achieved an integrated technology platform for PDMS-based stretchable microelectrode arrays (sMEAs). This platform features a set of technological advances: (1) we have fabricated uniform current density profile microelectrodes as small as 10 mum in diameter; (2) we have patterned high-resolution (feature as small as 10 mum), high-density (pitch as small as 20 mum) thin-film gold interconnects on PDMS substrate; (3) we have developed a multilayer wiring interconnect technology within the PDMS substrate to further boost the achievable integration density of such sMEA; and (4) we have invented a bonding technology---via-bonding---to facilitate high-resolution, high-density integration of the sMEA with integrated circuits (ICs) to form a compact implant. Taken together, this platform provides a high-resolution, high-density integrated system solution for neural and muscular surface interfacing. sMEAs of example designs are evaluated through in vitro and in vivo experimentations on their biocompatibility, surface conformability

  8. The Elements Of Adaptive Neural Expert Systems

    NASA Astrophysics Data System (ADS)

    Healy, Michael J.

    1989-03-01

    The generalization properties of a class of neural architectures can be modelled mathematically. The model is a parallel predicate calculus based on pattern recognition and self-organization of long-term memory in a neural network. It may provide the basis for adaptive expert systems capable of inductive learning and rapid processing in a highly complex and changing environment.

  9. Neural control of the immune system

    PubMed Central

    Sundman, Eva

    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 suggested that vagus nerve stimulation can improve symptoms in human rheumatoid arthritis. These discoveries have generated an increased interest in bioelectronic medicine, i.e., therapeutic delivery of electrical impulses that activate nerves to regulate immune system function. Here, we discuss the physiology and potential therapeutic implications of neural immune control. PMID:25039084

  10. A PDMS-based integrated stretchable microelectrode array (isMEA) for neural and muscular surface interfacing.

    PubMed

    Liang Guo; Guvanasen, G S; Xi Liu; Tuthill, C; Nichols, T R; DeWeerth, S P

    2013-02-01

    Numerous applications in neuroscience research and neural prosthetics, such as electrocorticogram (ECoG) recording and retinal prosthesis, involve electrical interactions with soft excitable tissues using a surface recording and/or stimulation approach. These applications require an interface that is capable of setting up high-throughput communications between the electrical circuit and the excitable tissue and that can dynamically conform to the shape of the soft tissue. Being a compliant material with mechanical impedance close to that of soft tissues, polydimethylsiloxane (PDMS) offers excellent potential as a substrate material for such neural interfaces. This paper describes an integrated technology for fabrication of PDMS-based stretchable microelectrode arrays (MEAs). Specifically, as an integral part of the fabrication process, a stretchable MEA is directly fabricated with a rigid substrate, such as a thin printed circuit board (PCB), through an innovative bonding technology-via-bonding-for integrated packaging. This integrated strategy overcomes the conventional challenge of high-density packaging for this type of stretchable electronics. Combined with a high-density interconnect technology developed previously, this stretchable MEA technology facilitates a high-resolution, high-density integrated system solution for neural and muscular surface interfacing. In this paper, this PDMS-based integrated stretchable MEA (isMEA) technology is demonstrated by an example design that packages a stretchable MEA with a small PCB. The resulting isMEA is assessed for its biocompatibility, surface conformability, electrode impedance spectrum, and capability to record muscle fiber activity when applied epimysially. PMID:23853274

  11. Echoes in correlated neural systems

    NASA Astrophysics Data System (ADS)

    Helias, M.; Tetzlaff, T.; Diesmann, M.

    2013-02-01

    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.

  12. Silicon-based wire electrode array for neural interfaces

    NASA Astrophysics Data System (ADS)

    Pei, Weihua; Zhao, Hui; Zhao, Shanshan; Fang, Xiaolei; Chen, Sanyuan; Gui, Qiang; Tang, Rongyu; Chen, Yuanfang; Hong, Bo; Gao, Xiaorong; Chen, Hongda

    2014-09-01

    Objectives. Metal-wire electrode arrays are widely used to record and stimulate neurons. Commonly, these devices are fabricated from a long insulated metal wire by cutting it into the proper length and using the cross-section as the electrode site. The assembly of a micro-wire electrode array with regular spacing is difficult. With the help of micro-machine technology, a silicon-based wire electrode array (SWEA) is proposed to simplify the assembling process and provide a wire-type electrode with tapered tips. Approach. Silicon wires with regular spacing coated with metal are generated from a silicon wafer through micro-fabrication and are ordered into a 3D array. A silicon wafer is cut into a comb-like structure with hexagonal teeth on both sides by anisotropic etching. To establish an array of silicon-based linear needles through isotropic wet etching, the diameters of these hexagonal teeth are reduced; their sharp edges are smoothed out and their tips are sharpened. The needle array is coated with a layer of parylene after metallization. The tips of the needles are then exposed to form an array of linear neural electrodes. With these linear electrode arrays, an array of area electrodes can be fabricated. Main results. A 6  ×  6 array of wire-type electrodes based on silicon is developed using this method. The time required to manually assemble the 3D array decreases significantly with the introduction of micro-fabricated 2D array. Meanwhile, the tip intervals in the 2D array are accurate and are controlled at no more than 1%. The SWEA is effective both in vitro and in vivo. Significance. Using this method, the SWEA can be batch-prepared in advance along with its parameters, such as spacing, length, and diameter. Thus, neural scientists can assemble proper electrode arrays in a short time.

  13. New User Interface Capabilities for Control Systems

    SciTech Connect

    Kasemir, Kay

    2009-01-01

    Latest technologies promise new control system User Interface (UI) features and greater interoperability of applications. New developments using Java and Eclipse aim to unify diverse control systems and make communication between applications seamless. Web based user interfaces can improve portability and remote access. Modern programming tools improve efficiency, support testing and facilitate shared code. This paper will discuss new developments aimed at improving control system interfaces and their development environment.

  14. Optimizing growth and post treatment of diamond for high capacitance neural interfaces.

    PubMed

    Tong, Wei; Fox, Kate; Zamani, Akram; Turnley, Ann M; Ganesan, Kumaravelu; Ahnood, Arman; Cicione, Rosemary; Meffin, Hamish; Prawer, Steven; Stacey, Alastair; Garrett, David J

    2016-10-01

    Electrochemical and biological properties are two crucial criteria in the selection of the materials to be used as electrodes for neural interfaces. For neural stimulation, materials are required to exhibit high capacitance and to form intimate contact with neurons for eliciting effective neural responses at acceptably low voltages. Here we report on a new high capacitance material fabricated using nitrogen included ultrananocrystalline diamond (N-UNCD). After exposure to oxygen plasma for 3 h, the activated N-UNCD exhibited extremely high electrochemical capacitance greater than 1 mF/cm(2), which originates from the special hybrid sp(2)/sp(3) structure of N-UNCD. The in vitro biocompatibility of the activated N-UNCD was then assessed using rat cortical neurons and surface roughness was found to be critical for healthy neuron growth, with best results observed on surfaces with a roughness of approximately 20 nm. Therefore, by using oxygen plasma activated N-UNCD with appropriate surface roughness, and considering the chemical and mechanical stability of diamond, the fabricated neural interfaces are expected to exhibit high efficacy, long-term stability and a healthy neuron/electrode interface. PMID:27424214

  15. Progress of Flexible Electronics in Neural Interfacing - A Self-Adaptive Non-Invasive Neural Ribbon Electrode for Small Nerves Recording.

    PubMed

    Xiang, Zhuolin; Yen, Shih-Cheng; Sheshadri, Swathi; Wang, Jiahui; Lee, Sanghoon; Liu, Yu-Hang; Liao, Lun-De; Thakor, Nitish V; Lee, Chengkuo

    2016-06-01

    A novel flexible neural ribbon electrode with a self-adaptive feature is successfully implemented for various small nerves recording. As a neural interface, the selective recording capability is characterized by having reliable signal acquisitions from the sciatic nerve and its branches such as the peroneal nerve, the tibial nerve, and the sural nerve. PMID:26568483

  16. A Low Noise Amplifier for Neural Spike Recording Interfaces.

    PubMed

    Ruiz-Amaya, Jesus; Rodriguez-Perez, Alberto; Delgado-Restituto, Manuel

    2015-01-01

    This paper presents a Low Noise Amplifier (LNA) for neural spike recording applications. The proposed topology, based on a capacitive feedback network using a two-stage OTA, efficiently solves the triple trade-off between power, area and noise. Additionally, this work introduces a novel transistor-level synthesis methodology for LNAs tailored for the minimization of their noise efficiency factor under area and noise constraints. The proposed LNA has been implemented in a 130 nm CMOS technology and occupies 0.053 mm-sq. Experimental results show that the LNA offers a noise efficiency factor of 2.16 and an input referred noise of 3.8 μVrms for 1.2 V power supply. It provides a gain of 46 dB over a nominal bandwidth of 192 Hz-7.4 kHz and consumes 1.92 μW. The performance of the proposed LNA has been validated through in vivo experiments with animal models. PMID:26437411

  17. A Low Noise Amplifier for Neural Spike Recording Interfaces

    PubMed Central

    Ruiz-Amaya, Jesus; Rodriguez-Perez, Alberto; Delgado-Restituto, Manuel

    2015-01-01

    This paper presents a Low Noise Amplifier (LNA) for neural spike recording applications. The proposed topology, based on a capacitive feedback network using a two-stage OTA, efficiently solves the triple trade-off between power, area and noise. Additionally, this work introduces a novel transistor-level synthesis methodology for LNAs tailored for the minimization of their noise efficiency factor under area and noise constraints. The proposed LNA has been implemented in a 130 nm CMOS technology and occupies 0.053 mm-sq. Experimental results show that the LNA offers a noise efficiency factor of 2.16 and an input referred noise of 3.8 μVrms for 1.2 V power supply. It provides a gain of 46 dB over a nominal bandwidth of 192 Hz–7.4 kHz and consumes 1.92 μW. The performance of the proposed LNA has been validated through in vivo experiments with animal models. PMID:26437411

  18. Bias voltages at microelectrodes change neural interface properties in vivo.

    PubMed

    Johnson, M D; Otto, K J; Williams, J C; Kipke, D R

    2004-01-01

    Rejuvenation of iridium microelectrode sites, which involves applying a 1.5 V bias for 4 s, has been shown to reduce site impedances of chronically implanted microelectrode arrays. This study applied complex impedance spectroscopy measurements to an equivalent circuit model of the electrode-tissue interface. Rejuvenation was found to cause a transient increase in electrode conductivity through an IrO2 layer and a decrease in the surrounding extracellular resistance by 85 +/- 1% (n=73, t-test p < 0.001) and a decrease in the immediate site resistance by 44 +/- 7% (n=73, t-test p<0.001). These findings may be useful as an intervention strategy to prolong the lifetime of chronic microelectrode implants for neuroprostheses. PMID:17271203

  19. Intelligent subsystem interface for modular hardware system

    NASA Technical Reports Server (NTRS)

    Krening, Douglas N. (Inventor); Lannan, Gregory B. (Inventor); Schneiderwind, Michael J. (Inventor); Schneiderwind, Robert A. (Inventor); Caffrey, Robert T. (Inventor)

    2000-01-01

    A single chip application specific integrated circuit (ASIC) which provides a flexible, modular interface between a subsystem and a standard system bus. The ASIC includes a microcontroller/microprocessor, a serial interface for connection to the bus, and a variety of communications interface devices available for coupling to the subsystem. A three-bus architecture, utilizing arbitration, provides connectivity within the ASIC and between the ASIC and the subsystem. The communication interface devices include UART (serial), parallel, analog, and external device interface utilizing bus connections paired with device select signals. A low power (sleep) mode is provided as is a processor disable option.

  20. Diagnosing Parkinson's Diseases Using Fuzzy Neural System

    PubMed Central

    Abiyev, Rahib H.; Abizade, Sanan

    2016-01-01

    This study presents the design of the recognition system that will discriminate between healthy people and people with Parkinson's disease. A diagnosing of Parkinson's diseases is performed using fusion of the fuzzy system and neural networks. The structure and learning algorithms of the proposed fuzzy neural system (FNS) are presented. The approach described in this paper allows enhancing the capability of the designed system and efficiently distinguishing healthy individuals. It was proved through simulation of the system that has been performed using data obtained from UCI machine learning repository. A comparative study was carried out and the simulation results demonstrated that the proposed fuzzy neural system improves the recognition rate of the designed system. PMID:26881009

  1. Layered carbon nanotube-polyelectrolyte electrodes outperform traditional neural interface materials.

    PubMed

    Jan, Edward; Hendricks, Jeffrey L; Husaini, Vincent; Richardson-Burns, Sarah M; Sereno, Andrew; Martin, David C; Kotov, Nicholas A

    2009-12-01

    The safety, function, and longevity of implantable neuroprosthetic and cardiostimulating electrodes depend heavily on the electrical properties of the electrode-tissue interface, which in many cases requires substantial improvement. While different variations of carbon nanotube materials have been shown to be suitable for neural excitation, it is critical to evaluate them versus other materials used for bioelectrical interfacing, which have not been done in any study performed so far despite strong interest to this area. In this study, we carried out this evaluation and found that composite multiwalled carbon nanotube-polyelectrolyte (MWNT-PE) multilayer electrodes substantially outperform in one way or the other state-of-the-art neural interface materials available today, namely activated electrochemically deposited iridium oxide (IrOx) and poly(3,4-ethylenedioxythiophene) (PEDOT). Our findings provide the concrete experimental proof to the much discussed possibility that carbon nanotube composites can serve as excellent new material for neural interfacing with a strong possibility to lead to a new generation of implantable electrodes. PMID:19785391

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

    PubMed Central

    Benyamini, Miri; Zacksenhouse, Miriam

    2015-01-01

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

  3. Dynamics and kinematics of simple neural systems

    SciTech Connect

    Rabinovich, M. |; Selverston, A.; Rubchinsky, L.; Huerta, R.

    1996-09-01

    The dynamics of simple neural systems is of interest to both biologists and physicists. One of the possible roles of such systems is the production of rhythmic patterns, and their alterations (modification of behavior, processing of sensory information, adaptation, control). In this paper, the neural systems are considered as a subject of modeling by the dynamical systems approach. In particular, we analyze how a stable, ordinary behavior of a small neural system can be described by simple finite automata models, and how more complicated dynamical systems modeling can be used. The approach is illustrated by biological and numerical examples: experiments with and numerical simulations of the stomatogastric central pattern generators network of the California spiny lobster. {copyright} {ital 1996 American Institute of Physics.}

  4. Dynamics and kinematics of simple neural systems

    NASA Astrophysics Data System (ADS)

    Rabinovich, Mikhail; Selverston, Allen; Rubchinsky, Leonid; Huerta, Ramón

    1996-09-01

    The dynamics of simple neural systems is of interest to both biologists and physicists. One of the possible roles of such systems is the production of rhythmic patterns, and their alterations (modification of behavior, processing of sensory information, adaptation, control). In this paper, the neural systems are considered as a subject of modeling by the dynamical systems approach. In particular, we analyze how a stable, ordinary behavior of a small neural system can be described by simple finite automata models, and how more complicated dynamical systems modeling can be used. The approach is illustrated by biological and numerical examples: experiments with and numerical simulations of the stomatogastric central pattern generators network of the California spiny lobster.

  5. Interfaces for Distributed Systems of Information Servers.

    ERIC Educational Resources Information Center

    Kahle, Brewster; And Others

    1992-01-01

    Describes two systems--Wide Area Information Servers (WAIS) and Rosebud--that provide protocol-based mechanisms for accessing remote full-text information servers. Design constraints, human interface design, and implementation are examined for five interfaces to these systems developed to run on the Macintosh or Unix terminals. Sample screen…

  6. Human-system Interfaces for Automatic Systems

    SciTech Connect

    OHara, J.M.; Higgins,J.; Fleger, S.; Barnes V.

    2010-11-07

    Automation is ubiquitous in modern complex systems, and commercial nuclear- power plants are no exception. Automation is applied to a wide range of functions including monitoring and detection, situation assessment, response planning, and response implementation. Automation has become a 'team player' supporting personnel in nearly all aspects of system operation. In light of its increasing use and importance in new- and future-plants, guidance is needed to conduct safety reviews of the operator's interface with automation. The objective of this research was to develop such guidance. We first characterized the important HFE aspects of automation, including six dimensions: levels, functions, processes, modes, flexibility, and reliability. Next, we reviewed literature on the effects of all of these aspects of automation on human performance, and on the design of human-system interfaces (HSIs). Then, we used this technical basis established from the literature to identify general principles for human-automation interaction and to develop review guidelines. The guidelines consist of the following seven topics: automation displays, interaction and control, automation modes, automation levels, adaptive automation, error tolerance and failure management, and HSI integration. In addition, our study identified several topics for additional research.

  7. A flexible microchannel electrode array for peripheral nerves to interface with neural prosthetics

    NASA Astrophysics Data System (ADS)

    Landrith, Ryan; Nothnagle, Caleb; Kim, Young-tae; Wijesundara, Muthu B. J.

    2016-05-01

    In order to control neural prosthetics by recording signals from peripheral nerves with the required specificity, high density electrode arrays that can be easily implanted on very small peripheral nerves (50μm-500μm) are needed. Interfacing with these small nerves is surgically challenging due to their size and fragile nature. To address this problem, a Flexible MicroChannel Electrode Array for interfacing with small diameter peripheral nerves and nerve fascicles was developed. The electrochemical characterization and electrophysiological recordings from the common peroneal nerve of a rat are presented along with demonstration of the surgical ease-of-use of the array.

  8. Switchable Polymer Based Thin Film Coils as a Power Module for Wireless Neural Interfaces

    PubMed Central

    Kim, S.; Zoschke, K.; Klein, M.; Black, D.; Buschick, K.; Toepper, M.; Tathireddy, P.; Harrison, R.; Solzbacher, F.

    2008-01-01

    Reliable chronic operation of implantable medical devices such as the Utah Electrode Array (UEA) for neural interface requires elimination of transcutaneous wire connections for signal processing, powering and communication of the device. A wireless power source that allows integration with the UEA is therefore necessary. While (rechargeable) micro batteries as well as biological micro fuel cells are yet far from meeting the power density and lifetime requirements of an implantable neural interface device, inductive coupling between two coils is a promising approach to power such a device with highly restricted dimensions. The power receiving coils presented in this paper were designed to maximize the inductance and quality factor of the coils and microfabricated using polymer based thin film technologies. A flexible configuration of stacked thin film coils allows parallel and serial switching, thereby allowing to tune the coil’s resonance frequency. The electrical properties of the fabricated coils were characterized and their power transmission performance was investigated in laboratory condition. PMID:18438447

  9. Multimodal optogenetic neural interfacing device fabricated by scalable optical fiber drawing technique.

    PubMed

    Davey, Christopher J; Argyros, Alexander; Fleming, Simon C; Solomon, Samuel G

    2015-12-01

    We present a novel approach to the design and manufacture of optrodes for use in the biomedical research field of optogenetic neural interfacing. Using recently developed optical fiber drawing techniques that involve co-drawing metal/polymer composite fiber, we have assembled and characterized a novel optrode with promising optical and electrical functionality. The fabrication technique is flexible, scalable, and amenable to extension to implantable optrodes with high-density arrays of multiple electrodes, waveguides, and drug delivery channels. PMID:26836662

  10. Development of the neural network algorithm projecting system Neural Architecture and its application in combining medical expert systems

    NASA Astrophysics Data System (ADS)

    Timofeew, Sergey; Eliseev, Vladimir; Tcherkassov, Oleg; Birukow, Valentin; Orbachevskyi, Leonid; Shamsutdinov, Uriy

    1998-04-01

    Some problems of creation of medical expert systems and the ways of their overcoming using artificial neural networks are discussed. The instrumental system for projecting neural network algorithms `Neural Architector', developed by the authors, is described. It allows to perform effective modeling of artificial neural networks and to analyze their work. The example of the application of the `Neural Architector' system in composing an expert system for diagnostics of pulmonological diseases is shown.

  11. On Building a Search Interface Discovery System

    NASA Astrophysics Data System (ADS)

    Shestakov, Denis

    A huge portion of the Web known as the deep Web is accessible via search interfaces to myriads of databases on the Web. While relatively good approaches for querying the contents of web databases have been recently proposed, one cannot fully utilize them having most search interfaces unlocated. Thus, the automatic recognition of search interfaces to online databases is crucial for any application accessing the deep Web. This paper describes the architecture of the I-Crawler, a system for finding and classifying search interfaces. The I-Crawler is intentionally designed to be used in the deep web characterization surveys and for constructing directories of deep web resources.

  12. Methods for estimating neural firing rates, and their application to brain-machine interfaces.

    PubMed

    Cunningham, John P; Gilja, Vikash; Ryu, Stephen I; Shenoy, Krishna V

    2009-11-01

    Neural spike trains present analytical challenges due to their noisy, spiking nature. Many studies of neuroscientific and neural prosthetic importance rely on a smoothed, denoised estimate of a spike train's underlying firing rate. Numerous methods for estimating neural firing rates have been developed in recent years, but to date no systematic comparison has been made between them. In this study, we review both classic and current firing rate estimation techniques. We compare the advantages and drawbacks of these methods. Then, in an effort to understand their relevance to the field of neural prostheses, we also apply these estimators to experimentally gathered neural data from a prosthetic arm-reaching paradigm. Using these estimates of firing rate, we apply standard prosthetic decoding algorithms to compare the performance of the different firing rate estimators, and, perhaps surprisingly, we find minimal differences. This study serves as a review of available spike train smoothers and a first quantitative comparison of their performance for brain-machine interfaces. PMID:19349143

  13. Microchannel neural interface manufacture by stacking silicone and metal foil laminae

    NASA Astrophysics Data System (ADS)

    Lancashire, Henry T.; Vanhoestenberghe, Anne; Pendegrass, Catherine J.; Ajam, Yazan Al; Magee, Elliot; Donaldson, Nick; Blunn, Gordon W.

    2016-06-01

    Objective. Microchannel neural interfaces (MNIs) overcome problems with recording from peripheral nerves by amplifying signals independent of node of Ranvier position. Selective recording and stimulation using an MNI requires good insulation between microchannels and a high electrode density. We propose that stacking microchannel laminae will improve selectivity over single layer MNI designs due to the increase in electrode number and an improvement in microchannel sealing. Approach. This paper describes a manufacturing method for creating MNIs which overcomes limitations on electrode connectivity and microchannel sealing. Laser cut silicone—metal foil laminae were stacked using plasma bonding to create an array of microchannels containing tripolar electrodes. Electrodes were DC etched and electrode impedance and cyclic voltammetry were tested. Main results. MNIs with 100 μm and 200 μm diameter microchannels were manufactured. High electrode density MNIs are achievable with electrodes present in every microchannel. Electrode impedances of 27.2 ± 19.8 kΩ at 1 kHz were achieved. Following two months of implantation in Lewis rat sciatic nerve, micro-fascicles were observed regenerating through the MNI microchannels. Significance. Selective MNIs with the peripheral nervous system may allow upper limb amputees to control prostheses intuitively.

  14. Stability of the interface between neural tissue and chronically implanted intracortical microelectrodes.

    PubMed

    Liu, X; McCreery, D B; Carter, R R; Bullara, L A; Yuen, T G; Agnew, W F

    1999-09-01

    The stability of the interface between neural tissue and chronically implanted microelectrodes is very important for obtaining reliable control signals for neuroprosthetic devices. Stability is also crucial for chronic microstimulation of the cerebral cortex. However, changes of the electrode-tissue interface can be caused by a variety of mechanisms. In the present study, intracortical microelectrode arrays were implanted into the pericruciate gyrus of cats and neural activities were recorded on a regular basis for several months. An algorithm based on cluster analysis and interspike interval analysis was developed to sort the extracellular action potentials into single units. We tracked these units based on their waveform and their response to somatic stimulation or stereotypical movements by the cats. Our results indicate that, after implantation, the electrode-tissue interface may change from day-to-day over the first 1-2 weeks, week-to-week for 1-2 months, and become quite stable thereafter. A stability index is proposed to quantify the stability of the electrode-tissue interface. The reasons for the pattern of changes are discussed. PMID:10498377

  15. Concept of software interface for BCI systems

    NASA Astrophysics Data System (ADS)

    Svejda, Jaromir; Zak, Roman; Jasek, Roman

    2016-06-01

    Brain Computer Interface (BCI) technology is intended to control external system by brain activity. One of main part of such system is software interface, which carries about clear communication between brain and either computer or additional devices connected to computer. This paper is organized as follows. Firstly, current knowledge about human brain is briefly summarized to points out its complexity. Secondly, there is described a concept of BCI system, which is then used to build an architecture of proposed software interface. Finally, there are mentioned disadvantages of sensing technology discovered during sensing part of our research.

  16. Kannada character recognition system using neural network

    NASA Astrophysics Data System (ADS)

    Kumar, Suresh D. S.; Kamalapuram, Srinivasa K.; Kumar, Ajay B. R.

    2013-03-01

    Handwriting recognition has been one of the active and challenging research areas in the field of pattern recognition. It has numerous applications which include, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. As there is no sufficient number of works on Indian language character recognition especially Kannada script among 15 major scripts in India. In this paper an attempt is made to recognize handwritten Kannada characters using Feed Forward neural networks. A handwritten Kannada character is resized into 20x30 Pixel. The resized character is used for training the neural network. Once the training process is completed the same character is given as input to the neural network with different set of neurons in hidden layer and their recognition accuracy rate for different Kannada characters has been calculated and compared. The results show that the proposed system yields good recognition accuracy rates comparable to that of other handwritten character recognition systems.

  17. Parietal Neural Prosthetic Control of a Computer Cursor in a Graphical-User-Interface Task

    PubMed Central

    Revechkis, Boris; Aflalo, Tyson NS; Kellis, Spencer; Pouratian, Nader; Andersen, Richard A

    2014-01-01

    Objective To date, the majority of Brain Machine Interfaces have been used to perform simple tasks with sequences of individual targets in otherwise blank environments. In this study we developed a more practical and clinically relevant task that approximated modern computers and graphical user interfaces (GUIs). This task could be problematic given the known sensitivity of areas typically used for BMIs to visual stimuli, eye movements, decision-making, and attentional control. Consequently, we sought to assess the effect of a complex, GUI-like task on the quality of neural decoding. Approach A male rhesus macaque monkey was implanted with two 96-channel electrode arrays in Area 5d of the superior parietal lobule. The animal was trained to perform a GUI-like “Face in a Crowd” task on a computer screen that required selecting one cued, icon-like, face image from a group of alternatives (the “Crowd”) using a neurally controlled cursor. We assessed whether the Crowd affected decodes of intended cursor movements by comparing it to a “Crowd Off” condition in which only the matching target appeared without alternatives. We also examined if training a neural decoder with the Crowd On rather than Off had any effect on subsequent decode quality. Main Results Despite the additional demands of working with the Crowd On, the animal was able to robustly perform the task under Brain Control. The presence of the Crowd did not itself affect decode quality. Training the decoder with the Crowd On relative to Off had no negative influence on subsequent decoding performance. Additionally, the subject was able to gaze around freely without influencing cursor position. Significance Our results demonstrate that area 5d recordings can be used for decoding in a complex, GUI-like task with free gaze. Thus, this area is a promising source of signals for neural prosthetics that utilize computing devices with GUI interfaces, e.g. personal computers, mobile devices, and tablet

  18. Parietal neural prosthetic control of a computer cursor in a graphical-user-interface task

    NASA Astrophysics Data System (ADS)

    Revechkis, Boris; Aflalo, Tyson NS; Kellis, Spencer; Pouratian, Nader; Andersen, Richard A.

    2014-12-01

    Objective. To date, the majority of Brain-Machine Interfaces have been used to perform simple tasks with sequences of individual targets in otherwise blank environments. In this study we developed a more practical and clinically relevant task that approximated modern computers and graphical user interfaces (GUIs). This task could be problematic given the known sensitivity of areas typically used for BMIs to visual stimuli, eye movements, decision-making, and attentional control. Consequently, we sought to assess the effect of a complex, GUI-like task on the quality of neural decoding. Approach. A male rhesus macaque monkey was implanted with two 96-channel electrode arrays in area 5d of the superior parietal lobule. The animal was trained to perform a GUI-like ‘Face in a Crowd’ task on a computer screen that required selecting one cued, icon-like, face image from a group of alternatives (the ‘Crowd’) using a neurally controlled cursor. We assessed whether the crowd affected decodes of intended cursor movements by comparing it to a ‘Crowd Off’ condition in which only the matching target appeared without alternatives. We also examined if training a neural decoder with the Crowd On rather than Off had any effect on subsequent decode quality. Main results. Despite the additional demands of working with the Crowd On, the animal was able to robustly perform the task under Brain Control. The presence of the crowd did not itself affect decode quality. Training the decoder with the Crowd On relative to Off had no negative influence on subsequent decoding performance. Additionally, the subject was able to gaze around freely without influencing cursor position. Significance. Our results demonstrate that area 5d recordings can be used for decoding in a complex, GUI-like task with free gaze. Thus, this area is a promising source of signals for neural prosthetics that utilize computing devices with GUI interfaces, e.g. personal computers, mobile devices, and tablet

  19. Electronic Neural Networks

    NASA Technical Reports Server (NTRS)

    Thakoor, Anil

    1990-01-01

    Viewgraphs on electronic neural networks for space station are presented. Topics covered include: electronic neural networks; electronic implementations; VLSI/thin film hybrid hardware for neurocomputing; computations with analog parallel processing; features of neuroprocessors; applications of neuroprocessors; neural network hardware for terrain trafficability determination; a dedicated processor for path planning; neural network system interface; neural network for robotic control; error backpropagation algorithm for learning; resource allocation matrix; global optimization neuroprocessor; and electrically programmable read only thin-film synaptic array.

  20. Integrated Neural Flight and Propulsion Control System

    NASA Technical Reports Server (NTRS)

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

    2001-01-01

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

  1. Interfacing the human into information systems

    NASA Astrophysics Data System (ADS)

    Santos, Eugene, Jr.; Brown, Scott M.

    2000-03-01

    The current state of user interfaces for large information spaces imposes an unmanageable cognitive burden upon the user. Determining how to get the right information into the right form with the right tool at the right time has become a monumental task. Interface agents address the problem of increasing task load by serving as either an assistant or associate, extracting and analyzing relevant information, providing information abstractions of that information, and providing timely, beneficial assistance to suers. Interface agents communicate with the user through the existing user interface and also adapt to user needs and behaviors. User modeling, on the other hand, is concerned with how to represent users' knowledge and interaction within a system to adapt the system to the needs of users. The inclusion of a user model within the overall system architecture allows the system to adapt its response to the preferences, biases, expertise level, goals and needs.

  2. Hypocretins, Neural Systems, Physiology, and Psychiatric Disorders.

    PubMed

    Li, Shi-Bin; Jones, Jeff R; de Lecea, Luis

    2016-01-01

    The hypocretins (Hcrts), also known as orexins, have been among the most intensely studied neuropeptide systems since their discovery about two decades ago. Anatomical evidence shows that the hypothalamic neurons that produce hypocretins/orexins project widely throughout the entire brain, innervating the noradrenergic locus coeruleus, the cholinergic basal forebrain, the dopaminergic ventral tegmental area, the serotonergic raphe nuclei, the histaminergic tuberomammillary nucleus, and many other brain regions. By interacting with other neural systems, the Hcrt system profoundly modulates versatile physiological processes including arousal, food intake, emotion, attention, and reward. Importantly, interruption of the interactions between these systems has the potential to cause neurological and psychiatric diseases. Here, we review the modulation of diverse neural systems by Hcrts and summarize potential therapeutic strategies based on our understanding of the Hcrt system's role in physiology and pathophysiological processes. PMID:26733323

  3. The PennBMBI: Design of a General Purpose Wireless Brain-Machine-Brain Interface System.

    PubMed

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

    2015-04-01

    In this paper, a general purpose wireless Brain-Machine-Brain Interface (BMBI) system is presented. The system integrates four battery-powered wireless devices for the implementation of a closed-loop sensorimotor neural interface, including a neural signal analyzer, a neural stimulator, a body-area sensor node and a graphic user interface implemented on the PC end. The neural signal analyzer features a four channel analog front-end with configurable bandpass filter, gain stage, digitization resolution, and sampling rate. The target frequency band is configurable from EEG to single unit activity. A noise floor of 4.69 μVrms is achieved over a bandwidth from 0.05 Hz to 6 kHz. Digital filtering, neural feature extraction, spike detection, sensing-stimulating modulation, and compressed sensing measurement are realized in a central processing unit integrated in the analyzer. A flash memory card is also integrated in the analyzer. A 2-channel neural stimulator with a compliance voltage up to ± 12 V is included. The stimulator is capable of delivering unipolar or bipolar, charge-balanced current pulses with programmable pulse shape, amplitude, width, pulse train frequency and latency. A multi-functional sensor node, including an accelerometer, a temperature sensor, a flexiforce sensor and a general sensor extension port has been designed. A computer interface is designed to monitor, control and configure all aforementioned devices via a wireless link, according to a custom designed communication protocol. Wireless closed-loop operation between the sensory devices, neural stimulator, and neural signal analyzer can be configured. The proposed system was designed to link two sites in the brain, bridging the brain and external hardware, as well as creating new sensory and motor pathways for clinical practice. Bench test and in vivo experiments are performed to verify the functions and performances of the system. PMID:25769171

  4. A lysinated thiophene-based semiconductor as a multifunctional neural bioorganic interface.

    PubMed

    Bonetti, Simone; Pistone, Assunta; Brucale, Marco; Karges, Saskia; Favaretto, Laura; Zambianchi, Massimo; Posati, Tamara; Sagnella, Anna; Caprini, Marco; Toffanin, Stefano; Zamboni, Roberto; Camaioni, Nadia; Muccini, Michele; Melucci, Manuela; Benfenati, Valentina

    2015-06-01

    Lysinated molecular organic semiconductors are introduced as valuable multifunctional platforms for neural cells growth and interfacing. Cast films of quaterthiophene (T4) semiconductor covalently modified with lysine-end moieties (T4Lys) are fabricated and their stability, morphology, optical/electrical, and biocompatibility properties are characterized. T4Lys films exhibit fluorescence and electronic transport as generally observed for unsubstituted oligothiophenes combined to humidity-activated ionic conduction promoted by the charged lysine-end moieties. The Lys insertion in T4 enables adhesion of primary culture of rat dorsal root ganglion (DRG), which is not achievable by plating cells on T4. Notably, on T4Lys, the number on adhering neurons/area is higher and displays a twofold longer neurite length than neurons plated on glass coated with poly-l-lysine. Finally, by whole-cell patch-clamp, it is shown that the biofunctionality of neurons cultured on T4Lys is preserved. The present study introduces an innovative concept for organic material neural interface that combines optical and iono-electronic functionalities with improved biocompatibility and neuron affinity promoted by Lys linkage and the softness of organic semiconductors. Lysinated organic semiconductors could set the scene for the fabrication of simplified bioorganic devices geometry for cells bidirectional communication or optoelectronic control of neural cells biofunctionality. PMID:25721438

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

    PubMed

    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. PMID:23853339

  6. Accelerating bioelectric functional development of neural stem cells by graphene coupling: Implications for neural interfacing with conductive materials.

    PubMed

    Guo, Rongrong; Zhang, Shasha; Xiao, Miao; Qian, Fuping; He, Zuhong; Li, Dan; Zhang, Xiaoli; Li, Huawei; Yang, Xiaowei; Wang, Ming; Chai, Renjie; Tang, Mingliang

    2016-11-01

    In order to govern cell-specific behaviors in tissue engineering for neural repair and regeneration, a better understanding of material-cell interactions, especially the bioelectric functions, is extremely important. Graphene has been reported to be a potential candidate for use as a scaffold and neural interfacing material. However, the bioelectric evolvement of cell membranes on these conductive graphene substrates remains largely uninvestigated. In this study, we used a neural stem cell (NSC) model to explore the possible changes in membrane bioelectric properties - including resting membrane potentials and action potentials - and cell behaviors on graphene films under both proliferation and differentiation conditions. We used a combination of single-cell electrophysiological recordings and traditional cell biology techniques. Graphene did not affect the basic membrane electrical parameters (capacitance and input resistance), but resting membrane potentials of cells on graphene substrates were more strongly negative under both proliferation and differentiation conditions. Also, NSCs and their progeny on graphene substrates exhibited increased firing of action potentials during development compared to controls. However, graphene only slightly affected the electric characterizations of mature NSC progeny. The modulation of passive and active bioelectric properties on the graphene substrate was accompanied by enhanced NSC differentiation. Furthermore, spine density, synapse proteins expressions and synaptic activity were all increased in graphene group. Modeling of the electric field on conductive graphene substrates suggests that the electric field produced by the electronegative cell membrane is much higher on graphene substrates than that on control, and this might explain the observed changes of bioelectric development by graphene coupling. Our results indicate that graphene is able to accelerate NSC maturation during development, especially with regard to

  7. NeuroArray: a universal interface for patterning and interrogating neural circuitry with single cell resolution.

    PubMed

    Li, Wei; Xu, Zhen; Huang, Junzhe; Lin, Xudong; Luo, Rongcong; Chen, Chia-Hung; Shi, Peng

    2014-01-01

    Recreation of neural network in vitro with designed topology is a valuable tool to decipher how neurons behave when interacting in hierarchical networks. In this study, we developed a simple and effective platform to pattern primary neurons in array formats for interrogation of neural circuitry with single cell resolution. Unlike many surface-chemistry-based patterning methods, our NeuroArray technique is specially designed to accommodate neuron's polarized morphologies to make regular arrays of cells without restricting their neurite outgrowth, and thus allows formation of freely designed, well-connected, and spontaneously active neural network. The NeuroArray device was based on a stencil design fabricated using a novel sacrificial-layer-protected PDMS molding method that enables production of through-structures in a thin layer of PDMS with feature sizes as small as 3 µm. Using the NeuroArray along with calcium imaging, we have successfully demonstrated large-scale tracking and recording of neuronal activities, and used such data to characterize the spiking dynamics and transmission within a diode-like neural network. Essentially, the NeuroArray is a universal patterning platform designed for, but not limited to neuron cells. With little adaption, it can be readily interfaced with other interrogation modalities for high-throughput drug testing, and for building neuron culture based live computational devices. PMID:24759264

  8. NeuroArray: A Universal Interface for Patterning and Interrogating Neural Circuitry with Single Cell Resolution

    NASA Astrophysics Data System (ADS)

    Li, Wei; Xu, Zhen; Huang, Junzhe; Lin, Xudong; Luo, Rongcong; Chen, Chia-Hung; Shi, Peng

    2014-04-01

    Recreation of neural network in vitro with designed topology is a valuable tool to decipher how neurons behave when interacting in hierarchical networks. In this study, we developed a simple and effective platform to pattern primary neurons in array formats for interrogation of neural circuitry with single cell resolution. Unlike many surface-chemistry-based patterning methods, our NeuroArray technique is specially designed to accommodate neuron's polarized morphologies to make regular arrays of cells without restricting their neurite outgrowth, and thus allows formation of freely designed, well-connected, and spontaneously active neural network. The NeuroArray device was based on a stencil design fabricated using a novel sacrificial-layer-protected PDMS molding method that enables production of through-structures in a thin layer of PDMS with feature sizes as small as 3 µm. Using the NeuroArray along with calcium imaging, we have successfully demonstrated large-scale tracking and recording of neuronal activities, and used such data to characterize the spiking dynamics and transmission within a diode-like neural network. Essentially, the NeuroArray is a universal patterning platform designed for, but not limited to neuron cells. With little adaption, it can be readily interfaced with other interrogation modalities for high-throughput drug testing, and for building neuron culture based live computational devices.

  9. An optical neural interface: in vivo control of rodent motor cortex with integrated fiberoptic and optogenetic technology

    NASA Astrophysics Data System (ADS)

    Aravanis, Alexander M.; Wang, Li-Ping; Zhang, Feng; Meltzer, Leslie A.; Mogri, Murtaza Z.; Schneider, M. Bret; Deisseroth, Karl

    2007-09-01

    Neural interface technology has made enormous strides in recent years but stimulating electrodes remain incapable of reliably targeting specific cell types (e.g. excitatory or inhibitory neurons) within neural tissue. This obstacle has major scientific and clinical implications. For example, there is intense debate among physicians, neuroengineers and neuroscientists regarding the relevant cell types recruited during deep brain stimulation (DBS); moreover, many debilitating side effects of DBS likely result from lack of cell-type specificity. We describe here a novel optical neural interface technology that will allow neuroengineers to optically address specific cell types in vivo with millisecond temporal precision. Channelrhodopsin-2 (ChR2), an algal light-activated ion channel we developed for use in mammals, can give rise to safe, light-driven stimulation of CNS neurons on a timescale of milliseconds. Because ChR2 is genetically targetable, specific populations of neurons even sparsely embedded within intact circuitry can be stimulated with high temporal precision. Here we report the first in vivo behavioral demonstration of a functional optical neural interface (ONI) in intact animals, involving integrated fiberoptic and optogenetic technology. We developed a solid-state laser diode system that can be pulsed with millisecond precision, outputs 20 mW of power at 473 nm, and is coupled to a lightweight, flexible multimode optical fiber, ~200 µm in diameter. To capitalize on the unique advantages of this system, we specifically targeted ChR2 to excitatory cells in vivo with the CaMKIIα promoter. Under these conditions, the intensity of light exiting the fiber (~380 mW mm-2) was sufficient to drive excitatory neurons in vivo and control motor cortex function with behavioral output in intact rodents. No exogenous chemical cofactor was needed at any point, a crucial finding for in vivo work in large mammals. Achieving modulation of behavior with optical control of

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

    NASA Technical Reports Server (NTRS)

    Curtis, Steven A. (Inventor)

    2011-01-01

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

  11. The quantum human central neural system.

    PubMed

    Alexiou, Athanasios; Rekkas, John

    2015-01-01

    In this chapter we present Excess Entropy Production for human aging system as the sum of their respective subsystems and electrophysiological status. Additionally, we support the hypothesis of human brain and central neural system quantumness and we strongly suggest the theoretical and philosophical status of human brain as one of the unknown natural Dirac magnetic monopoles placed in the center of a Riemann sphere. PMID:25416114

  12. Coal-shale interface detection system

    NASA Technical Reports Server (NTRS)

    Campbell, R. A.; Hudgins, J. L.; Morris, P. W.; Reid, H., Jr.; Zimmerman, J. E. (Inventor)

    1979-01-01

    A coal-shale interface detection system for use with coal cutting equipment consists of a reciprocating hammer on which an accelerometer is mounted to measure the impact of the hammer as it penetrates the ceiling or floor surface of a mine. A pair of reflectometers simultaneously view the same surface. The outputs of the accelerometer and reflectometers are detected and jointly registered to determine when an interface between coal and shale is being cut through.

  13. Analysis of complex systems using neural networks

    SciTech Connect

    Uhrig, R.E. . Dept. of Nuclear Engineering Oak Ridge National Lab., TN )

    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.

  14. Analysis of complex systems using neural networks

    SciTech Connect

    Uhrig, R.E. |

    1992-12-31

    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.

  15. An in vitro demonstration of CMOS-based optoelectronic neural interface device for optogenetics.

    PubMed

    Tokuda, T; Nakajima, S; Maezawa, Y; Noda, T; Sasagawa, K; Ishikawa, Y; Shiosaka, S; Ohta, J

    2013-01-01

    A CMOS-based neural interface device equipped with an integrated micro light source array for optogenetics was fabricated and demonstrated. A GaInN LED array formed on sapphire substrate was successfully assembled with a multifunctional CMOS image sensor that is capable of on-chip current injection. We demonstrated a functionality of light stimulation onto ChR2-expressed cells in an in vitro experiment. A ChR2-expressed cell were successfully stimulated with the light emitted from the fabricated device. PMID:24109808

  16. Decoding a new neural machine interface for control of artificial limbs.

    PubMed

    Zhou, Ping; Lowery, Madeleine M; Englehart, Kevin B; Huang, He; Li, Guanglin; Hargrove, Levi; Dewald, Julius P A; Kuiken, Todd A

    2007-11-01

    An analysis of the motor control information content made available with a neural-machine interface (NMI) in four subjects is presented in this study. We have developed a novel NMI-called targeted muscle reinnervation (TMR)-to improve the function of artificial arms for amputees. TMR involves transferring the residual amputated nerves to nonfunctional muscles in amputees. The reinnervated muscles act as biological amplifiers of motor commands in the amputated nerves and the surface electromyogram (EMG) can be used to enhance control of a robotic arm. Although initial clinical success with TMR has been promising, the number of degrees of freedom of the robotic arm that can be controlled has been limited by the number of reinnervated muscle sites. In this study we assess how much control information can be extracted from reinnervated muscles using high-density surface EMG electrode arrays to record surface EMG signals over the reinnervated muscles. We then applied pattern classification techniques to the surface EMG signals. High accuracy was achieved in the classification of 16 intended arm, hand, and finger/thumb movements. Preliminary analyses of the required number of EMG channels and computational demands demonstrate clinical feasibility of these methods. This study indicates that TMR combined with pattern-recognition techniques has the potential to further improve the function of prosthetic limbs. In addition, the results demonstrate that the central motor control system is capable of eliciting complex efferent commands for a missing limb, in the absence of peripheral feedback and without retraining of the pathways involved. PMID:17728391

  17. Control Strategies for the DAB Based PV Interface System

    PubMed Central

    El-Helw, Hadi M.; Al-Hasheem, Mohamed; Marei, Mostafa I.

    2016-01-01

    This paper presents an interface system based on the Dual Active Bridge (DAB) converter for Photovoltaic (PV) arrays. Two control strategies are proposed for the DAB converter to harvest the maximum power from the PV array. The first strategy is based on a simple PI controller to regulate the terminal PV voltage through the phase shift angle of the DAB converter. The Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT) technique is utilized to set the reference of the PV terminal voltage. The second strategy presented in this paper employs the Artificial Neural Network (ANN) to directly set the phase shift angle of the DAB converter that results in harvesting maximum power. This feed-forward strategy overcomes the stability issues of the feedback strategy. The proposed PV interface systems are modeled and simulated using MATLAB/SIMULINK and the EMTDC/PSCAD software packages. The simulation results reveal accurate and fast response of the proposed systems. The dynamic performance of the proposed feed-forward strategy outdoes that of the feedback strategy in terms of accuracy and response time. Moreover, an experimental prototype is built to test and validate the proposed PV interface system. PMID:27560138

  18. Control Strategies for the DAB Based PV Interface System.

    PubMed

    El-Helw, Hadi M; Al-Hasheem, Mohamed; Marei, Mostafa I

    2016-01-01

    This paper presents an interface system based on the Dual Active Bridge (DAB) converter for Photovoltaic (PV) arrays. Two control strategies are proposed for the DAB converter to harvest the maximum power from the PV array. The first strategy is based on a simple PI controller to regulate the terminal PV voltage through the phase shift angle of the DAB converter. The Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT) technique is utilized to set the reference of the PV terminal voltage. The second strategy presented in this paper employs the Artificial Neural Network (ANN) to directly set the phase shift angle of the DAB converter that results in harvesting maximum power. This feed-forward strategy overcomes the stability issues of the feedback strategy. The proposed PV interface systems are modeled and simulated using MATLAB/SIMULINK and the EMTDC/PSCAD software packages. The simulation results reveal accurate and fast response of the proposed systems. The dynamic performance of the proposed feed-forward strategy outdoes that of the feedback strategy in terms of accuracy and response time. Moreover, an experimental prototype is built to test and validate the proposed PV interface system. PMID:27560138

  19. A 96-channel neural stimulation system for driving AIROF microelectrodes.

    PubMed

    Hu, Z; Troyk, P; Cogan, S

    2004-01-01

    We present the design and testing of a 96-channel stimulation system to drive activated iridium oxide (AIROF) microelectrodes within safe charge-injection limits. Our system improves upon the traditional capacitively coupled, symmetric charge-balanced biphasic stimulation waveform so as to maximize charge-injection capacity without endangering the microelectrodes. It can deliver computer-controlled cathodic current pulse for to up to 96 AIROF microelectrodes and positively bias them during the inter-pulse interval. The stimulation system is comprised of (1) 12 custom-designed PCB boards each hosting an 8-channel ASIC chip, (2) a motherboard to communicate between these 12 boards and the PC, (3) the PC interface equipped with a DIO card and the corresponding software. We plan to use this system in animal experiments for intracortical neural stimulation of implanted electrodes within our visual prosthesis project. PMID:17271241

  20. Virtual reality interface devices in the reorganization of neural networks in the brain of patients with neurological diseases.

    PubMed

    Gatica-Rojas, Valeska; Méndez-Rebolledo, Guillermo

    2014-04-15

    Two key characteristics of all virtual reality applications are interaction and immersion. Systemic interaction is achieved through a variety of multisensory channels (hearing, sight, touch, and smell), permitting the user to interact with the virtual world in real time. Immersion is the degree to which a person can feel wrapped in the virtual world through a defined interface. Virtual reality interface devices such as the Nintendo® Wii and its peripheral nunchuks-balance board, head mounted displays and joystick allow interaction and immersion in unreal environments created from computer software. Virtual environments are highly interactive, generating great activation of visual, vestibular and proprioceptive systems during the execution of a video game. In addition, they are entertaining and safe for the user. Recently, incorporating therapeutic purposes in virtual reality interface devices has allowed them to be used for the rehabilitation of neurological patients, e.g., balance training in older adults and dynamic stability in healthy participants. The improvements observed in neurological diseases (chronic stroke and cerebral palsy) have been shown by changes in the reorganization of neural networks in patients' brain, along with better hand function and other skills, contributing to their quality of life. The data generated by such studies could substantially contribute to physical rehabilitation strategies. PMID:25206907

  1. Virtual reality interface devices in the reorganization of neural networks in the brain of patients with neurological diseases

    PubMed Central

    Gatica-Rojas, Valeska; Méndez-Rebolledo, Guillermo

    2014-01-01

    Two key characteristics of all virtual reality applications are interaction and immersion. Systemic interaction is achieved through a variety of multisensory channels (hearing, sight, touch, and smell), permitting the user to interact with the virtual world in real time. Immersion is the degree to which a person can feel wrapped in the virtual world through a defined interface. Virtual reality interface devices such as the Nintendo® Wii and its peripheral nunchuks-balance board, head mounted displays and joystick allow interaction and immersion in unreal environments created from computer software. Virtual environments are highly interactive, generating great activation of visual, vestibular and proprioceptive systems during the execution of a video game. In addition, they are entertaining and safe for the user. Recently, incorporating therapeutic purposes in virtual reality interface devices has allowed them to be used for the rehabilitation of neurological patients, e.g., balance training in older adults and dynamic stability in healthy participants. The improvements observed in neurological diseases (chronic stroke and cerebral palsy) have been shown by changes in the reorganization of neural networks in patients’ brain, along with better hand function and other skills, contributing to their quality of life. The data generated by such studies could substantially contribute to physical rehabilitation strategies. PMID:25206907

  2. Simulating neural systems with Xyce.

    SciTech Connect

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

    2012-12-01

    Sandia's 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.

  3. TOPICAL REVIEW: Prosthetic interfaces with the visual system: biological issues

    NASA Astrophysics Data System (ADS)

    Cohen, Ethan D.

    2007-06-01

    The design of effective visual prostheses for the blind represents a challenge for biomedical engineers and neuroscientists. Significant progress has been made in the miniaturization and processing power of prosthesis electronics; however development lags in the design and construction of effective machine brain interfaces with visual system neurons. This review summarizes what has been learned about stimulating neurons in the human and primate retina, lateral geniculate nucleus and visual cortex. Each level of the visual system presents unique challenges for neural interface design. Blind patients with the retinal degenerative disease retinitis pigmentosa (RP) are a common population in clinical trials of visual prostheses. The visual performance abilities of normals and RP patients are compared. To generate pattern vision in blind patients, the visual prosthetic interface must effectively stimulate the retinotopically organized neurons in the central visual field to elicit patterned visual percepts. The development of more biologically compatible methods of stimulating visual system neurons is critical to the development of finer spatial percepts. Prosthesis electrode arrays need to adapt to different optimal stimulus locations, stimulus patterns, and patient disease states.

  4. A 128-Channel Extreme Learning Machine-Based Neural Decoder for Brain Machine Interfaces.

    PubMed

    Chen, Yi; Yao, Enyi; Basu, Arindam

    2016-06-01

    Currently, state-of-the-art motor intention decoding algorithms in brain-machine interfaces are mostly implemented on a PC and consume significant amount of power. A machine learning coprocessor in 0.35- μm CMOS for the motor intention decoding in the brain-machine interfaces is presented in this paper. Using Extreme Learning Machine algorithm and low-power analog processing, it achieves an energy efficiency of 3.45 pJ/MAC at a classification rate of 50 Hz. The learning in second stage and corresponding digitally stored coefficients are used to increase robustness of the core analog processor. The chip is verified with neural data recorded in monkey finger movements experiment, achieving a decoding accuracy of 99.3% for movement type. The same coprocessor is also used to decode time of movement from asynchronous neural spikes. With time-delayed feature dimension enhancement, the classification accuracy can be increased by 5% with limited number of input channels. Further, a sparsity promoting training scheme enables reduction of number of programmable weights by ≈ 2X. PMID:26672048

  5. NEVESIM: event-driven neural simulation framework with a Python interface.

    PubMed

    Pecevski, Dejan; Kappel, David; Jonke, Zeno

    2014-01-01

    NEVESIM is a software package for event-driven simulation of networks of spiking neurons with a fast simulation core in C++, and a scripting user interface in the Python programming language. It supports simulation of heterogeneous networks with different types of neurons and synapses, and can be easily extended by the user with new neuron and synapse types. To enable heterogeneous networks and extensibility, NEVESIM is designed to decouple the simulation logic of communicating events (spikes) between the neurons at a network level from the implementation of the internal dynamics of individual neurons. In this paper we will present the simulation framework of NEVESIM, its concepts and features, as well as some aspects of the object-oriented design approaches and simulation strategies that were utilized to efficiently implement the concepts and functionalities of the framework. We will also give an overview of the Python user interface, its basic commands and constructs, and also discuss the benefits of integrating NEVESIM with Python. One of the valuable capabilities of the simulator is to simulate exactly and efficiently networks of stochastic spiking neurons from the recently developed theoretical framework of neural sampling. This functionality was implemented as an extension on top of the basic NEVESIM framework. Altogether, the intended purpose of the NEVESIM framework is to provide a basis for further extensions that support simulation of various neural network models incorporating different neuron and synapse types that can potentially also use different simulation strategies. PMID:25177291

  6. NEVESIM: event-driven neural simulation framework with a Python interface

    PubMed Central

    Pecevski, Dejan; Kappel, David; Jonke, Zeno

    2014-01-01

    NEVESIM is a software package for event-driven simulation of networks of spiking neurons with a fast simulation core in C++, and a scripting user interface in the Python programming language. It supports simulation of heterogeneous networks with different types of neurons and synapses, and can be easily extended by the user with new neuron and synapse types. To enable heterogeneous networks and extensibility, NEVESIM is designed to decouple the simulation logic of communicating events (spikes) between the neurons at a network level from the implementation of the internal dynamics of individual neurons. In this paper we will present the simulation framework of NEVESIM, its concepts and features, as well as some aspects of the object-oriented design approaches and simulation strategies that were utilized to efficiently implement the concepts and functionalities of the framework. We will also give an overview of the Python user interface, its basic commands and constructs, and also discuss the benefits of integrating NEVESIM with Python. One of the valuable capabilities of the simulator is to simulate exactly and efficiently networks of stochastic spiking neurons from the recently developed theoretical framework of neural sampling. This functionality was implemented as an extension on top of the basic NEVESIM framework. Altogether, the intended purpose of the NEVESIM framework is to provide a basis for further extensions that support simulation of various neural network models incorporating different neuron and synapse types that can potentially also use different simulation strategies. PMID:25177291

  7. Ultrasmall implantable composite microelectrodes with bioactive surfaces for chronic neural interfaces

    PubMed Central

    Kozai, Takashi D. Yoshida; Langhals, Nicholas B.; Patel, Paras R.; Deng, Xiaopei; Zhang, Huanan; Smith, Karen L.; Lahann, Joerg; Kotov, Nicholas A.; Kipke, Daryl R.

    2012-01-01

    Implantable neural microelectrodes that can record extracellular biopotentials from small, targeted groups of neurons are critical for neuroscience research and emerging clinical applications including brain-controlled prosthetic devices. The crucial material-dependent problem is developing microelectrodes that record neural activity from the same neurons for years with high fidelity and reliability. Here, we report the development of an integrated composite electrode consisting of a carbon-fibre core, a poly(p-xylylene)-based thin-film coating that acts as a dielectric barrier and that is functionalized to control intrinsic biological processes, and a poly(thiophene)-based recording pad. The resulting implants are an order of magnitude smaller than traditional recording electrodes, and more mechanically compliant with brain tissue. They were found to elicit much reduced chronic reactive tissue responses and enabled single-neuron recording in acute and early chronic experiments in rats. This technology, taking advantage of new composites, makes possible highly selective and stealthy neural interface devices towards realizing long-lasting implants. PMID:23142839

  8. Ultrasmall implantable composite microelectrodes with bioactive surfaces for chronic neural interfaces

    NASA Astrophysics Data System (ADS)

    Yoshida Kozai, Takashi D.; Langhals, Nicholas B.; Patel, Paras R.; Deng, Xiaopei; Zhang, Huanan; Smith, Karen L.; Lahann, Joerg; Kotov, Nicholas A.; Kipke, Daryl R.

    2012-12-01

    Implantable neural microelectrodes that can record extracellular biopotentials from small, targeted groups of neurons are critical for neuroscience research and emerging clinical applications including brain-controlled prosthetic devices. The crucial material-dependent problem is developing microelectrodes that record neural activity from the same neurons for years with high fidelity and reliability. Here, we report the development of an integrated composite electrode consisting of a carbon-fibre core, a poly(p-xylylene)-based thin-film coating that acts as a dielectric barrier and that is functionalized to control intrinsic biological processes, and a poly(thiophene)-based recording pad. The resulting implants are an order of magnitude smaller than traditional recording electrodes, and more mechanically compliant with brain tissue. They were found to elicit much reduced chronic reactive tissue responses and enabled single-neuron recording in acute and early chronic experiments in rats. This technology, taking advantage of new composites, makes possible highly selective and stealthy neural interface devices towards realizing long-lasting implants.

  9. An Analysis of EMG Electrode Configuration for Targeted Muscle Reinnervation Based Neural Machine Interface

    PubMed Central

    Huang, He; Zhou, Ping; Li, Guanglin; Kuiken, Todd A.

    2015-01-01

    Targeted muscle reinnervation (TMR) is a novel neural machine interface for improved myoelectric prosthesis control. Previous high-density (HD) surface electromyography (EMG) studies have indicated that tremendous neural control information can be extracted from the reinnervated muscles by EMG pattern recognition (PR). However, using a large number of EMG electrodes hinders clinical application of the TMR technique. This study investigated a reduced number of electrodes and the placement required to extract sufficient neural control information for accurate identification of user movement intents. An electrode selection algorithm was applied to the HD EMG recordings from each of 4 TMR amputee subjects. The results show that when using only 12 selected bipolar electrodes the average accuracy over subjects for classifying 16 movement intents was 93.0(±3.3)%, just 1.2% lower than when using the entire HD electrode complement. The locations of selected electrodes were consistent with the anatomical reinnervation sites. Additionally, a practical protocol for clinical electrode placement was developed, which does not rely on complex HD EMG experiment and analysis while maintaining a classification accuracy of 88.7±4.5%. These outcomes provide important guidelines for practical electrode placement that can promote future clinical application of TMR and EMG PR in the control of multifunctional prostheses. PMID:18303804

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

    PubMed

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

    2014-01-01

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

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

    PubMed Central

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

    2014-01-01

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

  12. Instantaneous estimation of motor cortical neural encoding for online brain-machine interfaces

    NASA Astrophysics Data System (ADS)

    Wang, Yiwen; Principe, Jose C.

    2010-10-01

    Recently, the authors published a sequential decoding algorithm for motor brain-machine interfaces (BMIs) that infers movement directly from spike trains and produces a new kinematic output every time an observation of neural activity is present at its input. Such a methodology also needs a special instantaneous neuronal encoding model to relate instantaneous kinematics to every neural spike activity. This requirement is unlike the tuning methods commonly used in computational neuroscience, which are based on time windows of neural and kinematic data. This paper develops a novel, online, encoding model that uses the instantaneous kinematic variables (position, velocity and acceleration in 2D or 3D space) to estimate the mean value of an inhomogeneous Poisson model. During BMI decoding the mapping from neural spikes to kinematics is one to one and easy to implement by simply reading the spike times directly. Due to the high temporal resolution of the encoding, the delay between motor cortex neurons and kinematics needs to be estimated in the encoding stage. Mutual information is employed to select the optimal time index defined as the lag for which the spike event is maximally informative with respect to the kinematics. We extensively compare the windowed tuning models with the proposed method. The big difference between them resides in the high firing rate portion of the tuning curve, which is rather important for BMI-decoding performance. This paper shows that implementing such an instantaneous tuning model in sequential Monte Carlo point process estimation based on spike timing provides statistically better kinematic reconstructions than the linear and exponential spike-tuning models.

  13. Olfactory instruction for fear: neural system analysis

    PubMed Central

    Canteras, Newton S.; Pavesi, Eloisa; Carobrez, Antonio P.

    2015-01-01

    Different types of predator odors engage elements of the hypothalamic predator-responsive circuit, which has been largely investigated in studies using cat odor exposure. Studies using cat odor have led to detailed mapping of the neural sites involved in innate and contextual fear responses. Here, we reviewed three lines of work examining the dynamics of the neural systems that organize innate and learned fear responses to cat odor. In the first section, we explored the neural systems involved in innate fear responses and in the acquisition and expression of fear conditioning to cat odor, with a particular emphasis on the role of the dorsal premammillary nucleus (PMd) and the dorsolateral periaqueductal gray (PAGdl), which are key sites that influence innate fear and contextual conditioning. In the second section, we reviewed how chemical stimulation of the PMd and PAGdl may serve as a useful unconditioned stimulus in an olfactory fear conditioning paradigm; these experiments provide an interesting perspective for the understanding of learned fear to predator odor. Finally, in the third section, we explored the fact that neutral odors that acquire an aversive valence in a shock-paired conditioning paradigm may mimic predator odor and mobilize elements of the hypothalamic predator-responsive circuit. PMID:26300721

  14. Neural system for structural health monitoring

    NASA Astrophysics Data System (ADS)

    Sundaresan, Mannur J.; Schulz, Mark J.; Ghoshal, Anindya; Martin, William N., Jr.; Pratap, Promod R.

    2001-08-01

    This is an overview paper that discusses the concept of an embeddable structural health monitoring system for use in composite and heterogeneous material systems. The sensor system is formed by integrating groups of autonomous unit cells into a structure, much like neurons in biological systems. Each unit cell consists of an embedded processor and a group of distributed sensors that gives the structure the ability to sense damage. In addition, each unit cell periodically updates a central processor on the status of health in its neighborhood. This micro-architectured synthetic nervous system has an advanced sensing capability based on new continuous sensor technology. This technology uses a plurality of serially connected piezoceramic nodes to form a distributed sensor capable of measuring waves generated in structures by damage events, including impact and crack propagation. Simulations show that the neural system can detect faint acoustic waves in large plates. An experiment demonstrates the use of a simple neural system that was able to measure simulated acoustic emissions that were not clearly recognizable by a single conventional piezoceramic sensor.

  15. Dynamic Artificial Neural Networks with Affective Systems

    PubMed Central

    Schuman, Catherine D.; Birdwell, J. Douglas

    2013-01-01

    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. PMID:24303015

  16. A Modular System of Interfacing Microcomputers.

    ERIC Educational Resources Information Center

    Martin, Peter

    1983-01-01

    Describes a system of interfacing allowing a range of signal conditioning and control modules to be connected to microcomputers, enabling execution of such experiments as: examining rate of cooling; control by light-activated switch; pH measurements; control frequency of signal generators; and making automated measurements of frequency response of…

  17. Interfaces for Distributed Systems of Information Servers.

    ERIC Educational Resources Information Center

    Kahle, Brewster M.; And Others

    1993-01-01

    Describes five interfaces to remote, full-text databases accessed through distributed systems of servers. These are WAIStation for the Macintosh, XWAIS for X-Windows, GWAIS for Gnu-Emacs; SWAIS for dumb terminals, and Rosebud for the Macintosh. Sixteen illustrations provide examples of display screens. Problems and needed improvements are…

  18. Garden Banks 388 ROV interface systems

    SciTech Connect

    Granhaug, O.; Brewster, D.; Soliah, J.; Dubea, C.

    1995-12-31

    ROV systems integration has become an important part of the planning and implementation of deep water field development. This paper provides an overview of the GB 388 subsea development project and describes the ROV interface systems in use on the various subsea production components. The paper continues with an account of the purpose-built ROV system developed for the project. Finally, the paper describes in some detail the specialized ROV tooling and intervention systems that have been developed to assist in the installation, operation and maintenance of the subsea production equipment. The subsea intervention solutions developed for the GB 388 development project have direct application to all deep water field development projects. ROV interface systems are an integral part of current and future subsea completion technology.

  19. Dynamical systems, attractors, and neural circuits

    PubMed Central

    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. PMID:27408709

  20. The desktop interface in intelligent tutoring systems

    NASA Technical Reports Server (NTRS)

    Baudendistel, Stephen; Hua, Grace

    1987-01-01

    The interface between an Intelligent Tutoring System (ITS) and the person being tutored is critical to the success of the learning process. If the interface to the ITS is confusing or non-supportive of the tutored domain, the effectiveness of the instruction will be diminished or lost entirely. Consequently, the interface to an ITS should be highly integrated with the domain to provide a robust and semantically rich learning environment. In building an ITS for ZetaLISP on a LISP Machine, a Desktop Interface was designed to support a programming learning environment. Using the bitmapped display, windows, and mouse, three desktops were designed to support self-study and tutoring of ZetaLISP. Through organization, well-defined boundaries, and domain support facilities, the desktops provide substantial flexibility and power for the student and facilitate learning ZetaLISP programming while screening the student from the complex LISP Machine environment. The student can concentrate on learning ZetaLISP programming and not on how to operate the interface or a LISP Machine.

  1. Development of closed-loop neural interface technology in a rat model: combining motor cortex operant conditioning with visual cortex microstimulation.

    PubMed

    Marzullo, Timothy Charles; Lehmkuhle, Mark J; Gage, Gregory J; Kipke, Daryl R

    2010-04-01

    Closed-loop neural interface technology that combines neural ensemble decoding with simultaneous electrical microstimulation feedback is hypothesized to improve deep brain stimulation techniques, neuromotor prosthetic applications, and epilepsy treatment. Here we describe our iterative results in a rat model of a sensory and motor neurophysiological feedback control system. Three rats were chronically implanted with microelectrode arrays in both the motor and visual cortices. The rats were subsequently trained over a period of weeks to modulate their motor cortex ensemble unit activity upon delivery of intra-cortical microstimulation (ICMS) of the visual cortex in order to receive a food reward. Rats were given continuous feedback via visual cortex ICMS during the response periods that was representative of the motor cortex ensemble dynamics. Analysis revealed that the feedback provided the animals with indicators of the behavioral trials. At the hardware level, this preparation provides a tractable test model for improving the technology of closed-loop neural devices. PMID:20144922

  2. A graphical, rule based robotic interface system

    NASA Technical Reports Server (NTRS)

    Mckee, James W.; Wolfsberger, John

    1988-01-01

    The ability of a human to take control of a robotic system is essential in any use of robots in space in order to handle unforeseen changes in the robot's work environment or scheduled tasks. But in cases in which the work environment is known, a human controlling a robot's every move by remote control is both time consuming and frustrating. A system is needed in which the user can give the robotic system commands to perform tasks but need not tell the system how. To be useful, this system should be able to plan and perform the tasks faster than a telerobotic system. The interface between the user and the robot system must be natural and meaningful to the user. A high level user interface program under development at the University of Alabama, Huntsville, is described. A graphical interface is proposed in which the user selects objects to be manipulated by selecting representations of the object on projections of a 3-D model of the work environment. The user may move in the work environment by changing the viewpoint of the projections. The interface uses a rule based program to transform user selection of items on a graphics display of the robot's work environment into commands for the robot. The program first determines if the desired task is possible given the abilities of the robot and any constraints on the object. If the task is possible, the program determines what movements the robot needs to make to perform the task. The movements are transformed into commands for the robot. The information defining the robot, the work environment, and how objects may be moved is stored in a set of data bases accessible to the program and displayable to the user.

  3. Sensory System for Implementing a Human—Computer Interface Based on Electrooculography

    PubMed Central

    Barea, Rafael; Boquete, Luciano; Rodriguez-Ascariz, Jose Manuel; Ortega, Sergio; López, Elena

    2011-01-01

    This paper describes a sensory system for implementing a human–computer interface based on electrooculography. An acquisition system captures electrooculograms and transmits them via the ZigBee protocol. The data acquired are analysed in real time using a microcontroller-based platform running the Linux operating system. The continuous wavelet transform and neural network are used to process and analyse the signals to obtain highly reliable results in real time. To enhance system usability, the graphical interface is projected onto special eyewear, which is also used to position the signal-capturing electrodes. PMID:22346579

  4. Implications of the Dependence of Neuronal Activity on Neural Network States for the Design of Brain-Machine Interfaces

    PubMed Central

    Panzeri, Stefano; Safaai, Houman; De Feo, Vito; Vato, Alessandro

    2016-01-01

    Brain-machine interfaces (BMIs) can improve the quality of life of patients with sensory and motor disabilities by both decoding motor intentions expressed by neural activity, and by encoding artificially sensed information into patterns of neural activity elicited by causal interventions on the neural tissue. Yet, current BMIs can exchange relatively small amounts of information with the brain. This problem has proved difficult to overcome by simply increasing the number of recording or stimulating electrodes, because trial-to-trial variability of neural activity partly arises from intrinsic factors (collectively known as the network state) that include ongoing spontaneous activity and neuromodulation, and so is shared among neurons. Here we review recent progress in characterizing the state dependence of neural responses, and in particular of how neural responses depend on endogenous slow fluctuations of network excitability. We then elaborate on how this knowledge may be used to increase the amount of information that BMIs exchange with brain. Knowledge of network state can be used to fine-tune the stimulation pattern that should reliably elicit a target neural response used to encode information in the brain, and to discount part of the trial-by-trial variability of neural responses, so that they can be decoded more accurately. PMID:27147955

  5. Nanoporous Gold as a Neural Interface Coating: Effects of Topography, Surface Chemistry, and Feature Size

    DOE PAGESBeta

    Chapman, Christopher A. R.; Chen, Hao; Stamou, Marianna; Biener, Juergen; Biener, Monika M.; Lein, Pamela J.; Seker, Erkin

    2015-02-23

    We report that designing neural interfaces that maintain close physical coupling of neurons to an electrode surface remains a major challenge for both implantable and in vitro neural recording electrode arrays. Typically, low-impedance nanostructured electrode coatings rely on chemical cues from pharmaceuticals or surface-immobilized peptides to suppress glial scar tissue formation over the electrode surface (astrogliosis), which is an obstacle to reliable neuron–electrode coupling. Nanoporous gold (np-Au), produced by an alloy corrosion process, is a promising candidate to reduce astrogliosis solely through topography by taking advantage of its tunable length scale. In the present in vitro study on np-Au’s interactionmore » with cortical neuron–glia co-cultures, we demonstrate that the nanostructure of np-Au achieves close physical coupling of neurons by maintaining a high neuron-to-astrocyte surface coverage ratio. Atomic layer deposition-based surface modification was employed to decouple the effect of morphology from surface chemistry. Additionally, length scale effects were systematically studied by controlling the characteristic feature size of np-Au through variations in the dealloying conditions. In conclusion, our results show that np-Au nanotopography, not surface chemistry, reduces astrocyte surface coverage while maintaining high neuronal coverage and may enhance neuron–electrode coupling through nanostructure-mediated suppression of scar tissue formation.« less

  6. Nanoporous Gold as a Neural Interface Coating: Effects of Topography, Surface Chemistry, and Feature Size

    SciTech Connect

    Chapman, Christopher A. R.; Chen, Hao; Stamou, Marianna; Biener, Juergen; Biener, Monika M.; Lein, Pamela J.; Seker, Erkin

    2015-02-23

    We report that designing neural interfaces that maintain close physical coupling of neurons to an electrode surface remains a major challenge for both implantable and in vitro neural recording electrode arrays. Typically, low-impedance nanostructured electrode coatings rely on chemical cues from pharmaceuticals or surface-immobilized peptides to suppress glial scar tissue formation over the electrode surface (astrogliosis), which is an obstacle to reliable neuron–electrode coupling. Nanoporous gold (np-Au), produced by an alloy corrosion process, is a promising candidate to reduce astrogliosis solely through topography by taking advantage of its tunable length scale. In the present in vitro study on np-Au’s interaction with cortical neuron–glia co-cultures, we demonstrate that the nanostructure of np-Au achieves close physical coupling of neurons by maintaining a high neuron-to-astrocyte surface coverage ratio. Atomic layer deposition-based surface modification was employed to decouple the effect of morphology from surface chemistry. Additionally, length scale effects were systematically studied by controlling the characteristic feature size of np-Au through variations in the dealloying conditions. In conclusion, our results show that np-Au nanotopography, not surface chemistry, reduces astrocyte surface coverage while maintaining high neuronal coverage and may enhance neuron–electrode coupling through nanostructure-mediated suppression of scar tissue formation.

  7. Nanoporous gold as a neural interface coating: effects of topography, surface chemistry, and feature size.

    PubMed

    Chapman, Christopher A R; Chen, Hao; Stamou, Marianna; Biener, Juergen; Biener, Monika M; Lein, Pamela J; Seker, Erkin

    2015-04-01

    Designing neural interfaces that maintain close physical coupling of neurons to an electrode surface remains a major challenge for both implantable and in vitro neural recording electrode arrays. Typically, low-impedance nanostructured electrode coatings rely on chemical cues from pharmaceuticals or surface-immobilized peptides to suppress glial scar tissue formation over the electrode surface (astrogliosis), which is an obstacle to reliable neuron-electrode coupling. Nanoporous gold (np-Au), produced by an alloy corrosion process, is a promising candidate to reduce astrogliosis solely through topography by taking advantage of its tunable length scale. In the present in vitro study on np-Au's interaction with cortical neuron-glia co-cultures, we demonstrate that the nanostructure of np-Au achieves close physical coupling of neurons by maintaining a high neuron-to-astrocyte surface coverage ratio. Atomic layer deposition-based surface modification was employed to decouple the effect of morphology from surface chemistry. Additionally, length scale effects were systematically studied by controlling the characteristic feature size of np-Au through variations in the dealloying conditions. Our results show that np-Au nanotopography, not surface chemistry, reduces astrocyte surface coverage while maintaining high neuronal coverage and may enhance neuron-electrode coupling through nanostructure-mediated suppression of scar tissue formation. PMID:25706691

  8. Nanoporous Gold as a Neural Interface Coating: Effects of Topography, Surface Chemistry, and Feature Size

    PubMed Central

    Chapman, Christopher A. R.; Chen, Hao; Stamou, Marianna; Biener, Juergen; Biener, Monika M.; Lein, Pamela J.; Seker, Erkin

    2015-01-01

    Designing neural-electrode interfaces that maintain close physical coupling of neurons to the electrode surface remains a major challenge for both implantable and in vitro neural recording electrode arrays. Typically, low-impedance nanostructured electrode coatings rely on chemical cues from pharmaceuticals or surface-immobilized peptides to suppress glial scar tissue formation over the electrode surface (astrogliosis), which is an obstacle to reliable neuron-electrode coupling. Nanoporous gold (np-Au), produced by an alloy corrosion process, is a promising candidate to reduce astrogliosis solely through topography by taking advantage of its tunable length scale. In the present in vitro study on np-Au’s interaction with cortical neuron-glia co-cultures, we demonstrate that the nanostructure of np-Au is achieving close physical coupling of neurons through maintaining a high neuron-to-astrocyte surface coverage ratio. Atomic layer deposition-based surface modification was employed to decouple the effect of morphology from surface chemistry. Additionally, length scale effects were systematically studied by controlling the characteristic feature size of np-Au through variations of the dealloying conditions. Our results show that np-Au nanotopography, not surface chemistry, reduces astrocyte surface coverage while maintaining high neuronal coverage, and may enhance the neuron-electrode coupling through nanostructure-mediated suppression of scar tissue formation. PMID:25706691

  9. Design and validation of a real-time spiking-neural-network decoder for brain-machine interfaces

    NASA Astrophysics Data System (ADS)

    Dethier, Julie; Nuyujukian, Paul; Ryu, Stephen I.; Shenoy, Krishna V.; Boahen, Kwabena

    2013-06-01

    Objective. Cortically-controlled motor prostheses aim to restore functions lost to neurological disease and injury. Several proof of concept demonstrations have shown encouraging results, but barriers to clinical translation still remain. In particular, intracortical prostheses must satisfy stringent power dissipation constraints so as not to damage cortex. Approach. One possible solution is to use ultra-low power neuromorphic chips to decode neural signals for these intracortical implants. The first step is to explore in simulation the feasibility of translating decoding algorithms for brain-machine interface (BMI) applications into spiking neural networks (SNNs). Main results. Here we demonstrate the validity of the approach by implementing an existing Kalman-filter-based decoder in a simulated SNN using the Neural Engineering Framework (NEF), a general method for mapping control algorithms onto SNNs. To measure this system’s robustness and generalization, we tested it online in closed-loop BMI experiments with two rhesus monkeys. Across both monkeys, a Kalman filter implemented using a 2000-neuron SNN has comparable performance to that of a Kalman filter implemented using standard floating point techniques. Significance. These results demonstrate the tractability of SNN implementations of statistical signal processing algorithms on different monkeys and for several tasks, suggesting that a SNN decoder, implemented on a neuromorphic chip, may be a feasible computational platform for low-power fully-implanted prostheses. The validation of this closed-loop decoder system and the demonstration of its robustness and generalization hold promise for SNN implementations on an ultra-low power neuromorphic chip using the NEF.

  10. A classifier neural network for rotordynamic systems

    NASA Astrophysics Data System (ADS)

    Ganesan, R.; Jionghua, Jin; Sankar, T. S.

    1995-07-01

    A feedforward backpropagation neural network is formed to identify the stability characteristic of a high speed rotordynamic system. The principal focus resides in accounting for the instability due to the bearing clearance effects. The abnormal operating condition of 'normal-loose' Coulomb rub, that arises in units supported by hydrodynamic bearings or rolling element bearings, is analysed in detail. The multiple-parameter stability problem is formulated and converted to a set of three-parameter algebraic inequality equations. These three parameters map the wider range of physical parameters of commonly-used rotordynamic systems into a narrow closed region, that is used in the supervised learning of the neural network. A binary-type state of the system is expressed through these inequalities that are deduced from the analytical simulation of the rotor system. Both the hidden layer as well as functional-link networks are formed and the superiority of the functional-link network is established. Considering the real time interpretation and control of the rotordynamic system, the network reliability and the learning time are used as the evaluation criteria to assess the superiority of the functional-link network. This functional-link network is further trained using the parameter values of selected rotor systems, and the classifier network is formed. The success rate of stability status identification is obtained to assess the potentials of this classifier network. The classifier network is shown that it can also be used, for control purposes, as an 'advisory' system that suggests the optimum way of parameter adjustment.

  11. Interfacing the expert: Characteristics and requirements for the user interface in expert systems

    NASA Technical Reports Server (NTRS)

    Potter, Andrew

    1987-01-01

    Because expert systems deal with new sets of problems presenting unique interface requirements, special issues requiring special attention are presented to user interface designers. External knowledge representation (how knowdedge is represented across the user interface), modes of user-system interdependence (advisory, cooperative, and autonomous), and management of uncertainty (deciding what actions to take or recommend based on incomplete evidence) are discussed.

  12. A Web Interface for Eco System Modeling

    NASA Astrophysics Data System (ADS)

    McHenry, K.; Kooper, R.; Serbin, S. P.; LeBauer, D. S.; Desai, A. R.; Dietze, M. C.

    2012-12-01

    We have developed the Predictive Ecosystem Analyzer (PEcAn) as an open-source scientific workflow system and ecoinformatics toolbox that manages the flow of information in and out of regional-scale terrestrial biosphere models, facilitates heterogeneous data assimilation, tracks data provenance, and enables more effective feedback between models and field research. The over-arching goal of PEcAn is to make otherwise complex analyses transparent, repeatable, and accessible to a diverse array of researchers, allowing both novice and expert users to focus on using the models to examine complex ecosystems rather than having to deal with complex computer system setup and configuration questions in order to run the models. Through the developed web interface we hide much of the data and model details and allow the user to simply select locations, ecosystem models, and desired data sources as inputs to the model. Novice users are guided by the web interface through setting up a model execution and plotting the results. At the same time expert users are given enough freedom to modify specific parameters before the model gets executed. This will become more important as more and more models are added to the PEcAn workflow as well as more and more data that will become available as NEON comes online. On the backend we support the execution of potentially computationally expensive models on different High Performance Computers (HPC) and/or clusters. The system can be configured with a single XML file that gives it the flexibility needed for configuring and running the different models on different systems using a combination of information stored in a database as well as pointers to files on the hard disk. While the web interface usually creates this configuration file, expert users can still directly edit it to fine tune the configuration.. Once a workflow is finished the web interface will allow for the easy creation of plots over result data while also allowing the user to

  13. Darwinian optimization of synthetic neural systems

    SciTech Connect

    Dress, W.B.

    1987-01-01

    This paper suggests a synthesis of computer science and artificial intelligence with the resurgent ideas of artificial neural systems and genetic algorithms cast in a classical Darwinian mold. Just as Darwin's original theory avoided the need for teleological arguments, the thrust of the approach set forth here for synthetic systems avoids the problem of programmer omniscience and the resulting brittle programs for precisely the same reasons. The price to be paid is one of many long computations on, perhaps, many parallel processors. Hardware development leading to parallel networks of RISC processors should meet the near-term needs of evolutionary parameter determination and allow the ensuring synthetic systems to function in real-time for certain tasks. In the longer term, special-purpose devices will be needed.

  14. Testing of the Automated Fluid Interface System

    NASA Technical Reports Server (NTRS)

    Johnston, A. S.; Tyler, Tony R.

    1998-01-01

    The Automated Fluid Interface System (AFIS) is an advanced development prototype satellite servicer. The device was designed to transfer consumables from one spacecraft to another. An engineering model was built and underwent development testing at Marshall Space Flight Center. While the current AFIS is not suitable for spaceflight, testing and evaluation of the AFIS provided significant experience which would be beneficial in building a flight unit.

  15. Geographic information system/watershed model interface

    USGS Publications Warehouse

    Fisher, Gary T.

    1989-01-01

    Geographic information systems allow for the interactive analysis of spatial data related to water-resources investigations. A conceptual design for an interface between a geographic information system and a watershed model includes functions for the estimation of model parameter values. Design criteria include ease of use, minimal equipment requirements, a generic data-base management system, and use of a macro language. An application is demonstrated for a 90.1-square-kilometer subbasin of the Patuxent River near Unity, Maryland, that performs automated derivation of watershed parameters for hydrologic modeling.

  16. Decentralized Multisensory Information Integration in Neural Systems

    PubMed Central

    Zhang, Wen-hao; Chen, Aihua

    2016-01-01

    How multiple sensory cues are integrated in neural circuitry remains a challenge. The common hypothesis is that information integration might be accomplished in a dedicated multisensory integration area receiving feedforward inputs from the modalities. However, recent experimental evidence suggests that it is not a single multisensory brain area, but rather many multisensory brain areas that are simultaneously involved in the integration of information. Why many mutually connected areas should be needed for information integration is puzzling. Here, we investigated theoretically how information integration could be achieved in a distributed fashion within a network of interconnected multisensory areas. Using biologically realistic neural network models, we developed a decentralized information integration system that comprises multiple interconnected integration areas. Studying an example of combining visual and vestibular cues to infer heading direction, we show that such a decentralized system is in good agreement with anatomical evidence and experimental observations. In particular, we show that this decentralized system can integrate information optimally. The decentralized system predicts that optimally integrated information should emerge locally from the dynamics of the communication between brain areas and sheds new light on the interpretation of the connectivity between multisensory brain areas. SIGNIFICANCE STATEMENT To extract information reliably from ambiguous environments, the brain integrates multiple sensory cues, which provide different aspects of information about the same entity of interest. Here, we propose a decentralized architecture for multisensory integration. In such a system, no processor is in the center of the network topology and information integration is achieved in a distributed manner through reciprocally connected local processors. Through studying the inference of heading direction with visual and vestibular cues, we show that

  17. Convolutional neural networks for P300 detection with application to brain-computer interfaces.

    PubMed

    Cecotti, Hubert; Gräser, Axel

    2011-03-01

    A Brain-Computer Interface (BCI) is a specific type of human-computer interface that enables the direct communication between human and computers by analyzing brain measurements. Oddball paradigms are used in BCI to generate event-related potentials (ERPs), like the P300 wave, on targets selected by the user. A P300 speller is based on this principle, where the detection of P300 waves allows the user to write characters. The P300 speller is composed of two classification problems. The first classification is to detect the presence of a P300 in the electroencephalogram (EEG). The second one corresponds to the combination of different P300 responses for determining the right character to spell. A new method for the detection of P300 waves is presented. This model is based on a convolutional neural network (CNN). The topology of the network is adapted to the detection of P300 waves in the time domain. Seven classifiers based on the CNN are proposed: four single classifiers with different features set and three multiclassifiers. These models are tested and compared on the Data set II of the third BCI competition. The best result is obtained with a multiclassifier solution with a recognition rate of 95.5 percent, without channel selection before the classification. The proposed approach provides also a new way for analyzing brain activities due to the receptive field of the CNN models. PMID:20567055

  18. Convergent evolution of neural systems in ctenophores

    PubMed Central

    Moroz, Leonid L.

    2015-01-01

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

  19. Convergent evolution of neural systems in ctenophores.

    PubMed

    Moroz, Leonid L

    2015-02-15

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

  20. Time-free spiking neural P systems.

    PubMed

    Pan, Linqiang; Zeng, Xiangxiang; Zhang, Xingyi

    2011-05-01

    Different biological processes take different times to be completed, which can also be influenced by many environmental factors. In this work, a realistic definition of nonsynchronized spiking neural P systems (SN P systems, for short) is considered: during the work of an SN P system, the execution times of spiking rules cannot be known exactly (i.e., they are arbitrary). In order to establish robust systems against the environmental factors, a special class of SN P systems, called time-free SN P systems, is introduced, which always produce the same computation result independent of the execution times of the rules. The universality of time-free SN P systems is investigated. It is proved that these P systems with extended rules (several spikes can be produced by a rule) are equivalent to register machines. However, if the number of spikes present in the system is bounded, then the power of time-free SN P systems falls, and in this case, a characterization of semilinear sets of natural numbers is obtained. PMID:21299423

  1. A user-system interface agent

    NASA Technical Reports Server (NTRS)

    Wakim, Nagi T.; Srivastava, Sadanand; Bousaidi, Mehdi; Goh, Gin-Hua

    1995-01-01

    Agent-based technologies answer to several challenges posed by additional information processing requirements in today's computing environments. In particular, (1) users desire interaction with computing devices in a mode which is similar to that used between people, (2) the efficiency and successful completion of information processing tasks often require a high-level of expertise in complex and multiple domains, (3) information processing tasks often require handling of large volumes of data and, therefore, continuous and endless processing activities. The concept of an agent is an attempt to address these new challenges by introducing information processing environments in which (1) users can communicate with a system in a natural way, (2) an agent is a specialist and a self-learner and, therefore, it qualifies to be trusted to perform tasks independent of the human user, and (3) an agent is an entity that is continuously active performing tasks that are either delegated to it or self-imposed. The work described in this paper focuses on the development of an interface agent for users of a complex information processing environment (IPE). This activity is part of an on-going effort to build a model for developing agent-based information systems. Such systems will be highly applicable to environments which require a high degree of automation, such as, flight control operations and/or processing of large volumes of data in complex domains, such as the EOSDIS environment and other multidisciplinary, scientific data systems. The concept of an agent as an information processing entity is fully described with emphasis on characteristics of special interest to the User-System Interface Agent (USIA). Issues such as agent 'existence' and 'qualification' are discussed in this paper. Based on a definition of an agent and its main characteristics, we propose an architecture for the development of interface agents for users of an IPE that is agent-oriented and whose resources

  2. A new architecture for neural signal amplification in implantable brain machine interfaces.

    PubMed

    ur Rehman, Sami; Kamboh, Awais M

    2013-01-01

    This paper reports a new architecture for variable gain-bandwidth amplification of neural signals to be used in implantable multi-channel recording systems. The two most critical requirements in such a front-end circuit are low power consumption and chip area, especially as number of channels increases. The presented architecture employs a single super-performing amplifier, with tunable gain and bandwidth, combined with several low-key preamplifiers and multiplexors for multi-channel recordings. This is in contrast to using copies of high performing amplifier for each channel as is typically reported in earlier literature. The resulting circuits consume lower power and require smaller area as compared to existing designs. Designed in 0.5 µmCMOS, the 8-channel prototype can simultaneously record Local Field Potentials and neural spikes, with an effective power consumption of 3.5 µW per channel and net core area of 0.407 mm(2). PMID:24110295

  3. Power system interface and umbilical system study

    NASA Technical Reports Server (NTRS)

    1980-01-01

    System requirements and basic design criteria were defined for berthing or docking a payload to the 25 kW power module which will provide electrical power and attitude control, cooling, data transfer, and communication services to free-flying and Orbiter sortie payloads. The selected umbilical system concept consists of four assemblies and command and display equipment to be installed at the Orbiter payload specialist station: (1) a movable platen assembly which is attached to the power system with EVA operable devices; (2) a slave platen assembly which is attached to the payload with EVA operable devices; (3) a fixed secondary platen permanently installed in the power system; and (4) a fixed secondary platen permanently installed on the payload. Operating modes and sequences are described.

  4. Systems and methods for monitoring a solid-liquid interface

    DOEpatents

    Stoddard, Nathan G; Lewis, Monte A.; Clark, Roger F

    2013-06-11

    Systems and methods are provided for monitoring a solid-liquid interface during a casting process. The systems and methods enable determination of the location of a solid-liquid interface during the casting process.

  5. Simulation of large systems with neural networks

    SciTech Connect

    Paez, T.L.

    1994-09-01

    Artificial neural networks (ANNs) have been shown capable of simulating the behavior of complex, nonlinear, systems, including structural systems. Under certain circumstances, it is desirable to simulate structures that are analyzed with the finite element method. For example, when we perform a probabilistic analysis with the Monte Carlo method, we usually perform numerous (hundreds or thousands of) repetitions of a response simulation with different input and system parameters to estimate the chance of specific response behaviors. In such applications, efficiency in computation of response is critical, and response simulation with ANNs can be valuable. However, finite element analyses of complex systems involve the use of models with tens or hundreds of thousands of degrees of freedom, and ANNs are practically limited to simulations that involve far fewer variables. This paper develops a technique for reducing the amount of information required to characterize the response of a general structure. We show how the reduced information can be used to train a recurrent ANN. Then the trained ANN can be used to simulate the reduced behavior of the original system, and the reduction transformation can be inverted to provide a simulation of the original system. A numerical example is presented.

  6. Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence.

    PubMed

    Li, Yongcheng; Sun, Rong; Zhang, Bin; Wang, Yuechao; Li, Hongyi

    2015-01-01

    Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems. PMID:25992579

  7. Used Fuel Management System Interface Analyses - 13578

    SciTech Connect

    Howard, Robert; Busch, Ingrid; Nutt, Mark; Morris, Edgar; Puig, Francesc; Carter, Joe; Delley, Alexcia; Rodwell, Phillip; Hardin, Ernest; Kalinina, Elena; Clark, Robert; Cotton, Thomas

    2013-07-01

    Preliminary system-level analyses of the interfaces between at-reactor used fuel management, consolidated storage facilities, and disposal facilities, along with the development of supporting logistics simulation tools, have been initiated to provide the U.S. Department of Energy (DOE) and other stakeholders with information regarding the various alternatives for managing used nuclear fuel (UNF) generated by the current fleet of light water reactors operating in the United States. An important UNF management system interface consideration is the need for ultimate disposal of UNF assemblies contained in waste packages that are sized to be compatible with different geologic media. Thermal analyses indicate that waste package sizes for the geologic media under consideration by the Used Fuel Disposition Campaign may be significantly smaller than the canisters being used for on-site dry storage by the nuclear utilities. Therefore, at some point along the UNF disposition pathway, there could be a need to repackage fuel assemblies already loaded and being loaded into the dry storage canisters currently in use. The implications of where and when the packaging or repackaging of commercial UNF will occur are key questions being addressed in this evaluation. The analysis demonstrated that thermal considerations will have a major impact on the operation of the system and that acceptance priority, rates, and facility start dates have significant system implications. (authors)

  8. Proceedings of intelligent engineering systems through artificial neural networks

    SciTech Connect

    Dagli, C.H. . Dept. of Engineering Management); Kumara, S.R. . Dept. of Industrial Management Systems Engineering); Shin, Y.C. . School of Mechanical Engineering)

    1991-01-01

    This book contains the edited versions of the technical presentation of ANNIE '91, the first international meeting on Artificial Neural Networks in Engineering. The conference covered the theory of Artificial Neural Networks and its contributions in the engineering domain and attracted researchers from twelve countries. The papers in this edited book are grouped into four categories: Artificial Neural Network Architectures; Pattern Recognition; Adaptive Control, Diagnosis and Process Monitoring; and Neuro-Engineering Systems.

  9. Support for User Interfaces for Distributed Systems

    NASA Technical Reports Server (NTRS)

    Eychaner, Glenn; Niessner, Albert

    2005-01-01

    An extensible Java(TradeMark) software framework supports the construction and operation of graphical user interfaces (GUIs) for distributed computing systems typified by ground control systems that send commands to, and receive telemetric data from, spacecraft. Heretofore, such GUIs have been custom built for each new system at considerable expense. In contrast, the present framework affords generic capabilities that can be shared by different distributed systems. Dynamic class loading, reflection, and other run-time capabilities of the Java language and JavaBeans component architecture enable the creation of a GUI for each new distributed computing system with a minimum of custom effort. By use of this framework, GUI components in control panels and menus can send commands to a particular distributed system with a minimum of system-specific code. The framework receives, decodes, processes, and displays telemetry data; custom telemetry data handling can be added for a particular system. The framework supports saving and later restoration of users configurations of control panels and telemetry displays with a minimum of effort in writing system-specific code. GUIs constructed within this framework can be deployed in any operating system with a Java run-time environment, without recompilation or code changes.

  10. Building intuitive 3D interfaces for virtual reality systems

    NASA Astrophysics Data System (ADS)

    Vaidya, Vivek; Suryanarayanan, Srikanth; Seitel, Mathias; Mullick, Rakesh

    2007-03-01

    An exploration of techniques for developing intuitive, and efficient user interfaces for virtual reality systems. Work seeks to understand which paradigms from the better-understood world of 2D user interfaces remain viable within 3D environments. In order to establish this a new user interface was created that applied various understood principles of interface design. A user study was then performed where it was compared with an earlier interface for a series of medical visualization tasks.

  11. A 700mV low power low noise implantable neural recording system design.

    PubMed

    An, Guanglei; Hutchens, Chriswell; Rennaker, Robert L

    2014-01-01

    A low power, low noise implantable neural recording interface for use in a Radio-Frequency Identification (RFID) is presented in this paper. A two stage neural amplifier and 8 bit Pipelined Analog to Digital Converter (ADC) are integrated in this system. The optimized number of amplifier stages demonstrates the minimum power and area consumption; The ADC utilizes a novel offset cancellation technique robust to device leakage to reduce the input offset voltage. The neural amplifier and ADC both utilize 700mV power supply. The midband gain of neural amplifier is 58.4dB with a 3dB bandwidth from 0.71 to 8.26 kHz. Measured input-referred noise and total power consumption are 20.7μVrms and 1.90 respectively. The ADC achieves 8 bit accuracy at 16Ksps with an input voltage of ±400mV. Combined simulation and measurement results demonstrate the neural recording interface's suitability for in situ neutral activity recording. PMID:25571498

  12. Neural networks for aircraft system identification

    NASA Technical Reports Server (NTRS)

    Linse, Dennis J.

    1991-01-01

    Artificial neural networks offer some interesting possibilities for use in control. Our current research is on the use of neural networks on an aircraft model. The model can then be used in a nonlinear control scheme. The effectiveness of network training is demonstrated.

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

    PubMed

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

    1995-12-01

    The development of a reliable helicopter gearbox health monitoring system (HMS) has been the subject of considerable research over the past 15 years. The deployment of such a system could lead to a significant saving in lives and vehicles as well as dramatically reduce the cost of helicopter maintenance. Recent research results indicate that a neural network-based system could provide a viable solution to the problem. This paper presents two neural network-based realizations of an HMS system. A hybrid (digital/analog) neural system is proposed as an extremely accurate off-line monitoring tool used to reduce helicopter gearbox maintenance costs. In addition, an all analog neural network is proposed as a real-time helicopter gearbox fault monitor that can exploit the ability of an analog neural network to directly compute the discrete Fourier transform (DFT) as a sum of weighted samples. Hardware performance results are obtained using the Integrated Neural Computing Architecture (INCA/1) analog neural network platform that was designed and developed at The Charles Stark Draper Laboratory. The results indicate that it is possible to achieve a 100% fault detection rate with 0% false alarm rate by performing a DFT directly on the first layer of INCA/1 followed by a small-size two-layer feed-forward neural network and a simple post-processing majority voting stage. PMID:8550948

  14. A recurrent neural network for closed-loop intracortical brain-machine interface decoders

    NASA Astrophysics Data System (ADS)

    Sussillo, David; Nuyujukian, Paul; Fan, Joline M.; Kao, Jonathan C.; Stavisky, Sergey D.; Ryu, Stephen; Shenoy, Krishna

    2012-04-01

    Recurrent neural networks (RNNs) are useful tools for learning nonlinear relationships in time series data with complex temporal dependences. In this paper, we explore the ability of a simplified type of RNN, one with limited modifications to the internal weights called an echostate network (ESN), to effectively and continuously decode monkey reaches during a standard center-out reach task using a cortical brain-machine interface (BMI) in a closed loop. We demonstrate that the RNN, an ESN implementation termed a FORCE decoder (from first order reduced and controlled error learning), learns the task quickly and significantly outperforms the current state-of-the-art method, the velocity Kalman filter (VKF), using the measure of target acquire time. We also demonstrate that the FORCE decoder generalizes to a more difficult task by successfully operating the BMI in a randomized point-to-point task. The FORCE decoder is also robust as measured by the success rate over extended sessions. Finally, we show that decoded cursor dynamics are more like naturalistic hand movements than those of the VKF. Taken together, these results suggest that RNNs in general, and the FORCE decoder in particular, are powerful tools for BMI decoder applications.

  15. Neural Network Based Intelligent Sootblowing System

    SciTech Connect

    Mark Rhode

    2005-04-01

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

  16. Straddle Carrier Interface and Dispatching System

    Energy Science and Technology Software Center (ESTSC)

    2012-09-13

    SCIDS is the Data Dispatching and Transfer Point (DDTP) component of a straddle carrier-based radiation detection system developed for the DOE Megaports Initiative for scanning shipping containers in transshipment ports. Its purpose is to communicate with a Radiation Detection Straddle Carrier (RDSC) developed by Detector Networks International, sending commands to the RDSC and receiving sensor data from the RDSC. Incoming sensor and status data from the RDSC is forwarded to a back-end data storage andmore » display system that is external to SCIDS. SCIDS provides a graphical user interface for port operations personnel that displays location and status of the RDSC and status of each container in the port, and accepts commands from the operator directing the scanning operations of the RDSC.« less

  17. The crew activity planning system bus interface unit

    NASA Technical Reports Server (NTRS)

    Allen, M. A.

    1979-01-01

    The hardware and software designs used to implement a high speed parallel communications interface to the MITRE 307.2 kilobit/second serial bus communications system are described. The primary topic is the development of the bus interface unit.

  18. CAD/CAM-Interface For Optical Systems And Optical Drawings

    NASA Astrophysics Data System (ADS)

    Wieder, Eckart

    1989-04-01

    It is explained why a general interface for optical data between CAD/CAM-Systems is necessary. The requirements for the interface are discussed. The philosophy of a solution is demonstrated and it is shown how to proceed.

  19. Neural Systems for Speech and Song in Autism

    ERIC Educational Resources Information Center

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

    2012-01-01

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

  20. Neural fuzzy modeling of anaerobic biological wastewater treatment systems

    SciTech Connect

    Tay, J.H.; Zhang, X.

    1999-12-01

    Anaerobic biological wastewater treatment systems are difficult to model because their performance is complex and varies significantly with different reactor configurations, influent characteristics, and operational conditions. Instead of conventional kinetic modeling, advanced neural fuzzy technology was employed to develop a conceptual adaptive model for anaerobic treatment systems. The conceptual neural fuzzy model contains the robustness of fuzzy systems, the learning ability of neural networks, and can adapt to various situations. The conceptual model was used to simulate the daily performance of two high-rate anaerobic wastewater treatment systems with satisfactory results obtained.

  1. Styles Of Programming In Neural Networks And Expert Systems

    NASA Astrophysics Data System (ADS)

    Duda, Richard O.

    1989-03-01

    Neural networks and expert systems provide different ways to reduce the programming effort required to build complex systems. Adaptive neural networks are programmed merely by training them with examples. Rule-based expert system are developed incrementally merely by adding rules. Although neural networks seem best suited for low-level sensory processing and expert systems seem best suited for high-level symbolic processing, strikingly similar issues arise when these approaches are used in large-scale applications. Illustrative examples of such applications are presented and discussed.

  2. Air support facilities. [interface between air and surface transportation systems

    NASA Technical Reports Server (NTRS)

    1975-01-01

    Airports are discussed in terms of the interface between the ground and air for transportation systems. The classification systems, design, facilities, administration, and operations of airports are described.

  3. A neural network hybrid expert system

    SciTech Connect

    Goulding, J.R. . Dept. of Mechanical Engineering)

    1991-01-01

    When knowledge-based expert rules, equations, and proprietary languages extend Computer Aided Design and Computer Aided Manufacturing (CAD CAM) software, previously designed mechanisms can be scaled to satisfy new design requirements in the shortest time. However, embedded design alternatives needed by design engineers during the product conception and rework stages are lacking, and an operator is required who has a thorough understanding of the intended design and the how-to expertise needed to create and optimize the mechanisms. By applying neural network technology to build an expert system, a robust design supervisor system emerged which automated the embedded intellectual operations (e.g. questioning, identifying, selecting, and coordinating the design process) to (1) select the best mechanisms necessary to design a power transmission gearbox from proven solutions; (2) aid the inexperienced operator in developing complex design solutions; and (3) provide design alternatives which add back-to-the-drawing board capabilities to knowledge-based mechanical CAD/CAM software programs. 15 refs., 2 figs.

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

    PubMed

    Zhang, Yanjun; Tao, Gang; Chen, Mou

    2016-09-01

    This paper presents a new study on the adaptive neural network-based control of a class of noncanonical nonlinear systems with large parametric uncertainties. Unlike commonly studied canonical form nonlinear systems whose neural network approximation system models have explicit relative degree structures, which can directly be used to derive parameterized controllers for adaptation, noncanonical form nonlinear systems usually do not have explicit relative degrees, and thus their approximation system models are also in noncanonical forms. It is well-known that the adaptive control of noncanonical form nonlinear systems involves the parameterization of system dynamics. As demonstrated in this paper, it is also the case for noncanonical neural network approximation system models. Effective control of such systems is an open research problem, especially in the presence of uncertain parameters. This paper shows that it is necessary to reparameterize such neural network system models for adaptive control design, and that such reparameterization can be realized using a relative degree formulation, a concept yet to be studied for general neural network system models. This paper then derives the parameterized controllers that guarantee closed-loop stability and asymptotic output tracking for noncanonical form neural network system models. An illustrative example is presented with the simulation results to demonstrate the control design procedure, and to verify the effectiveness of such a new design method. PMID:26285223

  5. System Identification of X-33 Neural Network

    NASA Technical Reports Server (NTRS)

    Aggarwal, Shiv

    2003-01-01

    present attempt, as a start, focuses only on the entry phase. Since the main engine remains cut off in this phase, there is no thrust acting on the system. This considerably simplifies the equations of motion. We introduce another simplification by assuming the system to be linear after some non-linearities are removed analytically from our consideration. Under these assumptions, the problem could be solved by Classical Statistics by employing the least sum of squares approach. Instead we chose to use the Neural Network method. This method has many advantages. It is modern, more efficient, can be adapted to work even when the assumptions are diluted. In fact, Neural Networks try to model the human brain and are capable of pattern recognition.

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

    NASA Astrophysics Data System (ADS)

    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.

  7. Neural joint control for Space Shuttle Remote Manipulator System

    NASA Technical Reports Server (NTRS)

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

    1992-01-01

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

  8. Neural network based expert system for compressor stall monitoring

    NASA Technical Reports Server (NTRS)

    Lo, Ching F.; Shi, G. Z.

    1991-01-01

    This research is designed to apply a new information processing technology, artificial neural networks, to monitoring compressor stall. The outputs of neural networks support the dynamic knowledge data base of an expert system. This is the open-loop mode to avoid compressor stall. The integration of a control system with neural networks is the closed-loop mode in stall avoidance. The feasibility of the concept has been demonstrated for the compressor of 16-foot transonic/supersonic propulsion wind tunnels. The construction of a prototpye expert system has been initiated.

  9. Java interface to a computer-aided diagnosis system for acute pulmonary embolism using PIOPED findings

    NASA Astrophysics Data System (ADS)

    Frederick, Erik D.; Tourassi, Georgia D.; Gauger, Matthew; Floyd, Carey E., Jr.

    1999-05-01

    An interface to a Computer Aided Diagnosis (CAD) system for diagnosis of Acute Pulmonary Embolism (PE) from PIOPED radiographic findings was developed. The interface is based on Internet technology which is user-friendly and available on a broad range of computing platforms. It was designed to be used as a research tool and as a data collection tool, allowing researchers to observe the behavior of a CAD system and to collect radiographic findings on ventilation-perfusion lung scans and chest radiographs. The interface collects findings from physicians in the PIOPED reporting format, processes those findings and presents them as inputs to an artificial neural network (ANN) previously trained on findings from 1,064 patients from the Prospective Investigation of Pulmonary Embolism Diagnosis (PIOPED) study. The likelihood of PE predicted by the ANN and by the physician using the system is then saved for later analysis.

  10. Neural network simulations of the nervous system.

    PubMed

    van Leeuwen, J L

    1990-01-01

    Present knowledge of brain mechanisms is mainly based on anatomical and physiological studies. Such studies are however insufficient to understand the information processing of the brain. The present new focus on neural network studies is the most likely candidate to fill this gap. The present paper reviews some of the history and current status of neural network studies. It signals some of the essential problems for which answers have to be found before substantial progress in the field can be made. PMID:2245130

  11. Docosahexaenoic acid in neural signaling systems.

    PubMed

    Crawford, Michael A

    2006-01-01

    Docosahexaenoic acid has been conserved in neural signalling systems in the cephalopods, fish, amphibian, reptiles, birds, mammals, primates and humans. This extreme conservation, despite wide genomic changes over 500 million years, testifies to a uniqueness of this molecule in the brain. The brain selectively incorporates docosahexaenoic acid and its rate of incorporation into the developing brain has been shown to be greater than ten times more efficient than its synthesis from the omega 3 fatty acids of land plant origin. Data has now been published demonstrating a significant influence of dietary omega 3 fatty acids on neural gene expression. As docosahexaenoic acid is the only omega 3 fatty acid in the brain, it is likely that it is the ligand involved. The selective uptake, requirement for function and stimulation of gene expression would have conferred an advantage to a primate which separated from the chimpanzees in the forests and woodlands and sought a different ecological niche. In view of the paucity of docosahexaenoic acid in the land food chain it is likely that the advantage would have been gained from a lacustrine or marine coastal habitat with access to food rich in docosahexaenoic acid and the accessory micronutrients, such as iodine, zinc, copper, manganese and selenium, of importance in brain development and protection against peroxidation. Land agricultural development has, in recent time, come to dominate the human food chain. The decline in use and availability of aquatic resources is not considered important by Langdon (2006) as he considers the resource was not needed for human evolution and can be replaced from the terrestrial food chain. This notion is not supported by the biochemistry nor the molecular biology. He misses the point that the shoreline hypothesis is not just dependent on docosahexaenoic acid but also on the other accessory nutrients specifically required by the brain. Moreover he neglects the basic principle of Darwinian

  12. Verification and Validation of Neural Networks for Aerospace Systems

    NASA Technical Reports Server (NTRS)

    Mackall, Dale; Nelson, Stacy; Schumman, Johann; Clancy, Daniel (Technical Monitor)

    2002-01-01

    The Dryden Flight Research Center V&V working group and NASA Ames Research Center Automated Software Engineering (ASE) group collaborated to prepare this report. The purpose is to describe V&V processes and methods for certification of neural networks for aerospace applications, particularly adaptive flight control systems like Intelligent Flight Control Systems (IFCS) that use neural networks. This report is divided into the following two sections: 1) Overview of Adaptive Systems; and 2) V&V Processes/Methods.

  13. Verification and Validation of Neural Networks for Aerospace Systems

    NASA Technical Reports Server (NTRS)

    Mackall, Dale; Nelson, Stacy; Schumann, Johann

    2002-01-01

    The Dryden Flight Research Center V&V working group and NASA Ames Research Center Automated Software Engineering (ASE) group collaborated to prepare this report. The purpose is to describe V&V processes and methods for certification of neural networks for aerospace applications, particularly adaptive flight control systems like Intelligent Flight Control Systems (IFCS) that use neural networks. This report is divided into the following two sections: Overview of Adaptive Systems and V&V Processes/Methods.

  14. Orbiter Interface Unit and Early Communication System

    NASA Technical Reports Server (NTRS)

    Cobbs, Ronald M.; Cooke, Michael P.; Cox, Gary L.; Ellenberger, Richard; Fink, Patrick W.; Haynes, Dena S.; Hyams, Buddy; Ling, Robert Y.; Neighbors, Helen M.; Phan, Chau T.; Prendergast, Kelly M.; Siekierski, James D.; Wade, Randall S.; Weisskopf, George A.; Yim, Hester J.; Adkins, Antha A.; Carl, James R..; Loh, Y. C.; Roberts, Charles; Steele, Douglas J.; DeSilva, Buveneka Kanishka; Killenb, Harold B.; Williams, Robert M.

    2004-01-01

    This report describes the Orbiter Interface Unit (OIU) and the Early Communication System (ECOMM), which are systems of electronic hardware and software that serve as the primary communication links for the International Space Station (ISS). When a space shuttle is at or near the ISS during assembly and resupply missions, the OIU sends groundor crew-initiated commands from the space shuttle to the ISS and relays telemetry from the ISS to the space shuttle s payload data systems. The shuttle then forwards the telemetry to the ground. In the absence of a space shuttle, the ECOMM handles communications between the ISS and Johnson Space Center via the Tracking and Data Relay Satellite System (TDRSS). Innovative features described in the report include (1) a "smart data-buffering algorithm that helps to preserve synchronization (and thereby minimize loss) of telemetric data between the OIU and the space-shuttle payload data interleaver; (2) an ECOMM antenna-autotracking algorithm that selects whichever of two phased-array antennas gives the best TDRSS signal and electronically steers that antenna to track the TDRSS source; and (3) an ECOMM radiation-latchup controller, which detects an abrupt increase in current indicative of radiation-induced latchup and temporarily turns off power to clear the latchup, restoring power after the charge dissipates.

  15. Neural network classification of autoregressive features from electroencephalogram signals for brain computer interface design

    NASA Astrophysics Data System (ADS)

    Huan, Nai-Jen; Palaniappan, Ramaswamy

    2004-09-01

    In this paper, we have designed a two-state brain-computer interface (BCI) using neural network (NN) classification of autoregressive (AR) features from electroencephalogram (EEG) signals extracted during mental tasks. The main purpose of the study is to use Keirn and Aunon's data to investigate the performance of different mental task combinations and different AR features for BCI design for individual subjects. In the experimental study, EEG signals from five mental tasks were recorded from four subjects. Different combinations of two mental tasks were studied for each subject. Six different feature extraction methods were used to extract the features from the EEG signals: AR coefficients computed with Burg's algorithm, AR coefficients computed with a least-squares (LS) algorithm and adaptive autoregressive (AAR) coefficients computed with a least-mean-square (LMS) algorithm. All the methods used order six applied to 125 data points and these three methods were repeated with the same data but with segmentation into five segments in increments of 25 data points. The multilayer perceptron NN trained by the back-propagation algorithm (MLP-BP) and linear discriminant analysis (LDA) were used to classify the computed features into different categories that represent the mental tasks. We compared the classification performances among the six different feature extraction methods. The results showed that sixth-order AR coefficients with the LS algorithm without segmentation gave the best performance (93.10%) using MLP-BP and (97.00%) using LDA. The results also showed that the segmentation and AAR methods are not suitable for this set of EEG signals. We conclude that, for different subjects, the best mental task combinations are different and proper selection of mental tasks and feature extraction methods are essential for the BCI design.

  16. Tuning of power system stabilizers using an artificial neural network

    SciTech Connect

    Hsu, Y.Y.; Chen, C.R. )

    1991-12-01

    This paper reports on tuning of power system stabilizers (PSS) which is investigated using an artificial neural network (ANN). To have good damping characteristics over a wide range of operating conditions, it is desirable to adapt the PSS parameters in real-time based on generator loading conditions. To do this, a pair of on-line measurements, i.e. generator real power output (P) and power factor (PF), which are representative of generator operating condition, are chosen as the input signals to the neural net. The outputs of the neural net are the desired PSS parameters. The neural net, once trained by a set of input-output patterns in the training set, can yield proper PSS parameters under any generator loading condition. Digital simulations of a synchronous machine subject to a major disturbance of three-phase fault under different operating conditions are performed to demonstrate the effectiveness of the proposed neural network.

  17. Dopamine system: manager of neural pathways.

    PubMed

    Hong, Simon

    2013-01-01

    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 provide the needed 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. PMID:24367324

  18. Dopamine system: manager of neural pathways

    PubMed Central

    Hong, Simon

    2013-01-01

    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 provide the needed 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. PMID:24367324

  19. HermesC: low-power wireless neural recording system for freely moving primates.

    PubMed

    Chestek, Cynthia A; Gilja, Vikash; Nuyujukian, Paul; Kier, Ryan J; Solzbacher, Florian; Ryu, Stephen I; Harrison, Reid R; Shenoy, Krishna V

    2009-08-01

    Neural prosthetic systems have the potential to restore lost functionality to amputees or patients suffering from neurological injury or disease. Current systems have primarily been designed for immobile patients, such as tetraplegics functioning in a rather static, carefully tailored environment. However, an active patient such as amputee in a normal dynamic, everyday environment may be quite different in terms of the neural control of movement. In order to study motor control in a more unconstrained natural setting, we seek to develop an animal model of freely moving humans. Therefore, we have developed and tested HermesC-INI3, a system for recording and wirelessly transmitting neural data from electrode arrays implanted in rhesus macaques who are freely moving. This system is based on the integrated neural interface (INI3) microchip which amplifies, digitizes, and transmits neural data across a approximately 900 MHz wireless channel. The wireless transmission has a range of approximately 4 m in free space. All together this device consumes 15.8 mA and 63.2 mW. On a single 2 A-hr battery pack, this device runs contiguously for approximately six days. The smaller size and power consumption of the custom IC allows for a smaller package (51 x 38 x 38 mm (3)) than previous primate systems. The HermesC-INI3 system was used to record and telemeter one channel of broadband neural data at 15.7 kSps from a monkey performing routine daily activities in the home cage. PMID:19497829

  20. Ocular attention-sensing interface system

    NASA Technical Reports Server (NTRS)

    Zaklad, Allen; Glenn, Floyd A., III; Iavecchia, Helene P.; Stokes, James M.

    1986-01-01

    The purpose of the research was to develop an innovative human-computer interface based on eye movement and voice control. By eliminating a manual interface (keyboard, joystick, etc.), OASIS provides a control mechanism that is natural, efficient, accurate, and low in workload.

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

    NASA Astrophysics Data System (ADS)

    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.

  2. Neural networks for self-learning control systems

    NASA Technical Reports Server (NTRS)

    Nguyen, Derrick H.; Widrow, Bernard

    1990-01-01

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

  3. Genetic learning in rule-based and neural systems

    NASA Technical Reports Server (NTRS)

    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.

  4. Implementations of learning control systems using neural networks

    NASA Technical Reports Server (NTRS)

    Sartori, Michael A.; Antsaklis, Panos J.

    1992-01-01

    The systematic storage in neural networks of prior information to be used in the design of various control subsystems is investigated. Assuming that the prior information is available in a certain form (namely, input/output data points and specifications between the data points), a particular neural network and a corresponding parameter design method are introduced. The proposed neural network addresses the issue of effectively using prior information in the areas of dynamical system (plant and controller) modeling, fault detection and identification, information extraction, and control law scheduling.

  5. Hybrid neural network and rule-based pattern recognition system capable of self-modification

    SciTech Connect

    Glover, C.W.; Silliman, M.; Walker, M.; Spelt, P.F. ); Rao, N.S.V. . Dept. of Computer Science)

    1990-01-01

    This paper describes a hybrid neural network and rule-based pattern recognition system architecture which is capable of self-modification or learning. The central research issue to be addressed for a multiclassifier hybrid system is whether such a system can perform better than the two classifiers taken by themselves. The hybrid system employs a hierarchical architecture, and it can be interfaced with one or more sensors. Feature extraction routines operating on raw sensor data produce feature vectors which serve as inputs to neural network classifiers at the next level in the hierarchy. These low-level neural networks are trained to provide further discrimination of the sensor data. A set of feature vectors is formed from a concatenation of information from the feature extraction routines and the low-level neural network results. A rule-based classifier system uses this feature set to determine if certain expected environmental states, conditions, or objects are present in the sensors' current data stream. The rule-based system has been given an a priori set of models of the expected environmental states, conditions, or objects which it is expected to identify. The rule-based system forms many candidate directed graphs of various combinations of incoming features vectors, and it uses a suitably chosen metric to measure the similarity between candidate and model directed graphs. The rule-based system must decide if there is a match between one of the candidate graphs and a model graph. If a match is found, then the rule-based system invokes a routine to create and train a new high-level neural network from the appropriate feature vector data to recognize when this model state is present in future sensor data streams. 12 refs., 3 figs.

  6. Brain-Machine Interactions for Assessing the Dynamics of Neural Systems

    PubMed Central

    Kositsky, Michael; Chiappalone, Michela; Alford, Simon T.; Mussa-Ivaldi, Ferdinando A.

    2008-01-01

    A critical advance for brain–machine interfaces is the establishment of bi-directional communications between the nervous system and external devices. However, the signals generated by a population of neurons are expected to depend in a complex way upon poorly understood neural dynamics. We report a new technique for the identification of the dynamics of a neural population engaged in a bi-directional interaction with an external device. We placed in vitro preparations from the lamprey brainstem in a closed-loop interaction with simulated dynamical devices having different numbers of degrees of freedom. We used the observed behaviors of this composite system to assess how many independent parameters − or state variables − determine at each instant the output of the neural system. This information, known as the dynamical dimension of a system, allows predicting future behaviors based on the present state and the future inputs. A relevant novelty in this approach is the possibility to assess a computational property – the dynamical dimension of a neuronal population – through a simple experimental technique based on the bi-directional interaction with simulated dynamical devices. We present a set of results that demonstrate the possibility of obtaining stable and reliable measures of the dynamical dimension of a neural preparation. PMID:19430593

  7. Multiple neural network approaches to clinical expert systems

    NASA Astrophysics Data System (ADS)

    Stubbs, Derek F.

    1990-08-01

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

  8. An online EEG-based brain-computer interface for controlling hand grasp using an adaptive probabilistic neural network.

    PubMed

    Hazrati, Mehrnaz Kh; Erfanian, Abbas

    2010-09-01

    This paper presents a new online single-trial EEG-based brain-computer interface (BCI) for controlling hand holding and sequence of hand grasping and opening in an interactive virtual reality environment. The goal of this research is to develop an interaction technique that will allow the BCI to be effective in real-world scenarios for hand grasp control. One of the major challenges in the BCI research is the subject training. Currently, in most online BCI systems, the classifier was trained offline using the data obtained during the experiments without feedback, and used in the next sessions in which the subjects receive feedback. We investigated whether the subject could achieve satisfactory online performance without offline training while the subjects receive feedback from the beginning of the experiments during hand movement imagination. Another important issue in designing an online BCI system is the machine learning to classify the brain signal which is characterized by significant day-to-day and subject-to-subject variations and time-varying probability distributions. Due to these variabilities, we introduce the use of an adaptive probabilistic neural network (APNN) working in a time-varying environment for classification of EEG signals. The experimental evaluation on ten naïve subjects demonstrated that an average classification accuracy of 75.4% was obtained during the first experiment session (day) after about 3 min of online training without offline training, and 81.4% during the second session (day). The average rates during third and eighth sessions are 79.0% and 84.0%, respectively, using previously calculated classifier during the first sessions, without online training and without the need to calibrate. The results obtained from more than 5000 trials on ten subjects showed that the method could provide a robust performance over different experiment sessions and different subjects. PMID:20510641

  9. Adaptive control of nonlinear systems using multistage dynamic neural networks

    NASA Astrophysics Data System (ADS)

    Gupta, Madan M.; Rao, Dandina H.

    1992-11-01

    In this paper we present a new architecture of neuron, called the dynamic neural unit (DNU). The topology of the proposed neuronal model embodies delay elements, feedforward and feedback signals weighted by the synaptic weights and a time-varying nonlinear activation function, and is thus different from the conventionally and assumed architecture of neurons. The learning algorithm for the proposed neuronal structure and the corresponding implementation scheme are presented. A multi-stage dynamic neural network is developed using the DNU as the basic processing element. The performance evaluation of the dynamic neural network is presented for nonlinear dynamic systems under various situations. The capabilities of the proposed neural network model not only account for the learning and control actions emulating some of the biological control functions, but also provide a promising parallel-distributed intelligent control scheme for large-scale complex dynamic systems.

  10. Recasting brain-machine interface design from a physical control system perspective.

    PubMed

    Zhang, Yin; Chase, Steven M

    2015-10-01

    With the goal of improving the quality of life for people suffering from various motor control disorders, brain-machine interfaces provide direct neural control of prosthetic devices by translating neural signals into control signals. These systems act by reading motor intent signals directly from the brain and using them to control, for example, the movement of a cursor on a computer screen. Over the past two decades, much attention has been devoted to the decoding problem: how should recorded neural activity be translated into the movement of the cursor? Most approaches have focused on this problem from an estimation standpoint, i.e., decoders are designed to return the best estimate of motor intent possible, under various sets of assumptions about how the recorded neural signals represent motor intent. Here we recast the decoder design problem from a physical control system perspective, and investigate how various classes of decoders lead to different types of physical systems for the subject to control. This framework leads to new interpretations of why certain types of decoders have been shown to perform better than others. These results have implications for understanding how motor neurons are recruited to perform various tasks, and may lend insight into the brain's ability to conceptualize artificial systems. PMID:26142906

  11. Prediction of fatigue crack propagation rate on the interface of wood-FRP using the artificial neural network (ANN)

    NASA Astrophysics Data System (ADS)

    Zhang, Liang; Jia, Junhui; Liu, Yongjun

    2008-11-01

    Crack propagation rate of the interface of fiber reinforced polymer (FRP) bonded to red maple wood, is analyzed and predicted using an artificial neural network (ANN) method. The performance of Multilayer Perceptron (MLP) and Modular Neural Network (MNN) is compared to obtain an optimal ANN model to predict the crack propagation rate. The effect of various parameters of the MNN and MLP models are investigated. The number of input vectors of MLP and MNN models is studied to see if this will affect the training and predicting performance by the scatter of input vectors. At last, a new method called sensitivity analysis is adopted to explore the influenced proportion of the input vectors and the effect of load ratio, frequency, et al., on the crack propagation rate.

  12. Interface management for the Mined Geologic Disposal System

    SciTech Connect

    Ashlock, K.J.

    1998-03-01

    The purpose of this paper is to present the interface management process that is to be used for Mined Geologic Disposal System (MGDS) development. As part of the systems engineering and integration performed on the Yucca Mountain Project (YMP), interface management is critical in the development of the potential MGDS. The application of interface management on the YMP directly addresses integration between physical elements of the MGDS and the organizations responsible for their development.

  13. Systems Engineering Interfaces: A Model Based Approach

    NASA Technical Reports Server (NTRS)

    Fosse, Elyse; Delp, Christopher

    2013-01-01

    Currently: Ops Rev developed and maintains a framework that includes interface-specific language, patterns, and Viewpoints. Ops Rev implements the framework to design MOS 2.0 and its 5 Mission Services. Implementation de-couples interfaces and instances of interaction Future: A Mission MOSE implements the approach and uses the model based artifacts for reviews. The framework extends further into the ground data layers and provides a unified methodology.

  14. A CMOS-based on-chip neural interface device equipped with integrated LED array for optogenetics.

    PubMed

    Tokuda, T; Miyatani, T; Maezawa, Y; Kobayashi, T; Noda, T; Sasagawa, K; Ohta, J

    2012-01-01

    A novel CMOS-based neural interface device equipped with an integrated micro light source array was proposed and demonstrated. Target application of the device is optogenetics. GaInN LED array formed on sapphire substrate was successfully assembled with a multifunctional CMOS image sensor which is capable of injecting current via any of the pixel. We demonstrated addressable LED operation with the present device. The device has advantages such as simultaneous multi-site stimulation and on-chip optical imaging, that are not available with previously reported LED array device for optogenetics. PMID:23367087

  15. Long term in vitro stability of fully integrated wireless neural interfaces based on Utah slant electrode array

    NASA Astrophysics Data System (ADS)

    Sharma, Asha; Rieth, Loren; Tathireddy, Prashant; Harrison, Reid; Solzbacher, Florian

    2010-02-01

    We herein report in vitro functional stability and recording longevity of a fully integrated wireless neural interface (INI). The INI uses biocompatible Parylene-C as an encapsulation layer, and was immersed in phosphate buffered saline (PBS) for a period of over 150 days. The full functionality (wireless radio-frequency power, command, and signal transmission) and the ability of INI to record artificial action potentials even after 150 days of PBS soaking without any change in signal/noise amplitude constitutes a major milestone in long term stability, and evaluate the encapsulation reliability, functional stability, and potential usefulness for future chronic implants.

  16. A board system for high-speed image analysis and neural networks.

    PubMed

    Sackinger, E; Graf, H P

    1996-01-01

    Two ANNA neural-network chips are integrated on a 6U VME board, to serve as a high-speed platform for a wide variety of algorithms used in neural-network applications as well as in image analysis. The system can implement neural networks of variable sizes and architectures, but can also be used for filtering and feature extraction tasks that are based on convolutions. The board contains a controller implemented with field programmable gate arrays (FPGA's), memory, and bus interfaces, all designed to support the high compute power of the ANNA chips. This new system is designed for maximum speed and is roughly 10 times faster than a previous board. The system has been tested for such tasks as text location, character recognition, and noise removal as well as for emulating cellular neural networks (CNN's). A sustained speed of up to two billion connections per second (GC/s) and a recognition speed of 1000 characters per second has been measured. PMID:18255573

  17. User interface for integrated computer aided design systems

    NASA Technical Reports Server (NTRS)

    Schwing, James L.

    1986-01-01

    The purpose was the development of a user interface and other appropriate tools to be used in Computer Aided Design systems which can integrate a wide variety of independently developed design and analysis tools. The interface was intended for the integration of programs to be used in the conceptual design of aerospace systems. A user's manual is included.

  18. Novel 3D plasmonic nano-electrodes for cellular investigations and neural interfaces

    NASA Astrophysics Data System (ADS)

    Malerba, Mario; Dipalo, Michele; Messina, Gabriele C.; Amin, Hayder; La Rocca, Rosanna; Shalabaeva, Victoria; Simi, Alessandro; Maccione, Alessandro; Berdondini, Luca; De Angelis, Francesco

    2014-08-01

    We propose the development of an innovative plasmonic-electronic multifunctional platform, capable at the same time of performing chemical analysis and electronic recordings from a cellular interface. The system, based on 3D hollow metallic nanotubes, integrated on customized multi-electrode-arrays, allows the study of neuronal signaling over different lengths, spanning from the molecular, to the cellular, to the network scale. Here we show that the same structures are efficient electric field enhancers, despite the continuous metal layer at the base, which connects them to the electric components of the integrated circuits. The methodology we propose, due to its simplicity and high throughput, has the potential for further improvements both in the field of plasmonics, and in the integration on large areas of commercial active electronic devices.

  19. Neural network based diagonal decoupling control of powered wheelchair systems.

    PubMed

    Nguyen, Tuan Nghia; Su, Steven; Nguyen, Hung T

    2014-03-01

    This paper proposes an advanced diagonal decoupling control method for powered wheelchair systems. This control method is based on a combination of the systematic diagonalization technique and the neural network control design. As such, this control method reduces coupling effects on a multivariable system, leading to independent control design procedures. Using an obtained dynamic model, the problem of the plant's Jacobian calculation is eliminated in a neural network control design. The effectiveness of the proposed control method is verified in a real-time implementation on a powered wheelchair system. The obtained results confirm that robustness and desired performance of the overall system are guaranteed, even under parameter uncertainty effects. PMID:23981543

  20. Variable neural adaptive robust control: a switched system approach.

    PubMed

    Lian, Jianming; Hu, Jianghai; Żak, Stanislaw H

    2015-05-01

    Variable neural adaptive robust control strategies are proposed for the output tracking control of a class of multiinput multioutput uncertain systems. The controllers incorporate a novel variable-structure radial basis function (RBF) network as the self-organizing approximator for unknown system dynamics. It can determine the network structure online dynamically by adding or removing RBFs according to the tracking performance. The structure variation is systematically considered in the stability analysis of the closed-loop system using a switched system approach with the piecewise quadratic Lyapunov function. The performance of the proposed variable neural adaptive robust controllers is illustrated with simulations. PMID:25881366

  1. On neural networks in identification and control of dynamic systems

    NASA Technical Reports Server (NTRS)

    Phan, Minh; Juang, Jer-Nan; Hyland, David C.

    1993-01-01

    This paper presents a discussion of the applicability of neural networks in the identification and control of dynamic systems. Emphasis is placed on the understanding of how the neural networks handle linear systems and how the new approach is related to conventional system identification and control methods. Extensions of the approach to nonlinear systems are then made. The paper explains the fundamental concepts of neural networks in their simplest terms. Among the topics discussed are feed forward and recurrent networks in relation to the standard state-space and observer models, linear and nonlinear auto-regressive models, linear, predictors, one-step ahead control, and model reference adaptive control for linear and nonlinear systems. Numerical examples are presented to illustrate the application of these important concepts.

  2. OCT detection of neural activity in American cockroach nervous system

    NASA Astrophysics Data System (ADS)

    Gorczyńska, Iwona; Wyszkowska, Joanna; Bukowska, Danuta; Ruminski, Daniel; Karnowski, Karol; Stankiewicz, Maria; Wojtkowski, Maciej

    2013-03-01

    We show results of a project which focuses on detection of activity in neural tissue with Optical Coherence Tomography (OCT) methods. Experiments were performed in neural cords dissected from the American cockroach (Periplaneta americana L.). Functional OCT imaging was performed with ultrahigh resolution spectral / Fourier domain OCT system (axial resolution 2.5 μm). Electrical stimulation (voltage pulses) was applied to the sensory cercal nerve of the neural cord. Optical detection of functional activation of the sample was performed in the connective between the terminal abdominal ganglion and the fifth abdominal ganglion. Functional OCT data were collected over time with the OCT beam illuminating selected single point in the connectives (i.e. OCT M-scans were acquired). Phase changes of the OCT signal were analyzed to visualize occurrence of activation in the neural cord. Electrophysiology recordings (microelectrode method) were also performed as a reference method to demonstrate electrical response of the sample to stimulation.

  3. Ground Systems Development Environment (GSDE) interface requirements analysis

    NASA Technical Reports Server (NTRS)

    Church, Victor E.; Philips, John; Hartenstein, Ray; Bassman, Mitchell; Ruskin, Leslie; Perez-Davila, Alfredo

    1991-01-01

    A set of procedural and functional requirements are presented for the interface between software development environments and software integration and test systems used for space station ground systems software. The requirements focus on the need for centralized configuration management of software as it is transitioned from development to formal, target based testing. This concludes the GSDE Interface Requirements study. A summary is presented of findings concerning the interface itself, possible interface and prototyping directions for further study, and results of the investigation of the Cronus distributed applications environment.

  4. Man-systems integration and the man-machine interface

    NASA Technical Reports Server (NTRS)

    Hale, Joseph P.

    1990-01-01

    Viewgraphs on man-systems integration and the man-machine interface are presented. Man-systems integration applies the systems' approach to the integration of the user and the machine to form an effective, symbiotic Man-Machine System (MMS). A MMS is a combination of one or more human beings and one or more physical components that are integrated through the common purpose of achieving some objective. The human operator interacts with the system through the Man-Machine Interface (MMI).

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

    SciTech Connect

    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.

  6. Space transportation system payload interface verification

    NASA Technical Reports Server (NTRS)

    Everline, R. T.

    1977-01-01

    The paper considers STS payload-interface verification requirements and the capability provided by STS to support verification. The intent is to standardize as many interfaces as possible, not only through the design, development, test and evaluation (DDT and E) phase of the major payload carriers but also into the operational phase. The verification process is discussed in terms of its various elements, such as the Space Shuttle DDT and E (including the orbital flight test program) and the major payload carriers DDT and E (including the first flights). Five tools derived from the Space Shuttle DDT and E are available to support the verification process: mathematical (structural and thermal) models, the Shuttle Avionics Integration Laboratory, the Shuttle Manipulator Development Facility, and interface-verification equipment (cargo-integration test equipment).

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

    PubMed

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

    2016-07-01

    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. PMID:27296463

  8. An implantable 64-channel neural interface with reconfigurable recording and stimulation.

    PubMed

    Wheeler, Jesse J; Baldwin, Keith; Kindle, Alex; Guyon, Daniel; Nugent, Brian; Segura, Carlos; Rodriguez, John; Czarnecki, Andrew; Dispirito, Hailey J; Lachapelle, John; Parks, Philip D; Moran, James; Widge, Alik S; Dougherty, Darin D; Eskandar, Emad N

    2015-08-01

    Next generation implantable medical devices will have the potential to provide more precise and effective therapies through adaptive closed-loop controllers that combine sensing and stimulation across larger numbers of electrode channels. A major challenge in the design of such devices is balancing increased functionality and channel counts with the miniaturization required for implantation within small anatomical spaces. Customized therapies will require adaptive systems capable of tuning which channels are sensed and stimulated to overcome variability in patient-specific needs, surgical placement of electrodes, and chronic physiological responses. In order to address these challenges, we have designed a miniaturized implantable fully-reconfigurable front-end system that is integrated into the distal end of an 8-wire lead, enabling up to 64 electrodes to be dynamically configured for sensing and stimulation. Full reconfigurability is enabled by two custom 32×2 cross-point switch (CPS) matrix ASICs which can route any electrode to either an amplifier with reprogrammable bandwidth and integrated ADC or to one of two independent stimulation channels that can be driven through the lead. The 8-wire circuit includes a digital interface for robust communication as well as a charge-balanced powering scheme for enhanced safety. The system is encased in a hermetic package designed to fit within a 14 mm bur-hole in the skull for neuromodulation of the brain, but could easily be adapted to enhance therapies across a broad spectrum of applications. PMID:26738108

  9. Decoding of grasping information from neural signals recorded using peripheral intrafascicular interfaces

    PubMed Central

    2011-01-01

    Background The restoration of complex hand functions by creating a novel bidirectional link between the nervous system and a dexterous hand prosthesis is currently pursued by several research groups. This connection must be fast, intuitive, with a high success rate and quite natural to allow an effective bidirectional flow of information between the user's nervous system and the smart artificial device. This goal can be achieved with several approaches and among them, the use of implantable interfaces connected with the peripheral nervous system, namely intrafascicular electrodes, is considered particularly interesting. Methods Thin-film longitudinal intra-fascicular electrodes were implanted in the median and ulnar nerves of an amputee's stump during a four-week trial. The possibility of decoding motor commands suitable to control a dexterous hand prosthesis was investigated for the first time in this research field by implementing a spike sorting and classification algorithm. Results The results showed that motor information (e.g., grip types and single finger movements) could be extracted with classification accuracy around 85% (for three classes plus rest) and that the user could improve his ability to govern motor commands over time as shown by the improved discrimination ability of our classification algorithm. Conclusions These results open up new and promising possibilities for the development of a neuro-controlled hand prosthesis. PMID:21892926

  10. FPGA implementation of a pyramidal Weightless Neural Networks learning system.

    PubMed

    Al-Alawi, Raida

    2003-08-01

    A hardware architecture of a Probabilistic Logic Neuron (PLN) is presented. The suggested model facilitates the on-chip learning of pyramidal Weightless Neural Networks using a modified probabilistic search reward/penalty training algorithm. The penalization strategy of the training algorithm depends on a predefined parameter called the probabilistic search interval. A complete Weightless Neural Network (WNN) learning system is modeled and implemented on Xilinx XC4005E Field Programmable Gate Array (FPGA), allowing its architecture to be configurable. Various experiments have been conducted to examine the feasibility and performance of the WNN learning system. Results show that the system has a fast convergence rate and good generalization ability. PMID:12964210

  11. Nonlinear control structures based on embedded neural system models.

    PubMed

    Lightbody, G; Irwin, G W

    1997-01-01

    This paper investigates in detail the possible application of neural networks to the modeling and adaptive control of nonlinear systems. Nonlinear neural-network-based plant modeling is first discussed, based on the approximation capabilities of the multilayer perceptron. A structure is then proposed to utilize feedforward networks within a direct model reference adaptive control strategy. The difficulties involved in training this network, embedded within the closed-loop are discussed and a novel neural-network-based sensitivity modeling approach proposed to allow for the backpropagation of errors through the plant to the neural controller. Finally, a novel nonlinear internal model control (IMC) strategy is suggested, that utilizes a nonlinear neural model of the plant to generate parameter estimates over the nonlinear operating region for an adaptive linear internal model, without the problems associated with recursive parameter identification algorithms. Unlike other neural IMC approaches the linear control law can then be readily designed. A continuous stirred tank reactor was chosen as a realistic nonlinear case study for the techniques discussed in the paper. PMID:18255659

  12. A Graphical User-Interface for Propulsion System Analysis

    NASA Technical Reports Server (NTRS)

    Curlett, Brian P.; Ryall, Kathleen

    1992-01-01

    NASA LeRC uses a series of computer codes to calculate installed propulsion system performance and weight. The need to evaluate more advanced engine concepts with a greater degree of accuracy has resulted in an increase in complexity of this analysis system. Therefore, a graphical user interface was developed to allow the analyst to more quickly and easily apply these codes. The development of this interface and the rationale for the approach taken are described. The interface consists of a method of pictorially representing and editing the propulsion system configuration, forms for entering numerical data, on-line help and documentation, post processing of data, and a menu system to control execution.

  13. Characterization of a-SiCx:H thin films as an encapsulation material for integrated silicon based neural interface devices

    PubMed Central

    Hsu, Jui-Mei; Tathireddy, Prashant; Rieth, Loren; Normann, A. Richard; Solzbacher, Florian

    2008-01-01

    A fully integrated, wireless neural interface device is being developed to free patients from the restriction and risk of infection associated with a transcutaneous wired connection. This device requires a hermetic, biocompatible encapsulation layer at the interface between the device and the neural tissue to maintain long-term recording/stimulating performance of the device. Hydrogenated amorphous silicon carbide (a-SiCx:H) films deposited by a plasma enhanced chemical vapor deposition using SiH4, CH4, and H2 precursors were investigated as the encapsulation layer for such device. Si-C bond density, measured by Fourier transform infrared absorption spectrometer, suggests that deposition conditions with increased hydrogen dilution, increased temperature, and low silane flow typically result in increase of Si-C bond density. From the variable angle spectroscopic ellipsometry measurement, no dissolution of a-SiCx:H was observed during soaking tests in 90°C phosphate buffered saline. Conformal coating of the a-SiCx:H in Utah electrode array was observed by scanning electron microscope. Electrical properties were studied by impedance spectroscopy to investigate the performance of a-SiCx:H as an encapsulation layer, and the results showed long term stability of the material. PMID:18437249

  14. [Design and Implementation of a Programmable Wireless Neural Stimulation System].

    PubMed

    Zhang, Zhang; Yu, Wencheng; Tan, Ye; Zeng, Jianmin; Xie, Guangjun

    2016-01-01

    The paper proposes and realizes a programmable wireless neural stimulation system which can be used as a solution of functional electrical stimulation to treat neural diseases. The system is composed of two parts: controller and neural stimulator. The controller can transmit pulse parameters to the stimulator wirelessly, and the stimulator can generate bidirectional pulses with charge balance. The simulator takes use of ADCs to sample on the bidirectional pulse output, which compared with preset amplitude to the DAC output voltage to realize the voltage calibration. Through the test, the whole system works stably and the output of the biphasic charge balanced circuit is definite. The stimulator output ranges from 0 to 5 V ajustably, and the frequency ranges from 1 Hz to 200 Hz ajustably, while the pulse width ranges from 500 μs to 1500 μs ajustably. The duration of the stimulation can be set from 10 s to 10 min. PMID:27197493

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

    SciTech Connect

    Vitela, J.E.; Hanebutte, U.R.; Reifman, J.

    1996-04-01

    An Autonomous Intelligent Cruise Control (AICC) has been designed using a feedforward artificial neural network, as an example for utilizing artificial neural networks for nonlinear control problems arising in intelligent transportation systems applications. The AICC is based on a simple nonlinear model of the vehicle dynamics. A Neural Network Controller (NNC) code developed at Argonne National Laboratory to control discrete dynamical systems was used for this purpose. In order to test the NNC, an AICC-simulator containing graphical displays was developed for a system of two vehicles driving in a single lane. Two simulation cases are shown, one involving a lead vehicle with constant velocity and the other a lead vehicle with varying acceleration. More realistic vehicle dynamic models will be considered in future work.

  16. Neural Network Control of a Magnetically Suspended Rotor System

    NASA Technical Reports Server (NTRS)

    Choi, Benjamin; Brown, Gerald; Johnson, Dexter

    1997-01-01

    Abstract Magnetic bearings offer significant advantages because of their noncontact operation, which can reduce maintenance. Higher speeds, no friction, no lubrication, weight reduction, precise position control, and active damping make them far superior to conventional contact bearings. However, there are technical barriers that limit the application of this technology in industry. One of them is the need for a nonlinear controller that can overcome the system nonlinearity and uncertainty inherent in magnetic bearings. This paper discusses the use of a neural network as a nonlinear controller that circumvents system nonlinearity. A neural network controller was well trained and successfully demonstrated on a small magnetic bearing rig. This work demonstrated the feasibility of using a neural network to control nonlinear magnetic bearings and systems with unknown dynamics.

  17. Microfabrication of 3D neural probes with combined electrical and chemical interfaces

    NASA Astrophysics Data System (ADS)

    John, Jessin; Li, Yuefa; Zhang, Jinsheng; Loeb, Jeffrey A.; Xu, Yong

    2011-10-01

    This paper reports a novel neural probe technology for the manufacture of 3D arrays of electrodes with integrated microchannels. This new technology is based on a silicon island structure and a simple folding procedure. This method simplifies the assembly or packaging process of 3D neural probes, leading to higher yield and lower cost. Prototypes with 3D arrays of electrodes have been successfully developed. Microchannels have been successfully integrated into the 3D neural probes via isotropic XeF2 gas phase etching and a parylene resealing process. The probes have been characterized by scanning electron microscopy imaging, optical imaging, impedance analysis, and atomic force microscopy characterization of the electrode surface. Preliminary animal tests have been carried out to demonstrate the recording functionality of the probes. Flow characteristics of the microchannels were also preliminarily measured.

  18. A closed-loop compressive-sensing-based neural recording system

    NASA Astrophysics Data System (ADS)

    Zhang, Jie; Mitra, Srinjoy; Suo, Yuanming; Cheng, Andrew; Xiong, Tao; Michon, Frederic; Welkenhuysen, Marleen; Kloosterman, Fabian; Chin, Peter S.; Hsiao, Steven; Tran, Trac D.; Yazicioglu, Firat; Etienne-Cummings, Ralph

    2015-06-01

    Objective. This paper describes a low power closed-loop compressive sensing (CS) based neural recording system. This system provides an efficient method to reduce data transmission bandwidth for implantable neural recording devices. By doing so, this technique reduces a majority of system power consumption which is dissipated at data readout interface. The design of the system is scalable and is a viable option for large scale integration of electrodes or recording sites onto a single device. Approach. The entire system consists of an application-specific integrated circuit (ASIC) with 4 recording readout channels with CS circuits, a real time off-chip CS recovery block and a recovery quality evaluation block that provides a closed feedback to adaptively adjust compression rate. Since CS performance is strongly signal dependent, the ASIC has been tested in vivo and with standard public neural databases. Main results. Implemented using efficient digital circuit, this system is able to achieve >10 times data compression on the entire neural spike band (500-6KHz) while consuming only 0.83uW (0.53 V voltage supply) additional digital power per electrode. When only the spikes are desired, the system is able to further compress the detected spikes by around 16 times. Unlike other similar systems, the characteristic spikes and inter-spike data can both be recovered which guarantes a >95% spike classification success rate. The compression circuit occupied 0.11mm2/electrode in a 180nm CMOS process. The complete signal processing circuit consumes <16uW/electrode. Significance. Power and area efficiency demonstrated by the system make it an ideal candidate for integration into large recording arrays containing thousands of electrode. Closed-loop recording and reconstruction performance evaluation further improves the robustness of the compression method, thus making the system more practical for long term recording.

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

    PubMed

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

    2006-01-01

    This paper presents a powerful new low power wireless system for sampling multiple channels of neural activity based on Texas Instruments MSP430 microprocessors and Nordic Semiconductor's ultra low power high bandwidth RF transmitters and receivers. The system's development process, component selection, features and test methodology are presented. PMID:17946727

  20. Airlift Deployment Analysis System (ADANS) development guidelines: User interface

    SciTech Connect

    Truett, L.F.; Loffman, R.S.; Stevens, S.S.

    1990-01-01

    This user interface document is the first in a series of development guidelines for the Airlift Deployment Analysis System (ADANS) project. This report documents the user interface design as it currently exists. These guidelines, which are specific to the current ADANS operating environment, will be used by the developers of ADANS to create a consistent, efficient, and logical user interface. A good user interface optimizes the interactions between a computer system and the personnel using the system and minimizes conditions that degrade human performance or cause human error. ADANS is still under development; as new capabilities become available to the ADANS development team, the ADANS user interface will be modified. Thus, a revision of this report is expected. 3 refs.

  1. Plasticity and neural stem cells in the enteric nervous system.

    PubMed

    Schäfer, Karl-Herbert; Van Ginneken, Chris; Copray, Sjef

    2009-12-01

    The enteric nervous system (ENS) is a highly organized part of the autonomic nervous system, which innervates the whole gastrointestinal tract by several interconnected neuronal networks. The ENS changes during development and keeps throughout its lifespan a significant capacity to adapt to microenvironmental influences, be it in inflammatory bowel diseases or changing dietary habits. The presence of neural stem cells in the pre-, postnatal, and adult gut might be one of the prerequisites to adapt to changing conditions. During the last decade, the ENS has increasingly come into the focus of clinical neural stem cell research, forming a considerable pool of neural crest derived stem cells, which could be used for cell therapy of dysganglionosis, that is, diseases based on the deficient or insufficient colonization of the gut by neural crest derived stem cells; in addition, the ENS could be an easily accessible neural stem cell source for cell replacement therapies for neurodegenerative disorders or traumatic lesions of the central nervous system. PMID:19943347

  2. Applications of neural networks in chemical engineering: Hybrid systems

    SciTech Connect

    Ferrada, J.J.; Osborne-Lee, I.W. ); Grizzaffi, P.A. )

    1990-01-01

    Expert systems are known to be useful in capturing expertise and applying knowledge to chemical engineering problems such as diagnosis, process control, process simulation, and process advisory. However, expert system applications are traditionally limited to knowledge domains that are heuristic and involve only simple mathematics. Neural networks, on the other hand, represent an emerging technology capable of rapid recognition of patterned behavior without regard to mathematical complexity. Although useful in problem identification, neural networks are not very efficient in providing in-depth solutions and typically do not promote full understanding of the problem or the reasoning behind its solutions. Hence, applications of neural networks have certain limitations. This paper explores the potential for expanding the scope of chemical engineering areas where neural networks might be utilized by incorporating expert systems and neural networks into the same application, a process called hybridization. In addition, hybrid applications are compared with those using more traditional approaches, the results of the different applications are analyzed, and the feasibility of converting the preliminary prototypes described herein into useful final products is evaluated. 12 refs., 8 figs.

  3. Neural prosthetic systems: current problems and future directions.

    PubMed

    Chestek, Cindy A; Cunningham, John P; Gilja, Vikash; Nuyujukian, Paul; Ryu, Stephen I; Shenoy, Krishna V

    2009-01-01

    By decoding neural activity into useful behavioral commands, neural prosthetic systems seek to improve the lives of severely disabled human patients. Motor decoding algorithms, which map neural spiking data to control parameters of a device such as a prosthetic arm, have received particular attention in the literature. Here, we highlight several outstanding problems that exist in most current approaches to decode algorithm design. These include two problems that we argue will unlikely result in further dramatic increases in performance, specifically spike sorting and spiking models. We also discuss three issues that have been less examined in the literature, and we argue that addressing these issues may result in dramatic future increases in performance. These include: non-stationarity of recorded waveforms, limitations of a linear mappings between neural activity and movement kinematics, and the low signal to noise ratio of the neural data. We demonstrate these problems with data from 39 experimental sessions with a non-human primate performing reaches and with recent literature. In all, this study suggests that research in cortically-controlled prosthetic systems may require reprioritization to achieve performance that is acceptable for a clinically viable human system. PMID:19963796

  4. Triple redundant computer system/display and keyboard subsystem interface

    NASA Technical Reports Server (NTRS)

    Gulde, F. J.

    1973-01-01

    Interfacing of the redundant display and keyboard subsystem with the triple redundant computer system is defined according to space shuttle design. The study is performed in three phases: (1) TRCS configuration and characteristics identification; (2) display and keyboard subsystem configuration and characteristics identification, and (3) interface approach definition.

  5. Reliability Modeling of Microelectromechanical Systems Using Neural Networks

    NASA Technical Reports Server (NTRS)

    Perera. J. Sebastian

    2000-01-01

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

  6. The intelligent user interface for NASA's advanced information management systems

    NASA Technical Reports Server (NTRS)

    Campbell, William J.; Short, Nicholas, Jr.; Rolofs, Larry H.; Wattawa, Scott L.

    1987-01-01

    NASA has initiated the Intelligent Data Management Project to design and develop advanced information management systems. The project's primary goal is to formulate, design and develop advanced information systems that are capable of supporting the agency's future space research and operational information management needs. The first effort of the project was the development of a prototype Intelligent User Interface to an operational scientific database, using expert systems and natural language processing technologies. An overview of Intelligent User Interface formulation and development is given.

  7. Flight Experiment Demonstration System (FEDS) functional description and interface document

    NASA Technical Reports Server (NTRS)

    Belcher, R. C.; Shank, D. E.

    1984-01-01

    This document presents a functional description of the Flight Experiment Demonstration System (FEDS) and of interfaces between FEDS and external hardware and software. FEDS is a modification of the Automated Orbit Determination System (AODS). FEDS has been developed to support a ground demonstration of microprocessor-based onboard orbit determination. This document provides an overview of the structure and logic of FEDS and details the various operational procedures to build and execute FEDS. It also documents a microprocessor interface between FEDS and a TDRSS user transponder and describes a software simulator of the interface used in the development and system testing of FEDS.

  8. Guidance for Human-system Interfaces to Automatic Systems

    SciTech Connect

    O'Hara, J.M.; Higgins, J.; Stephen Fleger; Valerie Barnes

    2010-09-27

    Automation is ubiquitous in modern complex systems, and commercial nuclear- power plants are no exception. Automation is applied to a wide range of functions, including monitoring and detection, situation assessment, response planning, and response implementation. Automation has become a 'team player' supporting personnel in nearly all aspects of system operation. In light of its increasing use and importance in new- and future-plants, guidance is needed to conduct safety reviews of the operator's interface with automation. The objective of this research was to develop such guidance. We first characterized the important HFE aspects of automation, including six dimensions: Levels, functions, processes, modes, flexibility, and reliability. Next, we reviewed literature on the effects of all of these aspects of automation on human performance, and on the design of human-system interfaces (HSIs). Then, we used this technical basis established from the literature to identify general principles for human-automation interaction and to develop review guidelines. The guidelines consist of the following seven topics: Automation displays, interaction and control, automation modes, automation levels, adaptive automation, error tolerance and failure management, and HSI integration.

  9. Integrated evolutionary computation neural network quality controller for automated systems

    SciTech Connect

    Patro, S.; Kolarik, W.J.

    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.

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

    NASA Technical Reports Server (NTRS)

    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.

  11. Ground Systems Development Environment (GSDE) interface requirements analysis: Operations scenarios

    NASA Technical Reports Server (NTRS)

    Church, Victor E.; Phillips, John

    1991-01-01

    This report is a preliminary assessment of the functional and data interface requirements to the link between the GSDE GS/SPF (Amdahl) and the Space Station Control Center (SSCC) and Space Station Training Facility (SSTF) Integration, Verification, and Test Environments (IVTE's). These interfaces will be involved in ground software development of both the control center and the simulation and training systems. Our understanding of the configuration management (CM) interface and the expected functional characteristics of the Amdahl-IVTE interface is described. A set of assumptions and questions that need to be considered and resolved in order to complete the interface functional and data requirements definitions are presented. A listing of information items defined to describe software configuration items in the GSDE CM system is included. It also includes listings of standard reports of CM information and of CM-related tools in the GSDE.

  12. Spatiotemporal stochastic resonance and its consequences in neural model systems.

    PubMed

    Balazsi, Gabor; Kish, Laszlo B.; Moss, Frank E.

    2001-09-01

    The realization of spatiotemporal stochastic resonance is studied in a two-dimensional FitzHugh-Nagumo system, and in a one-dimensional system of integrate-and-fire neurons. We show that spatiotemporal stochastic resonance occurs in these neural model systems, independent of the method of modeling. Moreover, the ways of realization are analogous in the two model systems. The biological implications and open questions are discussed. (c) 2001 American Institute of Physics. PMID:12779493

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

    NASA Technical Reports Server (NTRS)

    Yen, John

    1991-01-01

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

  14. Polar interface phonons in ionic toroidal systems

    NASA Astrophysics Data System (ADS)

    Nguyen, N. D.; Evrard, R.; Stroscio, Michael A.

    2016-09-01

    We use the dielectric continuum model to obtain the polar (Fuchs–Kliewer like) interface vibration modes of toroids made of ionic materials either embedded in a different material or in vacuum, with applications to nanotoroids specially in mind. We report the frequencies of these modes and describe the electric potential they produce. We establish the quantum-mechanical Hamiltonian appropriate for their interaction with electric charges. This Hamiltonian can be used to describe the effect of this interaction on different types of charged particles either inside or outside the torus.

  15. Polar interface phonons in ionic toroidal systems.

    PubMed

    Nguyen, N D; Evrard, R; Stroscio, Michael A

    2016-09-01

    We use the dielectric continuum model to obtain the polar (Fuchs-Kliewer like) interface vibration modes of toroids made of ionic materials either embedded in a different material or in vacuum, with applications to nanotoroids specially in mind. We report the frequencies of these modes and describe the electric potential they produce. We establish the quantum-mechanical Hamiltonian appropriate for their interaction with electric charges. This Hamiltonian can be used to describe the effect of this interaction on different types of charged particles either inside or outside the torus. PMID:27357246

  16. ScienceOrganizer System and Interface Summary

    NASA Technical Reports Server (NTRS)

    Keller, Richard M.; Norvig, Peter (Technical Monitor)

    2001-01-01

    ScienceOrganizer is a specialized knowledge management tool designed to enhance the information storage, organization, and access capabilities of distributed NASA science teams. Users access ScienceOrganizer through an intuitive Web-based interface that enables them to upload, download, and organize project information - including data, documents, images, and scientific records associated with laboratory and field experiments. Information in ScienceOrganizer is "threaded", or interlinked, to enable users to locate, track, and organize interrelated pieces of scientific data. Linkages capture important semantic relationships among information resources in the repository, and these assist users in navigating through the information related to their projects.

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

    PubMed

    Sylvester, Jared; Reggia, James

    2016-07-01

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

  18. Two Serial Data to Pulse Code Modulation System Interfaces

    NASA Technical Reports Server (NTRS)

    Hamory, Phil

    2006-01-01

    Two pulse code modulation (PCM) system interfaces for asynchronous serial data are described. One interface is for global positioning system (GPS) data on the NASA Dryden Flight Research Center (DFRC) F-15B (McDonnell Douglas Corporation, St. Louis, Missouri) airplane, tail number 836 (F-15B/836). The other is for flight control computer data on the duPont Aerospace (La Jolla, California) DP-1, a 53-percent scale model of the duPont Aerospace DP-2.

  19. Ground Systems Development Environment (GSDE) interface requirements and prototyping plan

    NASA Technical Reports Server (NTRS)

    Church, Victor E.; Philips, John; Bassman, Mitchell; Williams, C.

    1990-01-01

    This report describes the data collection and requirements analysis effort of the Ground System Development Environment (GSDE) Interface Requirements study. It identifies potential problems in the interfaces among applications and processors in the heterogeneous systems that comprises the GSDE. It describes possible strategies for addressing those problems. It also identifies areas for further research and prototyping to demonstrate the capabilities and feasibility of those strategies and defines a plan for building the necessary software prototypes.

  20. Neural Network Based System for Equipment Startup Surveillance

    Energy Science and Technology Software Center (ESTSC)

    1996-12-18

    NEBSESS is a system for equipment surveillance and fault detection which relies on a neural-network based means for diagnosing disturbances during startup and for automatically actuating the Sequential Probability Ratio Test (SPRT) as a signal validation means during steady-state operation.

  1. A layered neural network model applied to the auditory system

    NASA Astrophysics Data System (ADS)

    Travis, Bryan J.

    1986-08-01

    The structure of the auditory system is described with emphasis on the cerebral cortex. A layered neural network model incorporating much of the known structure of the cortex is applied to word discrimination. The concepts of iterated maps and atrractive fixed points are used to enable the model to recognize words despite variations in pitch, intensity and duration.

  2. Boron-Doped Nanocrystalline Diamond Electrodes for Neural Interfaces: In vivo Biocompatibility Evaluation

    PubMed Central

    Alcaide, María; Taylor, Andrew; Fjorback, Morten; Zachar, Vladimir; Pennisi, Cristian P.

    2016-01-01

    Boron-doped nanocrystalline diamond (BDD) electrodes have recently attracted attention as materials for neural electrodes due to their superior physical and electrochemical properties, however their biocompatibility remains largely unexplored. In this work, we aim to investigate the in vivo biocompatibility of BDD electrodes in relation to conventional titanium nitride (TiN) electrodes using a rat subcutaneous implantation model. High quality BDD films were synthesized on electrodes intended for use as an implantable neurostimulation device. After implantation for 2 and 4 weeks, tissue sections adjacent to the electrodes were obtained for histological analysis. Both types of implants were contained in a thin fibrous encapsulation layer, the thickness of which decreased with time. Although the level of neovascularization around the implants was similar, BDD electrodes elicited significantly thinner fibrous capsules and a milder inflammatory reaction at both time points. These results suggest that BDD films may constitute an appropriate material to support stable performance of implantable neural electrodes over time. PMID:27013949

  3. Boron-Doped Nanocrystalline Diamond Electrodes for Neural Interfaces: In vivo Biocompatibility Evaluation.

    PubMed

    Alcaide, María; Taylor, Andrew; Fjorback, Morten; Zachar, Vladimir; Pennisi, Cristian P

    2016-01-01

    Boron-doped nanocrystalline diamond (BDD) electrodes have recently attracted attention as materials for neural electrodes due to their superior physical and electrochemical properties, however their biocompatibility remains largely unexplored. In this work, we aim to investigate the in vivo biocompatibility of BDD electrodes in relation to conventional titanium nitride (TiN) electrodes using a rat subcutaneous implantation model. High quality BDD films were synthesized on electrodes intended for use as an implantable neurostimulation device. After implantation for 2 and 4 weeks, tissue sections adjacent to the electrodes were obtained for histological analysis. Both types of implants were contained in a thin fibrous encapsulation layer, the thickness of which decreased with time. Although the level of neovascularization around the implants was similar, BDD electrodes elicited significantly thinner fibrous capsules and a milder inflammatory reaction at both time points. These results suggest that BDD films may constitute an appropriate material to support stable performance of implantable neural electrodes over time. PMID:27013949

  4. Dissipative rendering and neural network control system design

    NASA Technical Reports Server (NTRS)

    Gonzalez, Oscar R.

    1995-01-01

    Model-based control system designs are limited by the accuracy of the models of the plant, plant uncertainty, and exogenous signals. Although better models can be obtained with system identification, the models and control designs still have limitations. One approach to reduce the dependency on particular models is to design a set of compensators that will guarantee robust stability to a set of plants. Optimization over the compensator parameters can then be used to get the desired performance. Conservativeness of this approach can be reduced by integrating fundamental properties of the plant models. This is the approach of dissipative control design. Dissipative control designs are based on several variations of the Passivity Theorem, which have been proven for nonlinear/linear and continuous-time/discrete-time systems. These theorems depend not on a specific model of a plant, but on its general dissipative properties. Dissipative control design has found wide applicability in flexible space structures and robotic systems that can be configured to be dissipative. Currently, there is ongoing research to improve the performance of dissipative control designs. For aircraft systems that are not dissipative active control may be used to make them dissipative and then a dissipative control design technique can be used. It is also possible that rendering a system dissipative and dissipative control design may be combined into one step. Furthermore, the transformation of a non-dissipative system to dissipative can be done robustly. One sequential design procedure for finite dimensional linear time-invariant systems has been developed. For nonlinear plants that cannot be controlled adequately with a single linear controller, model-based techniques have additional problems. Nonlinear system identification is still a research topic. Lacking analytical models for model-based design, artificial neural network algorithms have recently received considerable attention. Using

  5. Interface between an optical communications system and the host spacecraft

    NASA Astrophysics Data System (ADS)

    Barry, J. D.

    1988-06-01

    The current state of development of optical communications systems for intersatellite data transfer has reached an advanced level, with on-orbit operations planned for the early 1990s. The technology and engineering aspects of the development of the optical system have been evaluated in some detail and are widely known, but the interfaces between the optical system and the host spacecraft are not as well understood. Many aspects of the optical interfaces have been identified, are currently under investigation and are discussed here. These include the interface support needed from the host spacecraft, such as thermal, electrical, mechanical, optical, radiation protection and contamination control; spacecraft attitude and vibrational stability; spacecraft level interface testing, such as EMI/EMC, vibration and thermal vacuum tests; and lifetime and reliability testing.

  6. Observer's Interface for Solar System Target Specification

    NASA Astrophysics Data System (ADS)

    Roman, Anthony; Link, Miranda; Moriarty, Christopher; Stansberry, John A.

    2016-01-01

    When observing an asteroid or comet with HST, it has been necessary for the observer to manually enter the target's orbital elements into the Astronomer's Proposal Tool (APT). This allowed possible copy/paste transcription errors from the observer's source of orbital elements data. In order to address this issue, APT has now been improved with the capability to identify targets in and then download orbital elements from JPL Horizons. The observer will first use a target name resolver to choose the intended target from the Horizons database, and then download the orbital elements from Horizons directly into APT. A manual entry option is also still retained if the observer does not wish to use elements from Horizons. This new capability is available for HST observing, and it will also be supported for JWST observing. The poster shows examples of this new interface.

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

    PubMed

    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. PMID:27334956

  8. Resident database interfaces to the DAVID system, a heterogeneous distributed database management system

    NASA Technical Reports Server (NTRS)

    Moroh, Marsha

    1988-01-01

    A methodology for building interfaces of resident database management systems to a heterogeneous distributed database management system under development at NASA, the DAVID system, was developed. The feasibility of that methodology was demonstrated by construction of the software necessary to perform the interface task. The interface terminology developed in the course of this research is presented. The work performed and the results are summarized.

  9. Studying the glial cell response to biomaterials and surface topography for improving the neural electrode interface

    NASA Astrophysics Data System (ADS)

    Ereifej, Evon S.

    Neural electrode devices hold great promise to help people with the restoration of lost functions, however, research is lacking in the biomaterial design of a stable, long-term device. Current devices lack long term functionality, most have been found unable to record neural activity within weeks after implantation due to the development of glial scar tissue (Polikov et al., 2006; Zhong and Bellamkonda, 2008). The long-term effect of chronically implanted electrodes is the formation of a glial scar made up of reactive astrocytes and the matrix proteins they generate (Polikov et al., 2005; Seil and Webster, 2008). Scarring is initiated when a device is inserted into brain tissue and is associated with an inflammatory response. Activated astrocytes are hypertrophic, hyperplastic, have an upregulation of intermediate filaments GFAP and vimentin expression, and filament formation (Buffo et al., 2010; Gervasi et al., 2008). Current approaches towards inhibiting the initiation of glial scarring range from altering the geometry, roughness, size, shape and materials of the device (Grill et al., 2009; Kotov et al., 2009; Kotzar et al., 2002; Szarowski et al., 2003). Literature has shown that surface topography modifications can alter cell alignment, adhesion, proliferation, migration, and gene expression (Agnew et al., 1983; Cogan et al., 2005; Cogan et al., 2006; Merrill et al., 2005). Thus, the goals of the presented work are to study the cellular response to biomaterials used in neural electrode fabrication and assess surface topography effects on minimizing astrogliosis. Initially, to examine astrocyte response to various materials used in neural electrode fabrication, astrocytes were cultured on platinum, silicon, PMMA, and SU-8 surfaces, with polystyrene as the control surface. Cell proliferation, viability, morphology and gene expression was measured for seven days in vitro. Results determined the cellular characteristics, reactions and growth rates of astrocytes

  10. Cognitive Training for Impaired Neural Systems in Neuropsychiatric Illness

    PubMed Central

    Vinogradov, Sophia; Fisher, Melissa; de Villers-Sidani, Etienne

    2012-01-01

    Neuropsychiatric illnesses are associated with dysfunction in distributed prefrontal neural systems that underlie perception, cognition, social interactions, emotion regulation, and motivation. The high degree of learning-dependent plasticity in these networks—combined with the availability of advanced computerized technology—suggests that we should be able to engineer very specific training programs that drive meaningful and enduring improvements in impaired neural systems relevant to neuropsychiatric illness. However, cognitive training approaches for mental and addictive disorders must take into account possible inherent limitations in the underlying brain ‘learning machinery' due to pathophysiology, must grapple with the presence of complex overlearned maladaptive patterns of neural functioning, and must find a way to ally with developmental and psychosocial factors that influence response to illness and to treatment. In this review, we briefly examine the current state of knowledge from studies of cognitive remediation in psychiatry and we highlight open questions. We then present a systems neuroscience rationale for successful cognitive training for neuropsychiatric illnesses, one that emphasizes the distributed nature of neural assemblies that support cognitive and affective processing, as well as their plasticity. It is based on the notion that, during successful learning, the brain represents the relevant perceptual and cognitive/affective inputs and action outputs with disproportionately larger and more coordinated populations of neurons that are distributed (and that are interacting) across multiple levels of processing and throughout multiple brain regions. This approach allows us to address limitations found in earlier research and to introduce important principles for the design and evaluation of the next generation of cognitive training for impaired neural systems. We summarize work to date using such neuroscience-informed methods and indicate

  11. Studying the glial cell response to biomaterials and surface topography for improving the neural electrode interface

    NASA Astrophysics Data System (ADS)

    Ereifej, Evon S.

    Neural electrode devices hold great promise to help people with the restoration of lost functions, however, research is lacking in the biomaterial design of a stable, long-term device. Current devices lack long term functionality, most have been found unable to record neural activity within weeks after implantation due to the development of glial scar tissue (Polikov et al., 2006; Zhong and Bellamkonda, 2008). The long-term effect of chronically implanted electrodes is the formation of a glial scar made up of reactive astrocytes and the matrix proteins they generate (Polikov et al., 2005; Seil and Webster, 2008). Scarring is initiated when a device is inserted into brain tissue and is associated with an inflammatory response. Activated astrocytes are hypertrophic, hyperplastic, have an upregulation of intermediate filaments GFAP and vimentin expression, and filament formation (Buffo et al., 2010; Gervasi et al., 2008). Current approaches towards inhibiting the initiation of glial scarring range from altering the geometry, roughness, size, shape and materials of the device (Grill et al., 2009; Kotov et al., 2009; Kotzar et al., 2002; Szarowski et al., 2003). Literature has shown that surface topography modifications can alter cell alignment, adhesion, proliferation, migration, and gene expression (Agnew et al., 1983; Cogan et al., 2005; Cogan et al., 2006; Merrill et al., 2005). Thus, the goals of the presented work are to study the cellular response to biomaterials used in neural electrode fabrication and assess surface topography effects on minimizing astrogliosis. Initially, to examine astrocyte response to various materials used in neural electrode fabrication, astrocytes were cultured on platinum, silicon, PMMA, and SU-8 surfaces, with polystyrene as the control surface. Cell proliferation, viability, morphology and gene expression was measured for seven days in vitro. Results determined the cellular characteristics, reactions and growth rates of astrocytes

  12. Real-time data acquisition and control system for the measurement of motor and neural data.

    PubMed

    Bryant, Christopher L; Gandhi, Neeraj J

    2005-03-30

    This paper outlines a powerful, yet flexible real-time data acquisition and control system for use in the triggering and measurement of both analog and digital events. Built using the LabVIEW development architecture (version 7.1) and freely available, this system provides precisely timed auditory and visual stimuli to a subject while recording analog data and timestamps of neural activity retrieved from a window discriminator. The system utilizes the most recent real-time (RT) technology in order to provide not only a guaranteed data acquisition rate of 1 kHz, but a much more difficult to achieve guaranteed system response time of 1 ms. The system interface is windows-based and easy to use, providing a host of configurable options for end-user customization. PMID:15698659

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

    PubMed

    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. PMID:27182811

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

    SciTech Connect

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

    2015-05-01

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

  15. Three neural network based sensor systems for environmental monitoring

    SciTech Connect

    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.

  16. A Flexible Behavioral Learning System with Modular Neural Networks

    NASA Astrophysics Data System (ADS)

    Takeuchi, Johane; Shouno, Osamu; Tsujino, Hiroshi

    Future robots/agents will perform situated behaviors for each user. Flexible behavioral learning is required for coping with diverse and unexpected users' situations. Unexpected situations are usually not tractable for machine learning systems that are designed for pre-defined problems. In order to realize such a flexible learning system, we were trying to create a learning model that can function in several different kinds of state transitions without specific adjustments for each transition as a first step. We constructed a modular neural network model based on reinforcement learning. We expected that combining a modular architecture with neural networks could accelerate the learning speed of neural networks. The inputs of our neural network model always include not only observed states but also memory information for any transition. In pure Markov decision processes, memory information is not necessary, rather it can lead to lower performance. On the other hand, partially observable conditions require memory information to select proper actions. We demonstrated that the new learning model could actually learn those multiple kinds of state transitions with the same architectures and parameters, and without pre-designed models of environments. This paper describes the performances of constructed models using probabilistically fluctuated Markov decision processes including partially observable conditions. In the test transitions, the observed state probabilistically fluctuated. The new learning model could function in those complex transitions. In addition, the learning speeds of our model are comparable to a reinforcement learning algorithm implemented with a pre-defined and optimized table-representation of states.

  17. Control Interface and Tracking Control System for Automated Poultry Inspection

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A new visible/near-infrared inspection system interface was developed in order to conduct research to test and implement an automated chicken inspection system for online operation on commercial chicken processing lines. The spectroscopic system demonstrated effective spectral acquisition and data ...

  18. Wireless Recording in the Peripheral Nervous System with Ultrasonic Neural Dust.

    PubMed

    Seo, Dongjin; Neely, Ryan M; Shen, Konlin; Singhal, Utkarsh; Alon, Elad; Rabaey, Jan M; Carmena, Jose M; Maharbiz, Michel M

    2016-08-01

    The emerging field of bioelectronic medicine seeks methods for deciphering and modulating electrophysiological activity in the body to attain therapeutic effects at target organs. Current approaches to interfacing with peripheral nerves and muscles rely heavily on wires, creating problems for chronic use, while emerging wireless approaches lack the size scalability necessary to interrogate small-diameter nerves. Furthermore, conventional electrode-based technologies lack the capability to record from nerves with high spatial resolution or to record independently from many discrete sites within a nerve bundle. Here, we demonstrate neural dust, a wireless and scalable ultrasonic backscatter system for powering and communicating with implanted bioelectronics. We show that ultrasound is effective at delivering power to mm-scale devices in tissue; likewise, passive, battery-less communication using backscatter enables high-fidelity transmission of electromyogram (EMG) and electroneurogram (ENG) signals from anesthetized rats. These results highlight the potential for an ultrasound-based neural interface system for advancing future bioelectronics-based therapies. PMID:27497221

  19. Communications and control for electric power systems: Power system stability applications of artificial neural networks

    NASA Technical Reports Server (NTRS)

    Toomarian, N.; Kirkham, Harold

    1994-01-01

    This report investigates the application of artificial neural networks to the problem of power system stability. The field of artificial intelligence, expert systems, and neural networks is reviewed. Power system operation is discussed with emphasis on stability considerations. Real-time system control has only recently been considered as applicable to stability, using conventional control methods. The report considers the use of artificial neural networks to improve the stability of the power system. The networks are considered as adjuncts and as replacements for existing controllers. The optimal kind of network to use as an adjunct to a generator exciter is discussed.

  20. Development and testing of the Automated Fluid Interface System

    NASA Astrophysics Data System (ADS)

    Milton, Martha E.; Tyler, Tony R.

    1993-05-01

    The Automated Fluid Interface System (AFIS) is an advanced development program aimed at becoming the standard interface for satellite servicing for years to come. The AFIS will be capable of transferring propellants, fluids, gasses, power, and cryogens from a tanker to an orbiting satellite. The AFIS program currently under consideration is a joint venture between the NASA/Marshall Space Flight Center and Moog, Inc. An engineering model has been built and is undergoing development testing to investigate the mechanism's abilities.

  1. Development and testing of the Automated Fluid Interface System

    NASA Technical Reports Server (NTRS)

    Milton, Martha E.; Tyler, Tony R.

    1993-01-01

    The Automated Fluid Interface System (AFIS) is an advanced development program aimed at becoming the standard interface for satellite servicing for years to come. The AFIS will be capable of transferring propellants, fluids, gasses, power, and cryogens from a tanker to an orbiting satellite. The AFIS program currently under consideration is a joint venture between the NASA/Marshall Space Flight Center and Moog, Inc. An engineering model has been built and is undergoing development testing to investigate the mechanism's abilities.

  2. A Universal Intelligent System-on-Chip Based Sensor Interface

    PubMed Central

    Mattoli, Virgilio; Mondini, Alessio; Mazzolai, Barbara; Ferri, Gabriele; Dario, Paolo

    2010-01-01

    The need for real-time/reliable/low-maintenance distributed monitoring systems, e.g., wireless sensor networks, has been becoming more and more evident in many applications in the environmental, agro-alimentary, medical, and industrial fields. The growing interest in technologies related to sensors is an important indicator of these new needs. The design and the realization of complex and/or distributed monitoring systems is often difficult due to the multitude of different electronic interfaces presented by the sensors available on the market. To address these issues the authors propose the concept of a Universal Intelligent Sensor Interface (UISI), a new low-cost system based on a single commercial chip able to convert a generic transducer into an intelligent sensor with multiple standardized interfaces. The device presented offers a flexible analog and/or digital front-end, able to interface different transducer typologies (such as conditioned, unconditioned, resistive, current output, capacitive and digital transducers). The device also provides enhanced processing and storage capabilities, as well as a configurable multi-standard output interface (including plug-and-play interface based on IEEE 1451.3). In this work the general concept of UISI and the design of reconfigurable hardware are presented, together with experimental test results validating the proposed device. PMID:22163624

  3. Micromotion-induced dynamic effects from a neural probe and brain tissue interface

    NASA Astrophysics Data System (ADS)

    Polanco, Michael; Yoon, Hargsoon; Bawab, Sebastian

    2014-04-01

    Neural probes contain the potential to cause injury to surrounding neural cells due to a discrepancy in stiffness values between them and the surrounding brain tissue when subjected to mechanical micromotion of the brain. To evaluate the effects of the mechanical mismatch, a series of dynamic simulations are conducted to better understand the design enhancements required to improve the feasibility of the neuron probe. The simulations use a nonlinear transient explicit finite element code, LS-DYNA. A three-dimensional quarter-symmetry finite element model is utilized for the transient analysis to capture the time-dependent dynamic deformations on the brain tissue from the implant as a function of different frequency shapes and stiffness values. When micromotion-induced pulses are applied, reducing the neuron probe stiffness by three orders of magnitude leads up to a 41.6% reduction in stress and 39.1% reduction in strain. The simulation conditions assume a case where sheath bonding has begun to take place around the probe implantation site, but no full bond to the probe has occurred. The analyses can provide guidance on the materials necessary to design a probe for injury reduction.

  4. Dynamical system modeling via signal reduction and neural network simulation

    SciTech Connect

    Paez, T.L.; Hunter, N.F.

    1997-11-01

    Many dynamical systems tested in the field and the laboratory display significant nonlinear behavior. Accurate characterization of such systems requires modeling in a nonlinear framework. One construct forming a basis for nonlinear modeling is that of the artificial neural network (ANN). However, when system behavior is complex, the amount of data required to perform training can become unreasonable. The authors reduce the complexity of information present in system response measurements using decomposition via canonical variate analysis. They describe a method for decomposing system responses, then modeling the components with ANNs. A numerical example is presented, along with conclusions and recommendations.

  5. Benefits of Power Electronic Interfaces for Distributed Energy Systems

    SciTech Connect

    Kroposki, B.; Pink, C.; DeBlasio, R.; Thomas, H.; Simoes, M.; Sen, P. K.

    2006-01-01

    Optimization of overall electrical system performance is important for the long-term economic viability of distributed energy (DE) systems. With the increasing use of DE systems in industry and its technological advancement, it is becoming more important to understand the integration of these systems with the electric power systems. New markets and benefits for distributed energy applications include the ability to provide ancillary services, improve energy efficiency, enhance power system reliability, and allow customer choice. Advanced power electronic (PE) interfaces will allow DE systems to provide increased functionality through improved power quality and voltage/VAR support, increase electrical system compatibility by reducing the fault contributions, and flexibility in operations with various other DE sources, while reducing overall interconnection costs. This paper examines the system integration and optimization issues associated with DE systems and show the benefits of using PE interfaces for such applications.

  6. Neural network identification of power system transfer functions

    SciTech Connect

    Gillard, D.M.; Bollinger, K.E.

    1996-03-01

    This paper describes an investigation into the use of a multilayered neural network for measuring the transfer function of a power system for use in power system stabilizer (PSS) tuning and assessing PSS damping. The objectives are to quickly and accurately measure the transfer function relating the electric power output to the AVR PSS reference voltage input of a system with the plant operating under normal conditions. In addition, the excitation signal used in the identification procedure is such that it will not adversely affect the terminal voltage or the system frequency. This research emphasized the development of a neural network that is easily trained and robust to changing system conditions. Performance studies of the trained neural network are described. Simulation studies suggest the practical feasibility of the algorithm as a stand-alone identification package and as a portion of a self-tuning algorithm requiring identification in the strategy. The same technique applied to a forward modeling scheme can be used to test the damping contribution from different control strategies.

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

    SciTech Connect

    Calloway, T.B. Jr.; Jantzen, C.M.; Medich, L.; Spennato, N.

    1997-08-05

    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% of the instrument`s measured range, R{sup 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{sub 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.

  8. Statistical Physics of Neural Systems with Nonadditive Dendritic Coupling

    NASA Astrophysics Data System (ADS)

    Breuer, David; Timme, Marc; Memmesheimer, Raoul-Martin

    2014-01-01

    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. Advanced Power Electronic Interfaces for Distributed Energy Systems Part 1: Systems and Topologies

    SciTech Connect

    Kramer, W.; Chakraborty, S.; Kroposki, B.; Thomas, H.

    2008-03-01

    This report summarizes power electronic interfaces for DE applications and the topologies needed for advanced power electronic interfaces. It focuses on photovoltaic, wind, microturbine, fuel cell, internal combustion engine, battery storage, and flywheel storage systems.

  10. Interface requirements in nuclear medicine devices and systems

    SciTech Connect

    Maguire, G.Q. Jr.; Brill, A.B.; Noz, M.E.

    1982-01-01

    Interface designs for three nuclear medicine imaging systems, and computer networking strategies proposed for medical imaging departments are presented. Configurations for two positron-emission-tomography devices (PET III and ECAT) and a general-purpose tomography instrument (the UNICON) are analyzed in terms of specific performance parameters. Interface designs for these machines are contrasted in terms of utilization of standard versus custom modules, cost, and ease of modification, upgrade, and support. The requirements of general purpose systems for medical image analysis, display, and archiving, are considered, and a realizable state-of-the-art system is specfied, including a suggested timetable.

  11. Application of Dynamic Interface Technology in Scientific Research Management System

    NASA Astrophysics Data System (ADS)

    Bi-peng, Yan; Zhi-qiang, Li

    this paper tries to develop an Internet-based evaluation system for declaration of scientific research projects. The system, employing the integrating mode of C/S and B/S, accomplishes three functions: on-line declaration of scientific research projects, project management and on-line evaluation. The problem of dovetailing word text with database is properly solved. Furthermore, the efficiency of designation of the system is highly raised thanks to the adoption of XML store profile, DOM interface and dynamic interface technology.

  12. Application of Hierarchical Dissociated Neural Network in Closed-Loop Hybrid System Integrating Biological and Mechanical Intelligence

    PubMed Central

    Zhang, Bin; Wang, Yuechao; Li, Hongyi

    2015-01-01

    Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including ‘random’ and ‘4Q’ (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the ‘4Q’ cultures presented absolutely different activities, and the robot controlled by the ‘4Q’ network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems. PMID:25992579

  13. Neural Mechanisms and Information Processing in Recognition Systems

    PubMed Central

    Ozaki, Mamiko; Hefetz, Abraham

    2014-01-01

    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. PMID:26462936

  14. Neural Mechanisms and Information Processing in Recognition Systems.

    PubMed

    Ozaki, Mamiko; Hefetz, Abraham

    2014-01-01

    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. PMID:26462936

  15. Cross interaction of melanocortinergic and dopaminergic systems in neural modulation

    PubMed Central

    He, Zhi-Gang; Liu, Bao-Wen; Xiang, Hong-Bing

    2015-01-01

    Melanocortinergic and dopaminergic systems are widely distributed in the CNS and have been established as a crucial regulatory component in diverse physiological functions. The pharmacology of both melanocortinergic and dopaminergic systems including their individual receptors, signaling mechanisms, agonists and antagonists has been extensively studied. Several lines of evidence showed that there existed a cross interaction between the receptors of melanocortinergic and dopaminergic systems. The data available at present had expanded our understanding of melanocortinergic and dopaminergic system interaction in neural modulation, which will be main discussed in this paper. PMID:26823964

  16. Graphical interfaces for cooperative planning systems

    NASA Technical Reports Server (NTRS)

    Smith, Philip J.; Layton, Chuck; Mccoy, C. Elaine

    1990-01-01

    Based on a cognitive task analysis of 5 airline flight crews in a simulator study, researchers have designed a testbed for studying computer aids for en route flight path planning. This testbed runs on a Mac II controlling three color monitors, and is being used to study the design of aids for both dispatchers and flight crews. Specifically, the research focuses on design concepts for developing cooperative problem-solving systems. We use en route flight planning (selecting alternate routes or destinations due to unanticipated weather, traffic, malfunctions, etc.) as the context for studying the design of such systems. Researchers are currently exploring three questions in this test environment: (1) When interacting with a flight planning aid, how does the role of the pilot influence overall system performance; (2) Can the architecture for a cooperative planning system be built around Sacerdoti's (1983) concept of an abstraction hierarchy, where the pilot can interact with the system at many different levels of detail (but where the computer aid by default handles lower level details that the pilot has chosen not to deat with); and (3) Can graphical displays and direct manipulation of these displays provide perceptual enhancements (Larkin and Simon, 1987) of the pilot's problem-solving activities. Information is given in viewgraph form.

  17. A neuromime system for neural circuit analysis.

    PubMed

    Mitchell, C E; Friesen, W O

    1981-01-01

    A system of electronic analog neurons (neuromimes) for modeling the activity in small neuronal networks is described. The system consists of sixteen analogs that simulate the integrative neuronal properties at the axon hillock and sixty-four analogs that serve to simulate synaptic interactions. The neuromime properties are based on a potential model incorporating the following properties: membrane potential, threshold, refractory period, adaptation, post-inhibitory rebound, accommodation and pacemaker potential. Use of matrix switch boards provides for convenient interconnection of the neuromime elements, allowing the construction of even complex circuits. PMID:7236753

  18. Automation and Accountability in Decision Support System Interface Design

    ERIC Educational Resources Information Center

    Cummings, Mary L.

    2006-01-01

    When the human element is introduced into decision support system design, entirely new layers of social and ethical issues emerge but are not always recognized as such. This paper discusses those ethical and social impact issues specific to decision support systems and highlights areas that interface designers should consider during design with an…

  19. A standard interface between simulation programs and systems analysis software.

    PubMed

    Reichert, P

    2006-01-01

    A simple interface between simulation programs and systems analytical software is proposed. This interface is designed to facilitate linkage of environmental simulation programs with systems analytical software and thus can contribute to remedying the deficiency in applying systems analytical techniques to environmental modelling studies. The proposed concept, consisting of a text file interface combined with a batch mode simulation program call, is independent of model structure, operating system and programming language. It is open for implementation by academic and commercial simulation and systems analytical software developers and is very simple to implement. Its practicability is demonstrated by implementations for three environmental simulation packages (AQUASIM, SWAT and LEACHM) and two systems analytical program packages (UNCSIM, SUFI). The properties listed above and the demonstration of the ease of implementation of the approach are prerequisites for the stimulation of a widespread implementation of the proposed interface that would be beneficial for the dissemination of systems analytical techniques in the environmental and engineering sciences. Furthermore, such a development could stimulate the transfer of systems analytical techniques between different fields of application. PMID:16532757

  20. Interface Agent for Computer-based Tutoring Systems.

    ERIC Educational Resources Information Center

    Dang, Trang; Ghenniwa, Hamada; Kamel, Mohamed

    1999-01-01

    Proposes an interface agent for intelligent tutoring systems that creates a collaborative learning environment between the learner and the tutoring software. Describes implementation of a prototype using the IBM Agent Builder Environment Toolkit to use with an intelligent tutoring system for algebra and considers benefits in a lifelong learning…

  1. The role of the neural reward system in attention selection.

    PubMed

    Soder, Heather E; de Dios, Constanza; Potts, Geoffrey F

    2016-07-01

    The prefrontal cortex may play a role in attention selection using motivational information from the mesotelencephalic dopamine system, a neural system that responds to reward prediction violations. If so, neural indices of attention selection and reward prediction violation should have overlapping spatiotemporal distributions. Attention selection elicits a frontal event-related potential component around 200-300 ms, the frontal selection positivity. A component with similar spatiotemporal characteristics, the reward positivity is elicited in reward prediction designs to outcomes that are better than expected. The current study used dense sensor array recording in a sample of 41 participants performing visual oddball (attention) and a reward prediction 'slot machine-like' design to compare the spatiotemporal distributions of the frontal selection positivity and the reward positivity. The components did not differ in their peak latencies and had overlapping scalp topographies, supporting the hypothesis that these positivities represent attachment of incentive salience to perceptual representations in the prefrontal cortex. PMID:27232519

  2. Automated neural network-based instrument validation system

    NASA Astrophysics Data System (ADS)

    Xu, Xiao

    2000-10-01

    In a complex control process, instrument calibration is periodically performed to maintain the instruments within the calibration range, which assures proper control and minimizes down time. Instruments are usually calibrated under out-of-service conditions using manual calibration methods, which may cause incorrect calibration or equipment damage. Continuous in-service calibration monitoring of sensors and instruments will reduce unnecessary instrument calibrations, give operators more confidence in instrument measurements, increase plant efficiency or product quality, and minimize the possibility of equipment damage during unnecessary manual calibrations. In this dissertation, an artificial neural network (ANN)-based instrument calibration verification system is designed to achieve the on-line monitoring and verification goal for scheduling maintenance. Since an ANN is a data-driven model, it can learn the relationships among signals without prior knowledge of the physical model or process, which is usually difficult to establish for the complex non-linear systems. Furthermore, the ANNs provide a noise-reduced estimate of the signal measurement. More importantly, since a neural network learns the relationships among signals, it can give an unfaulted estimate of a faulty signal based on information provided by other unfaulted signals; that is, provide a correct estimate of a faulty signal. This ANN-based instrument verification system is capable of detecting small degradations or drifts occurring in instrumentation, and preclude false control actions or system damage caused by instrument degradation. In this dissertation, an automated scheme of neural network construction is developed. Previously, the neural network structure design required extensive knowledge of neural networks. An automated design methodology was developed so that a network structure can be created without expert interaction. This validation system was designed to monitor process sensors plant

  3. Spiking Neural P Systems with Neuron Division and Dissolution.

    PubMed

    Zhao, Yuzhen; Liu, Xiyu; Wang, Wenping

    2016-01-01

    Spiking neural P systems are a new candidate in spiking neural network models. By using neuron division and budding, such systems can generate/produce exponential working space in linear computational steps, thus provide a way to solve computational hard problems in feasible (linear or polynomial) time with a "time-space trade-off" strategy. In this work, a new mechanism called neuron dissolution is introduced, by which redundant neurons produced during the computation can be removed. As applications, uniform solutions to two NP-hard problems: SAT problem and Subset Sum problem are constructed in linear time, working in a deterministic way. The neuron dissolution strategy is used to eliminate invalid solutions, and all answers to these two problems are encoded as indices of output neurons. Our results improve the one obtained in Science China Information Sciences, 2011, 1596-1607 by Pan et al. PMID:27627104

  4. Data interfaces to the Space Station information system

    NASA Technical Reports Server (NTRS)

    Carper, Richard; Schulz, Fritz

    1988-01-01

    The general form of the information system to be implemented to provide the broad and flexible services required to support the diverse needs of the U.S. Space Station is discussed. Emphasis is placed on the interfaces to the SSIS (Space Station Information System) and major interfaces within the SSIS. A central theme of the SSIS is the use of international standards, where appropriate and available. These standards include those of the International Organization for Standards (ISO), the Telegraph and Telephone Consultative Committee (CCITT), and the Consultative Committee for Space Data Systems (CCSDS). The specific standards selected or under consideration are enumerated. The effect of the selections on the interfaces visible to users of the SSIS are described, and a status report on the progress of official adoption of the standards is presented.

  5. Mimicking the biological neural system using electronic logic circuits

    NASA Astrophysics Data System (ADS)

    Kirikera, Goutham R.; Shinde, Vishal; Kang, Inpil; Schulz, Mark J.; Shanov, Vesselin; Datta, Saurabh; Hurd, Doug; Westheider, Bo; Sundaresan, Mannur; Ghoshal, Anindya

    2004-07-01

    Detecting and locating cracks in structural components and joints that have high feature densities is a challenging problem in the field of Structural Health Monitoring. There have been advances in piezoelectric sensors, actuators, wave propagation, MEMS, and optical fiber sensors. However, few sensor-signal processing techniques have been applied to the monitoring of joints and complex structural geometries. This is in part because maintaining and analyzing a large amount of data obtained from a large number of sensors that may be needed to monitor joints for cracks is difficult. Reliable low cost assessment of the health of structures is crucial to maintain operational availability and productivity, reduce maintenance cost, and prevent catastrophic failure of large structures such as wind turbines, aircraft, and civil infrastructure. Recently, there have also been advances in development of simple passive techniques for health monitoring including a technique based on mimicking the biological neural system using electronic logic circuits. This technique aids in reducing the required number of data acquisition channels by a factor of ten or more and is able to predict the location of a crack within a rectangular grid or within an arbitrarily arranged network of continuous sensors or neurons. The current paper shows results obtained by implementing this method on an aluminum plate and joint. The plates were tested using simulated acoustic emissions and also loading via an MTS machine. The testing indicates that the neural system can monitor complex joints and detect acoustic emissions due to propagating cracks. High sensitivity of the neural system is needed, and further sensor development and testing on different types of joints is required. Also indicated is that sensor geometry, sensor location, signal filtering, and logic parameters of the neural system will be specific to the particular type of joint (material, thickness, geometry) being monitored. Also, a

  6. Generalized Predictive and Neural Generalized Predictive Control of Aerospace Systems

    NASA Technical Reports Server (NTRS)

    Kelkar, Atul G.

    2000-01-01

    The research work presented in this thesis addresses the problem of robust control of uncertain linear and nonlinear systems using Neural network-based Generalized Predictive Control (NGPC) methodology. A brief overview of predictive control and its comparison with Linear Quadratic (LQ) control is given to emphasize advantages and drawbacks of predictive control methods. It is shown that the Generalized Predictive Control (GPC) methodology overcomes the drawbacks associated with traditional LQ control as well as conventional predictive control methods. It is shown that in spite of the model-based nature of GPC it has good robustness properties being special case of receding horizon control. The conditions for choosing tuning parameters for GPC to ensure closed-loop stability are derived. A neural network-based GPC architecture is proposed for the control of linear and nonlinear uncertain systems. A methodology to account for parametric uncertainty in the system is proposed using on-line training capability of multi-layer neural network. Several simulation examples and results from real-time experiments are given to demonstrate the effectiveness of the proposed methodology.

  7. Establishing a Novel Modeling Tool: A Python-Based Interface for a Neuromorphic Hardware System

    PubMed Central

    Brüderle, Daniel; Müller, Eric; Davison, Andrew; Muller, Eilif; Schemmel, Johannes; Meier, Karlheinz

    2008-01-01

    Neuromorphic hardware systems provide new possibilities for the neuroscience modeling community. Due to the intrinsic parallelism of the micro-electronic emulation of neural computation, such models are highly scalable without a loss of speed. However, the communities of software simulator users and neuromorphic engineering in neuroscience are rather disjoint. We present a software concept that provides the possibility to establish such hardware devices as valuable modeling tools. It is based on the integration of the hardware interface into a simulator-independent language which allows for unified experiment descriptions that can be run on various simulation platforms without modification, implying experiment portability and a huge simplification of the quantitative comparison of hardware and simulator results. We introduce an accelerated neuromorphic hardware device and describe the implementation of the proposed concept for this system. An example setup and results acquired by utilizing both the hardware system and a software simulator are demonstrated. PMID:19562085

  8. Choosing the right video interface for military vision systems

    NASA Astrophysics Data System (ADS)

    Phillips, John

    2015-05-01

    This paper discusses how GigE Vision® video interfaces - the technology used to transfer data from a camera or image sensor to a mission computer or display - help designers reduce the cost and complexity of military imaging systems, while also improving usability and increasing intelligence for end-users. The paper begins with a detailed review of video connectivity approaches commonly used in military imaging systems, followed by an overview on the GigE Vision standard. With this background, the design, cost, and performance benefits that can be achieved when employing GigE Vision-compliant video interfaces in a vetronics retrofit upgrade project are outlined.

  9. Guidance for human interface with artificial intelligence systems

    NASA Technical Reports Server (NTRS)

    Potter, Scott S.; Woods, David D.

    1991-01-01

    The beginning of a research effort to collect and integrate existing research findings about how to combine computer power and people is discussed, including problems and pitfalls as well as desirable features. The goal of the research is to develop guidance for the design of human interfaces with intelligent systems. Fault management tasks in NASA domains are the focus of the investigation. Research is being conducted to support the development of guidance for designers that will enable them to make human interface considerations into account during the creation of intelligent systems.

  10. A Nanoscale Interface Promoting Molecular and Functional Differentiation of Neural Cells.

    PubMed

    Posati, Tamara; Pistone, Assunta; Saracino, Emanuela; Formaggio, Francesco; Mola, Maria Grazia; Troni, Elisabetta; Sagnella, Anna; Nocchetti, Morena; Barbalinardo, Marianna; Valle, Francesco; Bonetti, Simone; Caprini, Marco; Nicchia, Grazia Paola; Zamboni, Roberto; Muccini, Michele; Benfenati, Valentina

    2016-01-01

    Potassium channels and aquaporins expressed by astrocytes are key players in the maintenance of cerebral homeostasis and in brain pathophysiologies. One major challenge in the study of astrocyte membrane channels in vitro, is that their expression pattern does not resemble the one observed in vivo. Nanostructured interfaces represent a significant resource to control the cellular behaviour and functionalities at micro and nanoscale as well as to generate novel and more reliable models to study astrocytes in vitro. However, the potential of nanotechnologies in the manipulation of astrocytes ion channels and aquaporins has never been previously reported. Hydrotalcite-like compounds (HTlc) are layered materials with increasing potential as biocompatible nanoscale interface. Here, we evaluate the effect of the interaction of HTlc nanoparticles films with primary rat neocortical astrocytes. We show that HTlc films are biocompatible and do not promote gliotic reaction, while favouring astrocytes differentiation by induction of F-actin fibre alignment and vinculin polarization. Western Blot, Immunofluorescence and patch-clamp revealed that differentiation was accompanied by molecular and functional up-regulation of both inward rectifying potassium channel Kir 4.1 and aquaporin 4, AQP4. The reported results pave the way to engineering novel in vitro models to study astrocytes in a in vivo like condition. PMID:27503424

  11. A Nanoscale Interface Promoting Molecular and Functional Differentiation of Neural Cells

    PubMed Central

    Posati, Tamara; Pistone, Assunta; Saracino, Emanuela; Formaggio, Francesco; Mola, Maria Grazia; Troni, Elisabetta; Sagnella, Anna; Nocchetti, Morena; Barbalinardo, Marianna; Valle, Francesco; Bonetti, Simone; Caprini, Marco; Nicchia, Grazia Paola; Zamboni, Roberto; Muccini, Michele; Benfenati, Valentina

    2016-01-01

    Potassium channels and aquaporins expressed by astrocytes are key players in the maintenance of cerebral homeostasis and in brain pathophysiologies. One major challenge in the study of astrocyte membrane channels in vitro, is that their expression pattern does not resemble the one observed in vivo. Nanostructured interfaces represent a significant resource to control the cellular behaviour and functionalities at micro and nanoscale as well as to generate novel and more reliable models to study astrocytes in vitro. However, the potential of nanotechnologies in the manipulation of astrocytes ion channels and aquaporins has never been previously reported. Hydrotalcite-like compounds (HTlc) are layered materials with increasing potential as biocompatible nanoscale interface. Here, we evaluate the effect of the interaction of HTlc nanoparticles films with primary rat neocortical astrocytes. We show that HTlc films are biocompatible and do not promote gliotic reaction, while favouring astrocytes differentiation by induction of F-actin fibre alignment and vinculin polarization. Western Blot, Immunofluorescence and patch-clamp revealed that differentiation was accompanied by molecular and functional up-regulation of both inward rectifying potassium channel Kir 4.1 and aquaporin 4, AQP4. The reported results pave the way to engineering novel in vitro models to study astrocytes in a in vivo like condition. PMID:27503424

  12. A Nanoscale Interface Promoting Molecular and Functional Differentiation of Neural Cells

    NASA Astrophysics Data System (ADS)

    Posati, Tamara; Pistone, Assunta; Saracino, Emanuela; Formaggio, Francesco; Mola, Maria Grazia; Troni, Elisabetta; Sagnella, Anna; Nocchetti, Morena; Barbalinardo, Marianna; Valle, Francesco; Bonetti, Simone; Caprini, Marco; Nicchia, Grazia Paola; Zamboni, Roberto; Muccini, Michele; Benfenati, Valentina

    2016-08-01

    Potassium channels and aquaporins expressed by astrocytes are key players in the maintenance of cerebral homeostasis and in brain pathophysiologies. One major challenge in the study of astrocyte membrane channels in vitro, is that their expression pattern does not resemble the one observed in vivo. Nanostructured interfaces represent a significant resource to control the cellular behaviour and functionalities at micro and nanoscale as well as to generate novel and more reliable models to study astrocytes in vitro. However, the potential of nanotechnologies in the manipulation of astrocytes ion channels and aquaporins has never been previously reported. Hydrotalcite-like compounds (HTlc) are layered materials with increasing potential as biocompatible nanoscale interface. Here, we evaluate the effect of the interaction of HTlc nanoparticles films with primary rat neocortical astrocytes. We show that HTlc films are biocompatible and do not promote gliotic reaction, while favouring astrocytes differentiation by induction of F-actin fibre alignment and vinculin polarization. Western Blot, Immunofluorescence and patch-clamp revealed that differentiation was accompanied by molecular and functional up-regulation of both inward rectifying potassium channel Kir 4.1 and aquaporin 4, AQP4. The reported results pave the way to engineering novel in vitro models to study astrocytes in a in vivo like condition.

  13. Nonlinear dynamical system approaches towards neural prosthesis

    SciTech Connect

    Torikai, Hiroyuki; Hashimoto, Sho

    2011-04-19

    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.

  14. Novel conjugates of peptides and conjugated polymers for optoelectronics and neural interfaces

    NASA Astrophysics Data System (ADS)

    Bhagwat, Nandita

    Peptide-polymer conjugates are a novel class of hybrid materials that take advantage of each individual component giving the opportunity to generate materials with unique physical, chemical, mechanical, optical, and electronic properties. In this dissertation peptide-polymer conjugates for two different applications are discussed. The first set of peptide-polymer conjugates were developed as templates to study the intermolecular interactions between electroactive molecules by manipulating the intermolecular distances at nano-scale level. A PEGylated, alpha-helical peptide template was employed to effectively display an array of organic chromophores (oxadiazole containing phenylenevinylene oligomers, Oxa-PPV). Three Oxa-PPV chromophores were strategically positioned on each template, at distances ranging from 6 to 17 A from each other, as dictated by the chemical and structural properties of the peptide. The Oxa-PPV modified PEGylated helical peptides (produced via Heck coupling strategies) were characterized by a variety of spectroscopic methods. Electronic contributions from multiple pairs of chromophores on a scaffold were detectable; the number and relative positioning of the chromophores dictated the absorbance and emission maxima, thus confirming the utility of these polymer--peptide templates for complex presentation of organic chromophores. The rest of the thesis is focused on using poly(3,4-alkylenedioxythiophene) based conjugated polymers as coatings for neural electrodes. This thiophene derivative is of considerable current interest for functionalizing the surfaces of a wide variety of devices including implantable biomedical electronics, specifically neural bio-electrodes. Toward these ends, copolymer films of 3,4-ethylenedioxythiophene (EDOT) with a carboxylic acid functional EDOT (EDOTacid) were electrochemically deposited and characterized as a systematic function of the EDOTacid content (0, 25, 50, 75, and 100%). The chemical surface characterization

  15. Interfaces in Random Field Ising Systems

    NASA Astrophysics Data System (ADS)

    Seppälä, Eira

    2001-03-01

    Domain walls are studied in random field Ising magnets at T=0 in two and three dimensions using exact ground state calculations. In 2D below the random field strength dependent length scale Lb the walls exhibit a super-rough behavior with a roughness exponent greater than unity ζ ~= 1.20 ± 0.05. The nearest-neighbor height difference probability distribution depends on the system size below L_b. Above Lb domains become fractal, ζ ~= 1.(E. T. Seppälä, V. Petäjä, and M. J. Alava, Phys. Rev. E 58), R5217 (1998). The energy fluctuation exponent has a value θ=1, contradicting the exponent relation θ = 2ζ -1 due to the broken scale-invariance, below Lb and vanishes for system sizes above L_b. The broken scale-invariance should be manifest also in Kardar-Parisi-Zhang problem with random-field noise.(E. Frey, U. C. Täuber, and H. K. Janssen, Europhys. Lett. 47), 14 (1999). In 3D there exists a transition between ferromagnetic and paramagnetic phases at the critical random field strength (Δ/J)_c. Below (Δ/J)c the roughness exponent is also greater ζ ~= 0.73 ± 0.03 than the functional-renormalization-group calculation result ζ = (5-d)/3.(D. Fisher, Phys. Rev. Lett. 56), 1964 (1986).(P. Chauve, P. Le Doussal, and K. Wiese, cond-mat/0006056.) The height differences are system size dependent in 3D, as well. The behavior of the domain walls in 2D below Lb with a constant external field, i.e., the random-bulk wetting, is demonstrated.(E. T. Seppälä, I. Sillanpää, and M. J. Alava, unpublished.)

  16. Time-recovering PCI-AER interface for bio-inspired spiking systems

    NASA Astrophysics Data System (ADS)

    Paz-Vicente, R.; Linares-Barranco, A.; Cascado, D.; Vicente, S.; Jimenez, G.; Civit, A.

    2005-06-01

    Address Event Representation (AER) is an emergent neuromorphic interchip communication protocol that allows for real-time virtual massive connectivity between huge number neurons located on different chips. By exploiting high speed digital communication circuits (with nano-seconds timings), synaptic neural connections can be time multiplexed, while neural activity signals (with mili-seconds timings) are sampled at low frequencies. Also, neurons generate 'events' according to their activity levels. More active neurons generate more events per unit time, and access the interchip communication channel more frequently, while neurons with low activity consume less communication bandwidth. When building multi-chip muti-layered AER systems it is absolutely necessary to have a computer interface that allows (a) to read AER interchip traffic into the computer and visualize it on screen, and (b) inject a sequence of events at some point of the AER structure. This is necessary for testing and debugging complex AER systems. This paper presents a PCI to AER interface, that dispatches a sequence of events received from the PCI bus with embedded timing information to establish when each event will be delivered. A set of specialized states machines has been introduced to recovery the possible time delays introduced by the asynchronous AER bus. On the input channel, the interface capture events assigning a timestamp and delivers them through the PCI bus to MATLAB applications. It has been implemented in real time hardware using VHDL and it has been tested in a PCI-AER board, developed by authors, that includes a Spartan II 200 FPGA. The demonstration hardware is currently capable to send and receive events at a peak rate of 8,3 Mev/sec, and a typical rate of 1 Mev/sec.

  17. Operator interface to the ORIC control system

    SciTech Connect

    Ludemann, C.A.; Casstevens, B.J.

    1983-01-01

    The Oak Ridge Isochronous Cyclotron (ORIC) was built in the early 1960s with a hard-wired manual control system. Presently, it serves as a variable-energy heavy-ion cyclotron with an internal ion source, or as an energy booster for the new 25 MV tandem electrostatic accelerator of the Holifield Heavy Ion Facility. One factor which has kept the cyclotron the productive research tool it is today is the gradual transfer of its control functions to a computer-based system beginning in the 1970s. This particular placement of a computer between an accelerator and its operators afforded some unique challenges and opportunities that would not be encountered today. Historically, the transformation began at a time when computers were just beginning to gain acceptance as reliable operational tools. Veteran operators with tens of years of accelerator experience justifiably expressed skepticism that this improvement would aid them, particularly if they had to re-learn how to operate the machine. The confidence of the operators was gained when they realized that one of the primary principles of ergonomics was being upheld. The computer software and hardware was being designed to serve them and not the computer.

  18. Design of natural user interface of indoor surveillance system

    NASA Astrophysics Data System (ADS)

    Jia, Lili; Liu, Dan; Jiang, Mu-Jin; Cao, Ning

    2015-03-01

    Conventional optical video surveillance systems usually just record what they view, but they can't make sense of what they are viewing. With lots of useless video information stored and transmitted, waste of memory space and increasing the bandwidth are produced every day. In order to reduce the overall cost of the system, and improve the application value of the monitoring system, we use the Kinect sensor with CMOS infrared sensor, as a supplement to the traditional video surveillance system, to establish the natural user interface system for indoor surveillance. In this paper, the architecture of the natural user interface system, complex background monitoring object separation, user behavior analysis algorithms are discussed. By the analysis of the monitoring object, instead of the command language grammar, when the monitored object need instant help, the system with the natural user interface sends help information. We introduce the method of combining the new system and traditional monitoring system. In conclusion, theoretical analysis and experimental results in this paper show that the proposed system is reasonable and efficient. It can satisfy the system requirements of non-contact, online, real time, higher precision and rapid speed to control the state of affairs at the scene.

  19. Database interfaces on NASA's heterogeneous distributed database system

    NASA Technical Reports Server (NTRS)

    Huang, Shou-Hsuan Stephen

    1987-01-01

    The purpose of Distributed Access View Integrated Database (DAVID) interface module (Module 9: Resident Primitive Processing Package) is to provide data transfer between local DAVID systems and resident Data Base Management Systems (DBMSs). The result of current research is summarized. A detailed description of the interface module is provided. Several Pascal templates were constructed. The Resident Processor program was also developed. Even though it is designed for the Pascal templates, it can be modified for templates in other languages, such as C, without much difficulty. The Resident Processor itself can be written in any programming language. Since Module 5 routines are not ready yet, there is no way to test the interface module. However, simulation shows that the data base access programs produced by the Resident Processor do work according to the specifications.

  20. Neural networks for feedback feedforward nonlinear control systems.

    PubMed

    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. PMID:18267810

  1. Dynamics of a neural system with a multiscale architecture

    PubMed Central

    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

  2. Predictive and Neural Predictive Control of Uncertain Systems

    NASA Technical Reports Server (NTRS)

    Kelkar, Atul G.

    2000-01-01

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

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

    PubMed

    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. PMID:22736978

  4. Neural systems behind word and concept retrieval.

    PubMed

    Damasio, H; Tranel, D; Grabowski, T; Adolphs, R; Damasio, A

    2004-01-01

    Using both the lesion method and functional imaging (positron emission tomography) in large cohorts of subjects investigated with the same experimental tasks, we tested the following hypotheses: (A) that the retrieval of words which denote concrete entities belonging to distinct conceptual categories depends upon partially segregated regions in higher-order cortices of the left temporal lobe; and (B) that the retrieval of conceptual knowledge pertaining to the same concrete entities also depends on partially segregated regions; however, those regions will be different from those postulated in hypothesis A, and located predominantly in the right hemisphere (the second hypothesis tested only with the lesion method). The analyses provide support for hypothesis A in that several regions outside the classical Broca and Wernicke language areas are involved in name retrieval of concrete entities, and that there is a partial segregation in the temporal lobe with respect to the conceptual category to which the entities belong, and partial support for hypothesis B in that retrieval of conceptual knowledge is partially segregated from name retrieval in the lesion study. Those regions identified here are seen as parts of flexible, multi-component systems serving concept and word retrieval for concrete entities belonging to different conceptual categories. By comparing different approaches the article also addresses a number of method issues that have surfaced in recent studies in this field. PMID:15037130

  5. Neural correlates of learning in an electrocorticographic motor-imagery brain-computer interface

    PubMed Central

    Blakely, Tim M.; Miller, Kai J.; Rao, Rajesh P. N.; Ojemann, Jeffrey G.

    2014-01-01

    Human subjects can learn to control a one-dimensional electrocorticographic (ECoG) brain-computer interface (BCI) using modulation of primary motor (M1) high-gamma activity (signal power in the 75–200 Hz range). However, the stability and dynamics of the signals over the course of new BCI skill acquisition have not been investigated. In this study, we report 3 characteristic periods in evolution of the high-gamma control signal during BCI training: initial, low task accuracy with corresponding low power modulation in the gamma spectrum, followed by a second period of improved task accuracy with increasing average power separation between activity and rest, and a final period of high task accuracy with stable (or decreasing) power separation and decreasing trial-to-trial variance. These findings may have implications in the design and implementation of BCI control algorithms. PMID:25599079

  6. Design of an adaptive neural network based power system stabilizer.

    PubMed

    Liu, Wenxin; Venayagamoorthy, Ganesh K; Wunsch, Donald C

    2003-01-01

    Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system in order to damp the low frequency power system oscillations. To overcome the drawbacks of conventional PSS (CPSS), numerous techniques have been proposed in the literature. Based on the analysis of existing techniques, this paper presents an indirect adaptive neural network based power system stabilizer (IDNC) design. The proposed IDNC consists of a neuro-controller, which is used to generate a supplementary control signal to the excitation system, and a neuro-identifier, which is used to model the dynamics of the power system and to adapt the neuro-controller parameters. The proposed method has the features of a simple structure, adaptivity and fast response. The proposed IDNC is evaluated on a single machine infinite bus power system under different operating conditions and disturbances to demonstrate its effectiveness and robustness. PMID:12850048

  7. Interface between astrophysical datasets and distributed database management systems (DAVID)

    NASA Technical Reports Server (NTRS)

    Iyengar, S. S.

    1988-01-01

    This is a status report on the progress of the DAVID (Distributed Access View Integrated Database Management System) project being carried out at Louisiana State University, Baton Rouge, Louisiana. The objective is to implement an interface between Astrophysical datasets and DAVID. Discussed are design details and implementation specifics between DAVID and astrophysical datasets.

  8. DIII-D Neutral Beam control system operator interface

    SciTech Connect

    Harris, J.J.; Campbell, G.L.

    1993-10-01

    A centralized graphical user interface has been added to the DIII-D Neutral Beam (NB) control systems for status monitoring and remote control applications. This user interface provides for automatic data acquisition, alarm detection and supervisory control of the four NB programmable logic controllers (PLC) as well as the Mode Control PLC. These PLCs are used for interlocking, control and status of the NB vacuum pumping, gas delivery, and water cooling systems as well as beam mode status and control. The system allows for both a friendly user interface as well as a safe and convenient method of communicating with remote hardware that formerly required interns to access. In the future, to enable high level of control of PLC subsystems, complete procedures is written and executed at the touch of a screen control panel button. The system consists of an IBM compatible 486 computer running the FIX DMACS{trademark} for Windows{trademark} data acquisition and control interface software, a Texas Instruments/Siemens communication card and Phoenix Digital optical communications modules. Communication is achieved via the TIWAY (Texas Instruments protocol link utilizing both fiber optic communications and a copper local area network (LAN). Hardware and software capabilities will be reviewed. Data and alarm reporting, extended monitoring and control capabilities will also be discussed.

  9. Space Shuttle program communication and tracking systems interface analysis

    NASA Technical Reports Server (NTRS)

    Dodds, J. G.; Holmes, J. K.; Huth, G. K.; Iwasaki, R. S.; Nilsen, P. W.; Polydoros, A.; Sampaio, D. R.; Udalov, S.

    1984-01-01

    The Space Shuttle Program Communications and Tracking Systems Interface Analysis began April 18, 1983. During this time, the shuttle communication and tracking systems began flight testing. Two areas of analysis documented were a result of observations made during flight tests. These analyses involved the Ku-band communication system. First, there was a detailed analysis of the interface between the solar max data format and the Ku-band communication system including the TDRSS ground station. The second analysis involving the Ku-band communication system was an analysis of the frequency lock loop of the Gunn oscillator used to generate the transmit frequency. The stability of the frequency lock loop was investigated and changes to the design were reviewed to alleviate the potential loss of data due the loop losing lock and entering the reacquisition mode. Other areas of investigation were the S-band antenna analysis and RF coverage analysis.

  10. Defining Functional Gene-Circuit Interfaces in the Mouse Nervous System

    PubMed Central

    Soden, Marta E.; Gore, Bryan B.; Zweifel, Larry S.

    2013-01-01

    Complexity in the nervous system is established by developmental genetic programs, maintained by differential genetic profiles, and sculpted by experiential and environmental influence over gene expression. Determining how specific genes define neuronal phenotypes, shape circuit connectivity, and regulate circuit function is essential for understanding how the brain processes information, directs behavior, and adapts to changing environments. Mouse genetics has contributed greatly to current percepts of gene-circuit interfaces in behavior, but considerable work remains. Large-scale initiatives to map gene expression and connectivity in the brain, together with advanced techniques in molecular genetics, now allow detailed exploration of the genetic basis of nervous system function at the level of specific circuit connections. In this review, we highlight several key advances for defining the function of specific genes within a neural network. PMID:24007626

  11. Neural networks and logical reasoning systems: a translation table.

    PubMed

    Martins, J; Mendes, R V

    2001-04-01

    A correspondence is established between the basic elements of logic reasoning systems (knowledge bases, rules, inference and queries) and the structure and dynamical evolution laws of neural networks. The correspondence is pictured as a translation dictionary which might allow to go back and forth between symbolic and network formulations, a desirable step in learning-oriented systems and multicomputer networks. In the framework of Horn clause logics, it is found that atomic propositions with n arguments correspond to nodes with nth order synapses, rules to synaptic intensity constraints, forward chaining to synaptic dynamics and queries either to simple node activation or to a query tensor dynamics. PMID:14632170

  12. Functional Interface Considerations within an Exploration Life Support System Architecture

    NASA Technical Reports Server (NTRS)

    Perry, Jay L.; Sargusingh, Miriam J.; Toomarian, Nikzad

    2016-01-01

    As notional life support system (LSS) architectures are developed and evaluated, myriad options must be considered pertaining to process technologies, components, and equipment assemblies. Each option must be evaluated relative to its impact on key functional interfaces within the LSS architecture. A leading notional architecture has been developed to guide the path toward realizing future crewed space exploration goals. This architecture includes atmosphere revitalization, water recovery and management, and environmental monitoring subsystems. Guiding requirements for developing this architecture are summarized and important interfaces within the architecture are discussed. The role of environmental monitoring within the architecture is described.

  13. User Interface Technology Transfer to NASA's Virtual Wind Tunnel System

    NASA Technical Reports Server (NTRS)

    vanDam, Andries

    1998-01-01

    Funded by NASA grants for four years, the Brown Computer Graphics Group has developed novel 3D user interfaces for desktop and immersive scientific visualization applications. This past grant period supported the design and development of a software library, the 3D Widget Library, which supports the construction and run-time management of 3D widgets. The 3D Widget Library is a mechanism for transferring user interface technology from the Brown Graphics Group to the Virtual Wind Tunnel system at NASA Ames as well as the public domain.

  14. Research of neural network application in methane gas spectrum sensing system

    NASA Astrophysics Data System (ADS)

    Zhou, Meng-ran; Zhang, Haiqing; Wu, Hongwei; Yu, Gang

    2010-10-01

    Laser spectroscopy combined with neural network approach is a new method of monitoring coal mine gas. This research analyses of gas concentration and predicts the process of modeling using BP neural network finds changes law of concentration, gives the various parameters settings of neural network. Experimental results show that, BP neural network for early warning of gas concentration is feasible. The study meets an online, real-time, fast agreement of China's Coal Mine Gas monitoring systems.

  15. Properties of the stimulus router system, a novel neural prosthesis.

    PubMed

    Gan, Liu Shi; Prochazka, Arthur

    2010-02-01

    Various types of neural prostheses (NPs) have been developed to restore motor function after neural injury. Surface NPs are noninvasive and inexpensive, but are often poorly selective, activating nontargeted muscles and cutaneous sensory nerves that can cause discomfort or pain. Implantable NPs are highly selective, but invasive and costly. The stimulus router system (SRS) is a novel NP consisting of fully implanted leads that "capture" and route some of the current flowing between a pair of surface electrodes to the vicinity of a target nerve. An SRS lead consists of a "pick-up" terminal that is implanted subcutaneously under one of the surface electrodes and a "delivery" terminal that is secured on or near the target nerve. We have published a preliminary report on the basic properties of the SRS [L. S. Gan , "A new means of transcutaneous coupling for neural prostheses," IEEE Trans. Biomed. Eng., vol. 54, no. 3, pp. 509-517, Mar. 2007]. Here, we further characterize the SRS and identify aspects that maximize its performance as a motor NP. The surface current needed to activate nerves with an SRS, was found to depend on the proximity of the delivery terminal(s) to the nerve, electrode configurations, contact areas of the surface electrodes and implanted terminals, and the distance between the surface anode and the delivery terminal. PMID:19770087

  16. Neural network application to aircraft control system design

    NASA Technical Reports Server (NTRS)

    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.

  17. Neural network application to aircraft control system design

    NASA Technical Reports Server (NTRS)

    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.

  18. KCNQ potassium channels in sensory system and neural circuits

    PubMed Central

    Wang, Jing-jing; Li, Yang

    2016-01-01

    M channels, an important regulator of neural excitability, are composed of four subunits of the Kv7 (KCNQ) K+ channel family. M channels were named as such because their activity was suppressed by stimulation of muscarinic acetylcholine receptors. These channels are of particular interest because they are activated at the subthreshold membrane potentials. Furthermore, neural KCNQ channels are drug targets for the treatments of epilepsy and a variety of neurological disorders, including chronic and neuropathic pain, deafness, and mental illness. This review will update readers on the roles of KCNQ channels in the sensory system and neural circuits as well as discuss their respective mechanisms and the implications for physiology and medicine. We will also consider future perspectives and the development of additional pharmacological models, such as seizure, stroke, pain and mental illness, which work in combination with drug-design targeting of KCNQ channels. These models will hopefully deepen our understanding of KCNQ channels and provide general therapeutic prospects of related channelopathies. PMID:26687932

  19. A neural interface provides long-term stable natural touch perception.

    PubMed

    Tan, Daniel W; Schiefer, Matthew A; Keith, Michael W; Anderson, James Robert; Tyler, Joyce; Tyler, Dustin J

    2014-10-01

    Touch perception on the fingers and hand is essential for fine motor control, contributes to our sense of self, allows for effective communication, and aids in our fundamental perception of the world. Despite increasingly sophisticated mechatronics, prosthetic devices still do not directly convey sensation back to their wearers. We show that implanted peripheral nerve interfaces in two human subjects with upper limb amputation provided stable, natural touch sensation in their hands for more than 1 year. Electrical stimulation using implanted peripheral nerve cuff electrodes that did not penetrate the nerve produced touch perceptions at many locations on the phantom hand with repeatable, stable responses in the two subjects for 16 and 24 months. Patterned stimulation intensity produced a sensation that the subjects described as natural and without "tingling," or paresthesia. Different patterns produced different types of sensory perception at the same location on the phantom hand. The two subjects reported tactile perceptions they described as natural tapping, constant pressure, light moving touch, and vibration. Changing average stimulation intensity controlled the size of the percept area; changing stimulation frequency controlled sensation strength. Artificial touch sensation improved the subjects' ability to control grasping strength of the prosthesis and enabled them to better manipulate delicate objects. Thus, electrical stimulation through peripheral nerve electrodes produced long-term sensory restoration after limb loss. PMID:25298320

  20. Improving the Performance of a Neural-Machine Interface for Artificial Legs Using Prior Knowledge of Walking Environment

    PubMed Central

    Huang, He; Dou, Zhi; Zhang, Fan; Nunnery, Michael J.

    2013-01-01

    A previously developed neural-machine interface (NMI) based on neuromuscular-mechanical fusion has showed promise for recognizing user locomotion modes; however, errors of NMI during mode transitions were observed, which may challenge its real application. This study aimed to investigate whether or not the prior knowledge of walking environment could further improve the NMI performance. Linear Discriminant Analysis (LDA)-based classifiers were designed to identify user intent based on electromyographic (EMG) signals from residual muscles of leg amputees and ground reaction force (GRF) measured from the prosthetic leg. The prior knowledge of the terrain in front of the user adjusted the prior possibility in the discriminant function. Therefore, the boundaries of LDA were adaptive to the prior knowledge of the walking environment. This algorithm was evaluated on a dataset collected from one patient with a transfemoral (TF) amputation. The preliminary results showed that the NMI with adaptive prior possibilities outperformed the NMI without using the prior knowledge; it produced 98.7% accuracy for identifying tested locomotion modes, accurately predicted all the task transitions with 261–390 ms prediction time, and generated stable decision during task transitions. These results indicate the potential of using prior knowledge about walking environment to further improve the NMI for prosthetic legs. PMID:22255279

  1. Optical mass memory system (AMM-13). AMM/DBMS interface control document

    NASA Technical Reports Server (NTRS)

    Bailey, G. A.

    1980-01-01

    The baseline for external interfaces of a 10 to the 13th power bit, optical archival mass memory system (AMM-13) is established. The types of interfaces addressed include data transfer; AMM-13, Data Base Management System, NASA End-to-End Data System computer interconnect; data/control input and output interfaces; test input data source; file management; and facilities interface.

  2. Data-driven model comparing the effects of glial scarring and interface interactions on chronic neural recordings in non-human primates

    NASA Astrophysics Data System (ADS)

    Malaga, Karlo A.; Schroeder, Karen E.; Patel, Paras R.; Irwin, Zachary T.; Thompson, David E.; Bentley, J. Nicole; Lempka, Scott F.; Chestek, Cynthia A.; Patil, Parag G.

    2016-02-01

    Objective. We characterized electrode stability over twelve weeks of impedance and neural recording data from four chronically-implanted Utah arrays in two rhesus macaques, and investigated the effects of glial scarring and interface interactions at the electrode recording site on signal quality using a computational model. Approach. A finite-element model of a Utah array microelectrode in neural tissue was coupled with a multi-compartmental model of a neuron to quantify the effects of encapsulation thickness, encapsulation resistivity, and interface resistivity on electrode impedance and waveform amplitude. The coupled model was then reconciled with the in vivo data. Histology was obtained seventeen weeks post-implantation to measure gliosis. Main results. From week 1-3, mean impedance and amplitude increased at rates of 115.8 kΩ/week and 23.1 μV/week, respectively. This initial ramp up in impedance and amplitude was observed across all arrays, and is consistent with biofouling (increasing interface resistivity) and edema clearing (increasing tissue resistivity), respectively, in the model. Beyond week 3, the trends leveled out. Histology showed that thin scars formed around the electrodes. In the model, scarring could not match the in vivo data. However, a thin interface layer at the electrode tip could. Despite having a large effect on impedance, interface resistivity did not have a noticeable effect on amplitude. Significance. This study suggests that scarring does not cause an electrical problem with regard to signal quality since it does not appear to be the main contributor to increasing impedance or significantly affect amplitude unless it displaces neurons. This, in turn, suggests that neural signals can be obtained reliably despite scarring as long as the recording site has sufficiently low impedance after accumulating a thin layer of biofouling. Therefore, advancements in microelectrode technology may be expedited by focusing on improvements to the

  3. Neural Network Based Intrusion Detection System for Critical Infrastructures

    SciTech Connect

    Todd Vollmer; Ondrej Linda; Milos Manic

    2009-07-01

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

  4. Neural Network Target Identification System for False Alarm Reduction

    NASA Technical Reports Server (NTRS)

    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.

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

    PubMed Central

    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

  6. User interface and operational issues with thermionic space power systems

    NASA Technical Reports Server (NTRS)

    Dahlberg, R. C.; Fisher, C. R.

    1987-01-01

    Thermionic space power systems have unique features which facilitate predeployment operations, provide operational flexibility and simplify the interface with the user. These were studied in some detail during the SP-100 program from 1983 to 1985. Three examples are reviewed in this paper: (1) system readiness verification in the prelaunch phase; (2) startup, shutdown, and dormancy in the operations phase; (3) part-load operation in the operations phase.

  7. The Ctenophore Genome and the Evolutionary Origins of Neural Systems

    PubMed Central

    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.; Ptytsyn, Andrey; Reshetov, Denis; Mukherjee, Krishanu; Moroz, Tatiana P.; Bobkova, Yelena; Yu, Fahong; Kapitonov, Vladimir V.; Jurka, Jerzy; Bobkov, Yuri; 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.

    2015-01-01

    The origins of neural systems remain unresolved. In contrast to other basal metazoans, ctenophores, or comb jellies, have both complex nervous and mesoderm-derived muscular systems. These holoplanktonic predators also have sophisticated ciliated locomotion, behaviour and distinct development. Here, we present the draft genome of Pleurobrachia bachei, Pacific sea gooseberry, together with ten other ctenophore transcriptomes and show that they are remarkably distinct from other animal genomes in their content of neurogenic, immune and developmental genes. Our integrative analyses place Ctenophora as the earliest lineage within Metazoa. This hypothesis is supported by comparative analysis of multiple gene families, including the apparent absence of HOX genes, canonical microRNA machinery, and reduced immune complement in ctenophores. Although two distinct nervous systems are well-recognized in ctenophores, many bilaterian neuron-specific genes and genes of “classical” neurotransmitter pathways either are absent or, if present, are not expressed in neurons. Our metabolomic and physiological data are consistent with the hypothesis that ctenophore neural systems, and possibly muscle specification, evolved independently from those in other animals. PMID:24847885

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

    PubMed

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

    2014-06-01

    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 present the draft genome of Pleurobrachia bachei, Pacific sea gooseberry, together with ten other ctenophore transcriptomes, and show that they are remarkably distinct from other animal genomes in their content of neurogenic, immune and developmental genes. Our integrative analyses place Ctenophora as the earliest lineage within Metazoa. This hypothesis is supported by comparative analysis of multiple gene families, including the apparent absence of HOX genes, canonical microRNA machinery, and reduced immune complement in ctenophores. Although two distinct nervous systems are well recognized in ctenophores, many bilaterian neuron-specific genes and genes of 'classical' neurotransmitter pathways either are absent or, if present, are not expressed in neurons. Our metabolomic and physiological data are consistent with the hypothesis that ctenophore neural systems, and possibly muscle specification, evolved independently from those in other animals. PMID:24847885

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

    SciTech Connect

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

    1995-12-31

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

  10. Risk Interfaces to Support Integrated Systems Analysis and Development

    NASA Technical Reports Server (NTRS)

    Mindock, Jennifer; Lumpkins, Sarah; Shelhamer, Mark; Anton, Wilma; Havenhill, Maria

    2016-01-01

    Objectives for systems analysis capability: Develop integrated understanding of how a complex human physiological-socio-technical mission system behaves in spaceflight. Why? Support development of integrated solutions that prevent unwanted outcomes (Implementable approaches to minimize mission resources(mass, power, crew time, etc.)); Support development of tools for autonomy (need for exploration) (Assess and maintain resilience -individuals, teams, integrated system). Output of this exercise: -Representation of interfaces based on Human System Risk Board (HSRB) Risk Summary information and simple status based on Human Research Roadmap; Consolidated HSRB information applied to support communication; Point-of-Departure for HRP Element planning; Ability to track and communicate status of collaborations. 4

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

    PubMed

    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. PMID:25489985

  12. Interface flow of CO2-brine mixtures in wellbore systems

    NASA Astrophysics Data System (ADS)

    Carey, J. W.; Newell, D. L.; Williams, M.

    2010-12-01

    Long-term geologic sequestration of CO2 will require minimal leakage from the wells used to inject the CO2, wells used for monitoring, and pre-existing wells that penetrate the caprock system. Our previous field work and experimental studies have shown that wellbore leakage processes are dominated by interface flow along the casing-cement, cement-cement, or cement-caprock interfaces. In order to constrain potential leakage scenarios, it is necessary to determine the magnitude of CO2-brine flows as a function of interface properties in addition to the subsequent mineralogical evolution of the interfaces (dissolution of cement and rock, corrosion of steel, and precipitation of carbonates). To support this effort, we have conducted a series of experimental column and core-flood studies of CO2-brine flow through interfaces. The first set of experiments was conducted at atmospheric conditions in which water, HCl (pH = 3), and CO2-saturated water were flowed through cement cylinders containing a defect channel with a diameter of approximately 0.8 mm. In all cases, permeability remained constant over the course of about 30 hours at 5 ml/min. The aqueous solutions were strongly undersaturated, including with respect to calcite in the CO2-bearing system. The cement system appeared to have developed an unreactive leached layer that did not widen with time and limited further chemical reaction. In a second study, we conducted core-flood experiments of a composite system consisting of a half-cylinder of type G oilwell cement and a half-cylinder of fine-grained, quartz-rich sandstone separated by a 2.5 mm-thick interface containing crushed rock and cured cement fragments (125 - 250 μ m). The core was wrapped in copper foil to prevent CO2 migration outside the core holder. A mixture of supercritical CO2 and synthetic, low salinity reservoir brine was flooded through the core at ≈ 100 bar and 60 oC. Differential pressure across the core was used to determine changes in

  13. Cycles of insanity and creativity within contemplative neural systems.

    PubMed

    Thaler, Stephen L

    2016-09-01

    Random connection weight disturbances within an assembly of artificial neural networks (ANN) drive a progression of activation patterns that are tantamount to the memories and ideas nucleating within the brain's cortex. The numerical evaluation of these pattern-based notions by another, more placid system of ANNs governs the magnitude of weight disturbances administered to the former assembly, that perturbative intensity in turn controlling the novelty of the resulting ideational stream as well as the retention of newly formed concepts. In search of solution patterns to posed problems, such collaborating neural systems autonomously cycle between two extremes in mean synaptic perturbation level. The higher limit, characterized by chaos and inattentiveness to exogenous input patterns, is the regime in which ideas first form and incubate. The lower bound, marked by relative synaptic tranquility, is favorable to the reactivation and reinforcement of concepts first seeded during heightened perturbation. When considering this synthetic neural architecture as a cognitive model, the proposed source of such synaptic fluctuations is volume neurotransmitter release within cortex where both ideational and critic nets are commingled. As a result of their overlap, not only are the generative cortical networks suffused with neurotransmitters, but also those functioning in a critic role, leading to altered 'opinions' about the perturbation-driven stream of consciousness that then govern the injection of neurotransmitters into cortex. The likely effect of such chemical feedback is that the brain constantly cycles between states of idea generating chaos and perception stabilizing tranquility in much the same way that creative artificial neural systems do. Postulating that ideas are potentially useful or interesting false memories born within such turmoil, creativity appears to take place through a cyclic process consisting of alternating phases of (1) cognitive incapacitation

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

    PubMed Central

    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

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

    PubMed

    Yin, Ming; Ghovanloo, Maysam

    2009-08-01

    We have used a well-known technique in wireless communication, pulse width modulation (PWM) of time division multiplexed (TDM) signals, within the architecture of a novel wireless integrated neural recording (WINeR) system. We have evaluated the performance of the PWM-based architecture and indicated its accuracy and potential sources of error through detailed theoretical analysis, simulations, and measurements on a setup consisting of a 15-channel WINeR prototype as the transmitter and two types of receivers; an Agilent 89600 vector signal analyzer and a custom wideband receiver, with 36 and 75 MHz of maximum bandwidth, respectively. Furthermore, we present simulation results from a realistic MATLAB-Simulink model of the entire WINeR system to observe the system behavior in response to changes in various parameters. We have concluded that the 15-ch WINeR prototype, which is fabricated in a 0.5- mum standard CMOS process and consumes 4.5 mW from +/-1.5 V supplies, can acquire and wirelessly transmit up to 320 k-samples/s to a 75-MHz receiver with 8.4 bits of resolution, which is equivalent to a wireless data rate of approximately 2.56 Mb/s. PMID:19497823

  16. Incomplete fuzzy data processing systems using artificial neural network

    NASA Technical Reports Server (NTRS)

    Patyra, Marek J.

    1992-01-01

    In this paper, the implementation of a fuzzy data processing system using an artificial neural network (ANN) is discussed. The binary representation of fuzzy data is assumed, where the universe of discourse is decartelized into n equal intervals. The value of a membership function is represented by a binary number. It is proposed that incomplete fuzzy data processing be performed in two stages. The first stage performs the 'retrieval' of incomplete fuzzy data, and the second stage performs the desired operation on the retrieval data. The method of incomplete fuzzy data retrieval is proposed based on the linear approximation of missing values of the membership function. The ANN implementation of the proposed system is presented. The system was computationally verified and showed a relatively small total error.

  17. A Gamma Memory Neural Network for System Identification

    NASA Technical Reports Server (NTRS)

    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.

  18. Neural systems for speech and song in autism.

    PubMed

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

    2012-03-01

    Despite language disabilities in autism, music abilities are frequently preserved. Paradoxically, brain regions associated with these functions typically overlap, enabling investigation of neural organization supporting speech and song in autism. Neural systems sensitive to speech and song were compared in low-functioning autistic and age-matched control children using passive auditory stimulation during functional magnetic resonance and diffusion tensor imaging. Activation in left inferior frontal gyrus was reduced in autistic children relative to controls during speech stimulation, but was greater than controls during song stimulation. Functional connectivity for song relative to speech was also increased between left inferior frontal gyrus and superior temporal gyrus in autism, and large-scale connectivity showed increased frontal-posterior connections. Although fractional anisotropy of the left arcuate fasciculus was decreased in autistic children relative to controls, structural terminations of the arcuate fasciculus in inferior frontal gyrus were indistinguishable between autistic and control groups. Fractional anisotropy correlated with activity in left inferior frontal gyrus for both speech and song conditions. Together, these findings indicate that in autism, functional systems that process speech and song were more effectively engaged for song than for speech and projections of structural pathways associated with these functions were not distinguishable from controls. PMID:22298195

  19. ATCA-based ATLAS FTK input interface system

    NASA Astrophysics Data System (ADS)

    Okumura, Y.; Liu, T.; Olsen, J.; Iizawa, T.; Mitani, T.; Korikawa, T.; Yorita, K.; Annovi, A.; Beretta, M.; Gatta, M.; Sotiropoulou, C.-L.; Gkaitatzis, S.; Kordas, K.; Kimura, N.; Cremonesi, M.; Yin, H.; Xu, Z.

    2015-04-01

    The first stage of the ATLAS Fast TracKer (FTK) is an ATCA-based input interface system, where hits from the entire silicon tracker are clustered and organized into overlapping η-phi trigger towers before being sent to the tracking engines. First, FTK Input Mezzanine cards receive hit data and perform clustering to reduce data volume. Then, the ATCA-based Data Formatter system will organize the trigger tower data, sharing data among boards over full mesh backplanes and optic fibers. The board and system level design concepts and implementation details, as well as the operation experiences from the FTK full-chain testing, will be presented.

  20. Field Deployable Tritium Assay System Host Graphical User Interface Software

    Energy Science and Technology Software Center (ESTSC)

    1998-05-12

    The FDTASHOST software is a Graphical User Interface for the Field Deployable Tritium Assay System (FDTAS - Invention Disclosure SRS-96-09-091 has been submitted). The program runs on the Host computer which is located in the Laboratory and connected to the FDTAS remote field system via a modem over a phone line. The operator receives status information and messages from the Remote system. The operator can enter in commands to be executed by the remote systemmore » using the mouse and a pull down menu.« less

  1. A fuzzy neural network approach for power system evaluations

    NASA Astrophysics Data System (ADS)

    Moghaddas, Javad

    Every real-world dynamical system is nonlinear. Existing methods for solving a nonlinear problem entail linearizing the nonlinear problem and then using the different tools available for solving the linear system. These tools have been well understood for many decades. This research presents the application of Fuzzy Neural Network in reducing the large computational requirements associated with solving nonlinear systems. This approach utilizes fewer and faster steps for solving nonlinear problems. A practical use of this technique is power flow calculation where a large number of nonlinear equations are involved. The power flow problem is formulated as a nonlinear constrained optimization problem with the bus voltages, bus angles, real power injected into the buses, and reactive power injected into the buses as the problem variables. The equality and inequality constraints are appended to this objective function. Fuzzy rules based control is used to assist in choosing suitable penalty functions to form an augmented cost function. The linearized power flow equations at each iteration are translated to a scalar objective function of quadratic form. A neural network structure is given which implements the steepest descent method for minimizing the objective function. This research also presents the application of Fuzzy Clustering to power systems. The technique of Fuzzy Clustering reduces large system states into a few representative clusters, which are sufficient for reliability analysis. The method is then shown for optimal network decomposition based on Fuzzy Clustering. Fuzzy Clustering presents a powerful, globally oriented optimization method, which exploits the mechanism of natural response to reach optima or near optima. The results for an IEEE 14-Bus test system are given and the Fuzzy Clustering algorithm approach is found to produce significantly better solution.

  2. MRS (monitored retrievable storage) to transportation system interfaces

    SciTech Connect

    Row, T.H.; Croff, A.G.

    1987-01-01

    In March 1987, the US Department of Energy presented to Congress the proposal to construct and operate a facility for the monitored retrievable storage (MRS) of spent fuel at a site on the Clinch River in the Roane County portions of Oak Ridge. In discussing the MRS to Transportation System Interfaces, the authors provide a blending of the technical and institutional issues, for they do not believe the solutions to success of this enterprise lie wholly in one area. The authors cover: early chronology of the MRS; comparison of total-system life cycle cost estimates of the authorized system and improved-performance system (i.e., the system that includes a facility for MRS); transportation costs resulting from shipping, security and cask; assumptions for dedicated rail transport from MRS to repository; and significant results from the Total System Life Cycle Cost (TSLCC) analysis of the improved performance system. (AT)

  3. Neural crest and placode interaction during the development of the cranial sensory system.

    PubMed

    Steventon, Ben; Mayor, Roberto; Streit, Andrea

    2014-05-01

    In the vertebrate head, the peripheral components of the sensory nervous system are derived from two embryonic cell populations, the neural crest and cranial sensory placodes. Both arise in close proximity to each other at the border of the neural plate: neural crest precursors abut the future central nervous system, while placodes originate in a common preplacodal region slightly more lateral. During head morphogenesis, complex events organise these precursors into functional sensory structures, raising the question of how their development is coordinated. Here we review the evidence that neural crest and placode cells remain in close proximity throughout their development and interact repeatedly in a reciprocal manner. We also review recent controversies about the relative contribution of the neural crest and placodes to the otic and olfactory systems. We propose that a sequence of mutual interactions between the neural crest and placodes drives the coordinated morphogenesis that generates functional sensory systems within the head. PMID:24491819

  4. Effect of bias voltage and temperature on lifetime of wireless neural interfaces with Al ₂O₃ and parylene bilayer encapsulation.

    PubMed

    Xie, Xianzong; Rieth, Loren; Caldwell, Ryan; Negi, Sandeep; Bhandari, Rajmohan; Sharma, Rohit; Tathireddy, Prashant; Solzbacher, Florian

    2015-02-01

    The lifetime of neural interfaces is a critical challenge for chronic implantations, as therapeutic devices (e.g., neural prosthetics) will require decades of lifetime. We evaluated the lifetime of wireless Utah electrode array (UEA) based neural interfaces with a bilayer encapsulation scheme utilizing a combination of alumina deposited by Atomic Layer Deposition (ALD) and parylene C. Wireless integrated neural interfaces (INIs), equipped with recording version 9 (INI-R9) ASIC chips, were used to monitor the encapsulation performance through radio-frequency (RF) power and telemetry. The wireless devices were encapsulated with 52 nm of ALD Al2O3 and 6 μm of parylene C, and tested by soaking in phosphate buffered solution (PBS) at 57 °C for 4× accelerated lifetime testing. The INIs were also powered continuously through 2.765 MHz inductive power and forward telemetry link at unregulated 5 V. The bilayer encapsulated INIs were fully functional for ∼35 days (140 days at 37 °C equivalent) with consistent power-up frequencies (∼910 MHz), stable RF signal (∼-75 dBm), and 100 % command reception rate. This is ∼10 times of equivalent lifetime of INIs with parylene-only encapsulation (13 days) under same power condition at 37 °C. The bilayer coated INIs without continuous powering lasted over 1860 equivalent days (still working) at 37 °C. Those results suggest that bias stress is a significant factor to accelerate the failure of the encapsulated devices. The INIs failed completely within 5 days of the initial frequency shift of RF signal at 57 °C, which implied that the RF frequency shift is an early indicator of encapsulation/device failure. PMID:25653054

  5. Voltage pulses change neural interface properties and improve unit recordings with chronically implanted microelectrodes.

    PubMed

    Otto, Kevin J; Johnson, Matthew D; Kipke, Daryl R

    2006-02-01

    Current neuroprosthetic systems based on electro-physiological recording have an extended, yet finite working lifetime. Some posited lifetime-extension solutions involve improving device biocompatibility or suppressing host immune responses. Our objective was to test an alternative solution comprised of applying a voltage pulse to a microelectrode site, herein termed "rejuvenation." Previously, investigators have reported preliminary electrophysiological results by utilizing a similar voltage pulse. In this study we sought to further explore this phenomenon via two methods: 1) electrophysiology; 2) an equivalent circuit model applied to impedance spectroscopy data. The experiments were conducted via chronically implanted silicon-substrate iridium microelectrode arrays in the rat cortex. Rejuvenation voltages resulted in increased unit recording signal-to-noise ratios (10% +/- 2%), with a maximal increase of 195% from 3.74 to 11.02. Rejuvenation also reduced the electrode site impedances at 1 kHz (67% +/- 2%). Neither the impedance nor recording properties of the electrodes changed on neighboring microelectrode sites that were not rejuvenated. In the equivalent circuit model, we found a transient increase in conductivity, the majority of which corresponded to a decrease in the tissue resistance component (44% +/- 7%). These findings suggest that rejuvenation may be an intervention strategy to prolong the functional lifetime of chronically implanted microelectrodes. PMID:16485763

  6. Interface Management for a NASA Flight Project Using Model-Based Systems Engineering (MBSE)

    NASA Technical Reports Server (NTRS)

    Vipavetz, Kevin; Shull, Thomas A.; Infeld, Samatha; Price, Jim

    2016-01-01

    The goal of interface management is to identify, define, control, and verify interfaces; ensure compatibility; provide an efficient system development; be on time and within budget; while meeting stakeholder requirements. This paper will present a successful seven-step approach to interface management used in several NASA flight projects. The seven-step approach using Model Based Systems Engineering will be illustrated by interface examples from the Materials International Space Station Experiment-X (MISSE-X) project. The MISSE-X was being developed as an International Space Station (ISS) external platform for space environmental studies, designed to advance the technology readiness of materials and devices critical for future space exploration. Emphasis will be given to best practices covering key areas such as interface definition, writing good interface requirements, utilizing interface working groups, developing and controlling interface documents, handling interface agreements, the use of shadow documents, the importance of interface requirement ownership, interface verification, and product transition.

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

    PubMed Central

    Kinney, Justin P.; Bernstein, Jacob G.; Meyer, Andrew J.; Barber, Jessica B.; Bolivar, Marti; Newbold, Bryan; Scholvin, Jorg; Moore-Kochlacs, Caroline; Wentz, Christian T.; Kopell, Nancy J.; Boyden, Edward S.

    2015-01-01

    Driven by the increasing channel count of neural probes, there is much effort being directed to creating increasingly scalable electrophysiology data acquisition (DAQ) systems. However, all such systems still rely on personal computers for data storage, and thus are limited by the bandwidth and cost of the computers, especially as the scale of recording increases. Here we present a novel architecture in which a digital processor receives data from an analog-to-digital converter, and writes that data directly to hard drives, without the need for a personal computer to serve as an intermediary in the DAQ process. This minimalist architecture may support exceptionally high data throughput, without incurring costs to support unnecessary hardware and overhead associated with personal computers, thus facilitating scaling of electrophysiological recording in the future. PMID:26388740

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

    PubMed

    Kinney, Justin P; Bernstein, Jacob G; Meyer, Andrew J; Barber, Jessica B; Bolivar, Marti; Newbold, Bryan; Scholvin, Jorg; Moore-Kochlacs, Caroline; Wentz, Christian T; Kopell, Nancy J; Boyden, Edward S

    2015-01-01

    Driven by the increasing channel count of neural probes, there is much effort being directed to creating increasingly scalable electrophysiology data acquisition (DAQ) systems. However, all such systems still rely on personal computers for data storage, and thus are limited by the bandwidth and cost of the computers, especially as the scale of recording increases. Here we present a novel architecture in which a digital processor receives data from an analog-to-digital converter, and writes that data directly to hard drives, without the need for a personal computer to serve as an intermediary in the DAQ process. This minimalist architecture may support exceptionally high data throughput, without incurring costs to support unnecessary hardware and overhead associated with personal computers, thus facilitating scaling of electrophysiological recording in the future. PMID:26388740

  9. Image exploitation using multi-sensor/neural network systems

    SciTech Connect

    Uberbacher, E.C.; Xu, Y.; Lee, R.W.

    1995-12-31

    We have developed and evaluated a tool for change detection and other analysis tasks relevant to image exploitation. The tool, visGRAIL, integrates three key elements: (1) the use of multiple algorithms to extract information from images - feature extractors or {open_quotes}sensors{close_quotes}, (2) an algorithm to fuse the information - presently a neural network, and (3) empirical estimation of the fusion parameters based on a representative set of images. The system was applied to test images in the RADIUS Common Development Environment (RCDE). In a task designed to distinguish natural scenes from those containing various amounts of human-made objects and structure, the system classified correctly 95% of 350 images in a test set. This paper describes details of the feature extractors, and presents analyses of the discriminatory characteristics of the features. visGRAIL has been integrated into the RCDE.

  10. Effects of Fast Simple Numerical Calculation Training on Neural Systems

    PubMed Central

    Takeuchi, Hikaru; Nagase, Tomomi; Taki, Yasuyuki; Sassa, Yuko; Hashizume, Hiroshi; Nouchi, Rui; Kawashima, Ryuta

    2016-01-01

    Cognitive training, including fast simple numerical calculation (FSNC), has been shown to improve performance on untrained processing speed and executive function tasks in the elderly. However, the effects of FSNC training on cognitive functions in the young and on neural mechanisms remain unknown. We investigated the effects of 1-week intensive FSNC training on cognitive function, regional gray matter volume (rGMV), and regional cerebral blood flow at rest (resting rCBF) in healthy young adults. FSNC training was associated with improvements in performance on simple processing speed, speeded executive functioning, and simple and complex arithmetic tasks. FSNC training was associated with a reduction in rGMV and an increase in resting rCBF in the frontopolar areas and a weak but widespread increase in resting rCBF in an anatomical cluster in the posterior region. These results provide direct evidence that FSNC training alone can improve performance on processing speed and executive function tasks as well as plasticity of brain structures and perfusion. Our results also indicate that changes in neural systems in the frontopolar areas may underlie these cognitive improvements. PMID:26881117

  11. Neural Systems Underlying the Reappraisal of Personally Craved Foods

    PubMed Central

    Giuliani, Nicole R.; Mann, Traci; Tomiyama, A. Janet; Berkman, Elliot T.

    2014-01-01

    Craving of unhealthy food is a common target of self-regulation, but the neural systems underlying this process are understudied. In this study, participants used cognitive reappraisal to regulate their desire to consume idiosyncratically craved or not craved energy-dense foods, and neural activity during regulation was compared with each other and with the activity during passive viewing of energy-dense foods. Regulation of both food types elicited activation in classic top–down self-regulation regions including the dorsolateral prefrontal, inferior frontal, and dorsal anterior cingulate cortices. This main effect of regulation was qualified by an interaction, such that activation in these regions was significantly greater during reappraisal of craved (versus not craved) foods and several regions, including the dorsolateral prefrontal, inferior frontal, medial frontal, and dorsal anterior cingulate cortices, were uniquely active during regulation of personally craved foods. Body mass index significantly negatively correlated with regulation-related activation in the right dorsolateral PFC, thalamus, and bilateral dorsal ACC and with activity in nucleus accumbens during passive viewing of craved (vs. neutral, low-energy density) foods. These results suggest that several of the brain regions involved in the self-regulation of food craving are similar to other kinds of affective self-regulation and that others are sensitive to the self-relevance of the regulation target. PMID:24392892

  12. Human neural progenitor cells in central nervous system lesions.

    PubMed

    Åkesson, Elisabet; Sundström, Erik

    2016-02-01

    Various immature cells can be isolated from human embryonic and fetal central nervous system (CNS) residual tissue and potentially be used in cell therapy for a number of neurological diseases and CNS insults. Transplantation of neural stem and progenitor cells is essential for replacing lost cells, particularly in the CNS with very limited endogenous regenerative capacity. However, while dopamine released from transplanted cells can substitute the lost dopamine neurons in the experimental models of Parkinson's disease, stem and progenitor cells primarily have a neuroprotective effect, probably through the release of trophic factors. Understanding the therapeutic effects of transplanted cells is crucial to determine the design of clinical trials. During the last few years, a number of clinical trials for CNS diseases and insults such as amyotrophic lateral sclerosis (ALS), stroke, and spinal cord trauma using neural progenitor cells have been initiated. Data from these early studies will provide vital information on the safety of transplanting these cells, which still is a major concern. That the beneficial results observed in experimental models also can be repeated in the clinical setting is highly hoped for. PMID:26803559

  13. Neural map formation and sensory coding in the vomeronasal system.

    PubMed

    Brignall, Alexandra C; Cloutier, Jean-François

    2015-12-01

    Sensory systems enable us to encode a clear representation of our environment in the nervous system by spatially organizing sensory stimuli being received. The organization of neural circuitry to form a map of sensory activation is critical for the interpretation of these sensory stimuli. In rodents, social communication relies strongly on the detection of chemosignals by the vomeronasal system, which regulates a wide array of behaviours, including mate recognition, reproduction, and aggression. The binding of these chemosignals to receptors on vomeronasal sensory neurons leads to activation of second-order neurons within glomeruli of the accessory olfactory bulb. Here, vomeronasal receptor activation by a stimulus is organized into maps of glomerular activation that represent phenotypic qualities of the stimuli detected. Genetic, electrophysiological and imaging studies have shed light on the principles underlying cell connectivity and sensory map formation in the vomeronasal system, and have revealed important differences in sensory coding between the vomeronasal and main olfactory system. In this review, we summarize the key factors and mechanisms that dictate circuit formation and sensory coding logic in the vomeronasal system, emphasizing differences with the main olfactory system. Furthermore, we discuss how detection of chemosignals by the vomeronasal system regulates social behaviour in mice, specifically aggression. PMID:26329476

  14. General-purpose interface bus for multiuser, multitasking computer system

    NASA Technical Reports Server (NTRS)

    Generazio, Edward R.; Roth, Don J.; Stang, David B.

    1990-01-01

    The architecture of a multiuser, multitasking, virtual-memory computer system intended for the use by a medium-size research group is described. There are three central processing units (CPU) in the configuration, each with 16 MB memory, and two 474 MB hard disks attached. CPU 1 is designed for data analysis and contains an array processor for fast-Fourier transformations. In addition, CPU 1 shares display images viewed with the image processor. CPU 2 is designed for image analysis and display. CPU 3 is designed for data acquisition and contains 8 GPIB channels and an analog-to-digital conversion input/output interface with 16 channels. Up to 9 users can access the third CPU simultaneously for data acquisition. Focus is placed on the optimization of hardware interfaces and software, facilitating instrument control, data acquisition, and processing.

  15. Beamline Control and Instrumentation System using Industrial Interface Techniques

    NASA Astrophysics Data System (ADS)

    Enz, F.

    2010-06-01

    How should a beamline be designed, which satisfies the needs and requirements of scientists and is easy to build and operate? Today, most control and instrumentation systems for beamlines are based on scientific requirements. Scientific details of the beamline, e.g. vacuum and beam physics details; are usually extensively described. However, control system specifications are often reduced to few requirements, e.g. which beam-related device to use. Lots of these systems work perfectly from the physicist's point of view, but are hard to bring into service and operate and difficult to extend with additional equipment. To overcome this, the engineering company ENZ has developed components using industrial standard interfaces to guarantee high flexibility for equipment extension. Using special interface boards and galvanic isolation offers increased stability of motion control axes. This saves resources during commissioning and service. A control system was developed and installed at a Soft-X-ray beamline at ASP Melbourne. It is operated under EPICs on distributed embedded IOC's based on PC-hardware. Motion and vacuum systems, measurement devices, e.g. a Low-Current Monitor (LoCuM) for beam position monitoring, and parts of the equipment protection system were developed and most of them tested in cooperation with DELTA at the Technical University of Dortmund.

  16. Beamline Control and Instrumentation System using Industrial Interface Techniques

    SciTech Connect

    Enz, F.

    2010-06-23

    How should a beamline be designed, which satisfies the needs and requirements of scientists and is easy to build and operate? Today, most control and instrumentation systems for beamlines are based on scientific requirements. Scientific details of the beamline, e.g. vacuum and beam physics details; are usually extensively described. However, control system specifications are often reduced to few requirements, e.g. which beam-related device to use. Lots of these systems work perfectly from the physicist's point of view, but are hard to bring into service and operate and difficult to extend with additional equipment. To overcome this, the engineering company ENZ has developed components using industrial standard interfaces to guarantee high flexibility for equipment extension. Using special interface boards and galvanic isolation offers increased stability of motion control axes. This saves resources during commissioning and service. A control system was developed and installed at a Soft-X-ray beamline at ASP Melbourne. It is operated under EPICs on distributed embedded IOC's based on PC-hardware. Motion and vacuum systems, measurement devices, e.g. a Low-Current Monitor (LoCuM) for beam position monitoring, and parts of the equipment protection system were developed and most of them tested in cooperation with DELTA at the Technical University of Dortmund.

  17. Bringing Control System User Interfaces to the Web

    SciTech Connect

    Chen, Xihui; Kasemir, Kay

    2013-01-01

    With the evolution of web based technologies, especially HTML5 [1], it becomes possible to create web-based control system user interfaces (UI) that are cross-browser and cross-device compatible. This article describes two technologies that facilitate this goal. The first one is the WebOPI [2], which can seamlessly display CSS BOY [3] Operator Interfaces (OPI) in web browsers without modification to the original OPI file. The WebOPI leverages the powerful graphical editing capabilities of BOY and provides the convenience of re-using existing OPI files. On the other hand, it uses generic JavaScript and a generic communication mechanism between the web browser and web server. It is not optimized for a control system, which results in unnecessary network traffic and resource usage. Our second technology is the WebSocket-based Process Data Access (WebPDA) [4]. It is a protocol that provides efficient control system data communication using WebSocket [5], so that users can create web-based control system UIs using standard web page technologies such as HTML, CSS and JavaScript. WebPDA is control system independent, potentially supporting any type of control system.

  18. Impact of mental representational systems on design interface.

    SciTech Connect

    Brown-VanHoozer, S. A.

    1998-02-25

    The purpose of the studies conducted at Argonne National Laboratory is to understand the impact mental representational systems have in identifying how user comfort parameters influence how information is to best be presented. By understanding how each individual perceives information based on the three representational systems (visual, auditory and kinesthetic modalities), it has been found that a different approach must be taken in the design of interfaces resulting in an outcome that is much more effective and representative of the users mental model. This paper will present current findings and future theories to be explored.

  19. Brain-computer interface after nervous system injury.

    PubMed

    Burns, Alexis; Adeli, Hojjat; Buford, John A

    2014-12-01

    Brain-computer interface (BCI) has proven to be a useful tool for providing alternative communication and mobility to patients suffering from nervous system injury. BCI has been and will continue to be implemented into rehabilitation practices for more interactive and speedy neurological recovery. The most exciting BCI technology is evolving to provide therapeutic benefits by inducing cortical reorganization via neuronal plasticity. This article presents a state-of-the-art review of BCI technology used after nervous system injuries, specifically: amyotrophic lateral sclerosis, Parkinson's disease, spinal cord injury, stroke, and disorders of consciousness. Also presented is transcending, innovative research involving new treatment of neurological disorders. PMID:25193343

  20. INN: An Intelligent Negotiating Neural Network for Information Systems: A Design Model.

    ERIC Educational Resources Information Center

    Meghabghab, George V.; Meghabghab, Dania B.

    1994-01-01

    Presents an Intelligent Negotiating Neural Network Design Model for solving the problem of poor information retrieval in subject searches of online catalogs. The purpose of the network, its architecture, and three different sessions of the user interface are described. Nineteen figures showing screens of the three sessions are appended. (Contains…

  1. BOOK REVIEW: Theory of Neural Information Processing Systems

    NASA Astrophysics Data System (ADS)

    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

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

    PubMed

    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. PMID:18238018

  3. SRF Test Areas Cryogenic System Controls Graphical User Interface

    SciTech Connect

    DeGraff, B.D.; Ganster, G.; Klebaner, A.; Petrov, A.D.; Soyars, W.M.; /Fermilab

    2011-06-09

    Fermi National Accelerator Laboratory has constructed a superconducting 1.3 GHz cavity test facility at Meson Detector Building (MDB) and a superconducting 1.3 GHz cryomodule test facility located at the New Muon Lab Building (NML). The control of these 2K cryogenic systems is accomplished by using a Synoptic graphical user interface (GUI) to interact with the underlying Fermilab Accelerator Control System. The design, testing and operational experience of employing the Synoptic client-server system for graphical representation will be discussed. Details on the Synoptic deployment to the MDB and NML cryogenic sub-systems will also be discussed. The implementation of the Synoptic as the GUI for both NML and MDB has been a success. Both facilities are currently fulfilling their individual roles in SCRF testing as a result of successful availability of the cryogenic systems. The tools available for creating Synoptic pages will continue to be developed to serve the evolving needs of users.

  4. Molecular tailoring of interfaces for thin film on substrate systems

    NASA Astrophysics Data System (ADS)

    Grady, Martha Elizabeth

    Thin film on substrate systems appear most prevalently within the microelectronics industry, which demands that devices operate in smaller and smaller packages with greater reliability. The reliability of these multilayer film systems is strongly influenced by the adhesion of each of the bimaterial interfaces. During use, microelectronic components undergo thermo-mechanical cycling, which induces interfacial delaminations leading to failure of the overall device. The ability to tailor interfacial properties at the molecular level provides a mechanism to improve thin film adhesion, reliability and performance. This dissertation presents the investigation of molecular level control of interface properties in three thin film-substrate systems: photodefinable polyimide films on passivated silicon substrates, self-assembled monolayers at the interface of Au films and dielectric substrates, and mechanochemically active materials on rigid substrates. For all three materials systems, the effect of interfacial modifications on adhesion is assessed using a laser-spallation technique. Laser-induced stress waves are chosen because they dynamically load the thin film interface in a precise, noncontacting manner at high strain rates and are suitable for both weak and strong interfaces. Photodefinable polyimide films are used as dielectrics in flip chip integrated circuit packages to reduce the stress between silicon passivation layers and mold compound. The influence of processing parameters on adhesion is examined for photodefinable polyimide films on silicon (Si) substrates with three different passivation layers: silicon nitride (SiNx), silicon oxynitride (SiOxNy), and the native silicon oxide (SiO2). Interfacial strength increases when films are processed with an exposure step as well as a longer cure cycle. Additionally, the interfacial fracture energy is assessed using a dynamic delamination protocol. The high toughness of this interface (ca. 100 J/m2) makes it difficult

  5. Neural Mechanism of Facilitation System during Physical Fatigue

    PubMed Central

    Tanaka, Masaaki; Ishii, Akira; Watanabe, Yasuyoshi

    2013-01-01

    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. PMID:24278313

  6. DiAs User Interface: A Patient-Centric Interface for Mobile Artificial Pancreas Systems

    PubMed Central

    Keith-Hynes, Patrick; Guerlain, Stephanie; Mize, Benton; Hughes-Karvetski, Colleen; Khan, Momin; McElwee-Malloy, Molly; Kovatchev, Boris P.

    2013-01-01

    Background Recent in-hospital studies of artificial pancreas (AP) systems have shown promising results in improving glycemic control in patients with type 1 diabetes mellitus. The next logical step in AP development is to conduct transitional outpatient clinical trials with a mobile system that is controlled by the patient. In this article, we present the user interface (UI) of the Diabetes Assistant (DiAs), an experimental smartphone-based mobile AP system, and describe the reactions of a round of focus groups to the UI. This work is an initial inquiry involving a relatively small number of potential users, many of whom had never seen an AP system before, and the results should be understood in that light. Methods We began by considering how the UI of an AP system could be designed to make use of the familiar touch-based graphical UI of a consumer smartphone. After developing a working prototype UI, we enlisted a human factors specialist to perform a heuristic expert analysis. Next we conducted a formative evaluation of the UI through a series of three focus groups with N = 13 potential end users as participants. The UI was modified based upon the results of these studies, and the resulting DiAs system was used in transitional outpatient AP studies of adults in the United States and Europe. Results The DiAs UI was modified based on focus group feedback from potential users. The DiAs was subsequently used in JDRF- and AP@Home-sponsored transitional outpatient AP studies in the United States and Europe by 40 subjects for 2400 h with no adverse events. Conclusions Adult patients with type 1 diabetes mellitus are able to control an AP system successfully using a patient-centric UI on a commercial smartphone in a transitional outpatient environment. PMID:24351168

  7. Implantable neurotechnologies: a review of integrated circuit neural amplifiers.

    PubMed

    Ng, Kian Ann; Greenwald, Elliot; Xu, Yong Ping; Thakor, Nitish V

    2016-01-01

    Neural signal recording is critical in modern day neuroscience research and emerging neural prosthesis programs. Neural recording requires the use of precise, low-noise amplifier systems to acquire and condition the weak neural signals that are transduced through electrode interfaces. Neural amplifiers and amplifier-based systems are available commercially or can be designed in-house and fabricated using integrated circuit (IC) technologies, resulting in very large-scale integration or application-specific integrated circuit solutions. IC-based neural amplifiers are now used to acquire untethered/portable neural recordings, as they meet the requirements of a miniaturized form factor, light weight and low power consumption. Furthermore, such miniaturized and low-power IC neural amplifiers are now being used in emerging implantable neural prosthesis technologies. This review focuses on neural amplifier-based devices and is presented in two interrelated parts. First, neural signal recording is reviewed, and practical challenges are highlighted. Current amplifier designs with increased functionality and performance and without penalties in chip size and power are featured. Second, applications of IC-based neural amplifiers in basic science experiments (e.g., cortical studies using animal models), neural prostheses (e.g., brain/nerve machine interfaces) and treatment of neuronal diseases (e.g., DBS for treatment of epilepsy) are highlighted. The review concludes with future outlooks of this technology and important challenges with regard to neural signal amplification. PMID:26798055

  8. Implantable neurotechnologies: a review of integrated circuit neural amplifiers

    PubMed Central

    Greenwald, Elliot; Xu, Yong Ping; Thakor, Nitish V.

    2016-01-01

    Neural signal recording is critical in modern day neuroscience research and emerging neural prosthesis programs. Neural recording requires the use of precise, low-noise amplifier systems to acquire and condition the weak neural signals that are transduced through electrode interfaces. Neural amplifiers and amplifier-based systems are available commercially or can be designed in-house and fabricated using integrated circuit (IC) technologies, resulting in very large-scale integration or application-specific integrated circuit solutions. IC-based neural amplifiers are now used to acquire untethered/portable neural recordings, as they meet the requirements of a miniaturized form factor, light weight and low power consumption. Furthermore, such miniaturized and low-power IC neural amplifiers are now being used in emerging implantable neural prosthesis technologies. This review focuses on neural amplifier-based devices and is presented in two interrelated parts. First, neural signal recording is reviewed, and practical challenges are highlighted. Current amplifier designs with increased functionality and performance and without penalties in chip size and power are featured. Second, applications of IC-based neural amplifiers in basic science experiments (e.g., cortical studies using animal models), neural prostheses (e.g., brain/nerve machine interfaces) and treatment of neuronal diseases (e.g., DBS for treatment of epilepsy) are highlighted. The review concludes with future outlooks of this technology and important challenges with regard to neural signal amplification. PMID:26798055

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

    NASA Technical Reports Server (NTRS)

    Ramamoorthy, P. A.

    1991-01-01

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

  10. A Spiking Neural Network System for Robust Sequence Recognition.

    PubMed

    Yu, Qiang; Yan, Rui; Tang, Huajin; Tan, Kay Chen; Li, Haizhou

    2016-03-01

    This paper proposes a biologically plausible network architecture with spiking neurons for sequence recognition. This architecture is a unified and consistent system with functional parts of sensory encoding, learning, and decoding. This is the first systematic model attempting to reveal the neural mechanisms considering both the upstream and the downstream neurons together. The whole system is a consistent temporal framework, where the precise timing of spikes is employed for information processing and cognitive computing. Experimental results show that the system is competent to perform the sequence recognition, being robust to noisy sensory inputs and invariant to changes in the intervals between input stimuli within a certain range. The classification ability of the temporal learning rule used in the system is investigated through two benchmark tasks that outperform the other two widely used learning rules for classification. The results also demonstrate the computational power of spiking neurons over perceptrons for processing spatiotemporal patterns. In summary, the system provides a general way with spiking neurons to encode external stimuli into spatiotemporal spikes, to learn the encoded spike patterns with temporal learning rules, and to decode the sequence order with downstream neurons. The system structure would be beneficial for developments in both hardware and software. PMID:25879976

  11. 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

    NASA Astrophysics Data System (ADS)

    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

  12. Remote Neural Pendants In A Welding-Control System

    NASA Technical Reports Server (NTRS)

    Venable, Richard A.; Bucher, Joseph H.

    1995-01-01

    Neural network integrated circuits enhance functionalities of both remote terminals (called "pendants") and communication links, without necessitating installation of additional wires in links. Makes possible to incorporate many features into pendant, including real-time display of critical welding parameters and other process information, capability for communication between technician at pendant and host computer or technician elsewhere in system, and switches and potentiometers through which technician at pendant exerts remote control over such critical aspects of welding process as current, voltage, rate of travel, flow of gas, starting, and stopping. Other potential manufacturing applications include control of spray coating and of curing of composite materials. Potential nonmanufacturing uses include remote control of heating, air conditioning, and lighting in electrically noisy and otherwise hostile environments.

  13. Neural systems for landmark-based wayfinding in humans

    PubMed Central

    Epstein, Russell A.; Vass, Lindsay K.

    2014-01-01

    Humans and animals use landmarks during wayfinding to determine where they are in the world and to guide their way to their destination. To implement this strategy, known as landmark-based piloting, a navigator must be able to: (i) identify individual landmarks, (ii) use these landmarks to determine their current position and heading, (iii) access long-term knowledge about the spatial relationships between locations and (iv) use this knowledge to plan a route to their navigational goal. Here, we review neuroimaging, neuropsychological and neurophysiological data that link the first three of these abilities to specific neural systems in the human brain. This evidence suggests that the parahippocampal place area is critical for landmark recognition, the retrosplenial/medial parietal region is centrally involved in localization and orientation, and both medial temporal lobe and retrosplenial/medial parietal lobe regions support long-term spatial knowledge. PMID:24366141

  14. Strawberry Maturity Neural Network Detectng System Based on Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Xu, Liming

    The quick and non-detective detection of agriculture product is one of the measures to increase the precision and productivity of harvesting and grading. Having analyzed H frequency of different maturities in different light intensities, the results show that H frequency for the same maturity has little influence in different light intensities; Under the same light intensity, three strawberry maturities are changing in order. After having confirmed the H frequency section to distinguish the different strawberry maturity, the triplelayer feed-forward neural network system to detect strawberry maturity was designed by using genetic algorithm. The test results show that the detecting precision ratio is 91.7%, it takes 160ms to distinguish one strawberry. Therefore, the online non-detective detecting the strawberry maturity could be realized.

  15. Chemosensory signals and their receptors in the olfactory neural system.

    PubMed

    Ihara, S; Yoshikawa, K; Touhara, K

    2013-12-19

    Chemical communication is widely used among various organisms to obtain essential information from their environment required for life. Although a large variety of molecules have been shown to act as chemical cues, the molecular and neural basis underlying the behaviors elicited by these molecules has been revealed for only a limited number of molecules. Here, we review the current knowledge regarding the signaling molecules whose flow from receptor to specific behavior has been characterized. Discussing the molecules utilized by mice, insects, and the worm, we focus on how each organism has optimized its reception system to suit its living style. We also highlight how the production of these signaling molecules is regulated, an area in which considerable progress has been recently made. PMID:24045101

  16. Design of video interface conversion system based on FPGA

    NASA Astrophysics Data System (ADS)

    Zhao, Heng; Wang, Xiang-jun

    2014-11-01

    This paper presents a FPGA based video interface conversion system that enables the inter-conversion between digital and analog video. Cyclone IV series EP4CE22F17C chip from Altera Corporation is used as the main video processing chip, and single-chip is used as the information interaction control unit between FPGA and PC. The system is able to encode/decode messages from the PC. Technologies including video decoding/encoding circuits, bus communication protocol, data stream de-interleaving and de-interlacing, color space conversion and the Camera Link timing generator module of FPGA are introduced. The system converts Composite Video Broadcast Signal (CVBS) from the CCD camera into Low Voltage Differential Signaling (LVDS), which will be collected by the video processing unit with Camera Link interface. The processed video signals will then be inputted to system output board and displayed on the monitor.The current experiment shows that it can achieve high-quality video conversion with minimum board size.

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

    PubMed

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

    2015-04-01

    We developed an in vitro model of early neural cell development. The maturation of a normal neural cell was studied in vitro using Raman spectroscopy for 120 days. The Raman spectra datasets were analyzed by principal component analysis (PCA) to investigate the relationship between maturation stages and molecular composition changes in neural cells. According to the PCA, the Raman spectra datasets can be classified into four larger groups. Previous electrophysiological studies have suggested that a normal neural cell goes through three maturation states. The groups we observed by Raman analysis showed good agreement with the electrophysiological results, except with the addition of a fourth state. The results demonstrated that Raman analysis was powerful to investigate the daily changes in molecular composition of the growing neural cell. This in vitro model system may be useful for future studies of the effects of endocrine disrupters in the developing early neural system. PMID:25610999

  18. An integrated architecture of adaptive neural network control for dynamic systems

    SciTech Connect

    Ke, Liu; Tokar, R.; Mcvey, B.

    1994-07-01

    In this study, an integrated neural network control architecture for nonlinear dynamic systems is presented. Most of the recent emphasis in the neural network control field has no error feedback as the control input which rises the adaptation problem. The integrated architecture in this paper combines feed forward control and error feedback adaptive control using neural networks. The paper reveals the different internal functionality of these two kinds of neural network controllers for certain input styles, e.g., state feedback and error feedback. Feed forward neural network controllers with state feedback establish fixed control mappings which can not adapt when model uncertainties present. With error feedbacks, neural network controllers learn the slopes or the gains respecting to the error feedbacks, which are error driven adaptive control systems. The results demonstrate that the two kinds of control scheme can be combined to realize their individual advantages. Testing with disturbances added to the plant shows good tracking and adaptation.

  19. Early distinction system of mine fire in underground by using a neural-network system

    SciTech Connect

    Ohga, Kotaro; Higuchi, Kiyoshi

    1996-12-31

    In our laboratory, a new detection system using smell detectors was developed to detect the spontaneous combustion of coal and the combustion of other materials used underground. The results of experiments clearly the combustion of materials can be detected earlier by this detection system than by conventional detectors for gas and smoke, and there were significant differences between output data from each smell detector for coal, rubber, oil and wood. In order to discern the source of combustion gases, we have been developing a distinction system using a neural-network system. It has shown successful results in laboratory tests. This paper describes our detection system using smell detectors and our distinction system which uses a neural-network system, and presents results of experiments using both systems.

  20. Neural Circuit Recording from an Intact Cockroach Nervous System

    PubMed Central

    Titlow, Josh S.; Majeed, Zana R.; Hartman, H. Bernard; Burns, Ellen; Cooper, Robin L.

    2013-01-01

    The cockroach ventral nerve cord preparation is a tractable system for neuroethology experiments, neural network modeling, and testing the physiological effects of insecticides. This article describes the scope of cockroach sensory modalities that can be used to assay how an insect nervous system responds to environmental perturbations. Emphasis here is on the escape behavior mediated by cerci to giant fiber transmission in Periplaneta americana. This in situ preparation requires only moderate dissecting skill and electrophysiological expertise to generate reproducible recordings of neuronal activity. Peptides or other chemical reagents can then be applied directly to the nervous system in solution with the physiological saline. Insecticides could also be administered prior to dissection and the escape circuit can serve as a proxy for the excitable state of the central nervous system. In this context the assays described herein would also be useful to researchers interested in limb regeneration and the evolution of nervous system development for which P. americana is an established model organism. PMID:24300738

  1. Reconstruction of Flaw Profiles Using Neural Networks and Multi-Frequency Eddy Current System

    SciTech Connect

    Chady, T.; Caryk, M.

    2005-04-09

    The objective of this paper is to identify profiles of flaws in conducting plates. To solve this problem, application of a multi-frequency eddy current system (MFES) and artificial neural networks is proposed. Dynamic feed-forward neural networks with various architectures are investigated. Extended experiments with all neural models are carried out in order to select the most promising configuration. Data utilized for the experiments were obtained from the measurements performed on the Inconel plates with EDM flaws.

  2. Human-System Interfaces (HSIs) in Small Modular Reactors (SMRs)

    SciTech Connect

    Jacques V Hugo

    2014-10-01

    This book chapter describes the considerations for the selection of advanced human–system interfaces (HSIs) for the new generation of nuclear power plants. The chapter discusses the technologies that will be needed to support highly automated nuclear power plants, while minimising demands for numbers of operational staff, reducing human error and improving plant efficiency and safety. Special attention is paid to the selection and deployment of advanced technologies in nuclear power plants (NPPs). The chapter closes with an examination of how technologies are likely to develop over the next 10–15 years and how this will affect design choices for the nuclear industry.

  3. Development of sensitized pick coal interface detector system

    NASA Technical Reports Server (NTRS)

    Burchill, R. F.

    1979-01-01

    One approach for detection of the coal interface is measurement of the pick cutting hoads and shock through the use of pick strain gage load cells and accelerometers. The cutting drum of a long wall mining machine contains a number of cutting picks. In order to measure pick loads and shocks, one pick was instrumented and telementry used to transmit the signals from the drum to an instrument-type tape recorder. A data system using FM telemetry was designed to transfer cutting bit load and shock information from the drum of a longwall shearer coal mining machine to a chassis mounted data recorder.

  4. Methods and systems for monitoring a solid-liquid interface

    SciTech Connect

    Stoddard, Nathan G.; Clark, Roger F.; Kary, Tim

    2010-07-20

    Methods and systems are provided for monitoring a solid-liquid interface, including providing a vessel configured to contain an at least partially melted material; detecting radiation reflected from a surface of a liquid portion of the at least partially melted material that is parallel with the liquid surface; measuring a disturbance on the surface; calculating at least one frequency associated with the disturbance; and determining a thickness of the liquid portion based on the at least one frequency, wherein the thickness is calculated based on.times. ##EQU00001## where g is the gravitational constant, w is the horizontal width of the liquid, and f is the at least one frequency.

  5. Development of a multi-classification neural network model to determine the microbial growth/no growth interface.

    PubMed

    Fernández-Navarro, Francisco; Valero, Antonio; Hervás-Martínez, César; Gutiérrez, Pedro A; García-Gimeno, Rosa M; Zurera-Cosano, Gonzalo

    2010-07-15

    Boundary models have been recognized as useful tools to predict the ability of microorganisms to grow at limiting conditions. However, at these conditions, microbial behaviour can vary, being difficult to distinguish between growth or no growth. In this paper, the data from the study of Valero et al. [Valero, A., Pérez-Rodríguez, F., Carrasco, E., Fuentes-Alventosa, J.M., García-Gimeno, R.M., Zurera, G., 2009. Modelling the growth boundaries of Staphylococcus aureus: Effect of temperature, pH and water activity. International Journal of Food Microbiology 133 (1-2), 186-194] belonging to growth/no growth conditions of Staphylococcus aureus against temperature, pH and a(w) were divided into three categorical classes: growth (G), growth transition (GT) and no growth (NG). Subsequently, they were modelled by using a Radial Basis Function Neural Network (RBFNN) in order to create a multi-classification model that was able to predict the probability of belonging at one of the three mentioned classes. The model was developed through an over sampling procedure using a memetic algorithm (MA) in order to balance in part the size of the classes and to improve the accuracy of the classifier. The multi-classification model, named Smote Memetic Radial Basis Function (SMRBF) provided a quite good adjustment to data observed, being able to correctly classify the 86.30% of training data and the 82.26% of generalization data for the three observed classes in the best model. Besides, the high number of replicates per condition tested (n=30) produced a smooth transition between growth and no growth. At the most stringent conditions, the probability of belonging to class GT was higher, thus justifying the inclusion of the class in the new model. The SMRBF model presented in this study can be used to better define microbial growth/no growth interface and the variability associated to these conditions so as to apply this knowledge to a food safety in a decision-making process. PMID

  6. A system for neural recording and closed-loop intracortical microstimulation in awake rodents.

    PubMed

    Venkatraman, Subramaniam; Elkabany, Ken; Long, John D; Yao, Yimin; Carmena, Jose M

    2009-01-01

    There is growing interest in intracortical microstimulation as a means of providing sensory input in neuroprosthetic systems. We believe that precisely controlling the timing and parameters of stimulation in closed loop can significantly improve the efficacy of this technique. Here, we present a system for closed-loop microstimulation in awake rodents chronically implanted with multielectrode arrays. The system interfaces with existing commercial recording and stimulating hardware. Using custom-made hardware, we can stimulate and record from electrodes on the same implanted array and significantly reduce the stimulation artifact. Stimulation sequences can either be preprogrammed or triggered by neural or behavioral events. Specifically, this system can provide feedback stimulation in response to action potentials or features in the local field potential recorded on any of the electrodes within 15 ms. It can also trigger stimulation based on behavioral events, such as real-time tracking of rat whiskers captured with high-speed video. We believe that this system, which can be recreated easily, will help to significantly refine the technique of intracortical microstimulation and advance the field of neuroprostheses. PMID:19224714

  7. NNETS - NEURAL NETWORK ENVIRONMENT ON A TRANSPUTER SYSTEM

    NASA Technical Reports Server (NTRS)

    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. Control of Complex Dynamic Systems by Neural Networks

    NASA Technical Reports Server (NTRS)

    Spall, James C.; Cristion, John A.

    1993-01-01

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

  9. Development of sensitized pick coal interface detector system

    NASA Technical Reports Server (NTRS)

    Burchill, R. F.

    1982-01-01

    One approach for detection of the coal interface is measurement of pick cutting loads and shock through the use of pick strain gage load cells and accelerometers. The cutting drum of a long wall mining machine contains a number of cutting picks. In order to measure pick loads and shocks, one pick was instrumented and telemetry used to transmit the signals from the drum to an instrument-type tape recorder. A data system using FM telemetry was designed to transfer cutting bit load and shock information from the drum of a longwall shearer coal mining machine to a chassis mounted data recorder. The design of components in the test data system were finalized, the required instruments were assembled, the instrument system was evaluated in an above-ground simulation test, and an underground test series to obtain tape recorded sensor data was conducted.

  10. Prospects of brain-machine interfaces for space system control

    NASA Astrophysics Data System (ADS)

    Menon, Carlo; de Negueruela, Cristina; Millán, José del R.; Tonet, Oliver; Carpi, Federico; Broschart, Michael; Ferrez, Pierre; Buttfield, Anna; Tecchio, Franca; Sepulveda, Francisco; Citi, Luca; Laschi, Cecilia; Tombini, Mario; Dario, Paolo; Maria Rossini, Paolo; De Rossi, Danilo

    2009-02-01

    The dream of controlling and guiding computer-based systems using human brain signals has slowly but steadily become a reality. The available technology allows real-time implementation of systems that measure neuronal activity, convert their signals, and translate their output for the purpose of controlling mechanical and electronic systems. This paper describes the state of the art of non-invasive brain-machine interfaces (BMIs) and critically investigates both the current technological limits and the future potential that BMIs have for space applications. We present an assessment of the advantages that BMIs can provide and justify the preferred candidate concepts for space applications together with a vision of future directions for their implementation.

  11. CSI computer system/remote interface unit acceptance test results

    NASA Technical Reports Server (NTRS)

    Sparks, Dean W., Jr.

    1992-01-01

    The validation tests conducted on the Control/Structures Interaction (CSI) Computer System (CCS)/Remote Interface Unit (RIU) is discussed. The CCS/RIU consists of a commercially available, Langley Research Center (LaRC) programmed, space flight qualified computer and a flight data acquisition and filtering computer, developed at LaRC. The tests were performed in the Space Structures Research Laboratory (SSRL) and included open loop excitation, closed loop control, safing, RIU digital filtering, and RIU stand alone testing with the CSI Evolutionary Model (CEM) Phase-0 testbed. The test results indicated that the CCS/RIU system is comparable to ground based systems in performing real-time control-structure experiments.

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

    PubMed

    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

  13. On some classes of sequential spiking neural p systems.

    PubMed

    Zhang, Xingyi; Zeng, Xiangxiang; Luo, Bin; Pan, Linqiang

    2014-05-01

    Spiking neural P systems (SN P systems) are a class of distributed parallel computing devices inspired by the way neurons communicate by means of spikes; neurons work in parallel in the sense that each neuron that can fire should fire, but the work in each neuron is sequential in the sense that at most one rule can be applied at each computation step. In this work, with biological inspiration, we consider SN P systems with the restriction that at each step, one of the neurons (i.e., sequential mode) or all neurons (i.e., pseudo-sequential mode) with the maximum (or minimum) number of spikes among the neurons that are active (can spike) will fire. If an active neuron has more than one enabled rule, it nondeterministically chooses one of the enabled rules to be applied, and the chosen rule is applied in an exhaustive manner (a kind of local parallelism): the rule is used as many times as possible. This strategy makes the system sequential or pseudo-sequential from the global view of the whole network and locally parallel at the level of neurons. We obtain four types of SN P systems: maximum/minimum spike number induced sequential/pseudo-sequential SN P systems with exhaustive use of rules. We prove that SN P systems of these four types are all Turing universal as number-generating computation devices. These results illustrate that the restriction of sequentiality may have little effect on the computation power of SN P systems. PMID:24555456

  14. Advanced EVA system design requirements study: EVAS/space station system interface requirements

    NASA Technical Reports Server (NTRS)

    Woods, T. G.

    1985-01-01

    The definition of the Extravehicular Activity (EVA) systems interface requirements and accomodations for effective integration of a production EVA capability into the space station are contained. A description of the EVA systems for which the space station must provide the various interfaces and accomodations are provided. The discussion and analyses of the various space station areas in which the EVA interfaces are required and/or from which implications for EVA system design requirements are derived, are included. The rationale is provided for all EVAS mechanical, fluid, electrical, communications, and data system interfaces as well as exterior and interior requirements necessary to facilitate EVA operations. Results of the studies supporting these discussions are presented in the appendix.

  15. Interfacing with the Computational Brain

    PubMed Central

    Jackson, Andrew; Fetz, Eberhard E.

    2012-01-01

    Neuroscience is just beginning to understand the neural computations that underlie our remarkable capacity to learn new motor tasks. Studies of natural movements have emphasized the importance of concepts such as dimensionality reduction within hierarchical levels of redundancy, optimization of behavior in the presence of sensorimotor noise and internal models for predictive control. These concepts also provide a framework for understanding the improvements in performance seen in myoelectric-controlled interface (MCI) and brain-machine interface (BMI) paradigms. Recent experiments reveal how volitional activity in the motor system combines with sensory feedback to shape neural representations and drives adaptation of behavior. By elucidating these mechanisms, a new generation of intelligent interfaces can be designed to exploit neural plasticity and restore function after neurological injury. PMID:21659037

  16. Visualization and Manipulation of Neural Activity in the Developing Vertebrate Nervous System

    PubMed Central

    Zhang, Jiayi; Ackman, James B.; Dhande, Onkar S.; Crair, Michael C.

    2011-01-01

    Neural activity during vertebrate development has been unambiguously shown to play a critical role in sculpting circuit formation and function. Patterned neural activity in various parts of the developing nervous system is thought to modulate neurite outgrowth, axon targeting, and synapse refinement. The nature and role of patterned neural activity during development has been classically studied with in vitro preparations using pharmacological manipulations. In this review we discuss newly available and developing molecular–genetic tools for the visualization and manipulation of neural activity patterns specifically during development. PMID:22121343

  17. Polymer-surfactant systems in bulk and at fluid interfaces.

    PubMed

    Guzmán, Eduardo; Llamas, Sara; Maestro, Armando; Fernández-Peña, Laura; Akanno, Andrew; Miller, Reinhard; Ortega, Francisco; Rubio, Ramón G

    2016-07-01

    The interest of polymer-surfactant systems has undergone a spectacular development in the last thirty years due to their complex behavior and their importance in different industrial sectors. The importance can be mainly associated with the rich phase behavior of these mixtures that confers a wide range of physico-chemical properties to the complexes formed by polymers and surfactants, both in bulk and at the interfaces. This latter aspect is especially relevant because of the use of their mixture for the stabilization of dispersed systems such as foams and emulsions, with an increasing interest in several fields such as cosmetic, food science or fabrication of controlled drug delivery structures. This review presents a comprehensive analysis of different aspects related to the phase behavior of these mixtures and their intriguing behavior after adsorption at the liquid/air interface. A discussion of some physical properties of the bulk is also included. The discussion clearly points out that much more work is needed for obtaining the necessary insights for designing polymer-surfactant mixtures for specific applications. PMID:26608684

  18. Suboptimal Use of Neural Information in a Mammalian Auditory System

    PubMed Central

    Zilany, Muhammad S. A.; Huang, Nicholas J.; Abrams, Kristina S.; Idrobo, Fabio

    2014-01-01

    Establishing neural determinants of psychophysical performance requires both behavioral and neurophysiological metrics amenable to correlative analyses. It is often assumed that organisms use neural information optimally, such that any information available in a neural code that could improve behavioral performance is used. Studies have shown that detection of amplitude-modulated (AM) auditory tones by humans is correlated to neural synchrony thresholds, as recorded in rabbit at the level of the inferior colliculus, the first level of the ascending auditory pathway where neurons are tuned to AM stimuli. Behavioral thresholds in rabbit, however, are ∼10 dB higher (i.e., 3 times less sensitive) than in humans, and are better correlated to rate-based than temporal coding schemes in the auditory midbrain. The behavioral and physiological results shown here illustrate an unexpected, suboptimal utilization of available neural information that could provide new insights into the mechanisms that link neuronal function to behavior. PMID:24453321

  19. Man-Machine Interface System for Neuromuscular Training and Evaluation Based on EMG and MMG Signals

    PubMed Central

    de la Rosa, Ramon; Alonso, Alonso; Carrera, Albano; Durán, Ramon; Fernández, Patricia

    2010-01-01

    This paper presents the UVa-NTS (University of Valladolid Neuromuscular Training System), a multifunction and portable Neuromuscular Training System. The UVa-NTS is designed to analyze the voluntary control of severe neuromotor handicapped patients, their interactive response, and their adaptation to neuromuscular interface systems, such as neural prostheses or domotic applications. Thus, it is an excellent tool to evaluate the residual muscle capabilities in the handicapped. The UVa-NTS is composed of a custom signal conditioning front-end and a computer. The front-end electronics is described thoroughly as well as the overall features of the custom software implementation. The software system is composed of a set of graphical training tools and a processing core. The UVa-NTS works with two classes of neuromuscular signals: the classic myoelectric signals (MES) and, as a novelty, the myomechanic signals (MMS). In order to evaluate the performance of the processing core, a complete analysis has been done to classify its efficiency and to check that it fulfils with the real-time constraints. Tests were performed both with healthy and selected impaired subjects. The adaptation was achieved rapidly, applying a predefined protocol for the UVa-NTS set of training tools. Fine voluntary control was demonstrated to be reached with the myoelectric signals. And the UVa-NTS demonstrated to provide a satisfactory voluntary control when applying the myomechanic signals. PMID:22163515

  20. Neural network diagnostic system for dengue patients risk classification.

    PubMed

    Faisal, Tarig; Taib, Mohd Nasir; Ibrahim, Fatimah

    2012-04-01

    With the dramatic increase of the worldwide threat of dengue disease, it has been very crucial to correctly diagnose the dengue patients in order to decrease the disease severity. However, it has been a great challenge for the physicians to identify the level of risk in dengue patients due to overlapping of the medical classification criteria. Therefore, this study aims to construct a noninvasive diagnostic system to assist the physicians for classifying the risk in dengue patients. Systematic producers have been followed to develop the system. Firstly, the assessment of the significant predictors associated with the level of risk in dengue patients was carried out utilizing the statistical analyses technique. Secondly, Multilayer perceptron neural network models trained via Levenberg-Marquardt and Scaled Conjugate Gradient algorithms was employed for constructing the diagnostic system. Finally, precise tuning for the models' parameters was conducted in order to achieve the optimal performance. As a result, 9 noninvasive predictors were found to be significantly associated with the level of risk in dengue patients. By employing those predictors, 75% prediction accuracy has been achieved for classifying the risk in dengue patients using Scaled Conjugate Gradient algorithm while 70.7% prediction accuracy were achieved by using Levenberg-Marquardt algorithm. PMID:20703665

  1. An fMRI study of the interface between affective and cognitive neural circuitry in pediatric bipolar disorder

    PubMed Central

    Pavuluri, Mani N.; O’Connor, Megan Marlow; Harral, Erin M.; Sweeney, John A.

    2008-01-01

    The pathophysiology of pediatric bipolar disorder impacts both affective and cognitive brain systems. Understanding disturbances in the neural circuits subserving these abilities is critical for characterizing developmental aberrations associated with the disorder and developing improved treatments. Our objective is to use functional neuroimaging with pediatric bipolar disorder patients employing a task that probes the functional integrity of attentional control and affect processing. Ten euthymic unmedicated pediatric bipolar patients and healthy controls matched for age, sex, race, socioeconomic status, and IQ were scanned using functional magnetic resonance imaging. In a pediatric color word matching paradigm, subjects were asked to match the color of a word with one of two colored circles below. Words had either a positive, negative or neutral emotional valence, and were presented in 30 second blocks. In the negative affect condition, relative to the neutral condition, patients with bipolar disorder demonstrated greater activation of bilateral pregenual anterior cingulate cortex and left amygdala, and less activation in right rostral ventrolateral prefrontal cortex (PFC) and dorsolateral PFC at the junction of the middle frontal and inferior frontal gyri. In the positive affect condition, there was no reduced activation of PFC or increased amygdala activation. The pattern of reduced activation of ventrolateral PFC and greater amygdala activation in bipolar children in response to negative stimuli suggests both disinhibition of emotional reactivity in the limbic system and reduced function in PFC systems that regulate those responses. Higher cortical cognitive areas such as the dorsolateral PFC may also be adversely affected by exaggerated emotional responsivity to negative emotions. This pattern of functional alteration in affective and cognitive circuitry may contribute to the reduced capacity for affect regulation and behavioral self-control in pediatric

  2. 47 CFR 15.115 - TV interface devices, including cable system terminal devices.

    Code of Federal Regulations, 2014 CFR

    2014-10-01

    ... 47 Telecommunication 1 2014-10-01 2014-10-01 false TV interface devices, including cable system... FREQUENCY DEVICES Unintentional Radiators § 15.115 TV interface devices, including cable system terminal devices. (a) Measurements of the radiated emissions of a TV interface device shall be conducted with...

  3. 47 CFR 15.115 - TV interface devices, including cable system terminal devices.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... 47 Telecommunication 1 2011-10-01 2011-10-01 false TV interface devices, including cable system... FREQUENCY DEVICES Unintentional Radiators § 15.115 TV interface devices, including cable system terminal devices. (a) Measurements of the radiated emissions of a TV interface device shall be conducted with...

  4. 47 CFR 15.115 - TV interface devices, including cable system terminal devices.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 47 Telecommunication 1 2010-10-01 2010-10-01 false TV interface devices, including cable system... FREQUENCY DEVICES Unintentional Radiators § 15.115 TV interface devices, including cable system terminal devices. (a) Measurements of the radiated emissions of a TV interface device shall be conducted with...

  5. 47 CFR 15.115 - TV interface devices, including cable system terminal devices.

    Code of Federal Regulations, 2013 CFR

    2013-10-01

    ... 47 Telecommunication 1 2013-10-01 2013-10-01 false TV interface devices, including cable system... FREQUENCY DEVICES Unintentional Radiators § 15.115 TV interface devices, including cable system terminal devices. (a) Measurements of the radiated emissions of a TV interface device shall be conducted with...

  6. 47 CFR 15.115 - TV interface devices, including cable system terminal devices.

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... 47 Telecommunication 1 2012-10-01 2012-10-01 false TV interface devices, including cable system... FREQUENCY DEVICES Unintentional Radiators § 15.115 TV interface devices, including cable system terminal devices. (a) Measurements of the radiated emissions of a TV interface device shall be conducted with...

  7. RoboCon: Operator interface for robotic applications. Final report: RoboCon electrical interfacing -- system architecture, and Interfacing NDDS and LabView

    SciTech Connect

    Schempf, H.

    1998-04-30

    The first appendix contains detailed specifications of the electrical interfacing employed in Robocon. This includes all electrical signals and power requirement descriptions up to and including the interface entry points for external robots and systems. The reader is first presented with an overview of the overall Robocon electrical system, followed by sub-sections describing each module in detail. The appendices contain listings of power requirements and the electrical connectors and cables used, followed by an overall electrical system diagram. Custom electronics employed are also described. The Network Data Delivery Service (NDDS) is a real-time dissemination communications architecture which allows nodes on a network to publish data and subscribe to data published by other nodes while remaining anonymous. The second appendix explains how to facilitate a seamless interface between NDDS and LabView and provides sample source code used to implement an NDDS consumer which writes a string to a socket.

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

    PubMed Central

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

    2016-01-01

    Neural-like computing models are versatile computing mechanisms in the field of artificial intelligence. Spiking neural P systems (SN P systems for short) are one of the recently developed spiking neural network models inspired by the way neurons communicate. The communications among neurons are essentially achieved by spikes, i. e. short electrical pulses. In terms of motivation, SN P systems fall into the third generation of neural network models. In this study, a novel variant of SN P systems, namely SN P systems with self-organization, is introduced, and the computational power of the system is investigated and evaluated. It is proved that SN P systems with self-organization are capable of computing and accept the family of sets of Turing computable natural numbers. Moreover, with 87 neurons the system can compute any Turing computable recursive function, thus achieves Turing universality. These results demonstrate promising initiatives to solve an open problem arisen by Gh Păun. PMID:27283843

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

    PubMed

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

    2016-01-01

    Neural-like computing models are versatile computing mechanisms in the field of artificial intelligence. Spiking neural P systems (SN P systems for short) are one of the recently developed spiking neural network models inspired by the way neurons communicate. The communications among neurons are essentially achieved by spikes, i. e. short electrical pulses. In terms of motivation, SN P systems fall into the third generation of neural network models. In this study, a novel variant of SN P systems, namely SN P systems with self-organization, is introduced, and the computational power of the system is investigated and evaluated. It is proved that SN P systems with self-organization are capable of computing and accept the family of sets of Turing computable natural numbers. Moreover, with 87 neurons the system can compute any Turing computable recursive function, thus achieves Turing universality. These results demonstrate promising initiatives to solve an open problem arisen by Gh Păun. PMID:27283843

  10. Man--machine interface issues for space nuclear power systems

    SciTech Connect

    Nelson, W.R.; Haugset, K. )

    1991-01-10

    The deployment of nuclear reactors in space necessitates an entirely new set of guidelines for the design of the man--machine interface (MMI) when compared to earth-based applications such as commerical nuclear power plants. Although the design objectives of earth- and space-based nuclear power systems are the same, that is, to produce electrical power, the differences in the application environments mean that the operator's role will be significantly different for space-based systems. This paper explores the issues associated with establishing the necessary MMI guidelines for space nuclear power systems. The generic human performance requirements for space-based systems are described, and the operator roles that are utilized for the operation of current and advanced earth-based reactors are briefly summarized. The development of a prototype advanced control room, the Integrated Surveillance and Control System (ISACS) at the Organization for Economic Cooperation and Development (OECD) Halden Reactor Project is introduced. Finally, preliminary ideas for the use of the ISACS system as a test bed for establishing MMI guidelines for space nuclear systems are presented.

  11. FY07 Summary of System Interface and Support Systems R&D and Technical Issues Map

    SciTech Connect

    Steven R. Sherman

    2007-09-01

    This document provides a summary of research and development activities in the System Interface and Support Systems area of the DOE Nuclear Hydrogen Initiative in FY 2007. Project cost and performance data obtained from the PICS system, at least up through July 2007, are presented and analyzed. Brief summaries of accomplishments and references are provided. A mapping of System Interface and Support Systems technical issues versus the work performed is updated and presented. Lastly, near-term research plans are described, and recommendatioins are provided for additional research.

  12. FY 06 Status of System Interface and Support Systems R&D Areas

    SciTech Connect

    S.R. Sherman

    2006-09-01

    This document provides a summary of research and development activities performed in the Systems Interface and Support Systems area of the DOE Nuclear Hydrogen Initiative during FY 2006. Project cost and performance data obtained from the PICS system are presented and analyzed. Brief summaries of accomplishments and references are provided. A mapping of System Interface and Support Systems technical issues versus the work performed is updated and presented. Lastly, near-term research plans are given, and a description of the new UNLV high temperature heat exchanger program structure is provided.

  13. EOS ground data systems: A description and interface overview

    NASA Technical Reports Server (NTRS)

    Smith, Gene

    1993-01-01

    systems, their interfaces, and their roles.

  14. Functional imaging and the neural systems of chronic pain.

    PubMed

    Mackey, Sean C; Maeda, Fumiko

    2004-07-01

    Pain remains a serious health care problem affecting millions of individuals, costing billions of dollars, and causing an immeasurable amount of human suffering. In designing improved therapies, there is still much to learn about peripheral nociceptor, nerves, and the spinal cord, and brain stem modulatory systems. Nevertheless, it is the brain that presents us with an incredible opportunity to understand the experience we call pain. Functional neuroimaging is helping to unlock the secrets of the sensory and emotional components of pain and its autonomic responses. These techniques are helping us to understand that pain is not a static disease with the pathologic findings localized to the periphery but is instead a highly plastic condition affecting multiple central neural systems. Functional neuroimaging is transforming our understanding of the neurobiology of pain and will be instrumental in helping us to design more rational treatments ultimately aimed at reducing the impact of pain on our patients. It is opening windows into the function of the brain that were previously closed. PMID:15246336

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

    PubMed

    Wai, Rong-Jong; Lee, Jeng-Dao

    2008-01-01

    A magnetic-levitation (maglev) transportation system including levitation and propulsion control is a subject of considerable scientific interest because of highly nonlinear and unstable behaviors. In this paper, the dynamic model of a maglev transportation system including levitated electromagnets and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed first. Then, a model-based sliding-mode control (SMC) strategy is introduced. In order to alleviate chattering phenomena caused by the inappropriate selection of uncertainty bound, a simple bound estimation algorithm is embedded in the SMC strategy to form an adaptive sliding-mode control (ASMC) scheme. However, this estimation algorithm is always a positive value so that tracking errors introduced by any uncertainty will cause the estimated bound increase even to infinity with time. Therefore, it further designs an adaptive fuzzy-neural-network control (AFNNC) scheme by imitating the SMC strategy for the maglev transportation system. In the model-free AFNNC, online learning algorithms are designed to cope with the problem of chattering phenomena caused by the sign action in SMC design, and to ensure the stability of the controlled system without the requirement of auxiliary compensated controllers despite the existence of uncertainties. The outputs of the AFNNC scheme can be directly supplied to the electromagnets and LIM without complicated control transformations for relaxing strict constrains in conventional model-based control methodologies. The effectiveness of the proposed control schemes for the maglev transportation system is verified by numerical simulations, and the superiority of the AFNNC scheme is indicated in comparison with the SMC and ASMC strategies. PMID:18269938

  16. PRODIAG: Combined expert system/neural network for process fault diagnosis. Volume 2, Code manual

    SciTech Connect

    Reifman, J.; Wei, T.Y.C.

    1995-09-01

    We recommend the reader first review Volume 1 of this document, Code Theory, before reading Volume 2. In this volume we make extensive use of terms and concepts described and defined in Volume 1 which are not redefined here to the same extent. To try to reduce the amount of redundant information, we have restricted this volume to the presentation of the expert system code and refer back to the theory described in Volume 1 when necessary. Verification and validation of the results are presented in Volume 3, Application, of this document. Volume 3 also presents the implementation of the component characteristics diagnostic approach through artificial neural networks discussed in Volume 1. We decided to present the component characteristics approach in Volume 3, as opposed to write a separate code manual for it, because the approach, although general, requires a case-by-case analysis. The purpose of this volume is to present the details of the expert system (ES) portion o the PRODIAG process diagnostic program. In addition, we present here the graphical diagnostics interface (GDI) and illustrate the combined use of the ES and GDI with a sample problem. For completeness, we provide the file names of all files, programs and major subroutines of these two systems, ES and GDI, and their corresponding location in the Reactor Analysis Division (RA) computer network and Reactor Engineering Division (RE) computer network as of 30 September 1995.

  17. Interface between object-oriented systems. Technical report

    SciTech Connect

    Crowl, L.A.

    1987-04-01

    The Chrysalis operating system for the Butterfly Parallel Processor presents an object-oriented programming environment based on shared memory. However, because of Chrysalis's low-level orientation and its use of type-unsafe features of the C programming language, programs using the environment are difficult to program and highly error-prone. Using C as the primary programming language for the Butterfly does not fully realize the benefit of Chrysalis's object orientation. An object-oriented programming language is natural candidate for improving the Chrysalis environment. The C ++ programming language provides a number of advantages in developing such an interface. This paper reports the successes and problems encountered in the development of Chrysalis ++, a C ++ interface to Chrysalis ++ uncovered many strengths and weakness in C ++. Some apply to C ++ in general, others apply only to its adaptation to a parallel programming environment. It is important to note that C++ is a sequential language; it is use in a parallel programming environment is therefore outside the bounds of its design.

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

    NASA Astrophysics Data System (ADS)

    Ning, Min

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

  19. An operator interface design for a telerobotic inspection system

    NASA Technical Reports Server (NTRS)

    Kim, Won S.; Tso, Kam S.; Hayati, Samad

    1993-01-01

    The operator interface has recently emerged as an important element for efficient and safe interactions between human operators and telerobotics. Advances in graphical user interface and graphics technologies enable us to produce very efficient operator interface designs. This paper describes an efficient graphical operator interface design newly developed for remote surface inspection at NASA-JPL. The interface, designed so that remote surface inspection can be performed by a single operator with an integrated robot control and image inspection capability, supports three inspection strategies of teleoperated human visual inspection, human visual inspection with automated scanning, and machine-vision-based automated inspection.

  20. Characterization of a-SiC(x):H thin films as an encapsulation material for integrated silicon based neural interface devices.

    PubMed

    Hsu, Jui-Mei; Tathireddy, Prashant; Rieth, Loren; Normann, A Richard; Solzbacher, Florian

    2007-11-01

    A fully integrated, wireless neural interface device is being developed to free patients from the restriction and risk of infection associated with a transcutaneous wired connection. This device requires a hermetic, biocompatible encapsulation layer at the interface between the device and the neural tissue to maintain long-term recording/stimulating performance of the device. Hydrogenated amorphous silicon carbide (a-SiC(x):H) films deposited by a plasma enhanced chemical vapor deposition using SiH(4), CH(4), and H(2) precursors were investigated as the encapsulation layer for such device. Si-C bond density, measured by Fourier transform infrared absorption spectrometer, suggests that deposition conditions with increased hydrogen dilution, increased temperature, and low silane flow typically result in increase of Si-C bond density. From the variable angle spectroscopic ellipsometry measurement, no dissolution of a-SiC(x):H was observed during soaking tests in 90°C phosphate buffered saline. Conformal coating of the a-SiC(x):H in Utah electrode array was observed by scanning electron microscope. Electrical properties were studied by impedance spectroscopy to investigate the performance of a-SiC(x):H as an encapsulation layer, and the results showed long term stability of the material. PMID:18437249

  1. Discrete-time neural inverse optimal control for nonlinear systems via passivation.

    PubMed

    Ornelas-Tellez, Fernando; Sanchez, Edgar N; Loukianov, Alexander G

    2012-08-01

    This paper presents a discrete-time inverse optimal neural controller, which is constituted by combination of two techniques: 1) inverse optimal control to avoid solving the Hamilton-Jacobi-Bellman equation associated with nonlinear system optimal control and 2) on-line neural identification, using a recurrent neural network trained with an extended Kalman filter, in order to build a model of the assumed unknown nonlinear system. The inverse optimal controller is based on passivity theory. The applicability of the proposed approach is illustrated via simulations for an unstable nonlinear system and a planar robot. PMID:24807528

  2. Microfluidic hubs, systems, and methods for interface fluidic modules

    DOEpatents

    Bartsch, Michael S; Claudnic, Mark R; Kim, Hanyoup; Patel, Kamlesh D; Renzi, Ronald F; Van De Vreugde, James L

    2015-01-27

    Embodiments of microfluidic hubs and systems are described that may be used to connect fluidic modules. A space between surfaces may be set by fixtures described herein. In some examples a fixture may set substrate-to-substrate spacing based on a distance between registration surfaces on which the respective substrates rest. Fluidic interfaces are described, including examples where fluid conduits (e.g. capillaries) extend into the fixture to the space between surfaces. Droplets of fluid may be introduced to and/or removed from microfluidic hubs described herein, and fluid actuators may be used to move droplets within the space between surfaces. Continuous flow modules may be integrated with the hubs in some examples.

  3. Shuttle/payload communications and data systems interface analysis

    NASA Technical Reports Server (NTRS)

    Huth, G. K.

    1980-01-01

    The payload/orbiter functional command signal flow and telemetry signal flow are discussed. Functional descriptions of the various orbiter communication/avionic equipment involved in processing a command to a payload either from the ground through the orbiter by the payload specialist on the orbiter are included. Functional descriptions of the various orbiter communication/avionic equipment involved in processing telemetry data by the orbiter and transmitting the processed data to the ground are presented. The results of the attached payload/orbiter single processing and data handling system evaluation are described. The causes of the majority of attached payload/orbiter interface problems are delineated. A refined set of required flux density values for a detached payload to communicate with the orbiter is presented.

  4. NASA access mechanism: Graphical user interface information retrieval system

    NASA Technical Reports Server (NTRS)

    Hunter, Judy; Generous, Curtis; Duncan, Denise

    1993-01-01

    Access to online information sources of aerospace, scientific, and engineering data, a mission focus for NASA's Scientific and Technical Information Program, has always been limited to factors such as telecommunications, query language syntax, lack of standardization in the information, and the lack of adequate tools to assist in searching. Today, the NASA STI Program's NASA Access Mechanism (NAM) prototype offers a solution to these problems by providing the user with a set of tools that provide a graphical interface to remote, heterogeneous, and distributed information in a manner adaptable to both casual and expert users. Additionally, the NAM provides access to many Internet-based services such as Electronic Mail, the Wide Area Information Servers system, Peer Locating tools, and electronic bulletin boards.

  5. Methods and systems for monitoring a solid-liquid interface

    SciTech Connect

    Stoddard, Nathan G.; Clark, Roger F.

    2011-10-04

    Methods and systems are provided for monitoring a solid-liquid interface, including providing a vessel configured to contain an at least partially melted material; detecting radiation reflected from a surface of a liquid portion of the at least partially melted material; providing sound energy to the surface; measuring a disturbance on the surface; calculating at least one frequency associated with the disturbance; and determining a thickness of the liquid portion based on the at least one frequency, wherein the thickness is calculated based on L=(2m-1)v.sub.s/4f, where f is the frequency where the disturbance has an amplitude maximum, v.sub.s is the speed of sound in the material, and m is a positive integer (1, 2, 3, . . . ).

  6. Method and systems for a radiation tolerant bus interface circuit

    NASA Technical Reports Server (NTRS)

    Kinstler, Gary A. (Inventor)

    2007-01-01

    A bus management tool that allows communication to be maintained between a group of nodes operatively connected on two busses in the presence of radiation by transmitting periodically a first message from one to another of the nodes on one of the busses, determining whether the first message was received by the other of the nodes on the first bus, and when it is determined that the first message was not received by the other of the nodes, transmitting a recovery command to the other of the nodes on a second of the of busses. Methods, systems, and articles of manufacture consistent with the present invention also provide for a bus recovery tool on the other node that re-initializes a bus interface circuit operatively connecting the other node to the first bus in response to the recovery command.

  7. Space station automation and robotics study. Operator-systems interface

    NASA Technical Reports Server (NTRS)

    1984-01-01

    This is the final report of a Space Station Automation and Robotics Planning Study, which was a joint project of the Boeing Aerospace Company, Boeing Commercial Airplane Company, and Boeing Computer Services Company. The study is in support of the Advanced Technology Advisory Committee established by NASA in accordance with a mandate by the U.S. Congress. Boeing support complements that provided to the NASA Contractor study team by four aerospace contractors, the Stanford Research Institute (SRI), and the California Space Institute. This study identifies automation and robotics (A&R) technologies that can be advanced by requirements levied by the Space Station Program. The methodology used in the study is to establish functional requirements for the operator system interface (OSI), establish the technologies needed to meet these requirements, and to forecast the availability of these technologies. The OSI would perform path planning, tracking and control, object recognition, fault detection and correction, and plan modifications in connection with extravehicular (EV) robot operations.

  8. NASA Access Mechanism - Graphical user interface information retrieval system

    NASA Technical Reports Server (NTRS)

    Hunter, Judy F.; Generous, Curtis; Duncan, Denise

    1993-01-01

    Access to online information sources of aerospace, scientific, and engineering data, a mission focus for NASA's Scientific and Technical Information Program, has always been limited by factors such as telecommunications, query language syntax, lack of standardization in the information, and the lack of adequate tools to assist in searching. Today, the NASA STI Program's NASA Access Mechanism (NAM) prototype offers a solution to these problems by providing the user with a set of tools that provide a graphical interface to remote, heterogeneous, and distributed information in a manner adaptable to both casual and expert users. Additionally, the NAM provides access to many Internet-based services such as Electronic Mail, the Wide Area Information Servers system, Peer Locating tools, and electronic bulletin boards.

  9. An artificial neural network system for diagnosing gas turbine engine fuel faults

    SciTech Connect

    Illi, O.J. Jr.; Greitzer, F.L.; Kangas, L.J.; Reeve, T.

    1994-04-01

    The US Army Ordnance Center & School and Pacific Northwest Laboratories are developing a turbine engine diagnostic system for the M1A1 Abrams tank. This system employs Artificial Neural Network (AN) technology to perform diagnosis and prognosis of the tank`s AGT-1500 gas turbine engine. This paper describes the design and prototype development of the ANN component of the diagnostic system, which we refer to as ``TEDANN`` for Turbine Engine Diagnostic Artificial Neural Networks.

  10. A comparison of expert systems and neural networks applications in power generation

    SciTech Connect

    Rodriguez, G.; Mejia-Lavalle, M.

    1994-12-31

    Two application systems, Tube Failure Diagnosis (TFD) and Electric Generator Failure Diagnosis (EGFD), are discussed in the paper. The TFD system was built using two different approaches: one with rule-chaining search algorithms and the other with a new neural network paradigm. The EGFD system combines the two artificial intelligence approaches: rule-chaining and neural networks. An analysis of the advantages and disadvantages of the two technologies, as applied to power generation applications, is included.

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

    PubMed

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

    2014-09-15

    This paper reports the development of a computer vision system (CVS) for non-destructive characterization of honey based on colour and its correlated chemical attributes including ash content (AC), antioxidant activity (AA), and total phenolic content (TPC). Artificial neural network (ANN) models were applied to transform RGB values of images to CIE L*a*b* colourimetric measurements and to predict AC, TPC and AA from colour features of images. The developed ANN models were able to convert RGB values to CIE L*a*b* colourimetric parameters with low generalization error of 1.01±0.99. In addition, the developed models for prediction of AC, TPC and AA showed high performance based on colour parameters of honey images, as the R(2) values for prediction were 0.99, 0.98, and 0.87, for AC, AA and TPC, respectively. The experimental results show the effectiveness and possibility of applying CVS for non-destructive honey characterization by the industry. PMID:24767037

  12. Modeling neural adaptation in the frog auditory system

    NASA Astrophysics Data System (ADS)

    Wotton, Janine; McArthur, Kimberly; Bohara, Amit; Ferragamo, Michael; Megela Simmons, Andrea

    2005-09-01

    Extracellular recordings from the auditory midbrain, Torus semicircularis, of the leopard frog reveal a wide diversity of tuning patterns. Some cells seem to be well suited for time-based coding of signal envelope, and others for rate-based coding of signal frequency. Adaptation for ongoing stimuli plays a significant role in shaping the frequency-dependent response rate at different levels of the frog auditory system. Anuran auditory-nerve fibers are unusual in that they reveal frequency-dependent adaptation [A. L. Megela, J. Acoust. Soc. Am. 75, 1155-1162 (1984)], and therefore provide rate-based input. In order to examine the influence of these peripheral inputs on central responses, three layers of auditory neurons were modeled to examine short-term neural adaptation to pure tones and complex signals. The response of each neuron was simulated with a leaky integrate and fire model, and adaptation was implemented by means of an increasing threshold. Auditory-nerve fibers, dorsal medullary nucleus neurons, and toral cells were simulated and connected in three ascending layers. Modifying the adaptation properties of the peripheral fibers dramatically alters the response at the midbrain. [Work supported by NOHR to M.J.F.; Gustavus Presidential Scholarship to K.McA.; NIH DC05257 to A.M.S.

  13. A multichannel telemetry system for single unit neural recordings.

    PubMed

    Obeid, Iyad; Nicolelis, Miguel A L; Wolf, Patrick D

    2004-02-15

    We present the design, testing, and evaluation of a 16 channel wearable telemetry system to facilitate multichannel single unit recordings from freely moving test subjects. Our design is comprised of (1) a 16-channel analog front end board to condition and sample signals derived from implanted neural electrodes, (2) a digital board for processing and buffering the digitized waveforms, and (3) an index-card sized 486 PC equipped with an IEEE 802.11b wireless ethernet card. Digitized data (up to 12 bits of resolution at 31.25k samples/s per channel) is transferred to the PC and sent to a nearby host computer on a wireless local area network. Up to 12 of the 16 channels were transmitted simultaneously for sustained periods at a range of 9 m. The device measures 5.1 cm x 8.1 cm x 12.4 cm, weighs 235 g, and is powered from rechargeable lithium ion batteries with a lifespan of 45 min at maximum transmission power. The device was successfully used to record signals from awake, chronically implanted macaque and owl monkeys. PMID:14757342

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

    PubMed

    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. PMID:18255671

  15. Poly(3,4-ethylenedioxythiophene)/poly(styrenesulfonate)-poly(vinyl alcohol)/poly(acrylic acid) interpenetrating polymer networks for improving optrode-neural tissue interface in optogenetics.

    PubMed

    Lu, Yi; Li, Yanling; Pan, Jianqing; Wei, Pengfei; Liu, Nan; Wu, Bifeng; Cheng, Jinbo; Lu, Caiyi; Wang, Liping

    2012-01-01

    The field of optogenetics has been successfully used to understand the mechanisms of neuropsychiatric diseases through the precise spatial and temporal control of specific groups of neurons in a neural circuitry. However, it remains a great challenge to integrate optogenetic modulation with electrophysiological and behavioral read out methods as a means to explore the causal, temporally precise, and behaviorally relevant interactions of neurons in the specific circuits of freely behaving animals. In this study, an eight-channel chronically implantable optrode array was fabricated and modified with poly(3,4-ethylenedioxythiophene)/poly(styrenesulfonate)-poly(vinyl alcohol)/poly(acrylic acid) interpenetrating polymer networks (PEDOT/PSS-PVA/PAA IPNs) for improving the optrode-neural tissue interface. The conducting polymer-hydrogel IPN films exhibited a significantly higher capacitance and lower electrochemical impedance at 1 kHz as compared to unmodified optrode sites and showed significantly improved mechanical and electrochemical stability as compared to pure conducting polymer films. The cell attachment and neurite outgrowth of rat pheochromocytoma (PC12) cells on the IPN films were clearly observed through calcein-AM staining. Furthermore, the optrode arrays were chronically implanted into the hippocampus of SD rats after the lentiviral expression of synapsin-ChR2-EYFP, and light-evoked, frequency-dependant action potentials were obtained in freely moving animals. The electrical recording results suggested that the modified optrode arrays showed significantly reduced impedance and RMS noise and an improved SNR as compared to unmodified sites, which may have benefited from the improved electrochemical performance and biocompatibility of the deposited IPN films. All these characteristics are greatly desired in optogenetic applications, and the fabrication method of conducting polymer-hydrogel IPNs can be easily integrated with other modification methods to build a

  16. Interface solitons in one-dimensional locally coupled lattice systems

    SciTech Connect

    Hadzievski, Lj.; Gligoric, G.; Maluckov, A.; Malomed, B. A.

    2010-09-15

    Fundamental solitons pinned to the interface between two discrete lattices coupled at a single site are investigated. Serially and parallel-coupled identical chains (system 1 and system 2), with self-attractive on-site cubic nonlinearity, are considered in one dimension. In these two systems, which can be readily implemented as arrays of nonlinear optical waveguides, symmetric, antisymmetric, and asymmetric solitons are investigated by means of the variational approximation (VA) and numerical methods. The VA demonstrates that the antisymmetric solitons exist in the entire parameter space, while the symmetric and asymmetric modes can be found below some critical value of the coupling parameter. Numerical results confirm these predictions for the symmetric and asymmetric fundamental modes. The existence region of numerically found antisymmetric solitons is also limited by a certain value of the coupling parameter. The symmetric solitons are destabilized via a supercritical symmetry-breaking pitchfork bifurcation, which gives rise to stable asymmetric solitons, in both systems. The antisymmetric fundamental solitons, which may be stable or not, do not undergo any bifurcation. In bistability regions, stable antisymmetric solitons coexist with either symmetric or asymmetric solitons.

  17. Medical Signal-Conditioning and Data-Interface System

    NASA Technical Reports Server (NTRS)

    Braun, Jeffrey; Jacobus, charles; Booth, Scott; Suarez, Michael; Smith, Derek; Hartnagle, Jeffrey; LePrell, Glenn

    2006-01-01

    A general-purpose portable, wearable electronic signal-conditioning and data-interface system is being developed for medical applications. The system can acquire multiple physiological signals (e.g., electrocardiographic, electroencephalographic, and electromyographic signals) from sensors on the wearer s body, digitize those signals that are received in analog form, preprocess the resulting data, and transmit the data to one or more remote location(s) via a radiocommunication link and/or the Internet. The system includes a computer running data-object-oriented software that can be programmed to configure the system to accept almost any analog or digital input signals from medical devices. The computing hardware and software implement a general-purpose data-routing-and-encapsulation architecture that supports tagging of input data and routing the data in a standardized way through the Internet and other modern packet-switching networks to one or more computer(s) for review by physicians. The architecture supports multiple-site buffering of data for redundancy and reliability, and supports both real-time and slower-than-real-time collection, routing, and viewing of signal data. Routing and viewing stations support insertion of automated analysis routines to aid in encoding, analysis, viewing, and diagnosis.

  18. User interface for a tele-operated robotic hand system

    SciTech Connect

    Crawford, Anthony L

    2015-03-24

    Disclosed here is a user interface for a robotic hand. The user interface anchors a user's palm in a relatively stationary position and determines various angles of interest necessary for a user's finger to achieve a specific fingertip location. The user interface additionally conducts a calibration procedure to determine the user's applicable physiological dimensions. The user interface uses the applicable physiological dimensions and the specific fingertip location, and treats the user's finger as a two link three degree-of-freedom serial linkage in order to determine the angles of interest. The user interface communicates the angles of interest to a gripping-type end effector which closely mimics the range of motion and proportions of a human hand. The user interface requires minimal contact with the operator and provides distinct advantages in terms of available dexterity, work space flexibility, and adaptability to different users.

  19. Database interfaces on NASA's heterogeneous distributed database system

    NASA Technical Reports Server (NTRS)

    Huang, S. H. S.

    1986-01-01

    The purpose of the ORACLE interface is to enable the DAVID program to submit queries and transactions to databases running under the ORACLE DBMS. The interface package is made up of several modules. The progress of these modules is described below. The two approaches used in implementing the interface are also discussed. Detailed discussion of the design of the templates is shown and concluding remarks are presented.

  20. Evaluation of Different Speech and Touch Interfaces to In-Vehicle Music Retrieval Systems

    PubMed Central

    Garay-Vega, L.; Pradhan, A. K.; Weinberg, G.; Schmidt-Nielsen, B.; Harsham, B.; Shen, Y.; Divekar, G.; Romoser, M.; Knodler, M.; Fisher, D. L.

    2010-01-01

    In-vehicle music retrieval systems are becoming more and more popular. Previous studies have shown that they pose a real hazard to drivers when the interface is a tactile one which requires multiple entries and a combination of manual control and visual feedback. Voice interfaces exist as an alternative. Such interfaces can require either multiple or single conversational turns. In this study, each of 17 participants between the ages of 18 and 30 years old was asked to use three different music-retrieval systems (one with a multiple entry touch interface, the iPod™, one with a multiple turn voice interface, interface B, and one with a single turn voice interface, interface C) while driving through a virtual world. Measures of secondary task performance, eye behavior, vehicle control, and workload were recorded. When compared with the touch interface, the voice interfaces reduced the total time drivers spent with their eyes off the forward roadway, especially in prolonged glances, as well as both the total number of glances away from the forward roadway and the perceived workload. Furthermore, when compared with driving without a secondary task, both voice interfaces did not significantly impact hazard anticipation, the frequency of long glances away from the forward roadway, or vehicle control. The multiple turn voice interface (B) significantly increased both the time it took drivers to complete the task and the workload. The implications for interface design and safety are discussed. PMID:20380920